Reviewerly/exHarmony huggingface dataset:
Information bottleneck disentanglement based sparse representation for fair classification

Unlike current state-of-the-art methods based on data augmentation and adversarial frameworks to solve the fair classification problem, this paper proposes the Information Bottleneck Disentanglement Based Sparse Representation algorithm to ensure the fairness of extracted features. The algorithm assumes that each sample is collaboratively generated by a categorical latent variable and a sensitive or category-irrelevant latent variable. To ensure that these two latent variables correctly capture their corresponding features, we impose Information Bottleneck Disentanglement constraints on them. Specifically, the Information Bottleneck is used to obtain more relevant information while compressing the amount of information. Simultaneously, a mutual information minimization constraint enhances their independence. Additionally, by employing the sparse structure, we can further disentangle the discriminative and sensitive information under supervision guidance. Therefore, the algorithm can improve the discrimination of the categorical latent variable as much as possible without extracting the sensitive data into it. Extensive experiments on five public and challenging benchmark datasets reveal the effectiveness of our proposed method, demonstrating that it can ensure fairness without sacrificing classification performance.
Artificial nanophotonic neuron with internal memory for biologically inspired and reservoir network computing

Abstract Neurons with internal memory have been proposed for biological and bio-inspired neural networks, adding important functionality. We introduce an internal time-limited charge-based memory into a III–V nanowire (NW) based optoelectronic neural node circuit designed for handling optical signals in a neural network. The new circuit can receive inhibiting and exciting light signals, store them, perform a non-linear evaluation, and emit a light signal. Using experimental values from the performance of individual III–V NWs we create a realistic computational model of the complete artificial neural node circuit. We then create a flexible neural network simulation that uses these circuits as neuronal nodes and light for communication between the nodes. This model can simulate combinations of nodes with different hardware derived memory properties and variable interconnects. Using the full model, we simulate the hardware implementation for two types of neural networks. First, we show that intentional variations in the memory decay time of the nodes can significantly improve the performance of a reservoir network. Second, we simulate the implementation in an anatomically constrained functioning model of the central complex network of the insect brain and find that it resolves an important functionality of the network even with significant variations in the node performance. Our work demonstrates the advantages of an internal memory in a concrete, nanophotonic neural node. The use of variable memory time constants in neural nodes is a general hardware derived feature and could be used in a broad range of implementations.
Structural-aware motif-based prompt tuning for graph clustering

Graph clustering as a fundamental unsupervised task in graph mining, has garnered increasing attention in recent studies. However, existing methods often train a dedicated unsupervised model for each graph dataset and require laborious tuning. They often prioritize the noisy topological structure and overlook the generalizable implicit connections in local patterns, which hinders the efficient learning of both structure and semantic knowledge. To this end, we consider the powerful expressive ability of the prompt and propose a structural-aware motif-based prompt tuning for graph clustering framework, namely, MPTGC. Specifically, we first employ a motif-based prompt to match and identify important local patterns, thereby augmenting the structure and semantic information of the original input graph. This helps mitigate the impact of long-tailed distribution and noise in the graph structure. Then, we utilize pretext tasks to pretrain a transferable backbone that facilitates quick adaptation to the graph clustering task. Based on the pretrained model, we leverage the task token prompt function to reformulate the objective of the downstream graph clustering task, making it resemble a general feature reconstruction. Therefore, the pretrained model could be directly applied without tedious fine-tuning to obtain the prediction probability. Extensive experiments on benchmark datasets demonstrate the superiority of our method. The MPTGC gains the average improvements of 10.92% on ACC, 13.53% on NMI, 16.42% on ARI and 9.90% on F1 against the baseline methods on six benchmark datasets.
A neural network regression model for estimating the lifespan of a Fibre Bundle

Abstract Fibre Bundle Models (FBMs) use generalized distributions like the Weibull distribution to study the failure mechanics of disordered material under different load-sharing schemes. Here we attempt to use a simple neural network regression model to estimate the lifespan of Fibre Bundles for axial loading under the Global Load Sharing (GLS) scheme. We find that using neural networks can give a reliable estimate (within ∼2%) of the lifespan for different initial conditions. We also develop a semi-analytical expression for the lifespan of a bundle of fibres. The aim is to establish an empirical relationship using a neural network regression (NNR) method that helps us estimate the ultimate tensile strength. The expressions and methods developed here can be a precursor to future investigation under those cited in the following section(s).
Wavelet regularization benefits adversarial training

Adversarial training methods are frequently-used empirical defense methods against adversarial examples. While many regularization techniques demonstrate effectiveness when combined with adversarial training, these methods typically work in the time domain. However, as the adversarial vulnerability can be considered a high-frequency phenomenon, it is crucial to regulate adversarially-trained neural network models in the frequency domain to capture low-frequency and high-frequency features. Neural networks must fully utilize the detailed local features extracted by their receptive field. To address these challenges, we conduct a theoretical analysis of the regularization properties of wavelets, which can enhance adversarial training. We propose a wavelet regularization method based on the Haar wavelet decomposition named Wavelet Average Pooling. This wavelet regularization module is integrated into a wide residual neural network to form a new model called WideWaveletResNet. On the CIFAR-10 and CIFAR-100 datasets, our proposed Adversarial Wavelet Training method demonstrates considerable robustness against different types of attacks. It confirms our assumption that our wavelet regularization method can enhance adversarial robustness, particularly in deep and wide neural networks. We present a detailed comparison of different wavelet base functions and conduct visualization experiments of the Frequency Principle (F-Principle) and interpretability to demonstrate the effectiveness of our method. The code is available on the open-source website: https://github.com/momo1986/AdversarialWaveletTraining.
Initial validation of a closed loop filter and controller approach for active stabilization of thermomechanical distortions

Increasing technological progress in space technology is accompanied by an increased demand for accuracy. The alignment of optical instruments or antennas on the satellite platform requires structural stability that can no longer be achieved with traditional, passive methods, such as the use of materials with high stiffness or low coefficient of thermal expansion. Within the Infrared Astronomy Satellite Swarm Interferometry (IRASSI) project, an approach to actively stabilizing structures against low-frequency periodic heat induced deformations has been developed and simulated. In this study, the basic functionality of that thermomechanical FEM model is demonstrated by means of the thermal transfer functions between the changes in the applied heat and the resulting temperature changes, as well as a closed loop filter and controller approach consisting of a Linear Quadratic Regulator (LQR) and a Kalman filter. For this purpose, an experimental setup consisting of an aluminum plate with 14 temperature sensors, four heating elements for controlling the structure and one heating element for simulating the perturbation is used. The evaluation of the thermal transfer functions in the model compared to the experimental setup shows a deviation of the phase shift of less than 0.6% and an amplitude shift of less than 3.5% on average. A Kalman filter processes the temperature measurement in order to obtain an optimal estimate for the LQR controller, which then calculates the necessary change in the heating power of the four control heating elements to minimize the displacements at the desired two positions in x- and y-direction. The verification is performed by a camera system as an external validation tool, which is able to resolve micron-scale displacements by measuring the spatial displacement of a speckle pattern applied to the structure using sub-pixel accurate image correlation algorithms. While the range of values of the amplitudes of the displacements is 8.93 μ in the uncontrolled case, an improvement of more than 4.6 dB could be achieved by applying the filter and controller system. Thus, it is demonstrated that this method has the potential to further improve already highly stable structures or to make lighter and cheaper materials with poorer passive stability values usable through active structural stabilization.
A Linguistic Stylistic Analysis of Political Commentaries on Ondo 2020 Gubernatorial Election on Online Newspapers

This study investigated linguistic stylistic analysis of political commentaries on Ondo 2020 gubernatorial election. The researcher selected 4 political commentaries from two online newspapers (Sun and Independent). The focus of this study was to analyze the collected data based on the objectives of the study that cover syntactic, phonological, foregrounded elements and deviant features found in the selected political commentaries. The analysis was anchored on Leech and Short’s Checklist of Linguistic and Stylistic Categories as its theoretical framework while the method was qualitative research analysis. Descriptive survey method was however employed to analyze the collected data. Hence, the findings showed that sentences in the analyzed comments are predominantly non-simple sentences with mixtures of declarative, imperative and interrogative sentences. Phonological devices such as alliteration, assonance, and consonance fully have rhythmic patterns. Also, foregrounded elements such as phonetic spelling, and dominant deployment of deviational and non-deviational foregrounding to captivate readers’ attentions are evidential. Lastly, deviant features such as morphological, grammatical and lexical deviations manifest in the analyzed comments. Thus, the manifestation of linguistic features inherent in the analyzed data is an attestation that the choice of words in the use of language is peculiar to an individual writer.
Joint span and token framework for few-shot named entity recognition

Few-shot Named Entity Recognition (NER) is a challenging task that involves identifying new entity types using a limited number of labeled instances for training. Currently, the majority of Few-shot NER methods are based on span, which pay more attention to the boundary information of the spans as candidate entities and the entity-level information. However, these methods often overlook token-level semantic information, which can limit their effectiveness. To address this issue, we propose a novel Joint Span and Token (JST) framework that integrates both the boundary information of an entity and the semantic information of each token that comprises an entity. The JST framework employs span features to extract the boundary features of the entity and token features to extract the semantic features of each token. Additionally, to reduce the negative impact of the Other class, we introduce a method to separate named entities from the Other class in semantic space, which helps to improve the distinction between entities and the Other class. In addition, we used GPT to do data augmentation on the support sentences, generating similar sentences to the original ones. These sentences increase the diversity of the sample and the reliability of our model. Our experimental results on the Few-NERD1 and SNIPS2 datasets demonstrate that our model outperforms existing methods in terms of performance.
The Sunspot Number Forecasting Using a Hybridization Model of <scp>EMD</scp>, <scp>LSTM</scp> and Attention Mechanism

Sunspot number forecasting is a significant task for human beings in order to observe solar activity, and it is a classical chaotic time series. To improve the forecasting accuracy, we proposed a hybrid forecasting model based on empirical mode decomposition, long short‐term memory neural network and attention mechanism. First, the empirical mode decomposition is used to transfer the sunspot number to several sub‐components called IMFs and residual. Then, the IMFs and residual are input to the prediction module that is composed of long short‐term memory neural network and attention mechanism. Finally, the forecasting result is accumulated by the IMFs and residual through the predict module. The RMSE, MAE, MAPE and R 2 indexes of our method are 1.567, 1.048, 1.8% and 0.9997. We also compare with GRU, informer and XGBoost‐DL, our method improves accuracy above 24 and 18 of RMSE and MAE, respectively. The experiment results demonstrate our method has higher precision. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Collaborative fine-grained interaction learning for image–text sentiment analysis

Investigating interactions between image and text can effectively improve image–text sentiment analysis, but most existing methods do not explore image–text interaction at fine-grained level. In this paper, we propose a Memory-enhanced Collaborative Fine-grained Interaction Transformer (MCFIT) to learn collaborative fine-grained interaction between image and text. Specifically, a multi-branch encoder is designed to learn both fine-grained region-word and patch-word interactions. Meanwhile, Memory-enhanced Cross-Attention (MECA) is proposed to utilize patch and region information to improve region-word interaction and patch-word interaction learning, respectively. Therefore, collaborative fine-grained interaction can yield more accurate image–text interaction. Finally, to analyze the sentiments embedded in real-life Chinese image–text pairs, we build a large-scale Chinese image–text sentiment dataset (CISD) containing 54,931 image–text pairs. Extensive experiments conducted on four real-life datasets prove the effectiveness of collaborative fine-grained interaction and demonstrate that MCFIT outperforms the state-of-the-art baselines.
Addressing implicit bias in adversarial imitation learning with mutual information

Adversarial imitation learning (AIL) is a powerful method for automated decision systems due to training a policy efficiently by mimicking expert demonstrations. However, implicit bias is present in the reward function of these algorithms, which leads to sample inefficiency. To solve this issue, an algorithm, referred to as Mutual Information Generative Adversarial Imitation Learning (MI-GAIL), is proposed to correct the biases. In this study, we propose two guidelines for designing an unbiased reward function. Based on these guidelines, we shape the reward function from the discriminator by adding auxiliary information from a potential-based reward function. The primary insight is that the potential-based reward function provides more accurate rewards for actions identified in the two guidelines. We compare our algorithm with SOTA imitation learning algorithms on a family of continuous control tasks. Experiments results show that MI-GAIL is able to address the issue of bias in AIL reward functions and further improve sample efficiency and training stability.
NORA: Scalable OWL reasoner based on NoSQL databases and Apache Spark

Abstract Reasoning is the process of inferring new knowledge and identifying inconsistencies within ontologies. Traditional techniques often prove inadequate when reasoning over large Knowledge Bases containing millions or billions of facts. This article introduces NORA, a persistent and scalable OWL reasoner built on top of Apache Spark, designed to address the challenges of reasoning over extensive and complex ontologies. NORA exploits the scalability of NoSQL databases to effectively apply inference rules to Big Data ontologies with large ABoxes. To facilitate scalable reasoning, OWL data, including class and property hierarchies and instances, are materialized in the Apache Cassandra database. Spark programs are then evaluated iteratively, uncovering new implicit knowledge from the dataset and leading to enhanced performance and more efficient reasoning over large‐scale ontologies. NORA has undergone a thorough evaluation with different benchmarking ontologies of varying sizes to assess the scalability of the developed solution.
A Compact BRDF Scanner with Multi-conjugate Optics

Reflectance, represented as Bidirectional reflectance distribution functions (BRDFs), is an important scene property that we wish to obtain from the real world together with the scene’s 3D shape. BRDF acquisition, however, remains a difficult task because it is time-consuming and requires huge and costly devices. To make the BRDF acquisition easier and accessible to everyone, we develop a compact projector-camera-based BRDF scanner with coaxial multi-conjugate optics, where the sensor/source is conjugate with the Fourier transform plane of the objective lens, and the camera/projector pupil is conjugate with the sample plane. It consists of only off-the-shelf components, and the carefully designed conjugate optics make the entire system compact. BRDFs of outdoor objects are scanned to show the validity of our device.
Near-light Photometric Stereo with Symmetric Lights

This paper describes a linear solution method for near-light photometric stereo by exploiting symmetric light source arrangements. Unlike conventional non-convex optimization approaches, by arranging multiple sets of symmetric nearby light source pairs, our method derives a closed-form solution for surface normal and depth without requiring initialization. In addition, our method works as long as the light sources are symmetrically distributed about an arbitrary point even when the entire spatial offset is uncalibrated. Experiments showcase the accuracy of shape recovery accuracy of our method, achieving comparable results to the state-of-the-art calibrated near-light photometric stereo method while significantly reducing requirements of careful depth initialization and light calibration.
NeReF: Neural Refractive Field for Fluid Surface Reconstruction and Rendering

We present a novel Neural Refractive Field (NeReF) to recover wavefront of transparent fluids by simultaneously estimating the surface position and normal of the fluid front. Unlike prior arts that treat the reconstruction target as a single layer of the surface, NeReF is specifically formulated to recover a volumetric normal field with its corresponding density field. A query ray will be refracted by NeReF according to its accumulated refractive point and normal, and we employ the correspondences and uniqueness of refracted ray for NeReF optimization. We show NeReF, as a global optimization scheme, can more robustly tackle refraction distortions detrimental to traditional methods for correspondence matching. Furthermore, the continuous NeReF representation of wavefront enables view synthesis as well as normal integration. We validate our approach on both synthetic and real data and show it is particularly suitable for sparse multi-view acquisition. We hence build a small light field array and experiment on various surface shapes to demonstrate high fidelity NeReF reconstruction.
Glare Removal for Astronomical Images with High Local Dynamic Range

Veiling glare is a common imaging corruption, describing the phenomenon of light spreading from bright regions into otherwise dark regions. This leads to a reduction of contrast, locally limiting high dynamic range imaging capabilities in addition to what the sensor’s pixels are able to record. In astronomical imaging, which motivates the present study, high local dynamic ranges are ubiquitous, an extreme example being the contrast between the moon and the sun’s corona during a total solar eclipse. While this corruption phenomenon has been known for a long time, the field lacks a specialized method for this problem. Building on the structure of the glare-corruption operator, we devise an efficient framework which can be combined with off-the-shelf optimization methods to address the problem of glare removal. We also present a novel evaluation set consisting of pairs of images with and without glare. We show state-of-the art glare removal performance on this benchmark and other real world images, including telescope images of a total solar eclipse.
Blood stroke Classification using Proposed CNN Model

The faster medical treatment is provided, the better chances of recovery from a blood stroke. Early detection allows for prompt medical intervention, which can aid in the dissolution of the clot and the restoration of blood flow to the brain. This can minimize the damage caused by the stroke and reduce the risk of long-term disability or death. This study presents a proposed Convolutional Neural Network (CNN) model for the classification of blood stroke into two classes, blood clots or normal. For training the model, the Adam optimizer was used with a batch size of 32 and 220 epochs. The proposed model was evaluated using various performance metrics such as precision, recall, F1 score, and accuracy. The model had an overall accuracy of 92%, indicating that it can correctly classify cases of blood stroke. The findings of this study offer promising clues for the development of automated blood stroke detection systems based on deep learning models, which can help healthcare professionals make timely and accurate diagnoses.