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Using high-fidelity numerical simulations based on a lattice Boltzmann framework, the advection-enhanced transport of a passive scalar from a prolate spheroid in simple shear flow has been thoroughly investigated across various parameters, including the spheroid's aspect ratio, particle-to-fluid density ratio, Reynolds number, and Schmidt number. The Reynolds number is constrained to the range from 0 to 1, where the prolate spheroid tumbles around its minor axis, aligned with the vorticity axis, in an equilibrium state. Several key findings have emerged: 1) Particle inertia significantly influences the uniformity of the spheroid's tumbling, affecting flow patterns around the spheroid and, consequently, the modes of scalar transport; 2) Both uniform and non-uniform tumbling generate a scalar line in the fluid with elevated scalar concentration, which sweeps through the wake region and merges with clusters of previously formed scalar lines; 3) Fluid passing over the spheroid carries the passive scalar downstream along these scalar lines; 4) Variations in the uniformity of spheroid tumbling result in distinct flow patterns and scalar transport modes, leading to different transport rates; 5) Within the studied parameter ranges, increased particle inertia enhances the scalar transport rate; 6) When both fluid and particle inertia are minimal, the dimensionless scalar transport rate for different aspect ratios converges to a common dependence on the Peclet number. These phenomena are analyzed in detail.

Photovoltaic solar cell is one of the main renewable energy sources, and its power conversion efficiency (PCE) is improved by employing doping or heterojunction to reduce the photogenerated carrier recombination. Here, we propose a doping-free homojunction solar cell utilizing two-dimensional Janus semiconductors to achieve high PCE. Thanks to the intrinsic dipole of Janus structure, doping-free Janus homojunction has naturally not only a type-II band alignment to promote the photoexciton dissociation, but also a smaller effective bandgap to enhance light absorption. More importantly, the intrinsic electric field across the Janus structure will drive photoinduced electron and hole transfer from the interface to the opposite transport layers respectively, significantly enhancing the efficiency of carrier separation and transport. We illustrate the concept in titanium-based Janus monolayer homojunction, where the theoretically observed PCE reaches 23.22% of TiSSe homojunction. Our work opens a novel avenue to design low-cost, high-efficiency solar cells., Comment: 16 pages, 5 figures

The detection of abusive language remains a long-standing challenge with the extensive use of social networks. The detection task of abusive language suffers from limited accuracy. We argue that the existing detection methods utilize the fine-tuning technique of the pre-trained language models (PLMs) to handle downstream tasks. Hence, these methods fail to stimulate the general knowledge of the PLMs. To address the problem, we propose a novel Deep Prompt Multi-task Network (DPMN) for abuse language detection. Specifically, DPMN first attempts to design two forms of deep prompt tuning and light prompt tuning for the PLMs. The effects of different prompt lengths, tuning strategies, and prompt initialization methods on detecting abusive language are studied. In addition, we propose a Task Head based on Bi-LSTM and FFN, which can be used as a short text classifier. Eventually, DPMN utilizes multi-task learning to improve detection metrics further. The multi-task network has the function of transferring effective knowledge. The proposed DPMN is evaluated against eight typical methods on three public datasets: OLID, SOLID, and AbuseAnalyzer. The experimental results show that our DPMN outperforms the state-of-the-art methods., Comment: Accepted by the International Conference on Pattern Recognition (ICPR) 2024

Symmetry breaking generally induce exotic physical properties, particularly for low-dimensional materials. Herein we demonstrate that symmetry breaking induces a giant Stark effect in 2D Janus materials using group IV-V monolayers with a four-atom-layer structure as a model system, which are constructed by Ge and As element substitution of symmetrical SnSb monolayer. A linear giant Stark effect is found in Janus semiconductor monolayers, as verified by the band gap variation up to 134 meV of Sn2SbAs monolayer, which is 30 times larger than that of SnSb monolayer (4 meV) when the applied electric field is increased from -0.30 to 0.30 V/{\AA}. By considering the induced electronic field, we propose a generalized and effective formula that efficiently determines the band gap variation owing to Stark effect. The calculated results from proposed formula are well agreement with those from DFT-HSE06 functional. The giant Stark effect is originated from the large spatial separation of centers of the conduction band minimum and valence band maximum states of Janus structure due to its intrinsic potential gradient. The wide-range tuning of band gap under electronic field shows potential applications of 2D Janus materials in optoelectronic devices., Comment: 10 pages, 5 figures

Dialogue summarization task involves summarizing long conversations while preserving the most salient information. Real-life dialogues often involve naturally occurring variations (e.g., repetitions, hesitations) and existing dialogue summarization models suffer from performance drop on such conversations. In this study, we systematically investigate the impact of such variations on state-of-the-art dialogue summarization models using publicly available datasets. To simulate real-life variations, we introduce two types of perturbations: utterance-level perturbations that modify individual utterances with errors and language variations, and dialogue-level perturbations that add non-informative exchanges (e.g., repetitions, greetings). We conduct our analysis along three dimensions of robustness: consistency, saliency, and faithfulness, which capture different aspects of the summarization model's performance. We find that both fine-tuned and instruction-tuned models are affected by input variations, with the latter being more susceptible, particularly to dialogue-level perturbations. We also validate our findings via human evaluation. Finally, we investigate if the robustness of fine-tuned models can be improved by training them with a fraction of perturbed data and observe that this approach is insufficient to address robustness challenges with current models and thus warrants a more thorough investigation to identify better solutions. Overall, our work highlights robustness challenges in dialogue summarization and provides insights for future research.

Spin plays a key role in physical and chemical reactions, such as oxygen evolution and hydrogen evolution reactions (OER/HER); but the spin-activity correlation has remained unclear. Based on a transition metal (TM)-doped PtN2 monolayer model with a well-defined spin center as adsorption site, we here reveal that only active spin state can enhance the strength of hydrogen adsorption, while inert spin state offers very little influence. Specifically, the unpaired electron along the out-of-plane direction such as in dZ2 orbital, acting as an active spin state, will strongly hybridize with hydrogen, resulting in enhanced hydrogen binding energy because dZ2 orbital is just enough to accommodate two electrons to form a bonding orbital. While the in-plane unpaired electron such as in dX2-Y2 orbital, plays a negligible role in adsorbing hydrogen atom. This is verified by a series of single atom catalysts comprising of PtN2 monolayer by replacing Pt atom with a TM (Fe, Co, Ni, Ru, Rh, Pd, Os, or Ir) atom, or subsequent adsorbing a Cl atom. One of the most promising materials is Pd@PtN2-Cl that offers superior HER activity, even better than pure Pt. This work uncovers the nature of spin-activity correlation, thus paving the way for the design of high-performance catalysts through spin-engineering., Comment: 5 figures

The integration of external personalized context information into document-grounded conversational systems has significant potential business value, but has not been well-studied. Motivated by the concept of personalized context-aware document-grounded conversational systems, we introduce the task of context-aware passage retrieval. We also construct a dataset specifically curated for this purpose. We describe multiple baseline systems to address this task, and propose a novel approach, Personalized Context-Aware Search (PCAS), that effectively harnesses contextual information during passage retrieval. Experimental evaluations conducted on multiple popular dense retrieval systems demonstrate that our proposed approach not only outperforms the baselines in retrieving the most relevant passage but also excels at identifying the pertinent context among all the available contexts. We envision that our contributions will serve as a catalyst for inspiring future research endeavors in this promising direction.

This paper complements the empirical justification of the revised scheme in Part I of this work with a mathematical justification leveraging a semi-discrete analysis framework for assessing the splitting error of process coupling methods. The novelty of the framework is that splitting error is distinguished from the process time integration errors, i.e., the errors caused by discrete time integration of individual processes, leading to expressions that are more easily interpreted utilizing existing physical understanding of the processes that the terms represent. This application of this framework to dust life cycle in EAMv1 showcases such an interpretation, using the leading-order splitting error that results from the framework to confirm (i) that the original EAMv1 scheme artificially strengthens the effect of dry removal processes, and (ii) that the revised splitting reduces that artificial strengthening. While the error analysis framework is presented in the context of the dust life cycle in EAMv1, the framework can be broadly leveraged to evaluate process coupling schemes, both in other physical problems and for any number of processes. This framework will be particularly powerful when the various process implementations support a variety of time integration approaches. Whereas traditional local truncation error approaches require separate consideration of each combination of time integration methods, this framework enables evaluation of coupling schemes independent of particular time integration approaches for each process while still allowing for the incorporation of these specific time integration errors if so desired. The framework also explains how the splitting error terms result from (i) the integration of individual processes in isolation from other processes, and (ii) the choices of input state and timestep size for the isolated integration of processes.

An earlier study evaluating the dust life cycle in the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1) has revealed that the simulated global mean dust lifetime is substantially shorter when higher vertical resolution is used, primarily due to significant strengthening of dust dry removal in source regions. This paper demonstrates that the sequential splitting of aerosol emissions, dry removal, and turbulent mixing in the model's time integration loop, especially the calculation of dry removal after surface emissions and before turbulent mixing, is the primary reason for the vertical resolution sensitivity reported in that earlier study. Based on this reasoning, we propose a simple revision to the numerical process coupling scheme, which moves the application of the surface emissions to after dry removal and before turbulent mixing. The revised scheme allows newly emitted particles to be transported aloft by turbulence before being removed from the atmosphere, and hence better resembles the dust life cycle in the real world. Sensitivity experiments are conducted and analyzed to evaluate the impact of the revised coupling on the simulated aerosol climatology in EAMv1.

In this paper we explore the task of modeling semi-structured object sequences; in particular, we focus our attention on the problem of developing a structure-aware input representation for such sequences. Examples of such data include user activity on websites, machine logs, and many others. This type of data is often represented as a sequence of sets of key-value pairs over time and can present modeling challenges due to an ever-increasing sequence length. We propose a two-part approach, which first considers each key independently and encodes a representation of its values over time; we then self-attend over these value-aware key representations to accomplish a downstream task. This allows us to operate on longer object sequences than existing methods. We introduce a novel shared-attention-head architecture between the two modules and present an innovative training schedule that interleaves the training of both modules with shared weights for some attention heads. Our experiments on multiple prediction tasks using real-world data demonstrate that our approach outperforms a unified network with hierarchical encoding, as well as other methods including a record-centric representation and a flattened representation of the sequence.

We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot. Our information-acquisition-oriented dialogue system employs a novel adaptation of reinforced self-play so that the system can be transferred to various domains without in-domain dialogue data, and can carry out conversations both informative and attentive to users. Our extensive subjective and objective evaluations on three large public data corpora demonstrate the effectiveness of our system to deliver knowledge-intensive and attentive conversations and help end users substantially gain knowledge without reading passages. Our code and datasets are publicly available for follow-up research., Comment: 10 pages, accepted by NAACL 2022

Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available. The state-of-the-art technology for neural dense retrieval or re-ranking involves deep learning models with hundreds of millions of parameters. However, it is difficult and expensive to get such models to operate at an industrial scale, especially for cloud services that often need to support a big number of individually customized dialogue systems, each with its own text corpus. We report our work on enabling advanced neural dense retrieval systems to operate effectively at scale on relatively inexpensive hardware. We compare with leading alternative industrial solutions and show that we can provide a solution that is effective, fast, and cost-efficient., Comment: Accepted in NAACL-HLT 2022 Industry Track

Through high-fidelity numerical simulation, the effect of the arrangement of micropillars on the flow characteristics and momentum transport has been extensively investigated. The surface friction due to the complex flow characteristics and momentum transport mechanism has also been studied in depth. The micropillars are arranged in a quadrilateral, and different arrangements are acquired by changing the streamwise and spanwise distances between pillar rows. The results show that the streamwise and spanwise pillar distances have their own different influences. When the streamwise pillar distance is small, the micro eddies in the gaps between the streamwise neighboring pillars are significantly suppressed. The increase in the spanwise pillar distance enhances the momentum transport from the flow above pillar array to the flow in the spaces among micro pillars. When the spanwise pillar distance is small, the micro eddies in the gaps between the streamwise neighboring pillars connect with each other and form a tubular eddy between each pair of spanwise pillar rows. The tubular eddies significantly reduce the momentum transport from the upper flow to the lower flow. The increase in the streamwise pillar distance increase the momentum flux slightly. The surface friction can be decomposed into three components which are associated with two factors, the dilution effect of the number density of micro pillars and the multi-faceted effects of micro eddies. These two factors are determined by the streamwise and spanwise pillar distances. The dependence of the total friction and its components on the pillar distances has been thoroughly examined.

Through high-fidelity numerical simulation, the simple shear flow over regularly arranged micro pillars has been investigated. The essential issues to be addressed include the characteristics of a simple shear flow over quadrilateral array of micro pillars, the effect of fluid inertia on the basic flow pattern, and the decomposition of the complex surface friction. The results show that the flow is characterized by a series of microscale recirculating eddies in the gaps between the streamwise neighboring pillars. The recirculation of the micro eddies and the oscillation of the overhead flow climbing over the pillar tips create a local flow advection. At smaller Reynolds number, the fluid inertia is weak and the flow patterns are symmetrical about the pillar center. When the Reynolds number is sufficiently large, the fluid inertia takes effect and breaks the symmetrical patterns. The overhead flow tilts downward, forming a spiral long-range advection between the fluid flow above pillar array and the flow in the spaces among micro pillars. The local advection and long-range advection constitute the transport mechanism in wall-normal direction. On micro-structured walls, the total friction includes the reaction forces of micro pillars due to flow shear and flow pressure at pillar surfaces and the reaction force of bottom plane due to flow shear on bottom surface. For larger Reynolds numbers, fluid inertia prevents the fluid from flowing along the curved surface of micro pillars and reduces the equivalent shear stress of the pillar reaction force due to flow shear. At the same time, the fluid inertia makes the overhead flow impact the windward side of micro pillars more strongly and therefore increases the equivalent shear stress of the pillar reaction force due to flow pressure.

The heat and mass transfer characteristics in a simple shear flow over staggered herringbone structures are numerically investigated with the lattice Boltzmann method. Two flow motions are identified. The first is a spiral flow oscillation above the herringbone structures that advects heat and mass from the top plane to herringbone structures. The second is a flow recirculation in the grooves between herringbone ridges that advects heat and mass from the area around herringbone tips to the side walls of herringbone ridges and the bottom surfaces. These two basic flow motions couple together to form complex transport mechanisms. The results show that when advective heat and mass transfer takes effect at relatively larger Reynolds and Schmidt numbers, the dependence of the total transfer rate on the Schmidt number follows a power law, with the power being the same as that in the Dittus-Boelter equation for turbulent heat transfer. As Reynolds number increases, the dependence of the total transfer rate on Reynolds number also approaches a power law, and the power is close to that in the Dittus-Boelter equation.