Reviewerly/exHarmony huggingface dataset:
Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion

Document-level relation extraction (DocRE)aims to extract semantic relations among entity pairs in a document.Typical DocRE methods blindly take the full document as input, while a subset of the sentences in the document, noted as the evidence, are often sufficient for humans to predict the relation of an entity pair.In this paper, we propose an evidenceenhanced framework, EIDER, that empowers DocRE by efficiently extracting evidence and effectively fusing the extracted evidence in inference. 1We first jointly train an RE model with a lightweight evidence extraction model, which is efficient in both memory and runtime.Empirically, even training the evidence model on silver labels constructed by our heuristic rules can lead to better RE performance.We further design a simple yet effective inference process that makes RE predictions on both extracted evidence and the full document, then fuses the predictions through a blending layer.This allows EIDER to focus on important sentences while still having access to the complete information in the document.Extensive experiments show that EIDER outperforms state-ofthe-art methods on three benchmark datasets (e.g., by 1.37/1.26Ign F1/F1 on DocRED).Head:Hero of the Day Tail:the United States Rel:[country of origin] GT evidence sentences: [1,10] Extracted evidence: [1,10] Original document as input: [1] Load is the sixth studio album by the American heavy metal band Metallica, released on June 4, 1996 by Elektra Records in the United States … [9] It was certified 5×platinum … for shipping five million copies in the United States.[10] Four singles-"Hero of the Day", "Until It Sleeps", "Mama Said", and "King Nothing" -were released as part of the marketing campaign for the album.Prediction scores: NA: 17.63 country of origin: 14.79 Extracted evidence as input: [1] Load is the sixth studio album … released … in the United States … [10] Four singles -"Hero of the Day", … were released … for the album.Prediction scores: country of origin: 18.31 NA: 13.45 Final prediction of our model: country of origin (✓)
Single Object Tracking in Satellite Videos: A Correlation Filter-Based Dual-Flow Tracker

Satellite video (SV) can acquire rich spatiotemporal information on the Earth. Single object tracking (SOT) in SVs enables the continuous acquisition of the position and range of a specific object, expanding the field of remote-sensing applications. In SVs, objects are small with limited features and vulnerable to tracking drift. In this paper, a correlation filter-based dual-flow (DF) tracker is proposed to explore how the hybridization of spatial-spectral feature fusion and motion model can boost tracking. To represent small objects, the DF adaptively fuses complementary features using a state-aware indicator in feature flow. In motion flow, the indicator perceives the confidence of the feature flow. A dual-mode prediction model is then constructed to simulate the object&#x0027;s motion pattern, and cooperate linear and non-linear motion patterns to implement SOT in SVs. The ablation experiments demonstrate the dual-flow contributes to tracking. Experimental comparisons on 14 real SVs captured by the Jilin-1 satellite constellation show that DF achieves optimal performance with an area under the curve of 0.912 in the precision plot, 0.700 in the success plot, and a speed of 155.2 frames per second. This work would encourage the development of remote-sensing ground surveillance.
Locality-Guided Global-Preserving Optimization for Robust Feature Matching

Feature matching is a fundamental problem in many computer vision tasks. This paper proposes a novel effective framework for mismatch removal, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LOcality-guided Global-preserving Optimization</i> (LOGO). To identify inliers from a putative matching set generated by feature descriptor similarity, we introduce a fixed-point progressive approach to optimize a graph-based objective, which represents a two-class assignment problem regarding an affinity matrix containing global structures. We introduce a strategy that a small initial set with a high inlier ratio exploits the topology of the affinity matrix to elicit other inliers based on their reliable geometry, which enhances the robustness to outliers. Geometrically, we provide a locality-guided matching strategy, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> using local topology consensus as a criterion to determine the initial set, thus expanding to yield the final feature matching set. In addition, we apply local affine transformations based on reference points to determine the local consensus and similarity scores of nodes and edges, ensuring the validity and generality for various scenarios including complex nonrigid transformations. Extensive experiments demonstrate the effectiveness and robustness of the proposed LOGO, which is competitive with the current state-of-the-art methods. It also exhibits favorable potential for high-level vision tasks, such as essential and fundamental matrix estimation, image registration and loop closure detection.
PK-RNN-V E: A deep learning model approach to vancomycin therapeutic drug monitoring using electronic health record data

Vancomycin is a commonly used antimicrobial in hospitals, and therapeutic drug monitoring (TDM) is required to optimize its efficacy and avoid toxicities. Bayesian models are currently recommended to predict the antibiotic levels. These models, however, although using carefully designed lab observations, were often developed in limited patient populations. The increasing availability of electronic health record (EHR) data offers an opportunity to develop TDM models for real-world patient populations. Here, we present a deep learning-based pharmacokinetic prediction model for vancomycin (PK-RNN-V E) using a large EHR dataset of 5,483 patients with 55,336 vancomycin administrations. PK-RNN-V E takes the patient’s real-time sparse and irregular observations and offers dynamic predictions. Our results show that RNN-PK-V E offers a root mean squared error (RMSE) of 5.39 and outperforms the traditional Bayesian model (VTDM model) with an RMSE of 6.29. We believe that PK-RNN-V E can provide a pharmacokinetic model for vancomycin and other antimicrobials that require TDM.
Theoretical Circuit Design of an Efficient Spintronic Random Number Generator With an Internal Postprocessing Unit

In this paper, a spintronic true random number generator (<b>TRNG</b>) is designed using the stochastic switching feature of the Magnetic Tunnel Junction (<b>MTJ</b>) device in the sub-critical current regime. The proposed structure consumes low power and occupies a small area. Also, to improve the quality of random numbers production and compensate for the impact of process variations on the quality of the random output, the proposed TRNG includes an internal post-processing unit. Compared to state-of-the-art designs, using an internal post-processing unit reduces the proposed generator&#x0027;s area overhead and power consumption. The simulation results show that the TRNG proposed in this paper consumes up to 68&#x0025; less power and occupies up to 64&#x0025; smaller area than the state-of-the-art design. Also, due to the existence of the efficient post-processing unit, the proposed TRNG successfully passes the national institute of standards and technology (NIST) random number tests even in the presence of the fabrication process variations.
A novel image encryption scheme based on memristive chaotic system and combining bidirectional bit-level cyclic shift and dynamic DNA-level diffusion

In recent years, many researchers have leveraged various memristors to design many novel memristive chaotic systems with complex dynamics. Compared with other chaotic systems, applying these memristive chaotic systems to image encryption is expected to solve some key problems in this field. Therefore, exploiting a recently reported memristive chaotic system, this paper proposes a novel image encryption scheme based on the memristive chaotic system and combining bidirectional bit-level cyclic shift and dynamic DNA-level diffusion (IES-M-BD). First, a discrete memristive chaotic map is employed to generate chaotic sequences. Then, the plaintext image is shifted circularly on bit-level according to chaotic sequences and the hash value of the plaintext image. After that, the shifted matrix is recombined on the bit plane and encoded dynamically by DNA encoding rules. Next, dynamic DNA-level diffusion and DNA-level permutation are carried out in two rounds. Finally, the encrypted image is obtained after dynamic DNA decoding. Simulation tests and performance analyses are also carried out in this paper. The simulation results and the security analyses demonstrate that this encryption scheme has a high security level and can resist various attacks.
The use of the cloud services to support the math teachers training

The development of the information society and technological progress are significantly influenced by the learning tools. Therefore, to the variety of tools that could be used to support the study of any discipline new ones emerging lately are continuously being added. Along with the great deal of systems of computer mathematics (SCM), web-oriented versions of SCM mathematical applications and other math learning tools the cloud-based versions of mathematical software such as MapleNet, MATLAB web-server, WebMathematica and others are now being used. These tools accomplishment becomes the essential part of training mathematics teachers. Domestic and foreign experiences of using cloud services for forming professional competences of mathematics teachers are analyzed. The place of the CoCalc within the system of mathematical disciplines learning tools is investigated. The task of improving the math teachers’ ICT competence by means of cloud services use in the process of training is considered. Among the new forms of learning rising along with the cloud services dissemination are such as collaborative learning, inquiry-based learning, person-oriented learning. At the same time, the use of the appropriate cloud service in the study of some mathematical discipline improves the assimilation of the learning material and improves the knowledge acquisition process on most topics. The analysis of current research of Ukrainian scientists on the problem in question shows that the progress is underway as for further elaboration and implementation of new learning methods and techniques of using cloud services in the higher education institutions.
Low-loss chip-scale programmable silicon photonic processor

Chip-scale programmable optical signal processors are often used to flexibly manipulate the optical signals for satisfying the demands in various applications, such as lidar, radar, and artificial intelligence.Silicon photonics has unique advantages of ultra-high integration density as well as CMOS compatibility, and thus makes it possible to develop large-scale programmable optical signal processors.The challenge is the high silicon waveguides propagation losses and the high calibration complexity for all tuning elements due to the random phase errors.In this paper, we propose and demonstrate a programmable silicon photonic processor for the first time by introducing low-loss multimode photonic waveguide spirals and low-random-phase-error Mach-Zehnder switches.The present chip-scale programmable silicon photonic processor comprises a 1×4 variable power splitter based on cascaded Mach-Zehnder couplers (MZCs), four Ge/Si photodetectors, four channels of thermally-tunable optical delaylines.Each channel consists of a continuously-tuning phase shifter based on a waveguide spiral with a micro-heater and a digitally-tuning delayline realized with cascaded waveguide-spiral delaylines and MZSs for 5.68 ps time-delay step.Particularly, these waveguide spirals used here are designed to be as wide as 2 μm, enabling an ultralow propagation loss of 0.28 dB/cm.Meanwhile, these MZCs and MZ-Ss are designed with 2-μm-wide arm waveguides, and thus the random phase errors in the MZC/MZS arms are negligible, in which case the calibration for these MZSs/MZCs becomes easy and furthermore the power consumption for compensating the phase errors can be reduced greatly.Finally, this programmable silicon photonic processor is demonstrated successfully to verify a number of distinctively different functionalities, including tunable time-delay, microwave photonic beamforming, arbitrary optical signal filtering, and arbitrary waveform generation.
Disentangled Capsule Routing for Fast Part-Object Relational Saliency

Recently, the Part-Object Relational (POR) saliency underpinned by the Capsule Network (CapsNet) has been demonstrated to be an effective modeling mechanism to improve the saliency detection accuracy. However, it is widely known that the current capsule routing operations have huge computational complexity, which seriously limited the usability of the POR saliency models in real-time applications. To this end, this paper takes an early step towards a fast POR saliency inference by proposing a novel disentangled part-object relational network. Concretely, we disentangle horizontal routing and vertical routing from the original omnidirectional capsule routing, thus generating Disentangled Capsule Routing (DCR). This mechanism enjoys two advantages. On one hand, DCR that disentangles orthogonal 1D (i.e., vertical and horizontal) routing greatly reduces parameters and routing complexity, resulting in much faster inference than omnidirectional 2D routing adopted by existing CapsNets. On the other hand, thanks to the light POR cues explored by DCR, we could conveniently integrate the part-object routing process to different feature layers in CNN, rather than just applying it to the small-scaled one as in previous works. This helps to increase saliency inference accuracy. Compared to previous POR saliency detectors, DPORTNet infers visual saliency (5 ∼ 9 ) × faster, and is more accurate. DPORTNet is available under the open-source license at https://github.com/liuyi1989/DCR.
UAV Path Planning Model Based on R5DOS Model Improved A-Star Algorithm

In order to solve the problems of large amounts of calculation and long calculation times of the A-star algorithm in three-dimensional space, based on the R5DOS model, this paper proposes a three-dimensional space UAV path planning model. The improved R5DOS intersection model is combined with the improved A-star algorithm. Together, they construct a local search process, and the R5DOS path planning model is established by reducing the number of search nodes. The path planning model is simulated through MATLAB software and the model can greatly reduce the number of nodes and computational complexity of the A-star algorithm in three-dimensional spaces, while also reducing the calculation time of the UAV. Finally, we compare the improved A-star algorithm with the original A-star algorithm and the geometric A-star algorithm. The final fitting result proves that the improved A-star algorithm has a shorter computation time and fewer node visits. Overall, the simulation results confirm the effectiveness of the improved A-star algorithm and they can be used as a reference for future research on path planning algorithms.
Fractional-order artificial bee colony algorithm with application in robot path planning

Artificial bee colony (ABC) algorithm is a popular meta-heuristic optimization algorithm inspired by the foraging behaviors of honeybees. Although ABC has outstanding exploration ability, it has certain weakness like poor exploitation capability and low solution precision. Therefore, to tackle these weakness and improve the performance, an ABC algorithm based on fractional-order calculus (FOABC) is proposed. More specially, the memory properties of fractional-order calculus is incorporated into onlooker bee phase to improve the local-search capability. Each time generating a new candidate solution, the previous foraging behaviors stored in memory are referred. Another enhanced search strategy is adopted in employed bee phase to avoid losing the balance between diversification and intensification. To evaluate the performance of FOABC, several groups of experiments are conducted on CEC 2017 benchmark problems. According to the experimental studies, FOABC is outstanding in comparisons with ABC variants and also performs remarkably well in practical applications, namely robot path planning.
Large-scale coherent Ising machine based on optoelectronic parametric oscillator

Ising machines based on analog systems have the potential to accelerate the solution of ubiquitous combinatorial optimization problems. Although some artificial spins to support large-scale Ising machines have been reported, e.g., superconducting qubits in quantum annealers and short optical pulses in coherent Ising machines, the spin stability is fragile due to the ultra-low equivalent temperature or optical phase sensitivity. In this paper, we propose to use short microwave pulses generated from an optoelectronic parametric oscillator as the spins to implement a large-scale Ising machine with high stability. The proposed machine supports 25,600 spins and can operate continuously and stably for hours. Moreover, the proposed Ising machine is highly compatible with high-speed electronic devices for programmability, paving a low-cost, accurate, and easy-to-implement way toward solving real-world optimization problems.
Evaluating generative models in high energy physics

There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP). In order to use such alternative simulators in practice, we need well defined metrics to compare different generative models and evaluate their discrepancy from the true distributions. We present the first systematic review and investigation into evaluation metrics and their sensitivity to failure modes of generative models, using the framework of two-sample goodness-of-fit testing, and their relevance and viability for HEP. Inspired by previous work in both physics and computer vision, we propose two new metrics, the Fr\'echet and kernel physics distances (FPD and KPD), and perform a variety of experiments measuring their performance on simple Gaussian-distributed, and simulated high energy jet datasets. We find FPD, in particular, to be the most sensitive metric to all alternative jet distributions tested and recommend its adoption, along with the KPD and Wasserstein distances between individual feature distributions, for evaluating generative models in HEP. We finally demonstrate the efficacy of these proposed metrics in evaluating and comparing a novel attention-based generative adversarial particle transformer to the state-of-the-art message-passing generative adversarial network jet simulation model.
Causal Transportability for Visual Recognition

Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features. Our main observation is that image classifiers may perform poorly on out-of-distribution samples because spurious correlations between non-robust features and labels can be changed in a new environment. By analyzing procedures for out-of-distribution generalization with a causal graph, we show that standard classifiers fail because the association between images and labels is not transportable across settings. However, we then show that the causal effect, which severs all sources of confounding, remains invariant across domains. This motivates us to develop an algorithm to estimate the causal effect for image classification, which is transportable (i.e., invariant) across source and target environments. Without observing additional variables, we show that we can derive an estimand for the causal effect under empirical assumptions using representations in deep models as proxies. Theoretical analysis, empirical results, and visualizations show that our approach captures causal invariances and improves overall generalization.
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation

Mining structural priors in data is a widely recognized technique for hyperspectral image (HSI) denoising tasks, whose typical ways include model-based methods and data-based methods. The model-based methods have good generalization ability, while the runtime can hardly meet the fast processing requirements of the practical situations due to the large size of an HSI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbf {X}}\in \mathbb {R}^{\textrm {MN}\times B}$ </tex-math></inline-formula> . For the data-based methods, they perform relatively fast on new test data once they have been trained. However, their generalization ability is always insufficient. In this article, we propose a fast model-based approach via a novel regularizer named the representative coefficient total variation (RCTV) to simultaneously characterize the low-rank and local smooth properties. The RCTV regularizer is proposed based on the observation that the representative coefficient matrix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbf {U}}\in \mathbb {R}^{\textrm {MN}\times R} (R\ll B)$ </tex-math></inline-formula> obtained by orthogonally transforming the original HSI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbf {X}}$ </tex-math></inline-formula> can inherit the strong local-smooth prior of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbf {X}}$ </tex-math></inline-formula> . Since <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R/B$ </tex-math></inline-formula> is very small, the model based on the RCTV regularizer has lower time complexity. In addition, we find that the representative coefficient matrix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbf {U}}$ </tex-math></inline-formula> is robust to noise, and thus, the RCTV regularizer can somewhat promote the robustness of the HSI denoising model. Extensive experiments on mixed noise removal demonstrate that the proposed method realizes a perfect compromise between denoising performance and denoising speed compared with other state-of-the-art methods. Remarkably, the denoising speed of our proposed method outperforms all competing model-based techniques and is comparable with the deep learning-based approaches. The code of our algorithm is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/andrew-pengjj/rctv.git</uri> .