Miracle-dz/UniFAR-FTUs huggingface dataset:
High sensitivity ultraviolet graphene-metamaterial integrated electro-optic modulator enhanced by superlubricity

: Ultraviolet (UV) electro-optic modulation system based on graphene-plasmonic metamaterials nanomechanical system (NEMS) with superlubricity is investigated. Due to the strong optical absorption intensity of graphene in the UV region and the combination of metamaterial structure based on surface plasmons, the modulation depth of the UV NEMS electro-optic modulator approaches as high as 8.5 times compared to the counterpart modulator in visible light region. Meanwhile, the superlubricity significantly reduces the power consumption of the UV electro-optic modulation system due to its extremely low friction coefficient. It also significantly increases the response speed of the modulator, with response time down to nanoseconds. The modulation voltage can be equal to or less than 150 mV. The proposed electro-optic modulation system has a simple structure and high sensitivity, which is supposed to have important applications in UV optoelectronic devices and systems.
Physically informed data-driven modeling of active nematics

A continuum description is essential for understanding a variety of collective phenomena in active matter. However, building quantitative continuum models of active matter from first principles can be extremely challenging due to both the gaps in our knowledge and the complicated structure of nonlinear interactions. Here, we use a physically informed data-driven approach to construct a complete mathematical model of an active nematic from experimental data describing kinesin-driven microtubule bundles confined to an oil-water interface. We find that the structure of the model is similar to the Leslie-Ericksen and Beris-Edwards models, but there are appreciable and important differences. Rather unexpectedly, elastic effects are found to play no role in the experiments considered, with the dynamics controlled entirely by the balance between active stresses and friction stresses.
Evolving-to-Learn Reinforcement Learning Tasks with Spiking Neural Networks

Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks with local information, making them suitable for online learning on neuromorphic hardware. However, when such rules are implemented to learn different new tasks, they usually require a significant amount of work on task-dependent fine-tuning. This paper aims to make this process easier by employing an evolutionary algorithm that evolves suitable synaptic plasticity rules for the task at hand. More specifically, we provide a set of various local signals, a set of mathematical operators, and a global reward signal, after which a Cartesian genetic programming process finds an optimal learning rule from these components. Using this approach, we find learning rules that successfully solve an XOR and cart-pole task, and discover new learning rules that outperform the baseline rules from literature.
Differentially-Private Publication of Origin-Destination Matrices with Intermediate Stops

Conventional origin-destination (OD) matrices record the count of trips between pairs of start and end locations, and have been extensively used in transportation, traffic planning, etc. More recently, due to use case scenarios such as COVID-19 pandemic spread modeling, it is increasingly important to also record intermediate points along an individual's path, rather than only the trip start and end points. This can be achieved by using a multi-dimensional frequency matrix over a data space partitioning at the desired level of granularity. However, serious privacy constraints occur when releasing OD matrix data, and especially when adding multiple intermediate points, which makes individual trajectories more distinguishable to an attacker. To address this threat, we propose a technique for privacy-preserving publication of multi-dimensional OD matrices that achieves differential privacy (DP), the de-facto standard in private data release. We propose a family of approaches that factor in important data properties such as data density and homogeneity in order to build OD matrices that provide provable protection guarantees while preserving query accuracy. Extensive experiments on real and synthetic datasets show that the proposed approaches clearly outperform existing state-of-the-art.
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs

NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. It implements three kinds of Knowledge Graph Embedding (KGE) methods, including conventional KGEs, GNN-based KGEs, and Rule-based KGEs. With a unified framework, NeuralKG successfully reproduces link prediction results of these methods on benchmarks, freeing users from the laborious task of reimplementing them, especially for some methods originally written in non-python programming languages. Besides, NeuralKG is highly configurable and extensible. It provides various decoupled modules that can be mixed and adapted to each other. Thus with NeuralKG, developers and researchers can quickly implement their own designed models and obtain the optimal training methods to achieve the best performance efficiently. We built a website http://neuralkg.zjukg. org to organize an open and shared KG representation learning community. The library, experimental methodologies, and model reimplement results of NeuralKG are all publicly released at https://github.com/zjukg/NeuralKG.
Understanding Adversarial Robustness from Feature Maps of Convolutional Layers

The adversarial robustness of a neural network mainly relies on two factors: model capacity and antiperturbation ability. In this article, we study the antiperturbation ability of the network from the feature maps of convolutional layers. Our theoretical analysis discovers that larger convolutional feature maps before average pooling can contribute to better resistance to perturbations, but the conclusion is not true for max pooling. It brings new inspiration to the design of robust neural networks and urges us to apply these findings to improve existing architectures. The proposed modifications are very simple and only require upsampling the inputs or slightly modifying the stride configurations of downsampling operators. We verify our approaches on several benchmark neural network architectures, including AlexNet, VGG, RestNet18, and PreActResNet18. Nontrivial improvements in terms of both natural accuracy and adversarial robustness can be achieved under various attack and defense mechanisms. The code is available at https://github.com/MTandHJ/rcm.
Deep Dirichlet Uncertainty for Unsupervised Out-of-Distribution Detection of Eye Fundus Photographs in Glaucoma Screening

The development of automatic tools for early glaucoma diagnosis with color fundus photographs can significantly re-duce the impact of this disease. However, current state-of-the-art solutions are not robust to real-world scenarios, providing over-confident predictions for out-of-distribution cases. With this in mind, we propose a model based on the Dirich-let distribution that allows to obtain class-wise probabilities together with an uncertainty estimation without exposure to out-of-distribution cases. We demonstrate our approach on the AIROGS challenge, where we achieve a performance sim-ilar to other participants without requiring additional annotations or artificially generated out-of-distribution labels.
Internet of Nano, Bio-Nano, Biodegradable and Ingestible Things: A Survey

In recent years, advances in biotechnology, nanotechnology and materials science have led to development of revolutionizing applications in Internet of Things (IoT). In particular, the interconnection of nanomaterials, nanoimplants and nanobiosensors with existing IoT networks have inspired the concepts of Internet of Nano Things (IoNT), Internet of Bio-Nano Things (IoBNT), Internet of Biodegradable Things (IoBDT) and Internet of Ingestible Things (IoIT). To date, although there are several survey papers that addressed these concepts separately, there is no current survey covering all studies in IoNT, IoBNT, IoBDT and IoIT. Therefore, in this paper, we provide a complete overview of all recent work in these four areas. Furthermore, we emphasize the research challenges, potential applications, and open research areas.
Deep neural networks for fine-grained surveillance of overdose mortality

Surveillance of drug overdose deaths relies on death certificates for identification of the substances that caused death. Drugs and drug classes can be identified through the International Classification of Diseases, 10th Revision (ICD-10) codes present on death certificates, however ICD-10 codes do not always provide high levels of specificity in drug identification. To achieve more fine-grained identification of substances on death certificate, the free-text cause of death section, completed by the medical certifier, must be analyzed. Current methods for analyzing free-text death certificates rely solely on look-up tables for identifying specific substances, which must be frequently updated and maintained. To improve identification of drugs on death certificates, a deep learning named-entity recognition model was developed, utilizing data from the Kentucky Drug Overdose Fatality Surveillance System, years 2014-2019, which achieved an F1-score of 99.13%. This model can identify new drug misspellings and novel substances that are not present on current surveillance look-up tables, enhancing the surveillance of drug overdose deaths.
Does an increase in the cost of imported inputs hurt exports? Evidence from firms' network of foreign suppliers

This paper examines the relationship between changes in the cost of imported inputs and export performance using a novel dataset from Argentina which identifies domestic firms’ network of foreign suppliers. To guide my empirical strategy, I construct a heterogeneous firm model subject to quality choice and frictions in the market for foreign supplier. The model predicts that the impact of an increase in the cost of imported inputs to be increasing in the adjustments costs of supplier linkages and in the quality of the product exported. I take the model to the data by constructing firm-specific shocks using a shift-share analysis which exploits firms’ lagged exposure to foreign suppliers and finely defined import price shifts. Evidence suggests the presence of significant adjustment cost in firms’ foreign supplier linkages and strong complementarities between imported inputs and export performance, particularly of high-quality products.
On periodic and compactly supported least energy solutions to semilinear elliptic equations with non-Lipschitz nonlinearity

We discuss the existence and non-existence of periodic in one variable and compactly supported in the other variables least energy solutions for equations with non-Lipschitz nonlinearity of the form: − Δ u = λ u p − u q in R N + 1 , where 0 < q < p < 1 and λ ∈ R. The approach is based on the Nehari manifold method supplemented by a one-sided constraint given through the functional of the suitable Pohozaev identity. The limit value of the parameter λ, where the approach is applicable, corresponds to the existence of periodic in one variable and compactly supported in the other variables least energy solutions. This value is found through the extrem values of nonlinear generalized Rayleigh quotients and the so-called curve of the critical exponents of p, q. Important properties of the solutions are derived for suitable ranges of the parameters, such as that they are not trivial with respect to the periodic variable and do not coincide with compactly supported solutions on the entire space R N + 1 .
TerraPN: Unstructured terrain navigation through Online Self-Supervised Learning

—We present TerraPN, a novel method to learn the surface characteristics (texture, bumpiness, deformability, etc.) of complex outdoor terrains for autonomous robot navigation. Our method predicts navigability cost maps for different surfaces using patches of RGB images, odometry, and IMU data. Our method dynamically varies the resolution of the output cost map based on the scene to improve its computational efﬁciency. We present a novel extension to the Dynamic-Window Approach (DWA-O) to account for a surface’s navigability cost while computing robot trajectories. DWA-O also dynamically modulates the robot’s acceleration limits based on the variation in the robot-terrain interactions. In terms of perception, our method learns to predict navigability costs in ∼ 20 minutes for ﬁve different surfaces, compared to 3-4 hours for previous scene segmentation methods, and leads to a decrease in inference time. In terms of navigation, our method outperforms previous works in terms of vibration costs and generates robot velocities suitable for different surfaces.
Cluster Formation in GGD 12-15: Infall Motion with Rotation of the Natal Clump

We report results of observations of the GGD 12-15 region, where cluster formation is actively taking place, using various molecular emission lines. The C18O (J = 1−0) emission line reveals a massive clump in the region with a mass of ∼2800 M ⊙ distributed over ∼2 pc. The distribution of the C18O (J = 3−2) emission is similar to that of a star cluster forming therein, with an elliptical shape of ∼1 pc in size. A bipolar molecular outflow driven by IRS 9Mc, a constituent star of the cluster, is blowing in the direction perpendicular to the elongated C18O (J = 3−2) distribution, covering the entire clump. There is a massive core with a radius of 0.3 pc and a mass of 530 M ⊙ in the center of the clump. There are two velocity components around the core, which are prominent in a position–velocity (PV) diagram along the major axis of the clump. In addition, a PV diagram along the minor axis of the clump, which is parallel to the outflow, shows a velocity gradient opposite to that of the outflow. The velocity structure strongly indicates the infalling motion of the clump. Comparison of the observational data with a simple model of infalling oblate clumps indicates that the clump is undergoing gravitational contraction with rotation.
Microgrid Day-Ahead Scheduling Considering Neural Network based Battery Degradation Model

— Battery energy storage system (BESS) can effectively mitigate the uncertainty of variable renewable generation. Degradation is unpreventable for batteries such as the most popular Lithium-ion battery (LiB). The main causes of LiB degradation are loss of Li-ions, loss of electrolyte, and increase of internal resistance which are hard to model and predict. In this paper, we propose a data driven method to predict the battery degradation per a given scheduled battery operational profile. Particularly, a neural network based battery degradation (NNBD) model is proposed to quantify the battery degradation with inputs of major battery degradation factors. When incorporating the proposed NNBD model into microgrid day-ahead scheduling (MDS), we can establish a battery degradation based MDS (BDMDS) model that can consider the equivalent battery degradation cost precisely. Since the proposed NNBD model is highly non-linear and non-convex, BDMDS would be very hard to solve. To address this issue, a neural network and optimization decoupled heuristic (NNODH) algorithm is proposed in this paper to effectively solve this neural network embedded optimization problem. Simulation results demonstrate that the proposed NNODH algorithm is able to obtain the optimal solution with lowest total cost including normal operation cost and battery degradation cost.
Alloying Ratio Versus Cluster Size for Reversible Hydrogen Storage in Ni Doped Small Mg Clusters: Dispersion Corrected DFT Study

Dispersion corrected density functional theory (ωB97X-D DFT) method is used to study the molecular hydrogen adsorption in NinMgm (1 ≥ n ≥ 3, 1 ≥ m ≥ 9) clusters. All these clusters can effectively adsorb multiple H2 in the preferred binding energy (BE) range between physisorption and chemisorption, i.e., 0.1eV ≥ BE ≥ 0.8eV. H2 adsorption on NikMgk (Ni:Mg=1:1), NikMg2k (Ni:Mg=1:2) and NikMg3k (Ni:Mg=1:3) (k=1-3) clusters shows fascinating behaviours in terms of Ni:Mg alloying ratio and cluster size. In each Ni:Mg ratio, the number of adsorbed H2 in the heavier clusters (k=2, 3) becomes integral multiples of that in the lightest configuration (k=1). As a consequence, the gravimetric density of molecular hydrogen remains fixed at each Ni:Mg ratio irrespective of the cluster size. The corresponding values are 17.94 wt% (1:1), 14.46 wt% (1:2) and 13.28 wt% (1:3), which are significantly higher than the ultimate target of 6.5 wt% set by DOE, US. Molecular dynamics simulations further reveal that room temperature desorption of almost all H2 molecules are possible for all the clusters.
Observations of Flow Structure Changes with Aspect Ratio for Rotating Insect Wing Planforms

The effect of wing aspect ratio for flapping and rotating wings in insect-like flight regimes is not well understood. In this study the effect of aspect ratio on the vortex structures formed over altered fly wings has been investigated. A numerical model was employed to simulate the flow over these wings which were rotating at a constant angular velocity. It was found that increasing the wing aspect ratio resulted in the formation of a dual co-rotating vortex structure near the leading-edge of the wing and the strengthening of the spanwise velocity within the core of the downstream vortex. Increasing the wing aspect ratio also resulted in an unsteady region of flow being developed out near the wing tip in which vortex structures are shed from the wing. The formation of this region was found to be due to the interaction of the leading-edge vortex with the vorticity at the trailing edge and not due to leading-edge vortex shedding as the dual leading-edge vortex structure was found to remain attached to the wing for all aspect ratios. This interaction did result in the reduction of the lift and drag coefficients for high aspect ratios.
FastMimic: Model-Based Motion Imitation for Agile, Diverse and Generalizable Quadrupedal Locomotion

Robots operating in human environments need various skills, like slow and fast walking, turning, side-stepping, and many more. However, building robot controllers that can exhibit such a large range of behaviors is a challenging problem that requires tedious investigation for every task. We present a unified model-based control algorithm for imitating different animal gaits without expensive simulation training or real-world fine-tuning. Our method consists of stance and swing leg controllers using a centroidal dynamics model augmented with online adaptation techniques. We also develop a whole-body trajectory optimization procedure to fix the kinematic infeasibility of the reference animal motions. We demonstrate that our universal data-driven model-based controller can seamlessly imitate various motor skills, including trotting, pacing, turning, and side-stepping. It also shows better tracking capabilities in simulation and the real world against several baselines, including another model-based imitation controller and a learning-based motion imitation technique.
Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry

This letter proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The map is a collection of voxels; each contains one plane feature that enables the probabilistic representation of the environment and accurate registration of a new LiDAR scan. We further analyze the need for coarse-to-fine voxel mapping and then use a novel voxel map organized by a Hash table and octrees to build and update the map efficiently. We apply the proposed voxel map to an iterated extended Kalman filter and construct a maximum a posteriori probability problem for pose estimation. Experiments on the open KITTI dataset show the high accuracy and efficiency of our method compared to other state-of-the-art methods. Experiments on indoor and unstructured outdoor environments with solid-state LiDAR and non-repetitive scanning LiDAR further verify the adaptability of our mapping method to different environments and LiDAR scanning patterns (see our attached video1). Our codes and dataset are open-sourced on Github2
Chapter 6 : Extratropical Stratosphere – Troposphere Coupling

This chapter assesses the representation of the two-way coupling between the troposphere and the stratospheric polar vortices in the reanalysis products. This coupling is evaluated over a broad range of time scales, from sub-seasonal to decadal, with a particular emphasis on Sudden Stratospheric Warming (SSW) events, which are among the clearest manifestations of coupling between the tropospheric and stratospheric circulations. Coupled variability on synoptic to seasonal time scales is evaluated by comparing the timing, evolution, and dynamical consistency of SSW events and Final Warming events, and the representation of the Annular Mode indices. Variability on interannual time scales is evaluated by comparing the modulation of sub-seasonal stratosphere-troposphere coupling by El Niño-Southern Oscillation (ENSO) and the Quasi-Biennial Oscillation (QBO). Finally, variability on decadal time scales is evaluated by comparing atmospheric circulation trends driven by the depletion of stratospheric ozone over Antarctica. Blanca Ayarzagüena (1)Departamento de Física de la Tierra y Astrofísica, Universidad Complutense de Madrid (2)Instituto de Geociencias, CSIC-UCM Spain David Barriopedro Instituto de Geociencias, CSIC-UCM Spain Thomas J. Bracegirdle British Antarctic Survey UK Amy H. Butler National Oceanic and Atmospheric Administration/Chemical Sciences Laboratory USA Natalia Calvo Departamento de Física de la Tierra y Astrofísica, Universidad Complutense de Madrid Spain Steven C. Hardiman Met Office Hadley Centre UK Peter Hitchcock Department of Earth and Atmospheric Sciences, Cornell University USA Maddalen Iza Departamento de Física de la Tierra y Astrofísica, Universidad Complutense de Madrid Spain Ulrike Langematz Institut für Meteorologie, Freie Universität Berlin Germany Hua Lu British Antarctic Survey UK Gareth Marshall British Antarctic Survey UK Andrew Orr British Antarctic Survey UK Froila M. Palmeiro (1)Departamento de Física de la Tierra y Astrofísica, Universidad Complutense de Madrid (2)Barcelona Supercomputing Center Spain Seok-Woo Son School of Earth and Environmental Sciences, Seoul National University South Korea Masakazu Taguchi Department of Earth Science, Aichi University of Education Japan Edwin P. Gerber Courant Institute of Mathematical Sciences, New York University USA Patrick Martineau Japan Agency for Marine-Earth Science and Technology previously at: Research Center for Advanced Science and Technology, the University of Tokyo Japan Chapter lead authors
Indonesian Journal of Electrical Engineering and Computer Science

Received Jul 15, 2022 Revised Aug 29, 2022 Accepted Sep 13, 2022 Modeling legal text is a difficult task because of its unique features, such as lengthy texts, complex language structures, and technical terms. During the last decade, there has been a big rise in the number of legislative documents, which makes it hard for law professionals to keep up with legislation like analyzing judgements and implementing acts. The relevancy of topics is heavily influenced by the processing and presentation of legal documents in some contexts. The objective of this work is to understand the legal judgement corpus related to cases under the Hindu Marriage Act of India. The study looked into various methods to generate sentence embeddings from the judgement. This paper employs the power of the BERTopic algorithm for generating significant topics.