Miracle-dz/UniFAR-FTUs huggingface dataset:
Tensioning Process of Sanhao Arch Pylon Cable-Stayed Bridge

Sanhao cable-stayed bridge located in Liaoning Province of China features an impressive arch pylon system including two inclined steel arch ribs laterally across the bridge deck. The two 60m-high arch ribs stretches out from the same base on the main pier foundation and separates up to 55m at the top, which have the appearance of two tennis rackets. Ten pairs of longitudinal cables on both sides connect these two “tennis rackets” forming a triangle pylon system. Twenty pairs of inclined stay cables link the steel arch pylon and the prestressed concrete deck, which carry the self-weight of the deck and service loads. The tensioning process of the horizontal and inclined stay cables, especially the tensioning process of the individual strands for each cable, is the most challenging work of the bridge construction for achieving a sustainable structure, characterized by the smooth lines of the arch pylons and bridge deck, the safe stress of the steel arch ribs and the crack-free prestressed concrete deck.
Measurement of equivalent circuit parameters of inverter fed induction motors

For an accurate prediction of the steady state performance of inverter fed induction motors it is required to determine the parameters of the fundamental and time harmonic equivalent circuits of the motors. A new method to measure these parameters using digital techniques is presented. This method is applied to an experimental induction motor, and its parameters are measured. From this, some characteristics of the parameters, such as voltage characteristics of the exciting inductance and iron loss resistance and frequency characteristics of the secondary resistance and leakage inductance, are clarified. Finally the fundamental and harmonic currents of the experimental induction motor fed by a voltage source inverter are predicted using the equivalent circuit parameters obtained, and they are compared with the measured results.
Study on Product Customization Design Knowledge Framework Based on Ontology

In this paper, we concentrate on the ontology-Based design knowledge modeling technologies for the product form. For that, we study the ontology modeling theory, the representation of product design knowledge. From the view of the DKM, the product form prototype and its mathematical model will be established for Product Forms Innovative Design System (FIDS).
Transient thermohydraulic modeling of two-phase fluid systems

This paper presents a transient thermohydraulic modeling, initially developed for a capillary pumped loop in gravitational applications, but also possibly suitable for all kinds of two-phase fluid systems. Using finite volumes method, it is based on Navier-Stokes equations for transcribing fluid mechanical aspects. The main feature of this 1D-model is based on a network representation by analogy with electrical. This paper also proposes a parametric study of a counterflow condenser following the sensitivity to inlet mass flow rate and cold source temperature. The comparison between modeling results and experimental data highlights a good numerical evaluation of temperatures. Furthermore, the model is able to represent a pretty good dynamic evolution of hydraulic variables.
Weak Solutions to an Euler Alignment System with Singular Interactions in a Bounded Domain

Euler alignment systems appear as hydrodynamic limits of interacting self-propelled particle systems such as the (generalized) Cucker-Smale model. In this work, we study weak solutions to an Euler alignment system on smooth, bounded, connected domains. This particular Euler alignment system includes singular alignment, attraction, and repulsion interaction kernels which correspond to a Yukawa potential. We also include a confinement potential and self-propulsion. We embed the problem into an abstract Euler system to conclude that infinitely many weak solutions exist. We further show that we can construct solutions satisfying bounds on an energy quantity, and that the solutions satisfy a weak-strong uniqueness principle. Finally, we present an addition of leader-agents governed by controlled ODEs, and modification of the interactions to be Bessel potentials of fractional order $s>2$.
High Molecular-gas to Dust Mass Ratios Predicted in Most Quiescent Galaxies

Observations of cold molecular gas reservoirs are critical for understanding the shutdown of star formation in massive galaxies. While dust continuum is an efficient and affordable tracer, this method relies upon the assumption of a “normal” molecular-gas to dust mass ratio, δ GDR, typically of order 100. Recent null detections of quiescent galaxies in deep dust continuum observations support a picture where the cold gas and dust have been rapidly depleted or expelled. In this work, we present another viable explanation: a significant fraction of galaxies with low star formation per unit stellar mass are predicted to have extreme δ GDR ratios. We show that simulated massive quiescent galaxies at 0 < z < 3 in the simba cosmological simulations have δ GDR values that extend >4 orders of magnitude. The dust in most simulated quiescent galaxies is destroyed significantly more rapidly than the molecular gas depletes, and cannot be replenished. The transition from star-forming to quiescent halts dust formation via star formation processes, with dust subsequently destroyed by supernova shocks and thermal sputtering of dust grains embedded in hot plasma. After this point, the dust growth rate in the models is not sufficient to overcome the loss of >3 orders of magnitude in dust mass to return to normal values of δ GDR despite having high metallicity. Our results indicate that it is not straight forward to use a single observational indicator to robustly preselect exotic versus normal ratios. These simulations make strong predictions that can be tested with millimeter facilities.
Internet of Things (IoT) Connectivity in 6G: An Interplay of Time, Space, Intelligence, and Value

Internet of Things (IoT) connectivity has a prominent presence in the 5G wireless communication systems. As these systems are being deployed, there is a surge of research efforts and visions towards 6G wireless systems. In order to position the evolution of IoT within the 6G systems, this paper first takes a critical view on the way IoT connectivity is supported within 5G. Following that, the wireless IoT evolution is discussed through multiple dimensions: time, space, intelligence, and value. We also conjecture that the focus will broaden from IoT devices and their connections towards the emergence of complex IoT environments, seen as building blocks of the overall IoT ecosystem.
Towards Practical Evaluation of Android ICC Resolution Techniques

Inter-component communication (ICC) is a key mechanism in mobile apps, which enables the message-based control flow transferring and data passing between components. Effective ICC resolution requires precisely identifying entry points, tracking data values of ICC fields, modeling related framework APIs, etc. Due to the multiple complex characteristics involved, the evaluation of ICC resolution approaches is challenging. Although a set of ICC-related hand-made benchmarks are publicly available, which supports the assessments on parts of the characteristics, the practical evaluations on more complicated scenarios are not available due to the lack of a reliable oracle from real-world apps. In this paper, we carried out the research to answer three key questions: 1) to what extent can the existing benchmarks contribute to the evaluation of ICC resolution ; 2) how to construct the practical, representative, and reliable oracle of real-world apps ; 3) what are the pros and cons of the state-of-the-art ICC resolution tools. First, we performed pre-evaluations of tools on two sets of benchmarks, including four hand-made ones and a large-scale dataset with 4,000 real-world apps, using metrics adopted in existing works as well as three graph-based ones proposed by us. For a more practical evaluation, we designed a stack-based trace analysis approach to extract reliable ICC links and manually labeled the involved code characteristics for 1,452 links. With the newly constructed oracle, we conducted another evaluation of the existing tools. Based on the evaluation results, we identified the limitations of the existing tools and developed a new ICC resolution tool ICCBot , which achieves better performance on both the hand-made and real-world apps.
Arbitrary order principal directions and how to compute them

Curvature principal directions on geometric surfaces are a ubiquitous concept of Geometry Processing techniques. However they only account for order 2 differential quantities, oblivious of higher order differential behaviors. In this paper, we extend the concept of principal directions to higher orders for surfaces in R^3 by considering symmetric differential tensors. We further show how they can be explicitly approximated on point set surfaces and that they convey valuable geometric information, that can help the analysis of 3D surfaces.
Simulating cloud-aerosol interactions made by ship emissions

Satellite imagery can detect temporary cloud trails or ship tracks formed from aerosols emitted from large ships traversing our oceans, a phenomenon that global climate models cannot directly reproduce. Ship tracks are observable examples of marine cloud brightening, a potential solar climate intervention that shows promise in helping combat climate change. Whether or not a ship’s emission path visibly impacts the clouds above and how long a ship track visibly persists largely depends on the exhaust type and properties of the boundary layer with which it mixes. In order to be able to statistically infer the longevity of ship-emitted aerosols and characterize atmospheric conditions under which they form, a first step is to simulate, with mathematical surrogate model rather than an expensive physical model, the path of these cloud-aerosol interactions with parameters that are inferable from imagery. This will allow us to compare when/where we would expect to ship tracks to be visible, independent of atmospheric conditions, with what is actually observed from satellite imagery to be able to infer under what atmospheric conditions do ship tracks form. In this paper, we will discuss an approach to stochastically simulate the behavior of ship induced aerosols parcels within naturally generated clouds. Our method can use wind fields and potentially relevant atmospheric variables to determine the approximate movement and behavior of the cloudaerosol tracks, and uses a stochastic differential equation (SDE) to model the persistence behavior of cloud-aerosol paths. This SDE incorporates both a drift and diffusion term which describes the movement of aerosol parcels via wind and their diffusivity through the atmosphere, respectively. We successfully demonstrate our proposed approach with an example using simulated wind fields and ship paths.
Study on the tip-deflection of a piezoelectric bimorph cantilever in the static state

Bimorph-type bending actuators have been widely used in many applications as converters of electric input to mechanical motion, or vice versa. In this paper a new piezoelectric model of a multilayer piezoelectric cantilever is obtained, and the analytic expressions for the curvature, displacement and tip-deflection of the multilayer structure are derived in the static state. With the use of this model and the solution, the tip-deflections of the piezoelectric bimorph cantilever in different working situations are compared and the effect of the metal electrodes on the tip-deflection of the cantilever is considered. The above-mentioned methods and conclusions can be used for optimized design of PZT cantilever structure.
Pipeline for 3D reconstruction of the human body from AR/VR headset mounted egocentric cameras

In this paper, we propose a novel pipeline for the 3D reconstruction of the full body from egocentric viewpoints. 3-D reconstruction of the human body from egocentric viewpoints is a challenging task as the view is skewed and the body parts farther from the cameras are occluded. One such example is the view from cameras installed below VR headsets. To achieve this task, we first make use of conditional GANs to translate the egocentric views to full body third-person views. This increases the comprehensibility of the image and caters to occlusions. The generated third-person view is further sent through the 3D reconstruction module that generates a 3D mesh of the body. We also train a network that can take the third person full-body view of the subject and generate the texture maps for applying on the mesh. The generated mesh has fairly realistic body proportions and is fully rigged allowing for further applications such as real-time animation and pose transfer in games. This approach can be key to a new domain of mobile human telepresence.
VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks based on Path Planning and Deep Reinforcement Learning

Vehicular social networking is an emerging application of the promising Internet of Vehicles (IoV) which aims to achieve the seamless integration of vehicular networks and social networks. However, the unique characteristics of vehicular networks such as high mobility and frequent communication interruptions make content delivery to end-users under strict delay constrains an extremely challenging task. In this paper, we propose a social-aware vehicular edge computing architecture that solves the content delivery problem by using some of the vehicles in the network as edge servers that can store and stream popular content to close-by end-users. The proposed architecture includes three components. First, we propose a social-aware graph pruning search algorithm that computes and assigns the vehicles to the shortest path with the most relevant vehicular content providers. Secondly, we use a traffic-aware content recommendation scheme to recommend relevant content according to their social context. This scheme uses graph embeddings in which the vehicles are represented by a set of low-dimension vectors (vehicle2vec) to store information about previously consumed content. Finally, we propose a Deep Reinforcement Learning (DRL) method to optimize the content provider vehicles distribution across the network. The results obtained from a realistic traffic simulation show the effectiveness and robustness of the proposed system when compared to the state-of-the-art baselines.
Parallel Physics-Informed Neural Networks with Bidirectional Balance

As an emerging technology in deep learning, physics-informed neural networks (PINNs) have been widely used to solve various partial differential equations (PDEs) in engineering. However, PDEs based on practical considerations contain multiple physical quantities and complex initial boundary conditions, thus PINNs often returns incorrect results. Here we take heat transfer problem in multilayer fabrics as a typical example. It is coupled by multiple temperature fields with strong correlation, and the values of variables are extremely unbalanced among different dimensions. We clarify the potential difficulties of solving such problems by classic PINNs, and propose a parallel physics-informed neural networks with bidirectional balance. In detail, our parallel solving framework synchronously fits coupled equations through several multilayer perceptron. Moreover, we design two modules to balance forward process of data and back-propagation process of loss gradient. This bidirectional balance not only enables the whole network to converge stably, but also helps to fully learn various physical conditions in PDEs. We provide a series of ablation experiments to verify the effectiveness of the proposed methods. The results show that our approach makes the PINNs unsolvable problem solvable, and achieves excellent solving accuracy.
Optimizing robot planning domains to reduce search time for long-horizon planning

We have recently introduced a system that automatically generates robotic planning operators from human demonstrations. One feature of our system is the operator count, which keeps track of the application frequency of every operator within the demonstrations. In this extended abstract, we show that we can use the count to slim down domains with the goal of decreasing the search time for long-horizon planning goals. The conceptual idea behind our approach is that we would like to prioritize operators that have occurred more often in the demonstrations over those that were not observed so frequently. We, therefore, propose to limit the domain only to the most popular operators. If this subset of operators is not sufficient to find a plan, we iteratively expand this subset of operators. We show that this significantly reduces the search time for long-horizon planning goals.
Dam Deformation Monitoring Model Based on Neural Network with Ant Colony Optimization Algorithm

: Back propagation algorithm (BP) is widely used as a multilayer feedforward neural network model in the analysis of water engineering projects monitoring data, but it has low solution accuracy, slow search speed and easy to get into a local minimum. To overcome these shortcomings, in this paper, a new learning method of neural network with ant colony optimization (ACO) is introduced to achieve optimization solution of the model weights, the ACO-BP monitoring model of dam deformation is established as well. And actual examples show that the ant colony algorithm is effective and rapid.
Optimal Delay-Outage Analysis for Noise-Limited Wireless Networks With Caching, Computing, and Communications

Performance assessment and optimization for networks jointly performing caching, computing, and communication (3C) has recently drawn significant attention because many emerging applications require 3C functionality. However, studies in the literature mostly focus on the particular algorithms and setups of such networks, while their theoretical understanding and characterization has been less explored. To fill this gap, this paper conducts the asymptotic (scaling-law) analysis for the delay-outage tradeoff of noise-limited wireless edge networks with joint 3C. In particular, assuming the user requests for different tasks following a Zipf distribution, we derive the analytical expression for the optimal caching policy. Based on this, we next derive the closed-form expression for the optimum outage probability as a function of delay and other network parameters for the case that the Zipf parameter is smaller than 1. Then, for the case that the Zipf parameter is larger than 1, we derive the closed-form expressions for upper and lower bounds of the optimum outage probability. We provide insights and interpretations based on the derived expressions. Computer simulations validate our analytical results and insights.
FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy

: We address the problem of goal-directed cloth manipulation, a chal-lenging task due to the deformability of cloth. Our insight is that optical ﬂow, a technique normally used for motion estimation in video, can also provide an effective representation for corresponding cloth poses across observation and goal images. We introduce FabricFlowNet (FFN), a cloth manipulation policy that leverages ﬂow as both an input and as an action representation to improve performance. FabricFlowNet also elegantly switches between dual-arm and single-arm actions based on the desired goal. We show that FabricFlowNet signiﬁcantly outperforms state-of-the-art model-free and model-based cloth manipulation policies. We also present real-world experiments on a bimanual system, demonstrating effective sim-to-real transfer. Finally, we show that our method generalizes when trained on a single square cloth to other cloth shapes, such as T-shirts and rectangular cloths. Video and other supplementary materials are available at: https://sites.google. com/view/fabricﬂownet.
Spatially and Seamlessly Hierarchical Reinforcement Learning for State Space and Policy space in Autonomous Driving

Despite advances in hierarchical reinforcement learning, its applications to path planning in autonomous driving on highways are challenging. One reason is that conventional hierarchical reinforcement learning approaches are not amenable to autonomous driving due to its riskiness: the agent must move avoiding multiple obstacles such as other agents that are highly unpredictable, thus safe regions are small, scattered, and changeable over time. To overcome this challenge, we propose a spatially hierarchical reinforcement learning method for state space and policy space. The high-level policy selects not only behavioral sub-policy but also regions to pay mind to in state space and for outline in policy space. Subsequently, the low-level policy elaborates the short-term goal position of the agent within the outline of the region selected by the high-level command. The network structure and optimization suggested in our method are as concise as those of single-level methods. Experiments on the environment with various shapes of roads showed that our method finds the nearly optimal policies from early episodes, outperforming a baseline hierarchical reinforcement learning method, especially in narrow and complex roads. The resulting trajectories on the roads were similar to those of human strategies on the behavioral planning level.
BagBERT: BERT-based bagging-stacking for multi-topic classification

This paper describes our submission on the COVID-19 literature annotation task at Biocreative VII. We proposed an approach that exploits the knowledge of the globally non-optimal weights, usually rejected, to build a rich representation of each label. Our proposed approach consists of two stages: (1) A bagging of various initializations of the training data that features weakly trained weights, (2) A stacking of heterogeneous vocabulary models based on BERT and RoBERTa Embeddings. The aggregation of these weak insights performs better than a classical globally efficient model. The purpose is the distillation of the richness of knowledge to a simpler and lighter model. Our system obtains an Instance-based F1 of 92.96 and a Label-based micro-F1 of 91.35.
Automatically detecting data drift in machine learning classifiers

Classifiers and other statistics-based machine learning (ML) techniques generalize, or learn, based on various statistical properties of the training data. The assumption underlying statistical ML resulting in theoretical or empirical performance guarantees is that the distribution of the training data is representative of the production data distribution. This assumption often breaks; for instance, statistical distributions of the data may change. We term changes that affect ML performance `data drift' or `drift'. Many classification techniques compute a measure of confidence in their results. This measure might not reflect the actual ML performance. A famous example is the Panda picture that is correctly classified as such with a confidence of about 60\%, but when noise is added it is incorrectly classified as a Gibbon with a confidence of above 99\%. However, the work we report on here suggests that a classifier's measure of confidence can be used for the purpose of detecting data drift. We propose an approach based solely on classifier suggested labels and its confidence in them, for alerting on data distribution or feature space changes that are likely to cause data drift. Our approach identities degradation in model performance and does not require labeling of data in production which is often lacking or delayed. Our experiments with three different data sets and classifiers demonstrate the effectiveness of this approach in detecting data drift. This is especially encouraging as the classification itself may or may not be correct and no model input data is required. We further explore the statistical approach of sequential change-point tests to automatically determine the amount of data needed in order to identify drift while controlling the false positive rate (Type-1 error).