Miracle-dz/UniFAR-FTUs huggingface dataset:
Biological differences between melancholic and nonmelancholic depression subtyped by the cOre measure

Background: The purpose of this study was to compare melancholic patients rated by the CORE measure of observable psychomotor disturbance with nonmelancholic and control subjects across a set of biomarkers. Methods: Depressed patients were classified as melancholic or nonmelancholic by using the CORE measure. Both groups of patients, as well as control subjects, were compared for a set of clinical and laboratory measures. Serum levels of brain-derived neurotrophic factor, of two markers of oxidative stress (protein carbonyl content [PCC] and thiobarbituric acid reactive substances [TBARS]), and of several immunity markers (interleukin [IL]-2, IL-4, IL-6, IL-10, IL-17, tumor necrosis factor-alpha, and interferon-gamma) were analyzed. Results: Thirty-three depressed patients and 54 healthy controls were studied. Depressive patients showed higher IL-4, IL-6, and PCC values than healthy controls. Thirteen (39%) of the depressed patients were assigned as melancholic by the CORE measure. They generated lower interferon-gamma (compared with nonmelancholic depressed patients) and TBARS (compared with both the nonmelancholic subset and controls) and returned higher IL-6 levels than controls. Both depressive groups generated higher PCC scores than controls, with no difference between melancholic and nonmelancholic subsets. Conclusion: A sign-based measure to rate melancholia was able to replicate and extend biological findings discriminating melancholic depression. Signs of psychomotor disturbance may be a useful diagnostic measure of melancholia.
A Projected Proximal Gradient Method for Efficient Recovery of Spectrally Sparse Signals

This paper investigates the recovery of a spectrally sparse signal (SSS) from partially observed entries, with particular emphasis on computational efficiency for large scaled problems. We formulate the SSS recovery as a nonconvex low-rank Hankel matrix recovery problem. A projected proximal gradient method has been developed. It is an iterative process where each iteration involves two steps. The first subspace projection step finds the optimal solution in a low-rank and Hankel matrix space for given column and row subspaces. The second step optimizes all involved variables including the column and row subspaces by using a proximal gradient process. In both steps, sub-problems are formulated so that both the low-rank and the Hankel structures are fully exploited for computational efficiency. The combination of these two steps substantially improves the convergence rate. Numerical simulations demonstrate a significant improvement in efficiency compared with the benchmark algorithms.
Decision Making for Autonomous Driving in a Virtual Highway Environment based on Generative Adversarial Imitation Learning

Autonomous driving has gained significant attention as a rapidly advancing research field in recent years. This paper addresses the challenge of efficient highway driving by formulating it as a Markov Decision Process (MDP) and leveraging Reinforcement Learning (RL) techniques. To tackle the decision-making problem, Generative Adversarial Imitation Learning (GAIL) is employed to imitate expert behavior. In this study, we acquired expert data by running an agent within the simulation, enabling us to gather a larger volume of data. Then, the expert agent is trained by Deep Q-Network (DQN) using reward shaping. Our contribution in this study involves the application of Curriculum Learning (CL) specifically to highway scenarios, gradually increasing the complexity of the traffic environment to enhance the training process. In order to achieve a behavior comparable to that of a human driver, we proposed the use of the GAIL approach specifically for highway scenarios, aiming to increase the diversity of state and action pairs. Our experiments successfully demonstrated the effectiveness of this approach, as the autonomous driving agent effectively imitated expert behavior and achieved outstanding collision-free performance on highways.
Negative Binomial Optimization for Low-Count Overdispersed Sparse Signal Reconstruction

Low-photon count imaging has been typically modeled by Poisson statistics. This discrete probability distribution model assumes that the mean and variance of a signal are equal. In the presence of greater variability in a dataset than what is expected, the negative binomial distribution is a suitable overdispersed alternative to the Poisson distribution. In this work, we present a framework for reconstructing sparse signals in these low-count overdispersed settings. Specifically, we describe a gradient-based sequential quadratic optimization approach that minimizes the negative log-likelihood corresponding to the negative binomial distribution coupled with a sparsity-promoting regularization term. Numerical experiments on 1D and 2D sparse/compressible signals are presented.
Cochlear Filter-Based Cepstral Features for Dysarthric Severity-Level Classification

Severity-level classification of dysarthria helps in diagnosing a patient and choosing an appropriate course of treatment. This would also aid in redirecting the speech to an appropriate dysarthric Automatic Speech Recognition (ASR), as traditional ASR does not perform well on dysarthric speech. In the recent past, several approaches have been used to study the severity-level classification of dysarthria using state-of-the-art features, such as Short-Time Fourier Transform (STFT) and Mel Frequency Cepstral Coefficients (MFCC). This study investigates novel auditory transform-based Cochlear Filter Cepstral Coefficients (CFCC) features for dysarthric severity-level classification. Three DNN-based classifiers, namely, Convolutional Neural Network (CNN), Light-CNN (LCNN), and Residual Neural Network (ResNet) were employed on UA-Speech Corpus and TORGO corpus. Our proposed CFCC feature set yields an improved classification accuracy of 97.46% (98.99%), 94.92% (94.97%), and 96.66% (98.93%) on UA (Torgo)-corpus using CNN, LCNN and ResNet classifiers respectively. Furthermore, performance metrics, such as the Jaccard index, Matthew's Correlation Coefficient (MCC), $F$1-score, and Hamming loss are used to examine feature discrimination power of CFCC. Finally, latency period of CFCC was also analysed for practical deployment of system.
Location and Density Optimisation of a Two-Tier NOMA-Based 5G HetNet

With the ever increasing data demands, integration of small base stations (sBSs) within the existing networks has been proposed as a potential solution to absorb this upsurge in traffic demand. However, the resulting heterogeneous networks (HetNets), consisting of macro base stations (mBSs) and sBSs, present a complex network topology. In this paper, we investigate the challenge of optimally locating small cells to reduce their complexity and enhance their performance. The locations of the sBSs are therefore formulated as an optimisation problem. Locating small cells optimally reduces the required number of sBSs for a target HetNet performance and minimizes the over-loading of the mBSs. A uniform Non-Orthogonal Multiple Access (NOMA)-based HetNet is considered due to its high spectral efficiency properties that are attained by exploiting power domain multiplexing. This paper also utilizes simulated annealing, a meta-heuristic optimization algorithm, to optimize the network by proposing optimal candidate locations. A placement algorithm is then proposed to select the optimal set of locations viable for the deployment of small cells.
HWTune: A Hardware-Aware Database Tuning System with Deep Reinforcement Learning

Database knob tuning can improve database performance, such as increasing throughput and reducing latency. However, manually finding optimal knob settings is challenging and time-consuming. In recent years, deep reinforcement learning (DRL) has been widely used to automate database knob tuning, but it has several limitations. First, existing DRL methods ignore the strong correlation between the database and the hardware configuration, leading to improper utilization of hardware resources and prolonged tuning times. Second, the workloads of real production environments are dynamically changing, yet existing DRL methods do not account for workload characteristics, resulting in suboptimal knob recommendations. To address these problems, we propose HWTune, a hardware-aware and workload-adaptive automatic database tuning system based on DRL. HWTune first collects hardware information from the database deployment environment and employs the Knob Space Trimming (KST) algorithm to reduce the knob value space. It then applies the SHapley Additive exPlanations (SHAP) algorithm to rank the importance of knobs for different workloads. Finally, it utilizes the Fast Convergence Deep Deterministic Policy Gradient (FC-DDPG) model based on DDPG to recommend knobs. Experimental results demonstrate that compared to the state-of-the-art tuning methods, HWTune can considerably reduce tuning time while guaranteeing the tuning performance.
Fast Recovery from Multiple Link Failures in LEO Satellite Networks

This paper introduces a protection mechanism for complete recovery from multi-link failures in Low Earth Orbit (LEO) satellite networks. Inter-satellite links (ISLs) may break down frequently due to the interruption of wireless channels, the high-speed movement of satellites and the long distance for communication. As a proactive scheme for recovery, IP Fast Reroute (IPFRR) has been used to achieve fast rerouting via pre-calculating backup paths. Although IPFRR has effectively addressed the problem of single-link failures, the recovery from multi-link failures still needs further studies, especially on aspects such as recovery rate, computation complexity and storage cost. For better survivability of LEO satellite networks, we develop an IPFRR mechanism for recovering from multi-link failures. This new mechanism, called Grid Bypass Routing (GBR), assigns two addresses to every network device – a normal address and a protection address. When failures occur, routers will choose the protection address to deliver packets via alternative paths. We develop a technique to compute backup paths and prove that GBR can guarantee complete recovery. We evaluate GBR in the simulation, and the result shows that the advantage of its computational cost, forwarding table size and recovery rate makes GBR a more efficient and easily manageable IPFRR solution to link failures in LEO satellite networks.
ECHO: Empirical Characterization and Height Optimization of UAV-to-Underground Channels

This paper explores the nexus of two emerging Internet of Things (IoT) components in precision agriculture, which requires vast amounts of agriculture fields to be monitored from air and soil for food production with efficient resource utilization. On the one hand, unmanned aerial vehicles (UAVs) have gained interest in agricultural aerial inspection due to their ubiquity and observation scale. On the other hand, agricultural IoT devices, including buried soil sensors, have gained interest in improving natural resource efficiency in crop production. In this work, the path loss and fading characteristics in wireless links between a UAV and underground (UG) nodes (Air2UG link) are studied to design a UAV altitude optimization solution. A path loss model is developed for the Air2UG link, including fading in the channel, where fading is modeled using a Rician distribution and validated using the Kolmogorov-Smirnov test. Moreover, Rician-K is found to be dependent on the UAV altitude, which is modeled with a Gaussian function with an RMSE of 0.4 − 1.3 dB. Furthermore, a novel altitude optimization solution is presented to minimize the bit error rate (BER). Results show that the lowest possible altitude does not always minimize the BER. Optimizing the altitude reduces the Air2UG link BER by as much as 8.6-fold. Likewise, altitude optimization can minimize the impacts of increasing burial depth on the BER. Our results and analysis are the first in this field and can be exploited to optimize the altitude and resources of a UAV node to communicate with the sensors embedded in the soil efficiently.
Exact and Heuristic Methods for Simultaneous Sparse Coding

The simultaneous sparse coding (SSC) problem consists in approximating several data points as linear combinations of the same few basis elements selected within a given dictionary. It is key in many applications of machine learning and signal processing. Solving SSC up to global optimality has never been explored in the literature, to the best of our knowledge. In this paper, we propose a reformulation of SSC into a mixed-integer quadratic program (MIQP) solvable globally by generic solvers. We also consider a variant of SSC with nonnegativity constraints. We show experimentally that the global resolution can be applied in practice to medium-scale problems. For larger-scale problems, we propose a hybrid method that first uses a heuristic as a pre-processing to reduce the size of the original problem, and then solves the reduced problem exactly. Empirically, we show that our hybrid method outperforms existing heuristics on both synthetic data and in the unmixing of real-world hyperspectral images.
Active Flux based control of ALA Rotor SynRel with MTPA, Maximum Power Factor and Maximum Torque per Flux

High Energy conversion control of ac motor drives with seamless swapping from maximum torque per ampere (MTPA) to maximum power factor (MaxCosPhi) and then to maximum torque per flux (MTPF) to cover all the torque-speed envelope points, based on the “active flux“ concept with a case study for ALA-SynRel machine with experiments constitutes the contribution of the present paper.
Extremely Lightweight Neural Network for Large-Scale Image Compressive Sensing

In restoring a large-scale image from incomplete samples, one always introduces a blocking strategy to avoid a con-siderable memory burden. Nevertheless, certain applications, like medical imaging, reject to divide an image into patches. In this paper, we derive a variant of ADMM algorithm, which induces a non-convex optimization-based neural network architecture, for large-scale image compressive sensing. In the literature, the largest scale of natural images that can be reconstructed without blocking strategy is around $256 \times 256$, whereas our method is feasible for an image size as high as $1024 \times 1024$. In addition, our method can obtain better reconstruction performance and smaller model size than state-of-the-art methods using blocking strategy.
MoM Formulation for Large Impenetrable Scatterers Using Deep-Shadow Producing Multipole Kernels

For large scatterers the Method of Moments (MoM) can become numerically inefficient when used with the standard free-space Green’s function kernel because of the many line-of-sight interactions that need to be taken into account. In the case of impenetrable scatterers, these interactions are highly non-physical and kernel modifications that suppress these interactions may be used to increase the efficiency of the MoM solutions. Here we show that deep-shadow producing multipole kernels can be employed to significantly reduce the numerical complexity of such problems.
Impact of Imperfect SIC in NOMA Enabled Heterogeneous Cellular Network

To accommodate a large number of users requiring higher data rates and improved outage performance, a heterogeneous cellular network (HCN) uses a multi-tier architecture made up of a macro-base station (MBS) and pico-base station (PBS). Due to its superior spectrum efficiency, non-orthogonal multiple access (NOMA) has been identified as a suitable multiple-access strategy for the upcoming sixth-generation wireless networks. Successive interference cancellation (SIC) is an essential technique in NOMA that enables multiple users to share the same resource while maintaining high performance and fairness. Thus, it is essential to understand the performance guarantees of actual NOMA-enabled HCN systems (HCN-NOMA) using a multi-tier design under both ideal and imperfect SIC using a stochastic geometry. In multi-tier HCN, the outage probability and system throughput of orthogonal multiple access (OMA) and NOMA under perfect and imperfect SIC are evaluated and compared in this paper. It has been found that using NOMA rather than OMA results in performance gains of up to 40% at 25 dB SNR in HCN. Performance increases of up to 30% against OMA at 25 dB SNR can be observed, despite imperfections in the SIC at NOMA.
Model-Free Decentralized Training for Deep Learning Based Resource Allocation in Communication Networks

Decentralized deep learning (DL) based resource allocation (RA) in communication networks guarantees scalability and higher communication bandwidth efficiency compared to centralized RA. Although the RA is decentralized in such approaches, the policies are mostly trained in a centralized manner. In this paper, we investigate a decentralized model-free training approach based on zeroth-order optimization methods. Each user trains its individual policy-possibly with a unique structure-to guarantee the maximum global utility, e.g., sum rate (SR) of users. More importantly, during the training, the users need to share only scalar quantities with their neighbors, avoiding a large communication overhead. The training is also robust against a certain level of asynchrony between the users. The proposed approach relaxes the need for a computationally complex central server and offers the possibility for (re)training in dynamic environments in a model-free manner using the computational power at the edge. Numerical experiments show a competitive performance compared to centralized and federated training approaches.
Mobile Diminished Reality for Preserving 3D Visual Privacy

Privacy is an essential human right. While the world has witnessed massive improvement in immersive technologies and devices in recent years, making them more ubiquitous and accessible in our daily lives, this has led to privacy implications where users' private and sensitive information can easily be exploited in inference attacks. State-of-the-art privacy-preserving approaches are either 2D, focused on protecting a single type of information like faces, or screens, or based on the least privilege paradigm. Preserving privacy in virtual environments, specifically 3D visual privacy in Mixed Reality (MR) applications, remains generally an unsolved problem. To mitigate the risk of private content inadvertently leaking from an MR user's environment, we propose a privacy-preserving Diminished Reality framework to obfuscate 3D objects while preserving the realism of a 3D environment during a live MR feed. We demonstrate the privacy preservation ability during 3D rotation around the sensitive object, real-time performance of our 3D privacy-preserving platform, as well as its implementation using local computations on mobile devices. The proposed framework enables users to select private objects to obfuscate by either enclosing them with 3D virtual objects or detecting their contours. Once identified, the objects are tracked using optical flow and obfuscated in 3D using three inpainting methods. The framework is tested on Android devices and evaluated using frames-per-second (FPS), Peak Signal-to-Noise Ratio (PSNR) and privacy metrics, achieving an average of 15 FPS for 15% mask sizes, PSNR between 25 and 27, and maintaining above 90% privacy.
Detecting and Counting Area of Crop Fields in Sentinel-2 Images Using Mask R-CNN Algorithm

Agriculture is a vital activity in many countries around the world, and statistics play an important role in understanding its current state. By gathering data on production levels, land use, crop yields and other factors related to agriculture, governments can make informed decisions about how best to support this sector of their economy. In addition to being used by government officials for policy making purposes, agricultural statistics are also valuable resources for researchers studying the impact of climate change or food security issues. For this reason, the direction of agriculture of Djelfa made every season statistic of all the activities in the region. This work was made by the engineers and the agents of the direction by visiting the farm and investigate farmers. This task is difficult and will be more difficult according to the increase of the agriculture lands. The using of the technology may facilitate this task. The aim of this work is to use the novel technology like satellite images and artificial intelligence to compute areas of important crops on the region like cereal, forage. We try to apply on Sentinel-2 images a recent model of deep learning the mask R-CNN algorithm that allow to detect and mask objects which are in our case the crop fields.
Spatio-temporal variability and restoration effects on below-ground biodiversity and soil ecosystem functioning at the Thur floodplain, Switzerland

Organic matter (OM) decomposition rate depends on the quality of the OM, the soil physico-chemical conditions, the microclimate and the decomposer community. In a dynamic floodplain, OM distribution is strongly influenced by the flooding regime, but the determinants of decomposition rates remain partially understood. In a restored floodplain in northeast Switzerland, we selected seven different functional processes zones (FPZ) differing in their distance from the river, topography and vascular plant community. In each FPZ, we estimated the decomposition rates of Phalaris litter and cotton strips in litterbags of two different mesh sizes over two periods of time of eight and 12 months. The decay coefficient was significantly different among FPZs for Phalaris litter in the first period. Decomposition was faster in the more dynamic FPZ, but not in the site closest to the river. In the forests, decomposition rates measured over one year were higher than over eight months, indicating an acceleration of decay in spring. No correlation was found between Phalaris and cotton decomposition in any of the mesh size or harvest time. Small and large mesh size decomposition rates were significantly correlated in the first period, indicating a spatially stable proportion of small versus large decomposer community. This stability was lost in spring, indicating higher spatial variability in the decomposer community in this season. Flooding regime and soil temperature influenced only indirectly decomposition rates, while enzymatic activity, soil moisture and soil diversity and phosphorus content were directly linked to both the small and large decomposer community. Based on these results, we discuss the potential factors affecting the decomposition rates in an ecosystem subject to natural pulsing floods.
A scanless method for terahertz time-domain imaging

Terahertz (THz) time-domain imaging can uncover the spectral fingerprints of materials, detect changes in both refractive index and absorption, and reveal the inner structure of complex objects through time-of-flight measurements. Despite its potential, the lengthy acquisition typically required to spatially raster-scan the object and record the terahertz temporal waveforms for each spatial point limits practical applications. Our scanless imaging method sets the path for an unprecedented reduction of both system complexity and acquisition time, by addressing this crucial bottleneck. By judiciously exploiting natural wave diffraction, time-to-space encoding is applied to THz point detection and enables a nearly instantaneous capture of the terahertz waveforms. Multidimensional images are then reconstructed through a computational method. This approach opens the route for next-generation fast and compact THz imagers that are well-suited for high-repetition-rate laser sources.