Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite-Horizon Optimal Control of Continuous-Time Stochastic Systems with Terminal Cost of Wasserstein Distance

This study addresses a stochastic optimal control problem for continuous-time systems aimed at steering a probability distribution of the terminal state towards a desired probability distribution. The problem formulation incorporates the Wasserstein distance, a metric of the space of probability measures, in the cost functional. We provide an optimality condition for this optimal control problem in the form of Pontryagin's minimal principle. The condition is obtained by carefully examining the properties of the Wasserstein distance. Consequently, we obtain the optimality condition described by a forward-backward stochastic differential equation and a Kantorovich potential, which appears in optimal transport theory.
Transformer-Based Domain Adaptation for Multi-Modal Emotion Recognition in Response to Game Animation Videos

Emotion recognition researches necessitate the strategic selection of stimuli to evoke targeted emotions for robust physiological analyses. This study pioneers the use of Genshin Impact game animation videos to induce positive and neutral emotional states. Notably, it introduces a Transformer-based feature extractor, enhancing Domain-Adversarial Neural Networks (DANN) to advance domain adaptation capabilities. Leveraging the inherent advantages of the Transformer architecture, including parallel processing and handling intricate time-series Electroencephalogram (EEG) and eye movement data, this innovation is reinforced by a discriminator with gradient reversal layers, harmonizing source and target domain distributions. Empirical results demonstrate the effectiveness of the Transformer-based DANN model in cross-subject multimodal emotion recognition, achieving a remarkable prediction accuracy of 83.38% across 59 subjects.
An Evaluation of Social Goals in Philippine Marine Protected Areas

Tubajon (Misamis Oriental, Philippines) Marine Protected Area (MPA) was established by the adjacent community in 2002. It is a 22 hectare MPA that is situated adjacent to a coastal village where a majority of the residents are fisherfolk. In March of 2012, a household socio-economic survey was administered to residents (N = 150) near Tubajon MPA to determine demographics, socio-economics, attitudes and perceptions of Tubajon residents regarding the MPA. Additionally, ten key informant interviews were conducted with various local government unit officers, barangay captains (elected village leader), and Deputized Fish Wardens to further understand MPA performance in Tubajon. Overall, Tubajon resident felt the MPA was a positive marine resource management tool for their community. Residents reported both biological and social improvements since the MPA was established, but these social gains from the MPA were minimal. Changes in alternative livelihoods were only experienced by a few residents. Residents reported minimal involvement in the decision-making process in their community and very few were involved in environmental educational programs that would encourage community empowerment. Many key informants reported problems of poaching and the corruption of some MPA managers. Despite, negative MPA perceptions, lack of community involvement, and few alternative livelihoods, informants
A Collaborative Multi-Agent Nonlinear System Identification Algorithm with Spectral Regularization

We address nonlinear system identification in a multi-agent setting, where each agent collects inputoutput data from a different instance of the same process, possibly in a different operating condition. In particular, we introduce a novel scheme for nonlinear auto-regressive with exogenous input (NARX) model identification where agents make a tentative estimate of their individual parameters, based on local information only, while a cloud-based application further manipulates these estimates so as to disclose the common model structure and adjust the values of the individual parameters around some reference parameter vector. The proposed scheme is inspired by the spectral regularization framework recently introduced in multi-task feature learning and is shown to be competitive against state-of-the art cloud-based algorithms addressing the same problem but under more restrictive assumptions.
Exponential Extremum Seeking with Unbiased Convergence

We present a multivariable extremum seeking (ES) algorithm for static and dynamic maps that achieves unbiased convergence to the optimum exponentially, referred to as exponential ES. The conventional ES approach, which uses constant amplitude sinusoids, results in steady-state oscillations around the optimum and is unable to guarantee unbiased convergence. In contrast, our ES approach employs exponential decay and growth functions to gradually decrease the amplitude of the perturbation signal and increase the amplitude of the demodulation signal, respectively. This eliminates the steady-state oscillation. To achieve unbiased convergence, we choose an adaptation gain that is sufficiently larger than the decay rate of the perturbation so that the learning process outpaces the perturbation's waning. The stability analysis is based on state transformation, averaging, and singular perturbation methods applied to the transformed system resulting in local stability of the transformed system as well as local exponential stability of the original system. For numerical simulation, we consider the problem of source seeking by a 2D velocity actuated point.
Variation in Some Soil Properties, Organic Matter and Soil Compaction after Logging Activities

In Turkey, three types of logging methods are widely used in forest operations; manpower, animal power and mechanical power. In recent years, the most common method involves skidding with farm tractors. In addition, the use of skidding through wire drawing is often applied in the most regions. The objective of this study was to determine the effects of logging activities on soil properties, organic horizon, and soil compaction. Study area was selected from Saricam Sub-District Directorate in Kastamonu, Turkey. In the study area, logging activities were carried out by farm tractors and the skidding through wire drawing at two slope classes (30-60% and 60-100%). Mineral soil samples (0-5, 5-10, 10-15, and 15-20 cm soil depths) were taken from four micro-ecological sites; skid road, logging residuals subbase, mineral soil without topsoil, and control (no activity). The soil samples were analyzed for soil pH, soil texture, permeability, bulk density (BD), water holding capacity (WHC), organic matter (OM), skeleton volume (SW) and soil compaction. Results showed that there were significant differences in soil compaction, soil texture, SW and OM between the four micro-ecological sites. However, the variation differed according to the soil depths and soil texture. The mineral soil without topsoil showed the lowest SW, sand and OM, whereas, it had the highest soil compaction and clay. The deepest soil part had the highest soil compaction. The results also showed higher dispersion ratios (higher than 15) which indicated higher erosion risk due to high land slope and low permeability values.
Distributed Dynamic Event-Triggered Communication Mechanisms for Dynamic Average Consensus

This paper studies the dynamic average consensus problem of multi-agent systems under event-triggered communication. In this problem, each agent has access to a time-varying reference signal and aims to track the average of all reference signals. Distributed algorithms with event-triggered communication have been developed to achieve dynamic average consensus. Nevertheless, these existing event-triggered communication mechanisms cannot guarantee the existence of a designable positive minimum inter-event time (MIET), which is important in their practical implementation. Motivated by this observation, we propose a distributed dynamic event-triggered communication mechanism (ETCM) for each agent. It is shown that the proposed ETCM guarantees the existence of a positive MIET that is locally adjustable by tuning design parameters. It is also shown that the dynamic average consensus is achieved with any pre-specified level of accuracy. As an illustrative example, the theoretical results are applied to a networked battery energy storage system for state-of-charge balancing and desired total power tracking.
Enhancing Longitudinal Medical Image Segmentation through Spatial-temporal Fusion

Medical imaging research has seen advances in deep learning, but temporal aspects in time-series medical imaging data are often overlooked, leading to diagnostic limitations. This study proposes a spatial-temporal fusion approach for medical image segmentation by integrating a 3D UNet spatial network with a novel temporal network, DTransformer, capable of handling irregularly spaced sequences. The 3D UNet extracts spatial features, while DTransformer processes temporal information with time distance considerations using a novel self-attention mechanism. Experiments on a lung CT dataset show significant segmentation accuracy improvements with the fusion approach. DTransformer proves effective for unequally spaced sequences and boosts performance. And spatial-temporal fusion enhances medical image segmentation. Moveover, DTransformer's ability to manage temporal context and time distance holds promise for various tasks, indicating a new avenue for research.
Exploring the Wearable and Embeddable Solutions for Biopotential Signal Measurement: Dry and Non-Contact Technologies

With a rising interest in wearable solutions and smart fitness gadgets, unobtrusive monitoring of physiological parameters is a topic gaining more and more interest. Being applied over clothes or embedded in the fabric, non-contact capacitive electrodes present a particularly interesting solution. However, in the context of digitalization of the healthcare industry, the question of the possibility of combining such a biomedical signal acquisition with artificial intelligence for postprocessing is posed. Instead of using biomedical signals generators, in this paper, capacitive electrodes with high input impedance are designed and used for real-life acquisition of electrocardiograms (ECGs), measured on thumbs over a layer of thin cotton gloves. These measurements are then used to analyze the influence of geometrical and fabric parameters on the quality of measured biomedical signals. For this purpose, instead of noise analysis, a novel approach was adopted: descriptive statistical features are used to evaluate the performance using four machine learning classifiers. The results have confirmed the possibility of classification and paved the way to further investigation of employing machine learning for capacitive biomonitoring. Furthermore, it is confirmed that tighter coupling, thicker fabric of lower dielectric permittivity, and larger detection surface area ensure better performance.
A Loopless Distributed Algorithm for Personalized Bilevel Optimization

This paper studies a class of personalized distributed bilevel optimization problems over networks, where nodes aim at jointly optimizing the sum of outer-level objectives that depend on the solution of inner-level optimization problems. The existing algorithms for distributed bilevel optimization problems usually require extra computation loops for estimating hypergradients. To facilitate computational efficiency, we develop a loopless distributed algorithm that employs certain steps to approximate the optimal solution of innerlevel optimization problems, and track Hessian-inverse-vector products in a recursive manner. We prove that for stochastic nonconvex-strongly-convex problems, the proposed algorithm achieves the state of the art O(∊−2) communication cost, while improving the computational cost by O(1og(1/∊)). Numerical experiments validate our theoretical findings.
Model-Based Fairness Metric for Speaker Verification

Ensuring that technological advancements benefit all groups of people equally is crucial. The first step towards fairness is identifying existing inequalities. The naive comparison of group error rates may lead to wrong conclusions. We introduce a new method to determine whether a speaker verification system is fair toward several population subgroups. We propose to model miss and false alarm probabilities as a function of multiple factors, including the population group effects, e.g., male and female, and a series of confounding variables, e.g., speaker effects, language, nationality, etc. This model can estimate error rates related to a group effect without the influence of confounding effects. We experiment with a synthetic dataset where we control group and confounding effects. Our metric achieves significantly lower false positive and false negative rates w.r. t. baseline. We also experiment with VoxCeleb and NIST SRE21 datasets on different ASV systems and present our conclusions.
MulAxialGO: Multi-Modal Feature-Enhanced Deep Learning Model for Protein Function Prediction

Predicting protein function from sequences through machine learning can improve the understanding of novel proteins and biological mechanisms. Existing methods mainly rely on one-dimensional convolution or natural language processing (NLP) techniques to extract features from sequences, but they suffer from limited predictive performance. To address this challenge, we propose MulAxialGO, a new method that leverages multi-modal feature fusion to improve prediction accuracy. MulAxialGO integrates the prior features of a large-scale pre-trained protein language model and the posterior features of dynamic embedding coding and sequence homology. In addition, MulAxialGO employs a comprehensive image feature encoder to extract features from sequences, providing a novel perspective for protein function prediction. MulAxialGO is tested on two benchmark datasets and achieves state-of-the-art results. On the 2016 dataset, MulAxialGO significantly outperforms DeepGOPlus, improving molecular function by 4.5 points, biological process by 2.4 points and cellular component by 1.6 points for the AUPR metric. Similarly, on the NetGO dataset, MulAxialGO outperforms the state-of-the-art NetGO2.0, improving Fmax by 1.1 points for biological process and 2.3 points for cellular component.
PENGARUH LEVERAGE (DAR, DER, DAN TIER) TERHADAP ROE PERUSAHAAN PROPERTI DAN REAL ESTATE YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2010 – 2014

Penelitian ini bertujuan untuk menganalisis pengaruh leverage yang diukur dengan debt to asset ratio (DAR), debt to equity ratio (DER), dan times interest earned ratio (TIER) terhadap return on equity (ROE). Penelitian menggunakan pendekatan kuantitatif dengan desain kausalitas. Penelitian dilakukan pada perusahaan properti dan real estate yang terdaftar di Bursa Efek Indonesia periode 2010-2014. Pemilihan sampel menggunakan metode purposive sampling sehingga diperoleh 24 sampel perusahaan. Data penelitian merupakan data sekunder yaitu laporan keuangan yang telah diaudit dan diperoleh melalui website resmi Bursa Efek Indonesia. Hipotesis diuji dengan menggunakan metode regresi data panel. Hasil analisis menemukan bahwa rasio leverage yang diukur dengan DER dan TIER berpengaruh positif terhadap ROE, sedangkan DAR tidak memiliki pengaruh terhadap ROE.
Predicting Lymph Node Metastasis of Colorectal Cancer in CT Scans Using Attention-Based Multiple Instance Learning

Accurately predicting lymph node metastasis (LNM) in colorectal cancer (CRC) contributes to develop appropriate treatment plans and evaluate postoperative prognosis. In clinical practice, preoperative diagnosis of LNM in CRC mostly relies on computed tomography (CT). Nevertheless, CT-based diagnosis of metastatic lymph nodes has a substantial misdiagnosis rate and lacks consistency among clinicians. Additionally, existing deep learning models using CT scans to predict LNM in CRC have not yielded satisfactory results due to insufficient attention given to lymph nodes. To address these issues, we propose an attention-based multiple instance learning (MIL) framework for this challenging task. Our framework incorporates a global-local cross-attention (GLCA) feature fusion module that combines global information from CT slices with local information from lymph nodes. Furthermore, an attention-based pooling approach is utilized to extract critical features and generate a bag representation. Lastly, a nested-neural memory ordinary differential equations (N-nmODE) feature enhancement module significantly augments the expressive capacity of the bag representation. A series of empirical studies show that our method achieves an overall accuracy of 74.7% and AUC of 76.8%, which represents a marked improvement over the physicians specialising in CRC and outperforms existing state-of-the-art methods. Our source code is available at https://github.com/SCU-MI/CAN-MIL.
Achieving Linear Speedup with Network-Independent Learning Rates in Decentralized Stochastic Optimization

Decentralized stochastic optimization has become a crucial tool for addressing large-scale machine learning and control problems. In decentralized algorithms, all computing nodes are connected through a network topology, and each node communicates only with its direct neighbors. Decentralized algorithms can significantly reduce communication overhead by eliminating the need for global communication. However, existing research on the linear speedup analysis of decentralized stochastic algorithms is limited to the condition of network-dependent learning rates, which rarely holds in practice since the network connectivity is typically unknown to each node. As a result, it remains an open question whether a linear speedup bound can be achieved using network-independent learning rates. This paper provides an affirmative answer. By utilizing a new analysis framework, we prove that D-SGD and Exact-Diffusion, two representative decentralized stochastic algorithms, can achieve linear speedup with network-independent learning rates. Simulations are provided to validate our theories.
Ecological Restoration of Soil Plant System by Earthworms based on Image Segmentation Algorithm

The life activities of earthworms, such as feeding, burrowing and excreting metabolites, may have direct or indirect effects on soil properties and chemical behavior of heavy metals in soil. By studying the growth and changes of plant roots, we can get the ecological parameters of plant growth environment, such as temperature, humidity, wind speed, precipitation and evapotranspiration, and help us study various meteorological conditions. Based on digital image processing technology and knowledge of plant root structure, this paper designs a simulation model of earthworm ecological restoration of soil plant system based on image segmentation algorithm. In this paper, the automatic segmentation method of plant root system image is mainly divided into three steps, that is, firstly, the green component image of root system image is enhanced by Gaussian matched filter, then the center line of root system is extracted by Hessian matrix method from the processed image, and finally the root system pixels are detected. The results show that the error rate of this algorithm is reduced by 2.36% and 4.88% respectively compared with the comparison algorithm, which further demonstrates the effectiveness of this algorithm, and completely realizes the segmentation of plant roots.
Kalman-like Observer for Hybrid Systems with Linear Maps and Known Jump Times

We propose a hybrid Kalman-like observer for general hybrid systems with linear (time-varying) dynamics and output maps, where the solutions' jump times are exactly known. After defining a hybrid observability Gramian and the corresponding hybrid uniform complete observability, we show that the estimate provided by this observer converges asymptotically to the system solution if this observability holds together with some boundedness and invertibility conditions along the considered system solution. Then, under additional uniformity and strictness of the forgetting factors, we show exponential stability of the estimation error with an arbitrarily fast rate. The robust stability of this error against input disturbances and measurement noise is also studied. The results are illustrated on several benchmark examples, including switched systems, hybrid systems with discontinuous solutions, and continuous- time systems with multi-rate sporadic outputs.
Fault Diagnosis of Discrete Event Systems Under Attack

In this paper, we study the problem of fault diagnosis under cyber attacks in the context of partially-observed discrete event systems. An operator monitors the evolution of a system through the received observations and computes its current diagnosis state. The observation is corrupted by an attacker which has the ability to edit a subset of sensor readings by inserting or erasing some events. In this sense, the attacker may induce the operator to draw incorrect diagnostic conclusions based on the corrupted observation regarding the fault occurrence. In particular, the attack is harmful if a fault can be detected by the operator when looking at an uncorrupted observation, while it is not detected when looking at the corresponding corrupted observation. In addition, the attacker must remain stealthy, i.e., its presence should not be discovered by the operator. To this end, we propose a special structure, called a stealthy joint diagnoser, which describes the set of all possible stealthy attacks. We show how to use the stealthy joint diagnoser to perform fault diagnosis under attack. Finally, such a structure also allows one to establish if a stealthy harmful attack may be implemented.
Control of systems with multiplicative observation noise

We consider the control of a linear system observed over multiplicative-noise. Specifically, the controller must stabilize the system using a control action based on observations of the system state that have been multiplied by i.i. d. random variables. While there is a long history of work on this fundamental problem, much of it has focused on understanding the performance of linear controllers, and the optimal control strategy for such a system remains unknown. In this paper, we consider the case of uniform multiplicative observation noise, and provide a nonlinear control strategy based on the maximum a-posteriori (MAP) estimator of the state. We explicitly compute the convergence rates of different moments of the system under this control strategy, and find that the MAP-based strategy outperforms the best memoryless linear strategy when the “signal-to-noise” ratio (SNR) of the multiplicative noise, i.e. the ratio of the mean to the standard deviation, is low. In the high SNR regime we see that the MAP strategy is also a linear memoryless strategy, however, it is suboptimal and is outperformed by the optimal linear controller.
ConvUNET: a Novel Depthwise Separable ConvNet for Lung Nodule Segmentation

Lung nodule segmentation is usually considered a 3D semantic segmentation task. Due to the small size, diverse morphology, and low recognition of lung nodules, it is hard to segment any nodule precisely. To solve this problem, we propose a lightweight depthwise separable convolutional network named ConvUNET, which consists of a hierarchical encoder and a U-shaped decoder. Compared with some Transformer-based models (e.g., SwinUNETR) and ConvNeXt-based models (e.g., 3D UX-Net), our model has the advantages of fewer parameters, faster inference speed, and higher accuracy. We test the segmentation performance on the LUNA-16 and LNDb-19 datasets using standard 5-fold cross-validations, and the proposed method achieves competitive dice scores of 88.90% and 84.16%, respectively. Besides, it also shows considerable precision in segmenting lung nodules with diverse characteristics. Our source code is available at https://github.com/Xinkai-Tang/ConvUNET.