Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services

In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i.e., in different geographical locations and at different times, user click behavior will change significantly. Appropriate spatiotemporal enhancement modeling of user click behavior and large-scale sparse attributes is key to building an LBS model. Although most of existing methods have been proved to be effective, they are difficult to apply to takeaway scenarios due to insufficient modeling of spatiotemporal information. In this paper, we address this challenge by seeking to explicitly model the timing and locations of interactions and proposing a Spatiotemporal-Enhanced Network, namely StEN. In particular, StEN applies a Spatiotemporal Profile Activation module to capture common spatiotemporal preference through attribute features. A Spatiotemporal Preference Activation is further applied to model the personalized spatiotemporal preference embodied by behaviors in detail. Moreover, a Spatiotemporal-aware Target Attention mechanism is adopted to generate different parameters for target attention at different locations and times, thereby improving the personalized spatiotemporal awareness of the model.Comprehensive experiments are conducted on three large-scale industrial datasets, and the results demonstrate the state-of-the-art performance of our methods. In addition, we have also released an industrial dataset for takeaway industry to make up for the lack of public datasets in this community.
Physical Logic Enhanced Network for Small-Sample Bi-Layer Metallic Tubes Bending Springback Prediction

Bi-layer metallic tube (BMT) plays an extremely crucial role in engineering applications, with rotary draw bending (RDB) the high-precision bending processing can be achieved, however, the product will further springback. Due to the complex structure of BMT and the high cost of dataset acquisition, the existing methods based on mechanism research and machine learning cannot meet the engineering requirements of springback prediction. Based on the preliminary mechanism analysis, a physical logic enhanced network (PE-NET) is proposed. The architecture includes ES-NET which equivalent the BMT to the single-layer tube, and SP-NET for the final prediction of springback with sufficient singlelayer tube samples. Specifically, in the first stage, with the theory-driven preexploration and the data-driven pretraining, the ES-NET and SP-NET are constructed, respectively. In the second stage, under the physical logic, the PE-NET is assembled by ES-NET and SP-NET and then fine-tuned with the small sample BMT dataset and composite loss function. The validity and stability of the proposed method are verified by the FE simulation dataset, the small-sample dataset BMT springback angle prediction is achieved, and the method potential in interpretability and engineering applications are demonstrated.
Wasserstein-p bounds in the central limit theorem under local dependence

The central limit theorem (CLT) is one of the most fundamental results in probability; and establishing its rate of convergence has been a key question since the 1940s. For independent random variables, a series of recent works established optimal error bounds under the Wasserstein-p distance (with p>=1). In this paper, we extend those results to locally dependent random variables, which include m-dependent random fields and U-statistics. Under conditions on the moments and the dependency neighborhoods, we derive optimal rates in the CLT for the Wasserstein-p distance. Our proofs rely on approximating the empirical average of dependent observations by the empirical average of i.i. d. random variables. To do so, we expand the Stein equation to arbitrary orders by adapting the Stein's dependency neighborhood method. Finally we illustrate the applicability of our results by obtaining efficient tail bounds.
Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey

Data quality is a key factor in the development of trustworthy AI in healthcare. A large volume of curated datasets with controlled confounding factors can improve the accuracy, robustness, and privacy of downstream AI algorithms. However, access to high-quality datasets is limited by the technical difficulties of data acquisition, and large-scale sharing of healthcare data is hindered by strict ethical restrictions. Data synthesis algorithms, which generate data with distributions similar to real clinical data, can serve as a potential solution to address the scarcity of good quality data during the development of trustworthy AI. However, state-of-the-art data synthesis algorithms, especially deep learning algorithms, focus more on imaging data while neglecting the synthesis of non-imaging healthcare data, including clinical measurements, medical signals and waveforms, and electronic healthcare records (EHRs). Therefore, in this paper, we will review synthesis algorithms, particularly for non-imaging medical data, with the aim of providing trustworthy AI in this domain. This tutorial-style review paper will provide comprehensive descriptions of non-imaging medical data synthesis, covering aspects such as algorithms, evaluations, limitations, and future research directions.
Guiding Vector Fields for the Distributed Motion Coordination of Mobile Robots

In this article, we propose coordinating guiding vector fields to achieve two tasks simultaneously with a team of robots: first, the guidance and navigation of multiple robots to possibly different paths or surfaces typically embedded in 2-D or 3-D, and second, their motion coordination while tracking their prescribed paths or surfaces. The motion coordination is defined by desired parametric displacements between robots on the path or surface. Such a desired displacement is achieved by controlling the virtual coordinates, which correspond to the path or surface's parameters, between guiding vector fields. Rigorous mathematical guarantees underpinned by dynamical systems theory and Lyapunov theory are provided for the effective distributed motion coordination and navigation of robots on paths or surfaces from all initial positions. As an example for practical robotic applications, we derive a control algorithm from the proposed coordinating guiding vector fields for a Dubins-car-like model with actuation saturation. Our proposed algorithm is distributed and scalable to an arbitrary number of robots. Furthermore, extensive illustrative simulations and fixed-wing aircraft outdoor experiments validate the effectiveness and robustness of our algorithm.
Visible-Infrared Person Re-Identification Using Privileged Intermediate Information

Visible-infrared person re-identification (ReID) aims to recognize a same person of interest across a network of RGB and IR cameras. Some deep learning (DL) models have directly incorporated both modalities to discriminate persons in a joint representation space. However, this cross-modal ReID problem remains challenging due to the large domain shift in data distributions between RGB and IR modalities. % This paper introduces a novel approach for a creating intermediate virtual domain that acts as bridges between the two main domains (i.e., RGB and IR modalities) during training. This intermediate domain is considered as privileged information (PI) that is unavailable at test time, and allows formulating this cross-modal matching task as a problem in learning under privileged information (LUPI). We devised a new method to generate images between visible and infrared domains that provide additional information to train a deep ReID model through an intermediate domain adaptation. In particular, by employing color-free and multi-step triplet loss objectives during training, our method provides common feature representation spaces that are robust to large visible-infrared domain shifts. % Experimental results on challenging visible-infrared ReID datasets indicate that our proposed approach consistently improves matching accuracy, without any computational overhead at test time. The code is available at: \href{https://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPI}{https://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPI}
Task-Agnostic Adaptation for Safe Human-Robot Handover

Human-robot interaction (HRI) is an important component to improve the flexibility of modern production lines. However, in real-world applications, the task (\ie the conditions that the robot needs to operate on, such as the environmental lighting condition, the human subjects to interact with, and the hardware platforms) may vary and it remains challenging to optimally and efficiently configure and adapt the robotic system under these changing tasks. To address the challenge, this paper proposes a task-agnostic adaptable controller that can 1) adapt to different lighting conditions, 2) adapt to individual behaviors and ensure safety when interacting with different humans, and 3) enable easy transfer across robot platforms with different control interfaces. The proposed framework is tested on a human-robot handover task using the FANUC LR Mate 200id/7L robot and the Kinova Gen3 robot. Experiments show that the proposed task-agnostic controller can achieve consistent performance across different tasks.
Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal

As diffusion tensor imaging (DTI) gains popularity in cardiac imaging due to its unique ability to non-invasively assess the cardiac microstructure, deep learning-based Artificial Intelligence is becoming a crucial tool in mitigating some of its drawbacks, such as the long scan times. As it often happens in fast-paced research environments, a lot of emphasis has been put on showing the capability of deep learning while often not enough time has been spent investigating what input and architectural properties would benefit cardiac DTI acceleration the most. In this work, we compare the effect of several input types (magnitude images vs complex images), multiple dimensionalities (2D vs 3D operations), and multiple input types (single slice vs multi-slice) on the performance of a model trained to remove artefacts caused by a simultaneous multi-slice (SMS) acquisition. Despite our initial intuition, our experiments show that, for a fixed number of parameters, simpler 2D real-valued models outperform their more advanced 3D or complex counterparts. The best performance is although obtained by a real-valued model trained using both the magnitude and phase components of the acquired data. We believe this behaviour to be due to real-valued models making better use of the lower number of parameters, and to 3D models not being able to exploit the spatial information because of the low SMS acceleration factor used in our experiments.
Potential destination discovery for low predictability individuals based on knowledge graph

Travelers may travel to locations they have never visited, which we call potential destinations of them. Especially under a very limited observation, travelers tend to show random movement patterns and usually have a large number of potential destinations, which make them difficult to handle for mobility prediction (e.g., destination prediction). In this paper, we develop a new knowledge graph-based framework (PDPFKG) for potential destination discovery of low predictability travelers by considering trip association relationships between them. We first construct a trip knowledge graph (TKG) to model the trip scenario by entities (e.g., travelers, destinations and time information) and their relationships, in which we introduce the concept of private relationship for complexity reduction. Then a modified knowledge graph embedding algorithm is implemented to optimize the overall graph representation. Based on the trip knowledge graph embedding model (TKGEM), the possible ranking of individuals' unobserved destinations to be chosen in the future can be obtained by calculating triples' distance. Empirically. PDPFKG is tested using an anonymous vehicular dataset from 138 intersections equipped with video-based vehicle detection systems in Xuancheng city, China. The results show that (i) the proposed method significantly outperforms baseline methods, and (ii) the results show strong consistency with traveler behavior in choosing potential destinations. Finally, we provide a comprehensive discussion of the innovative points of the methodology.
Diffusion unit: Interpretable edge enhancement and suppression learning for 3D point cloud segmentation

3D point clouds are discrete samples of continuous surfaces which can be used for various applications. However, the lack of true connectivity information, i.e., edge information, makes point cloud recognition challenging. Recent edge-aware methods incorporate edge modeling into network designs to better describe local structures. Although these methods show that incorporating edge information is beneficial, how edge information helps remains unclear, making it difficult for users to analyze its usefulness. To shed light on this issue, in this study, we propose a new algorithm called Diffusion Unit (DU) that handles edge information in a principled and interpretable manner while providing decent improvement. First, we theoretically show that DU learns to perform task-beneficial edge enhancement and suppression. Second, we experimentally observe and verify the edge enhancement and suppression behavior. Third, we empirically demonstrate that this behavior contributes to performance improvement. Extensive experiments and analyses performed on challenging benchmarks verify the effectiveness of DU. Specifically, our method achieves state-of-the-art performance in object part segmentation using ShapeNet part and scene segmentation using S3DIS. Our source code is available at https://github.com/martianxiu/DiffusionUnit.
BuFF: Burst Feature Finder for Light-Constrained 3D Reconstruction

Robots operating in low-light conditions with conventional cameras face significant challenges due to the low signal-to-noise ratio in the images. Previous work has demonstrated the use of burst-imaging techniques to partially overcome this issue. This study proposes a novel feature finder that enhances vision-based reconstruction under extremely low-light conditions. The approach locates features with well-defined scale and apparent motion within each burst by jointly searching in a scale-slope space. We demonstrate improved performance in feature detection, camera pose estimation and reconstruction compared to state-of-the-art feature extractors on conventional and burst-merged images. This work opens avenues for robotic applications where low-light conditions often pose difficulties such as disaster recovery and drone delivery at night.
Effect of substrate surface on the wide bandgap SnO_2 thin films grown by spin coating

Tin (IV) oxide (SnO_2) sols have been synthesized from SnCl_2·2H_2O precursor solution by applying two different processing conditions. The prepared sols were then deposited on UV-Ozone-treated quartz and soda lime glass (SLG) substrates by spin coating. The as-synthesized film was soft baked at about 100 °C for 10 min. This process was repeated 5 times to get a compact film, followed by air annealing at 250 °C for 2 h. The pristine and annealed films were characterized by UV–Vis–NIR spectroscopy, Grazing Incident X-Ray Diffraction (GIXRD), and Field Emission Scanning Electron Microscope (FESEM). The effect of the substrate surface was investigated by measuring the contact angles with De-Ionized (DI) water. UV-Ozone treatment of substrate provides a cleaner surface to grow a homogeneous film. The electrical resistivity of annealed thin films was carried out by a four-point collinear probe employing the current reversal technique and found in the range of ~ 2 × 10^3 to 3 × 10^3 Ω·cm. Film thickness was found in the range of ~ 137–285 nm, measured by a stylus profilometer. UV–Vis–NIR transmission data revealed that all the thin-film samples showed maximum (82–89) % transmission in the visible range. The optical bandgap of the thin films was estimated to be ~ 3.75–4.00 eV and ~ 3.78–4.35 eV for the films grown on SLG and quartz substrates, respectively. Graphical abstract
Portable Resistive Plate Chambers for Muography in confined environments

Muography (or muon radiography) is an imaging technique that relies on the use of cosmogenic muons as a free and safe radiation source. It can be applied in various fields such as archaeology, civil engineering, geology, nuclear reactor monitoring, nuclear waste characterization, underground surveys, etc. In such applications, sometimes deploying muon detectors is challenging due to logistics, e.g. in a narrow underground tunnel or mine. Therefore, we are developing muon detectors whose design goals include portability, robustness, autonomy, versatility, and safety. Our portable muon detectors (or “muoscopes”) are based on Resistive Plate Chambers (RPC), planar detectors that use ionization in a thin gas gap to detect cosmic muons. Prototype RPCs of active area 16×16 cm2 and 28 × 28 cm2 were built in our laboratories at Louvain-la-Neuve (UCLouvain) and Ghent (UGent) to test and compare various design options. Benefiting from the experience gained in building and operating these prototypes, we are proceeding towards the development of improved prototypes with more advanced technical layout and readiness. In this paper we provide the status of our performance studies, including the cross-validation of the two types of prototypes in a joint data taking, and an outline of the direction ahead.
Toward Identification and Characterization of IoT Software Update Practices

—Software update systems are critical for ensuring systems remain free of bugs and vulnerabilities while they are in service. While many Internet of Things (IoT) devices are capable of outlasting desktops and mobile phones, their software update practices are not yet well understood. This paper discusses efforts toward characterizing the IoT software update landscape through network analysis of IoT device trafﬁc. Our results suggest that vendors do not currently follow security best practices, and that software update standards, while available, are not being deployed. We discuss our ﬁndings and give a research agenda for improving the overall security and transparency of software updates on IoT.
A unified analytical method to quantify three types of fast frequency response from inverter-based resources

With more inverter-based resources (IBRs), our power systems have lower frequency nadirs following N-1 contingencies, and undesired under-frequency load shedding (UFLS) can occur. To address this challenge, IBRs can be programmed to provide at least three types of fast frequency response (FFR), e.g., step response, proportional response (P/f droop response), and derivative response (synthetic inertia). However, these heterogeneous FFR challenge the study of power system frequency dynamics. Thus, this paper develops an analytical frequency nadir prediction method that allows for the consideration of all three potential forms of FFR provided by IBRs. The proposed method provides fast and accurate frequency nadir estimation after N-1 generation tripping contingencies. Our method is grounded on the closed-form solution for the frequency nadir, which is solved from the second-order system frequency response model considering the governor dynamics and three types of FFR. The simulation results in the IEEE 39-bus system with different types of FFR demonstrate that the proposed method provides an accurate and fast prediction of the frequency nadir under various disturbances.
1 3 A ug 2 00 6 Wolf-Rayet Populations at High Metallicity By

Observed properties of Wolf-Rayet stars at high metallicity are reviewed. Wolf-Rayet stars are more common at higher metallicity, as a result of stronger mass-loss during earlier evolutionary phases with late WC subtypes signatures of solar metallicity or higher. Similar numbers of early (WC4–7) and late (WC8–9) stars are observed in the Solar neighbourhood, whilst late subtypes dominate at higher metallicities, such as Westerlund 1 in the inner Milky Way and in M83. The observed trend to later WC subtype within metal-rich environments is intimately linked to a metallicity dependence of WR stars, in the sense that strong winds preferentially favour late subtypes. This has relevance to (a) the upper mass limit in metal-rich galaxies such as NGC 3049, due to softer ionizing fluxes from WR stars at high metallicity; (b) evolutionary models including a WR metallicity dependence provide a better match to the observed N(WC)/N(WN) ratio. The latter conclusion partially rests upon the assumption of constant line luminosities for WR stars, yet observations and theoretical atmospheric models reveal higher line fluxes at high metallicity.
Indices of El Nio and El Nio Modoki: An Improved El Nio Modoki Index

In recent years, El Nio Modoki (a type of pseudo-El Nio) has been distinguished as a unique large-scale ocean warming phenomenon happening in the central tropical Pacific that is quite different from the traditional El Nio. In this study, EOF analysis was used to successfully separate El Nio and El Nio Modoki. The abilities of the NINO3 index, NINO3.4 index, NINO1+2 index and NINO4 index in characterizing El Nio were explored in detail. The resulting suggestion was that, comparatively, NINO3 is the optimal index for monitoring El Nio among the four NINO indices, as the other NINO indices were found to be less good at distinguishing between El Nio and El Nio Modoki signals, or were easily disturbed by El Nio Modoki signals. Further, an improved El Nio Modoki index (IEMI) was introduced in the current paper to better represent the El Nio Modoki that is captured by the second leading EOF mode of monthly tropical Pacific sea surface temperature anomalies (SSTAs). The IEMI is an improvement of the El Nio Modoki index (EMI) through adjustments made to the inappropriate weight coefficients of the three boxes of EMI. The IEMI therefore overcomes the EMI’s inability to monitor the two historical El Nio Modoki events, as well as avoids the possible risk (present in the EMI) of excluding the interference of the El Nio signal. The realistic and potential advantages of the IEMI are clear.
Efficient Property Projections of Graph Queries over Relational Data

Specialized graph data management systems have made significant advances in storing and analyzing graph-structured data. However, a large fraction of the data of interest still resides in relational data-base systems (RDBMS) due to their maturity and security reasons. Recent studies, in view of composability, show that the execution of graph queries over relational databases, (i.e., a graph layer on top of RDBMS), can provide competitive performance compared to specialized graph databases. While using the standard property graph model for graph querying, one of the main bottlenecks for efficient query processing, under memory constraints, is property projections , i.e., to project properties of nodes along paths matching a given pattern. This is because graph queries produce a large number of matching paths, resulting in a lot of requests to the data storage or a large memory footprint, to access their properties. In this paper, we propose a set of novel techniques exploiting the inherent structure of the graph (aka, a graph projection cache manager ) to provide efficient property projections. The controlled memory footprint of our solution makes it practical in multi-tenant database deployments. The empirical results on a social graph show that our solution reduce the number of accesses to the data storage by more than an order of magnitude, resulting in graph queries being up to 3.1X faster than the baseline.