Miracle-dz/UniFAR-FTUs huggingface dataset:
New control methodology for a morphing wing demonstrator

A morphing wing can improve the aircraft aerodynamic performance by changing the wing airfoil depending on the flight conditions. In this paper, a new control methodology is presented for a morphing wing demonstrator tested in a subsonic wind tunnel in the open-loop configuration. Actuators integrated inside the wing are used to modify the flexible structure, which is an integral part of the wing. In this project, the actuators are made in-house and controlled with logic control, which is developed within the main frame of this work. The characterization of the flow (laminar or turbulent) over the wing is obtained starting from the pressure signals measured over the flexible part of the wing (upper surface). The signals are acquired by using some pressure sensors (Kulite sensors) incorporated in this flexible part of the wing upper surface. The technique used to collect Kulite pressure data and the post-processing methodology are explained. The recorded pressure data are sometimes subjected to noise, which is filtered before being processed. The standard deviation and power spectrum visualization of the pressure data approaches are used to evaluate the quality of the flow over the wing and estimate the transition point position in the area monitored by the Kulite sensors. In addition, infrared thermography visualization is implemented to observe the transition region over the entire wing upper surface, and to validate the methodology applied to the pressure data in this way. The demonstrator measures 1.5 m chordwise and 1.5 m spanwise. Four miniature actuators fixed on two actuation lines are used to morph the wing. The wing is also equipped with a rigid aileron. The experimental aerodynamic results obtained after post processing validate the numerical prediction for the transition location.
Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction

Conditional behavior prediction (CBP) builds up the foundation for a coherent interactive prediction and plan-ning framework that can enable more efficient and less conser-vative maneuvers in interactive scenarios. In CBP task, we train a prediction model approximating the posterior distribution of target agents' future trajectories conditioned on the future trajectory of an assigned ego agent. However, we argue that CBP may provide overly confident anticipation on how the autonomous agent may influence the target agents' behavior. Consequently, it is risky for the planner to query a CBP model. Instead, we should treat the planned trajectory as an intervention and let the model learn the trajectory distribution under intervention. We refer to it as the interventional behavior prediction (IBP) task. Moreover, to properly evaluate an IBP model with offline datasets, we propose a Shapley-value-based metric to verify if the prediction model satisfies the inherent temporal independence of an interventional distribution. We show that the proposed metric can effectively identify a CBP model violating the temporal independence, which plays an important role when establishing IBP benchmarks.
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training

Retrosynthesis prediction is one of the fundamental challenges in organic chemistry and related fields. The goal is to find reactants molecules that can synthesize product molecules. To solve this task, we propose a new graph-to-graph transformation model, G2GT, in which the graph encoder and graph decoder are built upon the standard transformer structure. We also show that self-training, a powerful data augmentation method that utilizes unlabeled molecule data, can significantly improve the model's performance. Inspired by the reaction type label and ensemble learning, we proposed a novel weak ensemble method to enhance diversity. We combined beam search, nucleus, and top-k sampling methods to further improve inference diversity and proposed a simple ranking algorithm to retrieve the final top-10 results. We achieved new state-of-the-art results on both the USPTO-50K dataset, with top1 accuracy of 54%, and the larger data set USPTO-full, with top1 accuracy of 50%, and competitive top-10 results.
Interfacial cavitation

Abstract Cavitation has long been recognized as a crucial predictor, or precursor, to the ultimate failure of various materials, ranging from ductile metals to soft and biological materials. Traditionally, cavitation in solids is defined as an unstable expansion of a void or a defect within a material. The critical applied load needed to trigger this instability -- the critical pressure -- is a lengthscale independent material property and has been predicted by numerous theoretical studies for a breadth of constitutive models. While these studies usually assume that cavitation initiates from defects in the bulk of an otherwise homogeneous medium, an alternative and potentially more ubiquitous scenario can occur if the defects are found at interfaces between two distinct media within the body. Such interfaces are becoming increasingly common in modern materials with the use of multimaterial composites and layer-by-layer additive manufacturing methods. However, a criterion to determine the threshold for interfacial failure, in analogy to the bulk cavitation limit, has yet to be reported. In this work, we fill this gap. Our theoretical model captures a lengthscale independent limit for interfacial cavitation, and is shown to agree with our observations at two distinct lengthscales, via two different experimental systems. To further understand the competition between the two cavitation modes (bulk versus interface), we expand our investigation beyond the elastic response to understand the ensuing unstable propagation of delamination at the interface. A phase diagram summarizes these results, showing regimes in which interfacial failure becomes the dominant mechanism.
C ONTINUAL L EARNING IN A M ODULAR E MBODIED A GENT VIA E ND - TO -E ND I NTERACTION

In this work we give a case study of an embodied machine-learning (ML) powered agent that improves itself via interactions with crowd-workers. The agent consists of a set of modules, some of which are learned, and others heuristic. While the agent is not “end-to-end” in the ML sense, end-to-end interaction is a vital part of the agent’s learning mechanism. We describe how the design of the agent works together with the design of multiple annotation interfaces to allow crowd-workers to assign credit to module errors from end-to-end interactions, and to label data for individual modules. Over multiple automated human-agent interaction, credit assignment, data annotation, and model re-training and re-deployment, rounds we demonstrate agent improvement.
Weak tension accelerates hybridization and dehybridization of short oligonucleotides

The hybridization and dehybridization of DNA subject to tension is relevant to fundamental genetic processes and to the design of DNA-based mechanobiology assays. While strong tension accelerates DNA melting and decelerates DNA annealing, the effects of tension weaker than 5 pN are less clear. In this study, we developed a DNA bow assay, which uses the bending rigidity of double-stranded DNA (dsDNA) to exert weak tension on a single-stranded DNA (ssDNA) target in the range of 2pN to 6pN. Combining this assay with single-molecule FRET, we measured the hybridization and dehybridization kinetics between a 15 nt ssDNA under tension and a 8-9 nt oligo, and found that both the hybridization and dehybridization rates monotonically increase with tension for various nucleotide sequences tested. These findings suggest that the nucleated duplex in its transition state is more extended than the pure dsDNA or ssDNA counterpart. Our simulations using the coarse-grained oxDNA2 model indicate that the increased extension of the transition state is due to exclusion interactions between unpaired ssDNA regions in close proximity to one another. This study highlights an example where the ideal worm-like chain models fail to explain the kinetic behavior of DNA in the low force regime.
GraphHop++: New Insights into GraphHop and Its Enhancement

—An enhanced label propagation (LP) method called GraphHop has been proposed recently. It outperforms graph convolutional networks (GCNs) in the semi-supervised node clas- siﬁcation task on various networks. Although the performance of GraphHop was explained intuitively with joint node attributes and labels smoothening, its rigorous mathematical treatment is lacking. In this paper, new insights into GraphHop are provided by analyzing it from a constrained optimization viewpoint. We show that GraphHop offers an alternate optimization to a certain regularization problem deﬁned on graphs. Based on this interpretation, we propose two ideas to improve GraphHop furthermore, which leads to GraphHop++. We conduct extensive experiments to demonstrate the effectiveness and efﬁciency of GraphHop++. It is observed that GraphHop++ outperforms all other benchmarking methods, including GraphHop, consistently on ﬁve test datasets as well as an object recognition task at extremely low label rates (i.e., 1, 2, 4, 8, 16, and 20 labeled samples per class).
Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on Signal Strength Represented by Antenna Gain Array Manifold

Inspection robots are widely used in the field of smart grid monitoring in substations, and partial discharge (PD) is an important sign of the insulation state of equipment. PD direction of arrival (DOA) algorithms using conventional beam forming and time difference of arrival (TDOA) require large-scale antenna arrays and high computational complexity, making them difficult to implement on inspection robots. To address this problem, a novel directional multiple signal classification (Dir-MUSIC) algorithm for PD direction finding based on signal strength is proposed, and a miniaturized directional spiral antenna circular array is designed in this paper. First, the Dir-MUSIC algorithm is derived based on the array manifold characteristics. This method uses strength intensity information rather than the TDOA information, which could reduce the computational difficulty and the requirement of array size. Second, the effects of signal-to-noise ratio (SNR) and array manifold error on the performance of the algorithm are discussed through simulations in detail. Then, according to the positioning requirements, the antenna array and its arrangement are developed and optimized. Simulation results suggested that the algorithm has reliable direction-finding performance in the form of six elements. Finally, the effectiveness of the algorithm is tested by using the designed spiral circular array in real scenarios. The experimental results show that the PD direction-finding error is 3.39°, which meets the need for partial discharge DOA estimation using inspection robots in substations.
THE COVARIOGRAM AND AN EXTENSION OF SIEGEL’S FORMULA

We extend a formula of C. L. Siegel in the geometry of numbers, allowing the body to contain an arbitrary number of interior lattice points. Our extension involves a lattice sum of the covariogram of any compact set K ⊂ R d . The Fourier methods herein also allow for more general admissible sets, due to the Poisson summation formula. As one of the consequences of these results, we obtain a new characterization of multi-tilings of Euclidean space by translations, using the lower bound on lattice sums of such covariograms.
Exact diagonalisation of photon Bose–Einstein condensates with thermo-optic interaction

Although photon Bose–Einstein condensates have already been used for studying many interesting effects, the precise role of the photon–photon interaction is not fully clarified up to now. In view of this, it is advantageous that these systems allow measuring both the intensity of the light leaking out of the cavity and its spectrum at the same time. Therefore, the photon–photon interaction strength can be determined once via analysing the condensate broadening and once via examining the interaction-induced modifications of the cavity modes. As the former method depends crucially on the concrete shape of the trapping potential and the spatial resolution of the used camera, interferometric methods promise more precise measurements. To this end, the present paper works out the impact of the photon–photon interaction upon the cavity modes. A quantum mechanical description of the photon–photon interaction, including the thermal cloud, builds the theoretical backbone of the method. An exact diagonalisation approach introduced here exposes how the effective photon–photon interaction modifies both the spectrum and the width of the photon gas. A comparison with a variational approach based on the Gross–Pitaevskii equation quantifies the contribution of the thermal cloud in the respective applications.
Etude de la lixiviation en tas du minerai cupro-cobaltifère dans les mines de la Province du Katanga en République Démocratique du Congo

The extraction of metal ore in the poor leads us to look for techniques and methods to extract a good amount of it (metal) low cost without passing the gangue in the solution. For this purpose, to determine leaching parameters seen to recover the metal in a low grade ore, we proceeded to test leaching guidelines heap ore preceded by size analysis and chemical different slices to determine the working parameters that can give satisfactory results. After these analyzes guidance, we opted for crushing the ore to determine the correct job settings for a good recovery of the metal contained in the ore poor and know the slice for perfect contact between the etching solution and ore for metal recovery. During testing after leaching, we noticed that when we respect the following parameters: a constant acid concentration of 70 g / l, a time of 64 hours, a particle size of 1,700 micron; we dissolved a good amount of metals or a yield ranging between 37.9 to 74.9% when the particle size of the ore pass 9423-850 micron. However, leaching of the composite sample of the same ore, gives a neighbor leach performance to which is recorded with the fine ore 70.8%.
Modeling the Cooperative Process of Learning a Task

In this paper we propose a mathematical model for a Transactive Memory System (TMS) involved in the cooperative process of learning a task. The model is based on an intertwined dynamics involving both the individuals level of expertise and the interaction network among the cooperators. The model shows that if all the agents are non-stubborn, then all of them are able to acquire the competence of the most expert members of the group, asymptotically reaching their level of proficiency. Conversely, when dealing with all stubborn agents, the capability to pass on the task depends on the connectedness properties of the interaction graph.
CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases

Knowledge bases (KBs) are often incomplete and constantly changing in practice. Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs is often overlooked. To this end, we propose a case-based reasoning approach, CBR-iKB, for knowledge base question answering (KBQA) with incomplete-KB as our main focus. Our method ensembles decisions from multiple reasoning chains with a novel nonparametric reasoning algorithm. By design, CBR-iKB can seamlessly adapt to changes in KBs without any task-specific training or fine-tuning. Our method achieves 100% accuracy on MetaQA and establishes new state-of-the-art on multiple benchmarks. For instance, CBR-iKB achieves an accuracy of 70% on WebQSP under the incomplete-KB setting, outperforming the existing state-of-the-art method by 22.3%.
Islands in Kerr–de Sitter spacetime and their flat limit

We use the quantum extremal island method to study the information paradox in certain cosmological setups known as three dimensional Kerr-de Sitter spacetimes. To do so, we couple an auxiliary ﬂat bath system to this spacetime in timelike singularity and measure entropy of Hawking radiation in its asymptotic regions where the gravity is weak. We show that adding the island regions to the entanglement wedge of the radiation causes its entropy to obey Page curve. The boundary of the island, i.e., quantum extremal surface is located outside the cosmological horizon in the region connected directly to the bath. Taking the ﬂat-space limit from the location of the island and its related calculation in the Kerr-de Sitter sapcetime results in the ﬂat-space cosmology (FSC) island, scrambling time and also Page time that were obtained in our previous paper. We repeat the same calculation for the pure de Sitter spacetime and show that our setup which neglects the eﬀect of backreaction, leads also to a quantum extremal surface outside the cosmological horizon. We calculate the scrambling time and conﬁrm the idea that pure de Sitter spacetime is a fast scrambler.
Image Processing via Multilayer Graph Spectra

—Graph signal processing (GSP) has become an important tool in image processing because of its ability to reveal underlying data structures. Many real-life multimedia datasets, however, exhibit heterogeneous structures across frames. Multilayer graphs (MLG), instead of traditional single-layer graphs, provide better representation of these datasets such as videos and hyperspectral images. To generalize GSP to multilayer graph models and develop multilayer analysis for image processing, this work introduces a tensor-based framework of multilayer graph signal processing (M-GSP) and present useful M-GSP tools for image processing. We then present guidelines for applying M-GSP in image processing and introduce several applications, including RGB image compression, edge detection and hyperspectral image segmentation. Successful experimental results demonstrate the efﬁcacy and promising futures of M-GSP in image processing.
Going beyond soft plus virtual

We present a formalism that sums up the soft-virtual (SV) and next-to-SV (NSV) diagonal contributions to inclusive colorless productions in hadron colliders to all orders in perturbative QCD. Using factorisation theorem, renormalisation group invariance and employing the transcendental structure of perturbative results, we show the exponential behavior of soft-collinear function. This allows us to predict certain SV and NSV terms to all orders from lower order information. We also present an integral representation for the coeﬃcient functions which is suitable for Mellin N -space resummation.
Decentralized non-convex optimization via bi-level SQP and ADMM

Decentralized non-convex optimization is important in many problems of practical relevance. Existing decentralized methods, however, typically either lack convergence guarantees for general non-convex problems, or they suffer from a high subproblem complexity. We present a novel bilevel SQP method, where the inner quadratic problems are solved via ADMM. A decentralized stopping criterion from inexact Newton methods allows the early termination of ADMM as an inner algorithm to improve computational efficiency. The method has local convergence guarantees for non-convex problems. Moreover, it only solves sequences of Quadratic Programs, whereas many existing algorithms solve sequences of Nonlinear Programs. The method shows competitive numerical performance for an optimal power flow problem.
Distinguishing citrus varieties based on genetic and compositional analyses

Simple sequence repeats (SSR) markers and secondary metabolite composition were used in combination to study seven varieties of citrus for the first time. With reference to established accessions of citrus, two of the varieties (Chanh Giay and Ma Nao Pan) were predicted to be Mexican key limes, while three were mandarin hybrids (Nagpur, Pontianak and Dalandan) and the remaining two (Qicheng and Mosambi) were related to the sweet orange. Notably, Dalandan was genetically more like a mandarin despite often referred to as an orange locally, whereas Mosambi was more likely to be a sweet orange hybrid although it has also been called a sweet lime due to its green peel and small size. Several key secondary metabolites such as polymethoxyflavones (sinensetin, tangeretin etc.), furanocoumarins (bergapten, citropten etc.) and volatiles (citronellol, α-sinensal etc.) were identified to be potential biomarkers for separation of citrus species. However, despite having similar genetic profiles, variations in the volatile profile of the two limes were observed; similarly, there were differences in the secondary metabolite profiles of the three mandarin hybrids despite having a common ancestral parent, highlighting the usefulness of genetic and compositional analyses in combination for revealing both origins and flavour profiles especially in citrus hybrids. This knowledge would be crucial for variety screening and selection for use in flavour or fragrance creation and application.
Economic Research and Education Policy: Project Star and Class Size Reduction

The use of randomized controlled trials (RCTs) and related randomization strategies to eliminate selection biases in establishing causality is a key element of the “modern experimentalist paradigm” (MEP). Yet, its emphasis on precisely identifying causal factors often limits its capacity to provide an evidence base for policy. We illustrate this through a detailed look at Project STAR, an extensively analyzed, well-funded, large-scale, rigorous RCT commissioned by the Tennessee legislature to help it decide whether to mandate statewide class-size reductions (CSR) from kindergarten to the third grade. Project STAR randomly assigned students to classes of different size and compared test results across these classes, to obtain an unbiased answer to the research question, “Does reducing class size improve test scores? ” However, this shed little light on whether reducing class size was a good use of increased education financing. Analyses of Project STAR ignored general equilibrium effects of CSR on both the demand for teachers and the value of test scores. Moreover, its emphasis on estimating average class-size effects in a particular setting diverted attention from their heterogeneity, and the need to understand how class size affects learning, and how its effect is moderated by circumstances. Rather than considering the full chain of evidence necessary for shaping class-size policy, Project STAR concentrated its effort on maximizing the accuracy of a single link in that chain; internal validity trumped policy relevance.
3D Organon VR Anatomy

3D Organon VR Anatomy. Medis Media Pty Ltd, 2/23 Illawong Str. , Surfers Paradise, Gold Coast, Queensland 4217, Australia; https://www.3dorganon.com/; prosupport@3dorganon.com; +61.4. 5190.5904; individual and institutional subscriptions, contact for pricing.
Asexual reproductive potential trumps virulence as a predictor of competitive ability in mixed infections

Summary Natural infections frequently involve several co‐infecting pathogen strains. These mixed infections can affect the extent of the infection, the transmission success of the pathogen and the eventual epidemic outcome. To date, few studies have investigated how mixed infections affect transmission between hosts. Zymoseptoria tritici is a highly diverse wheat pathogen in which multiple strains often coexist in the same lesion. Here we demonstrate that the most competitive strains often exclude their competitors during serial passages of mixed infections. The outcome of the competition depended on both the host genotype and the genotypes of the competing pathogen strains. Differences in virulence among the strains were not associated with competitive advantages during transmission, while differences in reproductive potential had a strong effect on strain competitive ability. Overall, our findings suggest that host specialization is determined mainly by the ability to successfully transmit offspring to new hosts during mixed infections.