Miracle-dz/UniFAR-FTUs huggingface dataset:
Magic NeRF Lens: Interactive Fusion of Neural Radiance Fields for Virtual Facility Inspection

Large industrial facilities such as particle accelerators and nuclear power plants are critical infrastructures for scientific research and industrial processes. These facilities are complex systems that not only require regular maintenance and upgrades but are often inaccessible to humans due to various safety hazards. Therefore, a virtual reality (VR) system that can quickly replicate real-world remote environments to provide users with a high level of spatial and situational awareness is crucial for facility maintenance planning. However, the exact 3D shapes of these facilities are often too complex to be accurately modeled with geometric primitives through the traditional rasterization pipeline. In this work, we develop Magic NeRF Lens, an interactive framework to support facility inspection in immersive VR using neural radiance fields (NeRF) and volumetric rendering. We introduce a novel data fusion approach that combines the complementary strengths of volumetric rendering and geometric rasterization, allowing a NeRF model to be merged with other conventional 3D data, such as a computer-aided design model. We develop two novel 3D magic lens effects to optimize NeRF rendering by exploiting the properties of human vision and context-aware visualization. We demonstrate the high usability of our framework and methods through a technical benchmark, a visual search user study, and expert reviews. In addition, the source code of our VR NeRF framework is made publicly available for future research and development.
Highly multimode solitons in step-index optical fiber

We report the generation of multimode solitons in step-index fibers. The solitons are superpositions of 5–10 temporally aligned transverse modes, they exhibit speckled beam profiles, and the spatio-spectral variation across the soliton can be complex. A greater understanding of multimode solitons should create a foundation for further research into complex multimode nonlinear phenomena in step-index fibers. With energies of tens of nanojoules and durations around 100 fs, the multimode solitons are speckled fields with peak powers that can exceed 300 kW, parameters that may be valuable in applications.
Are We Ready to Embrace Generative AI for Software Q&A?

Stack Overflow, the world's largest software Q&A (SQA) website, is facing a significant traffic drop due to the emergence of generative AI techniques. ChatGPT is banned by Stack Overflow after only 6 days from its release. The main reason provided by the official Stack Overflow is that the answers generated by ChatGPT are of low quality. To verify this, we conduct a comparative evaluation of human-written and ChatGPT-generated answers. Our methodology employs both automatic comparison and a manual study. Our results suggest that human-written and ChatGPT-generated answers are semantically similar, however, human-written answers outperform ChatGPT-generated ones consistently across multiple aspects, specifically by 10% on the overall score. We release the data, analysis scripts, and detailed results at https://github.com/maxxbw54/GAI4SQA.
Modelling reflected polarised light from close-in giant exoplanet WASP-96b using PolHEx (Polarisation of Hot Exoplanets)

We present the Polarisation of Hot Exoplanets (PolHEx) code for modelling the total flux (F) and degree of linear polarisation (P) of light spectra reflected by close-in, tidally locked exoplanets. We use the output from a global climate model (GCM) combined with a kinetic cloud model of hot Jupiter WASP-96b as a base to investigate effects of atmospheric longitudinal-latitudinal inhomogeneities on these spectra. We model F and P-spectra as functions of wavelength and planet orbital phase for various model atmospheres. We find different materials and sizes of cloud particles to impact the reflected flux F, and particularly the linear polarisation state P. A range of materials are used to form inhomogeneous mixed-material cloud particles (Al2O3, Fe2O3, Fe2SiO4, FeO, Fe, Mg2SiO4, MgO, MgSiO3, SiO2, SiO, TiO2), with Fe2O3, Fe, and FeO the most strongly absorbing species. The cloud particles near the relatively cool morning terminator are expected to have smaller average sizes and a narrower size distribution than those near the warmer evening terminator, which leads to different reflected spectra at the respective orbital phases . We also find differences in the spectra of F and P as functions of orbital phase for irregularly or spherically shaped cloud particles. This work highlights the importance of including polarisation in models and future observations of the reflection spectra of exoplanets.
Theory of the anomalous Hall effect in the transition metal pentatellurides 
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 and 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>HfTe</mml:mi>

The anomalous Hall effect has considerable impact on the progress of condensed matter physics and occurs in systems with time-reversal symmetry breaking. Here we theoretically investigate the anomalous Hall effect in nonmagnetic transition-metal pentatelluride $\mathrm{ZrTe_{5}}$ and $\mathrm{HfTe}_{5}$. In the presence of Zeeman splitting and Dirac mass, there is an intrinsic anomalous Hall conductivity induced by the Berry curvature in the semiclassical treatment. In a finite magnetic field, the anomalous Hall conductivity rapidly decays to zero for constant spin-splitting and vanishes for the magnetic-field-dependent Zeeman energy. A semiclassical formula is derived to depict the magnetic field dependence of the Hall conductivity, which is beneficial for experimental data analysis. Lastly, when the chemical potential is fixed in the magnetic field, a Hall conductivity plateau arises, which may account for the observed anomalous Hall effect in experiments.
Scaling Power Management in Cloud Data Centers: A Multi-Level Continuous-Time MDP Approach

Power management in multi-server data centers~especially at scale is a vital issue of increasing importance in cloud computing paradigm. Existing studies mostly consider thresholds on the number of idle servers to switch the servers on or off and suffer from scalability issues. As a natural approach in view~of~the Markovian assumption, we present a multi-level continuous-time Markov decision process (CTMDP) model based on state aggregation of multi-server data centers with setup times that interestingly overcomes the inherent intractability of traditional MDP approaches due to their colossal state-action space. The beauty of the presented model is that, while it keeps loyalty to the Markovian behavior, it approximates the calculation of the transition probabilities in a way that keeps the accuracy of the results at a desirable level. Moreover, near-optimal performance is attained at the expense of the increased state-space dimensionality by tuning the number of levels in the multi-level approach. The simulation results were promising and confirm that in many scenarios of interest, the proposed approach attains noticeable improvements, namely a near 50% reduction in the size of CTMDP while yielding better rewards as compared to existing fixed threshold-based policies and aggregation methods.
Staggered Schemes for Compressible Flow: A General Construction

This paper is focused on the approximation of the Euler equations of compressible fluid dynamics on a staggered mesh. With this aim, the flow parameters are described by the velocity, the density and the internal energy. The thermodynamic quantities are described on the elements of the mesh, and thus the approximation is only in $L^2$, while the kinematic quantities are globally continuous. The method is general in the sense that the thermodynamic and kinetic parameters are described by an arbitrary degree of polynomials. In practice, the difference between the degrees of the kinematic parameters and the thermodynamic ones {is set} to $1$. The integration in time is done using the forward Euler method but can be extended straightforwardly to higher-order methods. In order to guarantee that the limit solution will be a weak solution of the problem, we introduce a general correction method in the spirit of the Lagrangian staggered method described in \cite{Svetlana,MR4059382, MR3023731}, and we prove a Lax Wendroff theorem. The proof is valid for multidimensional versions of the scheme, even though most of the numerical illustrations in this work, on classical benchmark problems, are one-dimensional because we have easy access to the exact solution for comparison. We conclude by explaining that the method is general and can be used in different settings, for example, Finite Volume, or discontinuous Galerkin method, not just the specific one presented in this paper.
Spectral Applications of Vertex-Clique Incidence Matrices Associated with a Graph

Using the notions of clique partitions and edge clique covers of graphs, we consider the corresponding incidence structures. This connection furnishes lower bounds on the negative eigenvalues and their multiplicities associated with the adjacency matrix, bounds on the incidence energy, and on the signless Laplacian energy for graphs. For the more general and well-studied set S(G) of all real symmetric matrices associated with a graph G, we apply an extended version of an incidence matrix tied to an edge clique cover to establish several classes of graphs that allow two distinct eigenvalues.
TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-based Memory Network

Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials based on longitudinal patient electronic health records (EHRs) and eligibility criteria of trials. However, they either depend on trial-specific expert rules that cannot be generalized or perform matching more generally with a black-box model where the lack of interpretability makes the model results difficult to be adopted. To provide accurate and interpretable patient trial matching, we introduce a personalized dynamic tree-based memory network, TREEMENT. It utilizes hierarchical clinical ontologies to expand the personalized patient representation learned from sequential EHR data, and then uses an attentional beam-search query learned from eligibility criteria embedding to offer a granular level of alignment for improved performance and interpretability. We evaluate TREEMENT against existing models on real-world datasets and show that TREEMENT outperforms the top baseline by 7% in terms of error reduction in criteria-level matching and achieves state-of-the-art results at the trial-level too. Furthermore, we show TREEMENT offers good interpretability to make the model results easier for adoption.
UniMatch: A Unified User-Item Matching Framework for the Multi-purpose Merchant Marketing

When doing private domain marketing with cloud services, the merchants usually have to purchase different machine learning models for the multiple marketing purposes, leading to a very high cost. We present a unified user-item matching framework to simultaneously conduct item recommendation and user targeting with just one model. We empirically demonstrate that the above concurrent modeling is viable via modeling the user-item interaction matrix with the multinomial distribution, and propose a bidirectional bias-corrected NCE loss for the implementation. The proposed loss function guides the model to learn the user-item joint probability p(u,i) instead of the conditional probability p(i|u) or p(u|i) through correcting both the users and items’ biases caused by the in-batch negative sampling. In addition, our framework is model-agnostic enabling a flexible adaptation of different model architectures. Extensive experiments demonstrate that our framework results in significant performance gains in comparison with the state-of-the-art methods, with greatly reduced cost on computing resources and daily maintenance.
Information Structures in AC/DC Grids

The converters in an AC/DC grid form actuated boundaries between the AC and DC subgrids. We show how in both simple linear and balanced dq-frame models, the states on either side of these boundaries are coupled only by control inputs. This topological property imparts all AC/DC grids with poset-causal information structures. A practical benefit is that certain decentralized control problems that are hard in general are tractable for poset-causal systems. We also show that special cases like multi-terminal DC grids can have coordinated and leader-follower information structures.
Blind Image Quality Assessment Using Multi-Stream Architecture with Spatial and Channel Attention

BIQA (Blind Image Quality Assessment) is an important field of study that evaluates images automatically. Although significant progress has been made, blind image quality assessment remains a difficult task since images vary in content and distortions. Most algorithms generate quality without emphasizing the important region of interest. In order to solve this, a multi-stream spatial and channel attention-based algorithm is being proposed. This algorithm generates more accurate predictions with a high correlation to human perceptual assessment by combining hybrid features from two different backbones, followed by spatial and channel attention to provide high weights to the region of interest. Four legacy image quality assessment datasets are used to validate the effectiveness of our proposed approach. Authentic and synthetic distortion image databases are used to demonstrate the effectiveness of the proposed method, and we show that it has excellent generalization properties with a particular focus on the perceptual foreground information.
Electron paramagnetic resonance calculations for hydrogenated Si surfaces

Electron paramagnetic resonance (EPR) signatures, more specifically the elements of the electronic g tensor, are calculated within density functional theory for hydrogenated Si(111), Si(001), Si(113), Si(114), Si(112̄), and Si(110) surfaces. Thereby both perturbation theory and a more sophisticated Berry phase technique are applied. Specific defects on different surface orientations are shown to reproduce the resonances at ḡ = 2.0043 and ḡ = 2.0052 measured for hydrogenated microcrystalline silicon: The latter value is argued here to originate from regions with low hydrogen coverage; the resonance at ḡ = 2.0043 is shown to appear in positions with dihydride environment, where an H atom is directly bound to the silicon dangling-bond atoms. A third group of EPR signals with considerably larger ḡ values between 2.006 and 2.009 is predicted for highly symmetric dangling bonds resembling single dangling-bond defects in silicon bulk material. As the exact value depends strongly on local strain, this type of defect can explain a less intense signal with large g strain observed in microcrystalline as well as in amorphous material.
LOG-LIO: A LiDAR-Inertial Odometry With Efficient Local Geometric Information Estimation

Local geometric information, i.e., normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for data association, which further determines the direction of optimization and ultimately affects the accuracy of localization. However, estimating normal and distribution of points are time-consuming tasks even with the assistance of kdtree or volumetric maps. To achieve fast normal estimation, we look into the structure of LiDAR scan and propose a ring-based fast approximate least squares (Ring FALS) method. With the Ring structural information, estimating the normal requires only the range information of the points when a new scan arrives. To efficiently estimate the distribution of points, we extend the ikd-tree to manage the map in voxels and update the distribution of points in each voxel incrementally while maintaining its consistency with the normal estimation. We further fix the distribution after its convergence to balance the time consumption and the correctness of representation. Based on the extracted and maintained local geometric information, we devise a robust and accurate hierarchical data association scheme where point-to-surfel association is prioritized over point-to-plane. Extensive experiments on diverse public datasets demonstrate the advantages of our system compared to other state-of-the-art methods.
3Deformer: A Common Framework for Image-Guided Mesh Deformation

We propose 3Deformer, a general-purpose framework for interactive 3D shape editing. Given a source 3D mesh with semantic materials, and a user-specified semantic image, 3Deformer can accurately edit the source mesh following the shape guidance of the semantic image, while preserving the source topology as rigid as possible. Recent studies of 3D shape editing mostly focus on learning neural networks to predict 3D shapes, which requires high-cost 3D training datasets and is limited to handling objects involved in the datasets. Unlike these studies, our 3Deformer is a non-training and common framework, which only requires supervision of readily-available semantic images, and is compatible with editing various objects unlimited by datasets. In 3Deformer, the source mesh is deformed utilizing the differentiable renderer technique, according to the correspondences between semantic images and mesh materials. However, guiding complex 3D shapes with a simple 2D image incurs extra challenges, that is, the deform accuracy, surface smoothness, geometric rigidity, and global synchronization of the edited mesh should be guaranteed. To address these challenges, we propose a hierarchical optimization architecture to balance the global and local shape features, and propose further various strategies and losses to improve properties of accuracy, smoothness, rigidity, and so on. Extensive experiments show that our 3Deformer is able to produce impressive results and reaches the state-of-the-art level.
An Unmixing Algorithm Based on a Large Library of Shortwave Infrared Spectra

The unmixing algorithm described in this paper has been motivated by a “spectral library” of pure shortwave infrared reflectance spectra that we started building in the early 1990’s. The library currently consists of 493 samples, representing 60 nominally pure materials (mostly minerals, but also water, dry vegetation and several man-made materials). The algorithm, implemented in software called The Spectral AssistantTM (TSA), is designed to analyse quickly tens to hundreds of thousands of spectra measured from drill core or chips using CSIRO’s HyLoggerTM and HyChips TM instruments, and other commercial reflectance spectrometers. Individual samples typically are composed of a small number of minerals. Therefore, in order to avoid overfitting, the TSA algorithm ultilises fast subset selection procedures to identify the most likely minerals in the mixture. Other novel aspects of the algorithm include the simultaneous fitting of the low frequency background with mineral identification (which provides greater model flexibility) and the combined fitting being carried out in penalised canonical variate space (which has certain optimality properties under an idealised model). The performance of the algorithm is illustrated on a few key examples. Discussion includes its wider applicability, its limitations and possible future extensions and modifications.
Our Model Achieves Excellent Performance on MovieLens: What Does it Mean?

A typical benchmark dataset for recommender system (RecSys) evaluation consists of user-item interactions generated on a platform within a time period. The interaction generation mechanism partially explains why a user interacts with (e.g., like, purchase, rate) an item, and the context of when a particular interaction happened. In this study, we conduct a meticulous analysis of the MovieLens dataset and explain the potential impact of using the dataset for evaluating recommendation algorithms. We make a few main findings from our analysis. First, there are significant differences in user interactions at the different stages when a user interacts with the MovieLens platform. The early interactions largely define the user portrait which affects the subsequent interactions. Second, user interactions are highly affected by the candidate movies that are recommended by the platform's internal recommendation algorithm(s). Third, changing the order of user interactions makes it more difficult for sequential algorithms to capture the progressive interaction process. We further discuss the discrepancy between the interaction generation mechanism that is employed by the MovieLens system and that of typical real-world recommendation scenarios. In summary, the MovieLens platform demonstrates an efficient and effective way of collecting user preferences to address cold-starts. However, models that achieve excellent recommendation accuracy on the MovieLens dataset may not demonstrate superior performance in practice, for at least two kinds of differences: (i) the differences in the contexts of user-item interaction generation, and (ii) the differences in user knowledge about the item collections. While results on MovieLens can be useful as a reference, they should not be solely relied upon as the primary justification for the effectiveness of a recommendation system model.
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI

Despite advancements in conversational AI, language models encounter challenges to handle diverse conversational tasks, and existing dialogue dataset collections often lack diversity and comprehensiveness. To tackle these issues, we introduce DialogStudio: the largest and most diverse collection of dialogue datasets, unified under a consistent format while preserving their original information. Our collection encompasses data from open-domain dialogues, task-oriented dialogues, natural language understanding, conversational recommendation, dialogue summarization, and knowledge-grounded dialogues, making it an incredibly rich and diverse resource for dialogue research and model training.To further enhance the utility of DialogStudio, we identify the licenses for each dataset, design external knowledge and domain-aware prompts for selected dialogues to facilitate instruction-aware fine-tuning. To improve transparency and support dataset and task-based research, as well as language model pre-training, all datasets, licenses, codes, and models associated with DialogStudio will be made publicly accessible.
The Impacts of Registration Regime Implementation on IPO Pricing Efficiency

We study the impacts of regime changes and related rule implementations on IPOs initial return for China entrepreneurial boards (ChiNext and STAR). We propose that an initial return contains the issuer fair value and an investors overreaction and examine their magnitudes and determinants. Our findings reveal an evolution of IPO pricing in response to the progression of regulation changes along four dimensions: 1) governing regulation regime, 2) listing day trading restrictions, 3) listing rules for issuers, and 4) participation requirements for investors. We find that the most efficient regulation regime in Chinese IPO pricing has four characteristics: 1) registration system, 2) no hard return caps nor trading curbs that restrict the initial return; 3) more specific listing rules for issuers, and 4) more stringent participation requirements for investors. In all contexts, we show that the registration regime governing the STAR IPOs offers the most efficient pricing.