Miracle-dz/UniFAR-FTUs huggingface dataset:
Collective guiding of acoustically propelled nano- and microparticles

One of the most important potential applications of motile nano- and microdevices is targeted drug delivery. To realize this, biocompatible particles that can be guided collectively towards a target inside a patient's body are required. Acoustically propelled nano- and microparticles constitute a promising candidate for such biocompatible, artificial motile particles. The main remaining obstacle to targeted drug delivery by motile nano- and microdevices is to also achieve a reliable and biocompatible method for guiding them collectively to their target. Here, we propose such a method. As we confirm by computer simulations, it allows for the remote guiding of large numbers of acoustically propelled particles to a prescribed target by combining a space- and time-dependent acoustic field and a time-dependent magnetic field. The method works without detailed knowledge about the particle positions and for arbitrary initial particle distributions. With these features, it paves the way for the future application of motile particles as vehicles for targeted drug delivery in nanomedicine.
An Experimental Evaluation and Investigation of Waves of Misery in R-trees

Waves of misery is a phenomenon where spikes of many node splits occur over short periods of time in tree indexes. Waves of misery negatively affect the performance of tree indexes in insertion-heavy workloads. Waves of misery have been first observed in the context of the B-tree, where these waves cause unpredictable index performance. In particular, the performance of search and index-update operations deteriorate when a wave of misery takes place, but is more predictable between the waves. This paper investigates the presence or lack of waves of misery in several R-tree variants, and studies the extent of which these waves impact the performance of each variant. Interestingly, although having poorer query performance, the Linear and Quadratic R-trees are found to be more resilient to waves of misery than both the Hilbert and R*-trees. This paper presents several techniques to reduce the impact in performance of the waves of misery for the Hilbert and R*-trees. One way to eliminate waves of misery is to force node splits to take place at regular times before nodes become full to achieve deterministic performance. The other way is that upon splitting a node, do not split it evenly but rather at different node utilization factors. This allows leaf nodes not to fill at the same pace. We study the impact of two new techniques to mitigate waves of misery after the tree index has been constructed, namely Regular Elective Splits (RES, for short) and Unequal Random Splits (URS, for short). Our experimental investigation highlights the trade-offs in performance of the introduced techniques and the pros and cons of each technique.
Optical and near-infrared spectroscopy of Nova V2891 Cygni: Evidence for shock-induced dust formation

We present multi-epoch optical and near-infrared observations of the highly reddened, Fe ii class slow nova V2891 Cygni. The observations span 15 months since its discovery. The initial rapid brightening from quiescence, and the presence of a ∼35 day long pre-maximum halt, is well documented. The evidence that the current outburst of V2891 Cyg has undergone several distinct episodes of mass ejection is seen through time-varying P Cygni profiles of the O i 7773 Å line. A highlight is the occurrence of a dust formation event centred around ∼+273d, which coincides with a phase of coronal line emission. The dust mass is found to be ∼0.83 − 1.25 × 10−10 M⊙. There is strong evidence to suggest that the coronal lines are created by shock heating rather than by photoionization. The simultaneous occurrence of the dust and coronal lines (with varying velocity shifts) supports the possibility that dust formation is shock-induced. Such a route for dust formation has not previously been seen in a nova, although the mechanism has been proposed for dust formation in some core-collapse supernovae. Analysis of the coronal lines indicates a gas mass and temperature of 8.35–8.42 × 10−7 M⊙ and ∼(4.8 − 9.1) × 105 K respectively, and an overabundance of aluminium and silicon. A Case B analysis of the hydrogen lines yields a mass of the ionized gas of (8.60 ± 1.73) × 10−5 M⊙. The reddening and distance to the nova are estimated to be E(B − V) = 2.21 ± 0.15 and d = 5.50 kpc respectively.
A Compact Neural Network-based Algorithm for Robust Image Watermarking

Digital image watermarking seeks to protect the digital media information from unauthorized access, where the message is embedded into the digital image and extracted from it, even some noises or distortions are applied under various data processing including lossy image compression and interactive content editing. Traditional image watermarking solutions easily suffer from robustness when specified with some prior constraints, while recent deep learning-based watermarking methods could not tackle the information loss problem well under various separate pipelines of feature encoder and decoder. In this paper, we propose a novel digital image watermarking solution with a compact neural network, named Invertible Watermarking Network (IWN). Our IWN architecture is based on a single Invertible Neural Network (INN), this bijective propagation framework enables us to effectively solve the challenge of message embedding and extraction simultaneously, by taking them as a pair of inverse problems for each other and learning a stable invertible mapping. In order to enhance the robustness of our watermarking solution, we specifically introduce a simple but effective bit message normalization module to condense the bit message to be embedded, and a noise layer is designed to simulate various practical attacks under our IWN framework. Extensive experiments demonstrate the superiority of our solution under various distortions.
Automatic extraction of requirements expressed in industrial standards : a way towards machine readable standards ?

The project, under industrial funding, presented in this publication aims at the semantic analysis of a normative document describing requirements applicable to electrical appliances. The objective of the project is to build a semantic approach to extract and automatically process information related to the requirements contained in the standard. To this end, the project has been divided into three parts, covering the analysis of the requirements document, the extraction of relevant information and creation of the ontology and the comparison with other approaches. The first part of our work deals with the analysis of the requirements document under study. The study focuses on the specificity of the sentence structure, the use of particular words and vocabulary related to the representation of the requirements. The aim is to propose a representation facilitating the extraction of information, used in the second part of the study. In the second part, the extraction of relevant information is conducted in two ways: manual (the ontology being built by hand), semi-automatic (using semantic annotation software and natural language processing techniques). Whatever the method used, the aim of this extraction is to create the concept dictionary, then the ontology, enriched as the document is scanned and understood by the system. Once the relevant terms have been identified, the work focuses on identifying and representing the requirements, separating the textual writing from the information given in the tables. The automatic processing of requirements involves the extraction of sentences containing terms identified as relevant to a requirement. The identified requirement is then indexed and stored in a representation that can be used for query processing.
The incompressible energy cascade rate in anisotropic solar wind turbulence

Context. The presence of a magnetic guide field induces several types of anisotropy in solar wind turbulence. The energy cascade rate between scales in the inertial range depends strongly on the direction of this magnetic guide field, splitting the energy cascade according to the parallel and perpendicular directions with respect to magnetic guide field. Aims. Using more than two years of Parker Solar Probe (PSP) observations, the isotropy and anisotropy energy cascade rates are investigated. The variance and normalized fluctuation ratios, the kinetic and magnetic energies, and the normalized cross-helicity and residual energy are studied. The connection between the heliocentric distance, the local temperature of the plasma, and the energy cascade components is made. Methods. Using exact relations for fully developed incompressible magnetohydrodynamic (MHD) turbulence, the incompressible energy cascade rate is computed. In particular, using the isotropy and 2D and slab assumptions, the isotropic, perpendicular, and parallel energy cascade rate components are estimated. Results. The variance anisotropy ratios, for both velocity and magnetic fields, do not exhibit a dependence with respect to the heliocentric distance $r$ between 0.2 and 0.8 au. While the velocity normalized fluctuation ratio shows a dependence with $r$, the magnetic normalized fluctuation ratio does not. A strong correlation between the isotropic and anisotropic energy cascade rates and the temperature is found. A clear dominance of the perpendicular cascades over the parallel cascades as PSP approaches the Sun is observed. A dominant 2D cascade and/or geometry over the slab component in slow solar wind turbulence in the largest MHD scales is observed.
Meridional energy transport extremes and the general circulation of NH mid-latitudes: dominant weather regimes and preferred zonal wavenumbers

Abstract. The extratropical meridional energy transport in the atmosphere is fundamentally intermittent in nature, having extremes large enough to affect the net seasonal transport. Here, we investigate how these extreme transports are associated with the dynamics of the atmosphere at multiple scales, from planetary to synoptic. We use ERA5 reanalysis data to perform a wavenumber decomposition of meridional energy transport in the Northern Hemisphere mid-latitudes during winter and summer. We then relate extreme transport events to atmospheric circulation anomalies and dominant weather regimes, identified by clustering 500 hPa geopotential height fields. In general, planetary-scale waves determine the strength and meridional position of the synoptic-scale baroclinic activity with their phase and amplitude, but important differences emerge between seasons. During winter, large wavenumbers (k = 2 − 3) are key drivers of the meridional energy transport extremes, and planetary and synoptic-scale transport extremes virtually never co-occur. In summer, extremes are associated with higher wavenumbers (k = 4 − 6), identified as synoptic-scale motions. We link these waves and the transport extremes to recent results on exceptionally strong and persistent co-occurring summertime heat waves across the Northern Hemisphere mid-latitudes. We show that these events are typical, in terms of dominant regime patterns associated with extremely strong meridional energy transports.
Dense anomaly detection by robust learning on synthetic negative data

Standard machine learning is unable to accommodate inputs which do not belong to the training distribution. The resulting models often give rise to conﬁdent incorrect predictions which may lead to devastating consequences. This problem is especially demanding in the context of dense prediction since input images may be only partially anomalous. Previous work has addressed dense anomaly detection by discriminative training on mixed-content images. We extend this approach with synthetic negative patches which simultaneously achieve high inlier likelihood and uniform discriminative prediction. We generate synthetic negatives with normalizing ﬂows due to their out-standing distribution coverage and capability to generate samples at diﬀerent resolutions. We also propose to detect anomalies according to a principled information-theoretic criterion which can be consistently applied through training and inference. The resulting models set the new state of the art on standard benchmarks and datasets in spite of minimal computational overhead and refraining from auxiliary negative data.
Not All Voxels Are Equal: Semantic Scene Completion from the Point-Voxel Perspective

We revisit Semantic Scene Completion (SSC), a useful task to predict the semantic and occupancy representation of 3D scenes, in this paper. A number of methods for this task are always based on voxelized scene representations. Although voxel representations keep local structures of the scene, these methods suffer from heavy computation redundancy due to the existence of visible empty voxels when the network goes deeper. To address this dilemma, we propose our novel point-voxel aggregation network for this task. We first transfer the voxelized scenes to point clouds by removing these visible empty voxels and adopt a deep point stream to capture semantic information from the scene efficiently. Meanwhile, a light-weight voxel stream containing only two 3D convolution layers preserves local structures of the voxelized scenes. Furthermore, we design an anisotropic voxel aggregation operator to fuse the structure details from the voxel stream into the point stream, and a semantic-aware propagation module to enhance the up-sampling process in the point stream by semantic labels. We demonstrate that our model surpasses state-of-the-arts on two benchmarks by a large margin, with only the depth images as input.
Branching structure of genealogies in spatially growing populations and its implications for population genetics inference

Spatial models where growth is limited to the population edge have been instrumental to understanding the population dynamics and the clone size distribution in growing cellular populations, such as microbial colonies and avascular tumours. A complete characterization of the coalescence process generated by spatial growth is still lacking, limiting our ability to apply classic population genetics inference to spatially growing populations. Here, we start filling this gap by investigating the statistical properties of the cell lineages generated by the two dimensional Eden model, leveraging their physical analogy with directed polymers. Our analysis provides quantitative estimates for population measurements that can easily be assessed via sequencing, such as the average number of segregating sites and the clone size distribution of a subsample of the population. Our results not only reveal remarkable features of the genealogies generated during growth, but also highlight new properties that can be misinterpreted as signs of selection if non-spatial models are inappropriately applied.
On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks

In this effort we consider a reinforcement learning (RL) technique for solving personalization tasks with complex reward signals. In particular, our approach is based on state space clustering with the use of a simplistic K-means algorithm as well as conventional choices of the network architectures and optimization algorithms. Numerical examples demonstrate the efficiency of different RL procedures and are used to illustrate that this technique accelerates the agent’s ability to learn and does not restrict the agent’s performance.
Modelling, simulation, testing, and optimization of advanced hybrid vehicle powertrains

The internal combustion engine (ICE) vehicle has dominated the transportation market for nearly 100 years. Numerous concerns with continued use of fossil fuels arise, however, and these concerns have created an impetus to develop more efficient vehicles that release fewer emissions. There are several powertrain technologies that could supplant conventional ICEs as the dominant technology, most notably electric and hybrid powertrains. In order to achieve the levels of performance and cost of conventional powertrains, electric and hybrid powertrain designers must use design techniques and tools such as computer modelling, simulation and optimization. These tools facilitate development of a virtual prototype that allows the designer to rapidly see the effects of design modifications and precludes the need to manufacture multiple expensive physical prototypes. A comprehensive survey of the state of the art of commercialized hybrid vehicle powertrains is conducted, and the term multi-regime in ICE hybrid vehicle (ICEHV) modelling is introduced to describe designs that allow for multiple configurations and operating regimes. A dynamic mathematical model of a power-split architecture with two modes (or configurations) introduced by General Motors Corporation is developed and a steady-state version is programmed into the ADvanced VehIcle SimulatOR (ADVISOR) simulation software package. This ADVISOR model is applied to a commercial delivery vehicle, and the fuel consumption of
A parameter extraction method for microwave direct-coupled-resonator filters with the consideration of component losses

In this paper, a method is proposed to extract the parameters of direct-coupled-resonator filters such as the resonant frequencies, coupling coefficients, and Q factors. The ABCD matrix is firstly built from the simulation or measured response. The components of the filter in each stage can then be decomposed based on the derived functions. Since lossy components can be individually extracted, this method is applicable to lossy filter designs with non-uniform Q distribution. From the extracted parameters, it is clear to indicate the detuned components and is more efficient for them to be fine-tuned. Two filter examples are given finally to validate the proposed method.
Gated-diode characterization of the back-channel interface on irradiated SOI wafers

A silicon-on-insulator (SOI) MOSFET-based "gated-diode" technique has been applied to study the Si-film/buried-oxide interface of devices that were fabricated on a SIMOX wafer. Since the gated-diode technique is sensitive to recombination current caused by the presence of interface states, the samples were exposed to 10 keV X-ray irradiation to enhance the development of SOI back-channel interface states. The measured peak in the gated-diode current curve was observed to increase in magnitude and move in voltage as a function of exposure to radiation in a manner consistent with increasing interface states and increasing trapped oxide charge. Using a two-dimensional numerical simulator, with a single acceptor interface trap at 0.7 eV, the gated-diode current was modeled as a function of 10 keV X-ray exposure.
Terahertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade

Terahertz (THz) band communications are envisioned as a key technology for 6G and Beyond. As a fundamental wireless infrastructure, THz communication can boost abundant promising applications. In 2014, our team published two comprehensive roadmaps for the development and progress of THz communication networks, which helped the research community to start research on this subject afterwards. The topic of THz communications became very important and appealing to the research community due to 6G wireless systems design and development in recent years. Many papers are getting published covering different aspects of wireless systems using the THz band. With this paper, our aim is looking back to the last decade and revisiting the old problems and pointing out what has been achieved in the research community so far. Furthermore, in this paper, open challenges and new research directions still to be investigated for the THz band communication systems are presented, by covering diverse topics ranging from devices, channel behavior, communication and networking, to physical testbeds and demonstration systems. The key aspects presented in this paper will enable THz communications as a pillar of 6G and Beyond wireless systems in the next decade.
A Review on the Cahn-Hilliard Equation: Classical Results and Recent Advances in Dynamic Boundary Conditions

The Cahn–Hilliard equation is a fundamental model that describes the phase separation process in multi-component mixtures. It has been successfully extended to many different contexts in several scientific fields. In this survey article, we briefly review the derivation, structure as well as some analytical issues for the Cahn–Hilliard equation and its variants. Our focus will be placed on the well-posedness and long-time behavior of the Cahn–Hilliard equation in the classical setting and recent progresses on the dynamic boundary conditions accounting for non-trivial boundary effects.
nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task

NL2VIS - which translates natural language (NL) queries to corresponding visualizations (VIS) - has attracted more and more attention both in commercial visualization vendors and academic researchers. In the last few years, the advanced deep learning-based models have achieved human-like abilities in many natural language processing (NLP) tasks, which clearly tells us that the deep learning-based technique is a good choice to push the field of NL2VIS. However, a big balk is the lack of benchmarks with lots of (NL, VIS) pairs. We present nvBench, the first large-scale NL2VIS benchmark, containing 25,750 (NL, VIS) pairs from 750 tables over 105 domains, synthesized from (NL, SQL) benchmarks to support cross-domain NL2VIS task. The quality of nvBench has been extensively validated by 23 experts and 300+ crowd workers. Deep learning-based models training using nvBench demonstrate that nvBench can push the field of NL2VIS.
A Fast Row-Stochastic Decentralized Optimization Method Over Directed Graphs

In this paper, we introduce a fast row-stochastic decentralized algorithm, referred to as FRSD, to solve consensus optimization problems over directed communication graphs. The proposed algorithm only utilizes row-stochastic weights, leading to certain practical advantages in broadcast communication settings over those requiring column-stochastic weights. Under the assumption that each node-speciﬁc function is smooth and strongly convex, we show that the FRSD iterate sequence converges with a linear rate to the optimal consensus solution. In contrast to the existing methods for directed networks, FRSD enjoys linear convergence without employing a gradient tracking (GT) technique explicitly, rather it implements GT implicitly with the use of a novel momentum term, which leads to a signiﬁcant reduction in communication and storage overhead for each node when FRSD is implemented for solving high-dimensional problems over small-to-medium scale networks. In the numerical tests, we compare FRSD with other state-of-the-art methods, which use row-stochastic and/or column-stochastic weights.