Miracle-dz/UniFAR-FTUs huggingface dataset:
Radar Interferometric Experimental Reconstruction of Three-Dimensional Displacement Vectors and Mode Shapes for Masonry Constructions

Radar interferometry is an innovative measurement technique capable of remotely measuring vibrations in terms of displacement history. It apparently allows overcoming some major drawbacks of the plodding and time-consuming conventional accelerometric experimental setup for vibration testing. Indeed, radar interferometry does not require any access to the structure; moreover, its use is simpler and faster. Therefore, this technique appears very appealing for ambient vibration testing on structures, and in particular on architectural heritage masonry constructions. However, radar interferometry is still affected by some relevant limitations, that currently outweigh the advantages. One of the most important limitations is that displacements are measured only along the line-of-sight; therefore, it is not possible to determine the whole three-dimensional displacement vector for moving structural points and, consequently, to reconstruct three-dimensional mode shapes of structures, whose knowledge is crucial for characterizing the dynamic behavior of structures and for detecting eventual damages. In this paper, a theoretical and experimental approach for reconstructing both three-dimensional displacement vectors and mode shapes of masonry constructions is proposed. This approach is based on the simultaneous use of two synchronized radar interferometers, and on the application of a theoretical model assuming a specific kinematical constraint, which is generally plausible in the case of masonry constructions. The proposed approach has been validated through in-situ experimental tests on a masonry bell tower.
Seismic wave calculation at the Hz level for a general Earth

This dCSE project will concern the attempt to improve the performance of a seismic wave propagation code, SPECFEM3D-GLOBE. The original project plan was to become familiar with the code and then perform a series optimisations. Some runs were unstable, which required analysis of these cases to nd the source of the defects and consequently improve the code so that it runs stably and more eciently. This would be followed by merging two main sections of code into one, removing the need to write and read large temporary les to disk (up to at least 3 TB), boosting performance. Up to around 8% of the walltime of a calculation is taken up in mesh I/O, so removing this will achieve a speedup of the same value. This project was approved for 5 months eort
Dynamic Analysis of a Tracked Vehicle Based on a Subsystem Synthesis Method

This paper concerns about subsystem synthesis method for dynamic analysis of complex multi-body tracked vehicle systems which can be divided into several subsystems. Using subsystem synthesis method, subsystems in the complex model can be independently analyzed with virtual massless reference bodies. The virtual massless reference body has information of position, velocity and acceleration for base body. In the subsystem synthesis method, analyses of position, velocity and acceleration of the subsystems are carried out in a forward recursive fashion. For overall multi-body tracked vehicle system analysis, subsystems can be synthesized to the base body with effective matrix and vector from the virtual massless reference body of each subsystem. Using these effective matrix and vector of subsystems, equations of motion with base body can be solved independently. After then equations of motion of each subsystem can be solved using results of base body. Advantages of the proposed method are investigated in terms of computational efficiency and solution accuracy. In order to show the effectiveness of the proposed method, a multi-body tracked vehicle of seafloor massive sulfide has been analyzed.
Genetic of the mitochondrial gene in with a of (Viannia) in the Peruvian Andes

The genetic divergence caused by genetic drift and/or selection is suggested to affect the vectorial capacity and insecticide susceptibility of sand flies, as well as other arthropods. In the present study, cytochrome b ( cyt b) gene sequences were determined in 13 species circulating in Peru to establish a basis for analysis of the genetic structure, and the intraspecific genetic diversity was assessed in the Lutzomyia (Lu.) peruensis , a main vector species of Leishmania (Viannia) peruviana in Peruvian Andes. Analysis of intraspecific genetic diversity in the cyt b gene sequences from 36 Lu. peruensis identified 3 highly polymorphic sites in the middle region of the gene. Haplotype and gene network analyses were performed on the cyt b gene sequences of 130 Lu. peruensis in 9 Andean areas from 3 Departments (Ancash, Lima and La Libertad). The results showed that the populations of La Libertad were highly polymorphic and that their haplotypes were distinct from those of Ancash and Lima, where dominant haplotypes were observed, suggesting that a population bottleneck may have occurred in Ancash and Lima, but not in La Libertad. The present study indicated that the middle region of the cyt b gene is useful for the analysis of genetic structure in sand fly populations.
Tailored Uncertainty Estimation for Deep Learning Systems

Uncertainty estimation bears the potential to make deep learning (DL) systems more reliable. Standard techniques for uncertainty estimation, however, come along with specific combinations of strengths and weaknesses, e.g., with respect to estimation quality, generalization abilities and computational complexity. To actually harness the potential of uncertainty quantification, estimators are required whose properties closely match the requirements of a given use case. In this work, we propose a framework that, firstly, structures and shapes these requirements, secondly, guides the selection of a suitable uncertainty estimation method and, thirdly, provides strategies to validate this choice and to uncover structural weaknesses. By contributing tailored uncertainty estimation in this sense, our framework helps to foster trustworthy DL systems. Moreover, it anticipates prospective machine learning regulations that require, e.g., in the EU, evidences for the technical appropriateness of machine learning systems. Our framework provides such evidences for system components modeling uncertainty.
A study of tree-based methods and their combination

Tree-based methods are popular machine learning techniques used in various fields. In this work, we review their foundations and a general framework the importance sampled learning ensemble (ISLE) that accelerates their fitting process. Furthermore, we describe a model combination strategy called the adaptive regression by mixing (ARM), which is feasible for tree- based methods via ISLE. Moreover, three modified ISLEs are proposed, and their performance are evaluated on the real data sets.
Modelling ride-sourcing matching and pickup processes based on additive Gaussian Process Models

Matching and pickup processes are core features of ride-sourcing services. Previous studies have adopted abundant analytical models to depict the two processes and obtain operational insights; while the goodness of fit between models and data was dismissed. To simultaneously consider the fitness between models and data and analytically tractable formations, we propose a data-driven approach based on the additive Gaussian Process Model (AGPM) for ride-sourcing market modelling. The framework is tested based on real-world data collected in Hangzhou, China. We fit analytical models, machine learning models, and AGPMs, in which the number of matches or pickups are used as outputs and spatial, temporal, demand, and supply covariates are utilized as inputs. The results demonstrate the advantages of AGPMs in recovering the two processes in terms of estimation accuracy. Furthermore, we illustrate the modelling power of AGPM by utilizing the trained model to design and estimate idle vehicle relocation strategies.
VPNets: Volume-preserving neural networks for learning source-free dynamics

We propose volume-preserving networks (VPNets) for learning unknown source-free dynamical systems using trajectory data. We propose three modules and combine them to obtain two network architectures, coined R-VPNet and LA-VPNet. The distinct feature of the proposed models is that they are intrinsic volume-preserving. In addition, the corresponding approximation theorems are proved, which theoretically guarantee the expressivity of the proposed VPNets to learn source-free dynamics. The effectiveness, generalization ability and structure-preserving property of the VP-Nets are demonstrated by numerical experiments.
Impact of Helium Ion Implantation Dose and Annealing on Dense Near-Surface Layers of NV Centers

The implantation of diamonds with helium ions has become a common method to create hundreds-nanometers-thick near-surface layers of NV centers for high-sensitivity sensing and imaging applications; however, optimal implantation dose and annealing temperature are still a matter of discussion. In this study, we irradiated HPHT diamonds with an initial nitrogen concentration of 100 ppm using different implantation doses of helium ions to create 200-nm thick NV layers. We compare a previously considered optimal implantation dose of ∼1012 He+/cm2 to double and triple doses by measuring fluorescence intensity, contrast, and linewidth of magnetic resonances, as well as longitudinal and transversal relaxation times T1 and T2. From these direct measurements, we also estimate concentrations of P1 and NV centers. In addition, we compare the three diamond samples that underwent three consequent annealing steps to quantify the impact of processing at 1100 °C, which follows initial annealing at 800 °C. By tripling the implantation dose, we have increased the magnetic sensitivity of our sensors by 28±5%. By projecting our results to higher implantation doses, we demonstrate that it is possible to achieve a further improvement of up to 70%. At the same time, additional annealing steps at 1100 °C improve the sensitivity only by 6.6 ± 2.7%.
Morphodynamics of active nematic fluid surfaces

Abstract Morphodynamic equations governing the behaviour of active nematic fluids on deformable curved surfaces are constructed in the large deformation limit. Emphasis is placed on the formulation of objective rates that account for normal deformations whilst ensuring that tangential flows are Eulerian, and the use of the surface derivative (rather than the covariant derivative) in the nematic free energy, which elastically couples local order to out-of-plane bending of the surface. Focusing on surface geometry and its dynamical interplay with the hydrodynamics, several illustrative instabilities are then characterised. These include cases where the role of the Scriven–Love number and its nematic analogue are non-negligible, and where the active nematic forcing can be characterised by an analogue of the Föppl–von Kármán number. For the former, flows and changes to the nematic texture are coupled to surface geometry by viscous dissipation. This is shown to result in non-trivial relaxation dynamics for a nematic tube. For the latter, the nematic active forcing couples to the surface bending terms of the nematic free energy, resulting in extensile (active ruffling) and contractile (active pearling) instabilities in the tube shape, as well as active bend instabilities in the nematic texture. In comparison to the flat case, such bend instabilities now have a threshold set by the extrinsic curvature of the tube. Finally, we examine a topological defect located on an almost flat surface, and show that there exists a steady state where a combination of defect elasticity, activity and non-negligible spin connection drive a shape change in the surface.
Visualization and Optimization Techniques for High Dimensional Parameter Spaces

High dimensional parameter space optimization is crucial in many applications. The parameters affecting this performance can be both numerical and categorical in their type. The existing techniques of black-box optimization and visual analytics are good in dealing with numerical parameters but analyzing categorical variables in context of the numerical variables are not well studied. Hence, we propose a novel approach, to create an auto-tuning framework for storage systems optimization combining both direct optimization techniques and visual analytics research. While the optimization algorithm will be the core of the system, visual analytics will provide a guideline with the help of an external agent (expert) to provide crucial hints to narrow down the large search space for the optimization engine. As part of the initial step towards creating an auto-tuning engine for storage systems optimization, we created an Interactive Configuration Explorer \textit{ICE}, which directly addresses the need of analysts to learn how the dependent numerical variable is affected by the parameter settings given multiple optimization objectives. No information is lost as ICE shows the complete distribution and statistics of the dependent variable in context with each categorical variable. Analysts can interactively filter the variables to optimize for certain goals such as achieving a system with maximum performance, low variance, etc. Our system was developed in tight collaboration with a group of systems performance researchers and its final effectiveness was evaluated with expert interviews, a comparative user study, and two case studies. We also discuss our research plan for creating an efficient auto-tuning framework combining black-box optimization and visual analytics for storage systems performance optimization.
Symbolic analysis meets federated learning to enhance malware identifier

The manual methods to create detection rules are no longer practical in the anti-malware product since the number of malware threats has been growing over past years. Thus, the turn to machine learning approaches is a promising way to make malware recognition more efficient. The traditional centralized machine learning requires a large amount of data to train a model with excellent performance. To boost the malware detection, the training data might be on various kind of data sources such as data on the host, network, and cloud-based anti-malware components, or even, data from different enterprises. To avoid the expenses of data collection as well as the leakage of private data, we present a federated learning system to identify malware through behavioral graphs, i.e., system call dependency graphs. It is based on a deep learning model including a graph autoencoder and a multiclass classifier module. This model is trained by a secure learning protocol among clients to preserve the private data against inference attacks. Using the model to identify malware, we achieve the accuracy of for homogeneous graph data and for inhomogeneous graph data.
Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking

There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. \acPL optimization with the REINFORCE algorithm can be used for optimizing black-box objective functions over permutations. In particular, it can be used for optimizing fairness measures. However, though effective for queries with a moderate number of repeating sessions, \acPL optimization has room for improvement for queries with a small number of repeating sessions. In this paper, we present a novel way of representing permutation distributions, based on the notion of permutation graphs. Similar to~\acPL, our distribution representation, called~\acPPG, can be used for black-box optimization of fairness. Different from~\acPL, where pointwise logits are used as the distribution parameters, in~\acPPG pairwise inversion probabilities together with a reference permutation construct the distribution. As such, the reference permutation can be set to the best sampled permutation regarding the objective function, making~\acPPG suitable for both deterministic and stochastic rankings. Our experiments show that~\acPPG, while comparable to~\acPL for larger session repetitions (i.e., stochastic ranking), improves over~\acPL for optimizing fairness metrics for queries with one session (i.e., deterministic ranking). Additionally, when accurate utility estimations are available, e.g., in tabular models, the performance of \acPPG in fairness optimization is significantly boosted compared to lower quality utility estimations from a learning to rank model, leading to a large performance gap with PL. Finally, the pairwise probabilities make it possible to impose pairwise constraints such as "item $d_1$ should always be ranked higher than item $d_2$. '' Such constraints can be used to simultaneously optimize the fairness metric and control another objective such as ranking performance.
Oracle Guided Image Synthesis with Relative Queries

Isolating and controlling specific features in the outputs of generative models in a user-friendly way is a difficult and open-ended problem. We develop techniques that allow an oracle user to generate an image they are envisioning in their head by answering a sequence of relative queries of the form \textit{"do you prefer image $a$ or image $b$? "} Our framework consists of a Conditional VAE that uses the collected relative queries to partition the latent space into preference-relevant features and non-preference-relevant features. We then use the user's responses to relative queries to determine the preference-relevant features that correspond to their envisioned output image. Additionally, we develop techniques for modeling the uncertainty in images' predicted preference-relevant features, allowing our framework to generalize to scenarios in which the relative query training set contains noise.
Persistent current oscillations in a double-ring quantum gas

Vorticity in closed quantum fluid circuits is known to arise in the form of persistent currents. In this work, we develop a method to engineer transport of the quantized vorticity between density-coupled ring-shaped atomic Bose-Einstein condensates in experimentally accessible regimes. Introducing a tunable weak link between the rings, we observe and characterize the controllable periodic transfer of the current and investigate the role of temperature on suppressing these oscillations via a range of complementary state-of-the-art numerical methods. Our setup paves the way for precision measurements of local acceleration and rotation.
Design and analysis of a new flexure-based XY stage

This article presents the design, modeling, and experimental testing of a novel piezo-driven XY stage with parallel, decoupled, and compact kinematic structure. The structural design of the stage is based on a hybrid compliant mechanism employing the right-circular double-rocker mechanism and the leaf-type parallelogram mechanism. The proposed XY stage is capable of producing a large workspace range, an excellent decoupled motion, and a suitable resonant frequency. By means of the pseudorigid-body-model method, the theoretical models of the XY stage are derived. Using the finite element analysis simulations, the optimal structural parameters are acquired, and the theoretical models are analyzed and validated. A prototype of the proposed stage was finally manufactured, and several experimental investigations were performed to validate its performances. The experimental results show that the XY stage has a large amplification ratio of 7.48 and a large workspace range of 150.3 µm × 147.9 µm. In addition, the parasitic motion along the y-axis (x-axis) accounts for 0.94% (0.74%) of the x-axis (y-axis) motion, which indicates that the stage possesses excellent decoupling characteristics.
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance

Existing metrics for assessing question generation not only require costly human reference but also fail to take into account the input context of generation, rendering the lack of deep understanding of the relevance between the generated questions and input contexts. As a result, they may wrongly penalize a legitimate and reasonable candidate question when it (1) involves complicated reasoning with the context or (2) can be grounded by multiple evidences in the context.In this paper, we propose QRelScore, a context-aware Relevance evaluation metric for Question Generation.Based on off-the-shelf language models such as BERT and GPT2, QRelScore employs both word-level hierarchical matching and sentence-level prompt-based generation to cope with the complicated reasoning and diverse generation from multiple evidences, respectively.Compared with existing metrics, our experiments demonstrate that QRelScore is able to achieve a higher correlation with human judgments while being much more robust to adversarial samples.
Learned Gradient of a Regularizer for Plug-and-Play Gradient Descent

The Plug-and-Play (PnP) framework allows integrating advanced image denoising priors into optimization algorithms, to eﬃciently solve a variety of image restoration tasks. The Plug-and-Play alternating direction method of multipliers (ADMM) and the Regularization by Denoising (RED) algorithms are two examples of such methods that made a breakthrough in image restoration. However, while the former method only applies to proximal algorithms, it has recently been shown that there exists no regularization that explains the RED algorithm when the denoisers lack Jacobian symmetry, which happen to be the case of most practical denoisers. To the best of our knowledge, there exists no method for training a network that directly represents the gradient of a regularizer, which can be directly used in Plug-and-Play gradient-based algorithms. We show that it is possible to train a denoiser along with a network that corresponds to the gradient of its regularizer. We use this gradient of the regularizer in gradient-based optimization methods and obtain better results comparing to other generic Plug-and-Play approaches. We also show that the regularizer can be used as a pre-trained network for unrolled gradient descent. Lastly, we show that the resulting denoiser allows for a quick convergence of the Plug-and-Play ADMM.
Accurate CMB covariance matrices: exact calculation and approximations

Accurate covariance matrices are required for a reliable estimation of cosmological parameters from pseudo-power spectrum estima- tors. In this work, we focus on the analytical calculation of covariance matrices. We consider the case of observations of the Cosmic Microwave Background in temperature and polarization on a small footprint such as in the SPT-3G experiment, which observes 4% of the sky. Power spectra evaluated on small footprints are expected to have large correlations between modes, and these need to be accurately modelled. We present, for the ﬁrst time, an algorithm that allows an e ﬃ cient (but computationally expensive) exact calcu- lation of analytic covariance matrices. Using it as our reference, we test the accuracy of existing fast approximations of the covariance matrix. We ﬁnd that, when the power spectrum is binned in wide bandpowers, current approaches are correct up to the 5% level on the SPT-3G small sky footprint. Furthermore, we propose a new approximation which improves over the previous ones reaching a precision of 1% in the wide bandpowers case and generally more than 4 times more accurate than current approaches. Finally, we de- rive the covariance matrices for mask-corrected power spectra estimated by the PolSpice code. In particular, we include, in the case of a small sky fraction, the e ﬀ ect of the apodization of the large scale modes. While we considered the speciﬁc case of the CMB, our results are applicable to any other cosmological probe which requires the calculation of pseudo-power spectrum covariance matrices.