Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive particle swarm architecture search based on multi-level convolutions for functional brain network classification

Recently, the functional brain network (FBN) classification methods based on deep neural networks (DNNs) have around a lot of scientific interest. However, these DNN architectures are manually designed by human experts through trial-and-error testing, which not only requires rich parameter tuning experience and large labor costs, but also a fixed manual architecture cannot consistently guarantee good performance across different data distributions and scenarios. To solve this problem, we propose an adaptive particle swarm architecture search method based on multi-level convolutions, which can automatically design suitable DNN architectures for various FBN classification tasks. Specifically, to effectively extract multi-level features at FBN, we construct three multi-level convolution units to form candidate architectures. These units can extract edge-level, node-level, and graph-level features respectively. The parameters of these units will be searched using the particle swarm-based NAS framework. Additionally, to alleviate the difficulty of searching in a vast search space, we propose a novel adaptive updating strategy. This strategy adaptively locks specific elements of the particle vector based on historical information and the search epochs, which can effectively search within a subset of the vast search space. We conduct systematic experiments on ABIDE I, ABIDE II, and ADHD-200 datasets with different atlases. The experimental results demonstrate that our method achieves competitive accuracies of 74.71%, 73.03%, and 74.39% on the CC200 atlas, and 71.42%, 73.91%, and 69.96% on the AAL atlas respectively.
Harnessing Transfer Learning for Dementia Prediction: Leveraging Sex-Different Mild Cognitive Impairment Prognosis

This paper presents a machine learning-based prediction for dementia, leveraging transfer learning to reuse the knowledge learned from prediction of mild cognitive impairment, a precursor of dementia. We also examine the impacts of temporal aspects of longitudinal data and sex differences. The methodology encompasses key components such as setting the duration window, comparing different modeling strategies, conducting comprehensive evaluations, and examining the sex-specific impacts of simulated scenarios. The findings reveal that cognitive deficits in females, once detected at the mild cognitive impairment stage, tend to deteriorate over time, while males exhibit more diverse decline across various characteristics without highlighting specific ones. However, the underlying reasons for these sex differences remain unknown and warrant further investigation.
Maintaining Synchronous Operation of a Damperless Dual-Port Tooth-Coil-Winding PMSG

The stability of a wind turbine-rotated synchronous generator must be maintained during a sudden change in wind speed or direction. In integral slot winding machines this task is taken care of by rotor damper windings. Tooth-coil permanent magnet generators cannot, however, have a damper winding in the rotor because of the high harmonic content of the air gap. In this paper, we study a possibility to stabilize PMSG damperless synchronous operation by dividing the generator stator winding into two parts - power winding (PW) and control winding (CW). We call this machine a dual-port PMSG (DPPMSG). The PW is directly connected to the grid. Therefore, the generator rotates, in average, at a fixed speed. On the other hand, the CW is connected to the grid through a four-quadrant power converter. The CW has three different tasks: It is responsible for the startup process, damping of speed oscillations and, of course, power generation. Electrical and magnetic decoupling of the winding sets is ensured by concentrated wound coils. The number of stator coils allocated to a winding set determines its power level. Different power levels of winding sets are analytically analyzed for wind gust speeds according to the IEC 61 400 standard. The results are verified by experimental tests.
Derivative Feedback Control Using Reinforcement Learning

In this paper, the model-free state-derivative and output-derivative feedback control of continuous-time dynamic systems are considered. First, an online iterative algorithm is designed following the reinforcement learning framework, in which measurements of the system state derivatives are collected to synthesize an optimal state-derivative feedback control law. The proposed approach employs the integral reinforcement learning technique to iteratively solve a derivative feedback algebraic Riccati equation. The iterative algorithm is extended to the output-derivative feedback case by introducing a state-parametrization scheme that reconstructs the state-derivative signal from the output derivatives and input data. Based on this parametrization, we develop an online iterative algorithm based on the reinforcement learning framework to determine the optimal output-derivative feedback controller. Convergence of the iterative algorithms to the analytical optimal control solutions is demonstrated. Numerical simulations motivated by practical applications demonstrate the benefits of our method compared to the standard output feedback reinforcement learning algorithms.
Macro Evaluation of a GaAs 3000 Gate Array

A 3000 equivalent gate, 64 signal I/O, configurable-cell GaAs array is reported. This enhancement/depletion-mode (E/D) array has been designed to interface with silicon ECL, CMOS, and TTL families. A custom designed, surface mount leaded chip carrier package has been developed with the array. Total array power is about 2.5 Watts plus 20 mW per ECL output driver for this single supply, 160 mil square chip. A special diagnostic circuit personalization is described. This circuit allows a complete assessment of the array logic macro library functional characteristics and the inter-cell fan-in and fan-out circuit loading effects. The diagnostic circuit includes a circuit designed to emulate the silicon 10181 ECL 4-Bit ALU, a popular 10K ECL component.
Spinal Lesions Classification and Localization with ACAT-Net from X-ray Images

X-ray images play an important role in the diagnosis of spinal diseases because of their convenient collection and easy observation. But it is time-consuming and challenging for radiologists to examine the differences between the vertebrae to diagnose abnormalities and locate lesions. Many existing methods try to extract the global features of radiographs and do not make full use of adjacent vertebrae variations. In this paper, we propose a novel Axial-aware neural network with Consecutive Attention Transformer (CAT), namely ACAT-Net, which takes advantage of the convolutional neural network and transformer as a new deep learning framework. A deep convolutional network extracts features of anteroposterior and lateral X-ray images that may have abnormalities in them. The consecutive attention transformer block is then used to focus on the morphological differences of axial adjacent vertebrae on the spines. The ingenious structure we designed can significantly reduce the amount of network parameters. Extensive experiments on clinical and public datasets show that our method is remarkably superior to other existing approaches in the spine X-ray image analysis.
A Method for Automatical Extraction of Typical Disaster-bearing Targets from LiDAR Point Cloud in Coastal Zone

This paper presents a method based on georeferenced feature image to automatically extract typical disaster-bearing targets from coastal LiDAR data. Firstly, the noise and the water surface of LiDAR point cloud are removed by using the elevation histogram. Secondly, by analyzing the spatial distribution of point cloud, the georeferenced feature image of point cloud is generated. Finally, the image processing method and the corresponding relationship between the threedimensional point cloud and the two-dimensional feature image are used to realize the automatic extraction of the targets. In this paper, the LiDAR cloud data of Haidian Island acquired by ALS70, is used as experimental data to verify the feasibility and practicability of the proposed method. Keywords-coastal zone;disaster bearing targets;LiDAR;object extraction
Evaluating the Slope‐Area Method to Accurately Identify Stream Channel Heads in Three Physiographic Regions

Estimation of stream channel heads is an important task since ephemeral channels play a significant role in the transport of sediment and materials to perennial streams. The slope‐area method utilizes digital elevation model (DEM) and related information to develop slope‐area threshold relationships used to estimate the position of channel heads in the watershed. A total of 162 stream channel heads were mapped across the three physiographic regions of Alabama, including the Southwestern Appalachians (51), Piedmont/Ridge and Valley (61), and Coastal Plains (51). Using Geographic Information System and DEM, the local slope and drainage area for each mapped channel head was calculated and region‐specific models were developed and evaluated. Results demonstrated the local slope and drainage area had an inverse and strong correlation in the Piedmont/Ridge and Valley region (r2 = 0.71) and the Southwestern Appalachian region (r2 = 0.61). Among three physiographic regions, the weakest correlation was observed in the Coastal Plain region (r2 = 0.45). By comparing the locations of modeled channel heads to those located in the field, calculated reliability and sensitivity indices indicated model accuracy and reliance were weak to moderate. However, the slope‐area method helped define the upstream boundaries of a more detailed channel network than that derived from the 1:24,000‐scale National Hydrography Dataset, which is commonly used for planning and regulatory purposes.
Economic Dispatch for DC Microgrids: An Optimal Power Sharing Approach with Batteries

In this paper, we propose a hierarchical control structure comprising three layers which is able to (i) achieve economic dispatch for islanded DC microgrids, (ii) compensate load and generation disturbances with batteries performing power sharing and (iii) stabilize non-passive constant power loads. The batteries are charged economically optimally by the third layer controller such the state of charges (SOCs) remain constant. The proportional power sharing of the batteries is achieved by employing a novel control law which solves the linearized steady-state power flow equations in real time. The microgrid is stabilized by using voltage controllers for batteries and active damping elements. A numeric method to verify closed-loop asymptotic stability is derived. The power sharing of the batteries and stability achieved with the proposed control is demonstrated in a simulation.
Simultaneous Synchronization and Topology Identification of Complex Dynamical Networks

We propose a new method for simultaneous synchronization and topology identification of a complex dynamical network that relies on the edge-agreement framework and on adaptive-control approaches by design of an auxiliary network. Our method guarantees the identification of the unknown topology and it guarantees that once the topology is identified the complex network achieves synchronization. Under our identification algorithm we are able to provide stability results for the estimation errors in the form of uniform semiglobal practical asymptotic stability. Finally, we demonstrate the effectiveness of our approach with an illustrating example.
FedARC: Federated Learning for Multi-Center Tuberculosis Chest X-ray Diagnosis with Adaptive Regularizing Contrastive Representation

Tuberculosis (TB) poses a significant global health threat and leads to millions of deaths annually. While early diagnosis and treatment can substantially enhance survival prospects, it continues to present a major challenge, particularly in developing countries. In recent years, machine learning has emerged as a valuable tool for tuberculosis diagnosis. However, the training of a dependable diagnostic model necessitates a large volume of data, typically distributed across multiple medical centers. To safeguard data privacy across various centers, we have incorporated federated learning (FL) into TB diagnosis. However, conventional FL methods suffer from substantial performance degradation due to the considerable variation in TB data distribution across different centers. Consequently, we introduce a novel personalized FL approach, FedARC, to address this issue. To mitigate data heterogeneity across centers, we guide the objective function for each center with adaptive regularization to align it with the stationary point of the global loss, thereby enabling the model to converge towards the global optimum. Simultaneously, model-contrastive learning enables the exploration of the specific attributes of each client, enabling the local model to learn more generalizable features. Extensive experimental results on five publicly available chest X-ray image datasets demonstrate the significant outperformance of our proposed method over state-of-the-art methods in diverse settings.
On Nash Equilibria for Decentralized Symbolic Control of Interconnected Finite State Systems

In this paper we consider a pair of interconnected nondeterministic and metric finite state systems and address a decentralized symbolic control problem where controllers are designed for enforcing local specifications expressed in terms of regular languages, up to a desired accuracy. The considered control architecture is decentralized, i.e. each controller can only communicate with the corresponding plant. Since plant systems are interconnected, the part of the specification that can be enforced on one system depends on the one on the other system. We show how this dependency can be nicely formalized in terms of equilibria and in particular, of Nash equilibria. When controlled plants are at a Nash equilibrium, deviation of each plant from its control strategy may correspond to a loss in terms of the part of specification enforced. Algorithms are proposed which converge, when an equilibrium exists, to Nash equilibria.
Prediction of Drug Targets based on In Vitro Activity Profiles Toward Drug Repurposing for Rare Diseases

Over 300 million people are suffering from rare diseases, most of which have limited treatment options. Therefore, discovering new treatments for rare diseases is imperative. Drug repurposing, which identifies new uses for approved drugs, is considered one of the viable and risk-managed strategies for disease treatments. To promote the drug repurposing process, we introduced a prediction model to uncover novel relationships between gene targets and chemical compounds. In our previous study, we identified enriched genes for compounds from the Toxicology in the 21st Century program (Tox21) 10K library, to extend that study for enriched gene target prediction, we developed machine learning (ML) models including Support Vector Machine; K-Nearest Neighbors; Random Forest; and extreme gradient boosting (XGBoost), by using Tox21 bioassay screening data. All four models perform well with f1_score over 0.7, and XGBoost has the best performance with four different multi-label prediction embedding algorithms, including Binary Relevance; Label Powerset; Classifier Chain; Multi-Output Classifier. Our study explored a reliable method to predict potential gene targets from in vitro activity profile data toward drug repurposing.
Successive-approximation-register ADC Implementation in 180 nm CMOS Technology for Bio-medical Applications

A microelectronic system designed to handle mixed-signal circuits necessitates the integration of both an analogue-to-digital converter (A/D) and a digital-to-analogue converter (D/A). In this work, our primary focus lies in the realm of wireless biomedical implantable devices. The core objective is to develop a high-performance Analog-to-Digital Converter (ADC) that will effectively interface with various bio-sensors, pacemakers, and other health monitoring devices. This ADC is crucial for establishing seamless communication between the implanted medical devices and the human body, thereby enhancing the overall healthcare system. For biomedical applications high speed is not required but low power is needed. To resolve the power consumption issue in this work A 10-bit Successive Approximation Register (SAR) ADC is implemented in 180 nm CMOS technology with 1 volt supply voltage. Traditional SAR ADC Architecture uses the sample and hold, comparator, SAR logic, and DAC circuit but in this architecture, Sampling is performed through the DAC switches which minimize the overall power consumption of ADC. The simulation results of the circuits show that the ADC archives 8.92-bit ENOB and consumes very low power about 168 nanowatts.
Distributed Algorithms for Edge-Agreements: More than Consensus

In this paper, we propose distributed algorithms for multi-agent systems to achieve edge-agreements. Different from consensus, where all agents' states converge to be the same value, the edge agreement is characterized by linear constraints defined for edges, i.e. one linear constraint involving two neighboring agents' states for each edge. Such agreement allows more general coordination among agents, with consensus on a special case. Given the underlying graph of the multi-agent system is undirected (not necessarily to be connected), we propose two discrete-time distributed algorithms that enable all agents' states to converge to constants satisfying edge agreements. Besides theoretical proofs, effectiveness of the proposed algorithms is also shown by simulations on a four-agent multi-agent system.
Out-of-Equilibrium Fluctuations Drive Correlations Between Enzyme and Metabolic Product Levels

Enzyme-driven catalysis of a substrate into a product forms the fundamental backbone of cellular metabolic pathways. In the deterministic formulation of such a reaction scheme, the equilibrium level of the metabolic product is independent of the steady-state enzyme, so that any perturbation in enzyme levels causes a transient change in metabolic product levels that perfectly adapts to the original enzyme-independent steady state. In this work, we consider a stochastic formulation of the problem, where enzyme levels constantly fluctuate due to the inherently noisy gene expression process as well as to the extrinsic noise in substrate availability. Our results show that such out-of-equilibrium fluctuations can result in positive (or negative) enzyme-product and substrate-product correlations, whose behavior qualitatively and quantitatively changes in different scenarios characterized by perturbations of nominal parameters and variable noise levels.
DR-ConvNeXt: A Dilated Residual Network based on ConvNeXt for Lung Nodule Features Prediction

The increasing application of Low Dose Spiral CT (LDCT) has enhanced lung nodule detection, promoting early diagnosis and better cure outcomes. Addressing the challenges of false positives and the radiologists’ growing workload, we refined the ConvNeXt model by integrating dilated residual modules for precise lung nodule detection. This led to a 9.48% improvement in 2D accuracy on the Luna16 dataset, attaining 87.24%. The 3D feature accuracy reached 81.78%, a leap of 21.6%, in 10-fold cross-validation. For the LNDB dataset, 2D classification surged to 95.74% (a 1.78% boost), and 3D prediction precision escalated from 72.02% to 80.73%.
Multi-omics clustering based on interpretable and discriminative features for cancer subtyping

Recent advances in multi-omics databases have enabled biomedical researchers to explore complex cancer systems across hierarchical biological levels. Although there are numerous multi-omics clustering methods, most of them directly integrate heterogeneous features of different omics which may include redundancy or noise and lead to poor clustering results. In this paper, we propose a novel multi-omics clustering method for cancer subtyping which extracts interpretable and discriminative features from different omics before data integration. The proposed method utilizes the clinical information of each omics to supervise the process of extracting interpretable and discriminative features based on SHAP (SHapley Additive exPlanation) values. The shared nearest neighbor-based approach is then applied to calculate the similarity matrix of the extracted features. Finally, we integrate the similarity matrices of different omics and apply spectral clustering on the integrated similarity matrix to obtain the clustering result. Experimental results conducted on four different cancer datasets on three levels of omics demonstrate the superior performance of the proposed method in comparison to the existing multi-omics clustering methods.
Optimal Control of Autonomous Vehicles for Flow Smoothing in Mixed-Autonomy Traffic

This article studies the optimal control of autonomous vehicles over a given time horizon to smooth traffic. We model the dynamics of a mixed-autonomy platoon as a system of non-linear ODEs, where the acceleration of human-driven vehicles is governed by a car-following model, and the acceleration of autonomous vehicles is to be controlled. We formulate the car-following task as an optimal control problem and propose a computational method to solve it. Our approach uses an adjoint formulation to compute gradients of the optimization problem explicitly, resulting in more accurate and efficient numerical computations. The gradients are then used to solve the problem using gradient-based optimization solvers. We consider an instance of the problem with the objective of improving the fuel efficiency of the vehicles in the platoon. The effectiveness of the proposed approach is demonstrated through numerical experiments. We apply the proposed approach to different scenarios of lead vehicle trajectories and platoon sizes. The results suggest that introducing an AV can produce significant energy savings for the platoon. It also reveals that the solution is agnostic to the platoon size thus the fuel saving is mainly due to optimizing the trajectory of the AV.
A 0.063-mm2 1.75-nW Biofuel Cell-Input Biosensing/Data-Storing System with 5.5-GHz Wireless Backscatter Data-Readout in 65-nm CMOS for Self-Powered Smart Contact Lenses

This paper proposes a 1.75-nW continuous tear-glucose monitoring/data-storing IC using 5.5-GHz wireless backscatter data-readout for self-powered Smart Contact Lenses (SCL). It consists of the passive communication block using intermediate interference modulation, a self-controlled dual-oscillator-based analog to digital converter, and 8×8-bit shift-register-based memory. This work enabled stand-alone longterm sensing with on-chip data storage. The 0.063 mm2 chip implemented in 65-nm CMOS successfully demonstrated the correct operation of the data-storing/burst-read-out system for SCLs 4 cm away from the 15 dBm 5.5 GHz transmitter.
A Power-efficient Continuous-time Delta-Sigma Resistor-to-Digital Converter for Humidity Sensing

This paper presents an energy-efficient resistor-to-digital converter (RDC) designed for detecting a wide range of relative humidity. The proposed RDC leverages a deltasigma architecture, enabling a high linear readout range while maintaining excellent power efficiency. The design achieves a resolution of 0.2 Ω with a rapid measurement time of 0.3 ms, while consuming only 87 μW. The energy efficiency Figure of Merit (FoM) is 10.12 pJ per measurement.