Miracle-dz/UniFAR-FTUs huggingface dataset:
Coordination in Markov Games with Asymmetric Information

We study coordination in Markov games with asymmetric information. We consider a model where the state consists of different components, each representing the private type of each player. Players' actions depend on their private types and the public observation of past actions. The state components evolve as independent Markov processes conditioned on actions. We propose a solution concept called perfect correlated equilibrium (PCE), realized by a correlation device that observes only the public information of past actions. At time t, the device generates a prescription profile from a commonly known joint distribution, and sends each player a prescription privately before they act. Players are expected to take actions according to the prescriptions at equilibrium by evaluating the suggested prescription at the private types. We introduce “structured” PCE (sPCE), in which the correlation device generates prescriptions based on the common action history through a common belief on the state. We motivate sPCE by showing that any payoff profile induced by a general device can be induced by a structured one. We show that when the correlation device is using structured strategies, players' rationality constraints can be characterized through appropriate Markov decision processes (MDPs). Based on this characterization, we develop a backward dynamic approach, with which one can verify if a structured device is feasible, or even design a structured PCE in a backward recursive manner. Finally, we consider a specific example demonstrating how coordination can improve social welfare.
ERU-Net: An Enhanced Regression U-Net with Attention Gate and Adaptive Feature Fusion Block for Brain Age Prediction

The brain age is one of important biomarkers for identifying neurodegenerative diseases. Most existing works construct prediction models based on brain MRIs by deep learning methods while multi-scale features of MRI have been paid less attention. So, this paper proposes a new brain age predicting model, ERU-Net, which introduces an adaptive feature fusion (AFF) block into U-Net, for exploiting multi-scale features for high estimating accuracy. Moreover, the attention gate is also applied to enhance this model’s ability to extract important features among various brain regions. On a dataset of 6318 healthy people, ERU-Net achieves MAE = 2.85 and Pearson correlation coefficient R = 0.98, which outperforms other existing models for brain age estimation. In addition, by the results on the neurodegenerative disease datasets, it is clear that for the index of brain age gap (BAG), there are significantly statistical differences between healthy subjects and neurodegenerative patients such as Aizheimer’s disease, mild cognitive impairment, schizophrenia and Parkinson disease.
An Effective Tuberculosis Detection System Based on Improved Faster R-CNN with RoI Align Method

Tuberculosis(TB) is a serious public health threat in the world. Detecting and treating TB in its early stages can significantly improve the survival rate of patients and serve as the most effective approach for TB prevention and treatment. Using deep learning models to diagnose TB is highly accurate and efficient, making it a competitive option for early diagnosis. We built an improved Faster R-CNN model, which can classify TB X-ray images and detect TB lesions with bounding boxes. Our model has been trained using the large TB dataset TBX11K, which contains 11,200 X-ray images and provides the bounding box annotation information in json files. Our model uses region proposal network to generate anchor boxes, and determines the features in each anchor belonging to the object or background. In the next step, we extract features from boxes of different sizes to ensure the length of output results is equal. Compared with the original Faster R-CNN, we replace region of interest(RoI) pooling with RoI align to avoid quantization problems. Our system can precisely capture and classify disease symptoms in X-ray images with an accuracy of over 90%, and this study contributes to the research of computer-aided TB diagnosis.
Exploring Diverse Polarimetric Information Contents from SAR Data

This paper evaluates three target descriptors derived from full-polarimetric SAR data. A 2 × 1 real positive target vector is defined for coherent targets based on the mean and standard deviation of complex eigenvalues of the 2 × 2 Sinclair matrix. This vector is the basis for a novel scattering-type parameter that effectively distinguishes canonical targets. In the case of incoherent targets, the scattering purity and complexity parameters are characterized using the mean and standard deviation of real positive eigenvalues from a Hermitian positive semi-definite 3 × 3 coherency (covariance) matrix. The paper demonstrates the effectiveness of these parameters through experimental analysis involving a mixture of canonical targets and further evaluates their performance using Radarsat-2 full-polarimetric SAR data.
An Adaptive and Autonomous System-On-Chip with Data Analysis for μs-Latency Closed-Loop Optogenetics

In this paper, we report a mixed-signal chip in 0.13-μm designed for long term autonomous operation that can trigger low-latency stimulation upon the detection of complex neural firing patterns. This adaptive autonomous neural IC (AANIC) includes: i) circuits for multi-site optogenetics stimulation and multi-unit electrophysiology recording, ii) specialized digital cores to detect, compress, and classify the neural signals in situ and iii) a programmable processor, connected to the specialized cores, running the closed-loop algorithm. The AANIC can process 10 neural channels in parallel, and stimulate through two independent precise current-sources, and is integrated within a tiny wireless optogenetic platform. The AANIC has a latency of 0.6 ms, a power of 165 μW/Ch, an effective number of bits (ENOB) of 8.68 bits (OSR=25), a tunable bandwidth (1-6.5 kHz), and is tested in vivo in freely-moving experiments involving a mouse model of temporal lobe epilepsy expressing ChR2 in its inhibitory neurons.
Towards Learning-based Surgical Planning of Glioma Resection via a Contrastively Constrained Siamese Neural Network

The risk assessment of craniotomy paths is a vital stage in the automated surgical planning of glioma resection. However, current methods heavily rely on the manually-assigned risk coefficients tied to factors like path length and intersecting brain structures. This overreliance on empirical coefficients poses limitations in terms of their adaptability to broader cohorts. In this paper, we propose an innovative risk assessment approach for automated surgical planning by leveraging deep learning techniques. Specifically, we develop a Siamese Neural Network (SNN) with two weight-sharing branches, and also compile a glioma dataset, containing the extracted brain vessels, regions, and the craniotomy region on the scalp. During training, SNN learns to predict two risk values for a pair of paths, which are sampled inside and outside the craniotomy region, respectively. A contrastive loss is then employed to guide SNN by penalizing the higher predicted risk inside the craniotomy region or the lower risk outside the region. Such contrastive constraint empowers effective learning of SNN in our unique scenario, where the true risk values of surgical paths are unavailable. During inference, the trained SNN generates a risk map on the scalp, and the path is determined by finding the local minimum on the risk map. To the best of our knowledge, this work proposes one of the first attempts to successfully incorporate deep learning techniques into the risk assessment process of glioma resection planning. Quantitative and neurosurgeons’ qualitative evaluations of six testing cases proved the effectiveness and superiority of our learning-based approach. A consensus is reached that our method shows great potential to help surgeons make safer and more accurate plans in practice.
Robust Meta-Learning of Vehicle Yaw Rate Dynamics via Conditional Neural Processes

Trajectory planners of autonomous vehicles usually rely on physical models to predict the vehicle behavior. However, despite their suitability, physical models have some shortcomings. On the one hand, simple models suffer from larger model errors and more restrictive assumptions. On the other hand, complex models are computationally more demanding and depend on environmental and operational parameters. In each case, the drawbacks can be associated to a certain degree to the physical modeling of the yaw rate dynamics. Therefore, this paper investigates the yaw rate prediction based on conditional neural processes (CNP), a data-driven meta-learning approach, to simultaneously achieve low errors, adequate complexity and robustness to varying parameters. Thus, physical models can be enhanced in a targeted manner to provide accurate and computationally efficient predictions to enable safe planning in autonomous vehicles. High fidelity simulations for a variety of driving scenarios and different types of cars show that CNP makes it possible to employ and transfer knowledge about the yaw rate based on current driving dynamics in a human-like manner, yielding robustness against changing environmental and operational conditions.
An Early Depression Detection Model on Social Media using Emotional and Causal Features

Early depression detection is essential to enable healthcare professionals to effectively intervene and treat the depressive conditions. In existing research, an increasing number of psychological manifestations of depression are incorporated, and demonstrated effective by integrating them into computational models, particularly the emotional cues of depression. Although emotional factors are effective in depression detection, few studies have paid attention to the underlying depressive causal factors hidden within social media text for depression detection. In this paper, we propose a novel partitioned filtering network model to extract emotional and causal features for predicting the depressive users on social media. Experimental results demonstrate the proposed model achieves superior performance over recent baseline models on the dataset by highlighting the emotional and causal factors.
Knowledge-guided Network Pruning for EEG-based Emotion Recognition

With the development of deep learning in EEG-related tasks, the complexity of learning models has gradually increased. These complex models often result in long inference times, high energy consumption, and an increased risk of overfitting. Therefore, model compression has become an important consideration. Although some EEG models have used lightweight techniques, such as separable convolution, no existing work has directly attempted to compress EEG models to reduce their complexity. In this paper, we integrate neuroscience knowledge into EEG model pruning recovery, and innovatively propose two loss functions in the learning process, the knowledge-guided region-wise loss that enforces the classification evidence consistent with the importance of the prefrontal lobe, and the knowledge-guided sample-wise loss that constrains the learning process by distinguishing the importance of different samples.
Online Learning of Effective Turbine Wind Speed in Wind Farms

To develop better wind farm controllers that can meet more complex objectives, methods of modeling the wind turbine wakes at low computational expense are needed. Gaussian process (GP) regression offers a computationally inexpensive framework for learning complex functions from noisy measurements with very few datapoints. In this work, an online learning approach is presented to learn the rotor-averaged wind velocity at downstream wind turbines with GPs, using the available datastream of wind field measurements and wind turbine control set-points. This framework can readily be integrated into model-based controls methods because the model a) is updated online at low computational expense, b) assumes a mathematically favorable Gaussian form, and c) explicitly quantifies the stochastic nature of the wake field so that the trade-off between exploration and exploitation, and the uncertainty in the prediction, can be utilized. We show that a GP-learned model can match true values with errors within 0.5% on average, with as few as 5 training data points.
Machine Learning-Assisted Optical Detection of Multilayer Hexagonal Boron Nitride for Enhanced Characterization and Analysis

Biofilms are ubiquitous in aqueous environments, exerting significant influence on diverse surfaces, including metals prone to microbiologically influenced corrosion (MIC). This multifaceted phenomenon demands interdisciplinary collaborations to combat its far-reaching implications. In this context, our research delves into the intricate characterization of twodimensional (2D) materials, particularly hexagonal boron nitride (hBN), which is crucial for advancing corrosion prevention coatings. The nanoscale dimensions of 2D materials pose challenges in microstructural analysis and defect identification, necessitating labor-intensive traditional techniques. To address these complexities, we utilized two unsupervised machine learning models, namely, (a) K-means clustering, and (b) Gaussian Mixture Model (GMM), which enabled clear differentiation between multilayer hBN (MLhBN) and cracks. Our approach will streamline the characterization process and facilitate the extraction of thin layers with enhanced accuracy.
Mode Collapse Alleviation of Reinforcement Learning-based GANs in Drug Design

De novo drug design is a challenging task that involves understanding the principles of chemistry, chemical properties, and the rules that govern molecular interactions. Deep learning-based generative models, such as MolGAN, offer a promising approach for generating new molecules with the desired chemical properties from molecular graphs. Such models often combine a discrete generative adversarial network (GAN) and reinforcement learning (RL) to produce highly valid and novel molecules. However, the severe mode collapse problem leads to low performance. This study aims to alleviate and investigate the effect of multiple factors on mode collapse. We conducted experiments on different sampling methods, training epochs, and datasets of various volumes and evaluated the experimental results using performance metrics such as validity, uniqueness, novelty, and diversity. The experimental results demonstrate that noise sampling distributions, training epochs, and training data volumes affect performance. The experimental results provide a direction for mitigating the mode collapse problem for RL-based discrete GANs.
DEPHIDES: Deep Learning Based Phishing Detection System

In today’s digital landscape, the increasing prevalence of internet-connected devices, including smartphones, personal computers, and IoT devices, has enabled users to perform a wide range of daily activities such as shopping, banking, and communication in the online world. However, cybercriminals are capitalizing on the Internet’s anonymity and the ease of conducting cyberattacks. Phishing attacks have become a popular method for acquiring sensitive user information, including passwords, bank account details, social security numbers and more, often through social engineering and messaging tools. To protect users from such threats, it is essential to establish sophisticated phishing detection systems on computing devices. Many of these systems leverage machine learning techniques for accurate classification. In recent years, deep learning algorithms have gained prominence, especially when dealing with large datasets. This study presents the development of a phishing detection system based on deep learning, employing five different algorithms: artificial neural networks, convolutional neural networks, recurrent neural networks, bidirectional recurrent neural networks, and attention networks. The system primarily focuses on the fast classification of web pages using URLs. To assess the system’s performance, a relatively extensive dataset of labeled URLs, comprising approximately five million records, was collected and shared. The experimental results indicate that convolutional neural networks achieved the highest performance, boasting a detection accuracy of 98.74% for phishing attacks. This research underscores the effectiveness of deep learning algorithms, particularly in enhancing cybersecurity in the face of evolving cyber threats.
STLDA: A Spatiotemporal Linear Discriminant Analysis for Single-trial ERP-based Depression Recognition

Event-related potentials (ERP) within the Electroencephalogram (EEG), in particular, are electric signals induced by stimuli that can reflect specific cognitive activities of the brain. Therefore, ERP can be used as an objective biomarker to distinguish patients with depression from healthy individuals. However, the analysis and classification of single-trial ERP are difficult due to the high trial-to-trial variability and the low signal-to-noise ratio (SNR). Therefore, how to improve the SNR and the single-trial classification accuracy has received much attention. In this study, we proposed a spatiotemporal linear discriminant analysis (STLDA) method for single-trial ERP-based depression recognition, which can obtain optimal temporal and spatial filters for ERP signals to enhance their SNR significantly and preserve the spatiotemporal characteristics of the signals for depression recognition using the minimum distance to mean (MDM) classification strategy. Experimental results on two public datasets showed that our method achieved higher classification accuracy in comparison with some baseline methods. Further, the depression recognition performance of our method is almost equal to the traditional trial-averaged strategy.
Predicting Associations between circRNAs and Drug Sensitivity using Heterogeneous Graphs and Graph Attention Networks

Discovering associations between circular RNAs (circRNAs) and cellular drug sensitivity is essential for understanding drug efficacy and therapeutic resistance. Traditional experimental methods to verify such associations are costly and time-consuming. Thus, the development of efficient computational methods for predicting circRNA-drug associations is crucial. In this study, we introduce a novel computational predictor called HETACDA, aimed at predicting potential circRNA-drug sensitivity associations. HETACDA constructs a heterogeneous graph network, incorporating the characteristic structure graph of drugs and circRNAs, along with the circRNA-drug sensitivity topology graph. By employing a graph convolutional network, the drug embedding vector is computed from the molecular structure of the drug. Through a graph attention mechanism, HETACDA assigns distinct attention weights to nodes in order to emphasize the contribution of various neighborhood nodes to the central node. Subsequently, an association score between circRNA-drug sensitivity is predicted using a three-layer fully connected neural network. Extensive experimental comparisons against several state-of-the-art methods highlight the effectiveness of our proposed framework. The availability of our source code and datasets on GitHub (https://github.com/xiaoxiaojun131421/HETACDA) facilitates replication and further research in this area.
Enhancing Protein Solubility Prediction through Pre-trained Language Models and Graph Convolutional Neural Networks

Achieving optimal protein solubility is pivotal for efficient high-throughput purification, especially in industrial settings. However, conventional experimental techniques for assessing protein solubility in such contexts are not only costly but also time-intensive. Currently, numerous methods are available for predicting protein solubility, yet their effectiveness remains limited. Most of these approaches are predominantly sequence-based, failing to harness the invaluable structural insights inherent in proteins. Addressing these limitations, we introduce PPSol, an innovative protein solubility prediction methodology. Operating on protein sequences, PPSol employs ESM2 to predict protein contact maps, forming the basis for constructing protein graphs. Subsequently, well-established techniques are employed to predict protein feature representations as node features, including the utilization of the Position-Specific Scoring Matrix (PSSM). The resulting graph is fed into a graph convolutional neural network (GCN), enabling the acquisition of spatial structural information from proteins. Concurrently, ESM2-generated features undergo dimensional reduction via fully connected layers, integrating into every layer of the GCN for precise protein solubility prediction. Our approach excels through the fusion of pre-trained protein language models and GCNs, surpassing existing methodologies. Notably, PPSol attains state-of-the-art performance, showcasing a remarkable 2.8% enhancement in AUROC performance com-pared to prior strategies.
IMT-LUSS: A Novel Inception Meets Transformer-based Lung Ultrasound Scoring Model in Pneumonia

Pneumonia is a contagious disease that poses a great threat to human health. The real-time and free-radiation of lung ultrasound (LUS) makes it an essential tool for diagnosing pneumonia. This paper aims to explore an automatic detection model based on lung ultrasound imaging, called IMT-LUSS, which can achieve effective lung scoring. The model combines inception with transformer, intends to incorporate multi-scale information while considering global information. Firstly, the input is compressed using a stem block. Then it is incorporated into the multi-scale inception meet transformer (IMT) block for information representation, which includes a flexible inception module composed of three convolutional branches with different receptive fields and a pooling branch, and a feature encoding module improved by the multi-head self-attention mechanism with depth-wise convolution. Finally, automatic scoring of LUS images is completed based on feature representation information. 19330 LUS images were employed to verify the proposed IMTLUSS model. Experimental results demonstrate that this model has a great LUS scoring performance with high accuracy of 99.44 ± 0.14%. Meanwhile, the ablation experiments on structure and comparative experiments with other models proved its significant superiority, indicating the potential in future clinical applications.
Scattering Power Components from Dual-Pol SLC and GRD SAR Data

Methods to extract scattering power components and their importance for land cover applications have been extensively studied in radar polarimetry. Motivated by the advancements, this study introduces a novel method to obtain the scattering power components from the dual-pol SLC and GRD SAR data. The idea is conceived from the two proposed indices: DpRBI and DpRSI, that characterize scattering from the built-up (i.e., "dihedral-like") and "surface-like" targets, respectively. These two indices are suitably exploited to obtain the weights for the convex splitting of the total scattered power employing the scattering power factorization framework. The proposed method is applied to dual-pol Sentinel-1 SLC and GRD data acquired over Barcelona, Spain, and Milan, Italy.
AI-Powered Software Testing: The Impact of Large Language Models on Testing Methodologies

Software testing is a crucial aspect of the software development lifecycle, ensuring the delivery of high-quality, reliable, and secure software systems. With the advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP), Large Language Models (LLMs) have emerged as powerful tools capable of understanding and processing natural language texts easly. This article investigates the application of AI-based software testing, with a specific focus on the impact of LLMs in traditional testing methodologies. Through a comprehensive review of relevant literature and SeturDigital’s 25 year testing experience, this article explores the potential benefits, challenges, and prospects of integrating LLMs into software testing.