Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Sentiment in S&P 500 volatility prediction with the use of Deep Learning

The automatic recognition of the impact of the sentiment in online space can help teachers to teach an information literacy and a critical thinking. The paper focuses on the gauging of the magnitude by which the added sentimental component affects the predictions of the S&P 500 market index. The sentiment of market’s participants bears increased significance in the age of social media, which is reflected in periods of increased social media activity followed by increased market action regarding commodity. The paper presents an experiment containing a multitude of deep learning methods in order to ascertain the level of improvement to the volatility prediction provided by the addition of the sentiment to the market data. Primarily, we will focus on the comparison of LSTM, N-Beats and Temporal Fusion Transformer methods and results of each method with and without the component of sentiment in the training data.
Dual consistency loss for contour-aware segmentation in medical images

Medical image segmentation is a paramount task for several clinical applications, namely for the diagnosis of pathologies, for treatment planning, and for aiding image-guided surgeries. With the development of deep learning, Convolutional Neural Networks (CNN) have become the state-of-the-art for medical image segmentation. However, issues are still raised concerning the precise object boundary delineation, since traditional CNNs can produce non-smooth segmentations with boundary discontinuities. In this work, a U-shaped CNN architecture is proposed to generate both pixel-wise segmentation and probabilistic contour maps of the object to segment, in order to generate reliable segmentations at the object’s boundaries. Moreover, since the segmentation and contour maps must be inherently related to each other, a dual consistency loss that relates the two outputs of the network is proposed. Thus, the network is enforced to consistently learn the segmentation and contour delineation tasks during the training. The proposed method was applied and validated on a public dataset of cardiac 3D ultrasound images of the left ventricle. The results obtained showed the good performance of the method and its applicability for the cardiac dataset, showing its potential to be used in clinical practice for medical image segmentation.Clinical Relevance— The proposed network with dual consistency loss scheme can improve the performance of state-of-the-art CNNs for medical image segmentation, proving its value to be applied for computer-aided diagnosis.
Comparative Study Of Maximum Power Point Tracking Techniques For Photovoltaic Systems

This paper presents a comparative study among maximum power point tracking methods for photovoltaic systems. The comparison takes into account steady state error, dynamic response and efficiency in a large power range. In special, an extensive bibliography and a classification of many maximum power point tracking methods is presented. Computational simulations with fast changes in the solar irradiance have been done and the best maximum power point tracking technique is chosen. Experimental results corresponding to the operation of a photovoltaic converter controlled by a digital signal processor are also presented.
Resource Allocation in Cell-Free MU-MIMO Multicarrier System with Finite Blocklength

The explosive growth of data results in more scarce spectrum resources. It is important to optimize the system performance under limited resources. In this paper, we investigate the weighted throughput (WPT) maximization for cell-free (CF) multiuser (MU) MIMO multicarrier (MC) systems through resource allocation (RA) in finite blocklength regime (FBL) while ensuring the quality of service (QoS) of each user under the constraints of total power consumption. Since the channels vary in different subcarriers and inter-user interference strengths, the WPT can be maximized by scheduling the best users in each time-frequency (TF) resource and advanced beamforming design (BF). With this motivation, we propose a joint user scheduling (US) and BF algorithm to address an mixed integer nonlinear programming (MINLP) problem. Numerical results demonstrate that the proposed RA scheme outperforms the comparison schemes. And the CF system in our scenario is capable of achieving higher spectral efficiency (SE) than the centralized antenna systems (CAS).
105 GHz Multipath Propagation Measurements and Path Loss Model for Sub-THz Indoor Short-Range Communications

This paper reports a first wideband indoor channel measurement at the 105 GHz sub-terahertz (sub-THz) band and analyzes the multipath characteristics in terms of the omnidirectional path-loss and angular characteristics. The measurement campaigns with the 4 GHz bandwidth are performed focusing on indoor short-range communication scenarios in a conference room, corridor, and office room, which have been considered in the primary 60 GHz communication systems standardized by the IEEE 802.15. 3c/11ad. Moreover, to draw full understanding to scale the 60 GHz indoor channel models and 60 GHz system designs for the 105 GHz band, we also conduct 60 GHz channel measurements in the same environment with few modifications in the channel-sounding system and performed the comparison between these two bands. Based on these measurements, we demonstrate the affinity that exists between 105 GHz and 60 GHz bands in terms of path loss exponent and angular characteristics of multipath components, shedding light on the hypothesis that several system designs of the 60 GHz communication systems (e.g., beam switching for non-line-of sight conditions) can be applied to the 105 GHz sub-THz communication systems.
Federated Learning-assisted Self-supervised CNN for Monkeypox Diagnosis

Monkeypox (Mpox) is a contagious viral illness that affects both humans and animals, and its early diagnosis is critical for the effective management and prevention of this disease. This work proposes a federated learning-assisted self-supervised convolutional neural network (CNN) for Mpox identification from skin lesion images. The demand for domain experts for ground truth annotations is reduced with the help of self-supervised learning, as it can process unlabeled data. The self-supervision is driven by a framework called simple contrastive learning for representations (SimCLR), which extracts discriminative patterns of Mpox from the skin lesion images. Federated learning (FL), on the other hand, enables a privacy-preserved collaborative training approach to build the Mpox classifier on vast diverse datasets from several healthcare institutions, remotely. Thorough experimental study on a publicly available benchmark dataset, the proposed approach achieves competitive performances.
Prerequisite studies for numerical flow modeling to locate safe drinking water wells in the zone of arsenic polluted groundwater in the Yamuna sub-basin, West Bengal, India

Arsenic beyond permissible limit from tube-well water has been reported from Yamuna sub-basin, which was initially giving potable water. Locating safe water tube-wells using MODFLOW software can give long-term remedy. Hydrostratigraphy of the study domain is conceptualized up to a depth of 140 m and three principal sediment groups were defined viz., Clay, Sand, and Sand and Gravel. This study indicates that the arsenic concentration in sediment and groundwater does not reveal depth-wise trend. Deviation Factor (DF) indicates irregular trend of concentration levels in sediment as well groundwater depth-wise. Statements on acquisition of arsenic free groundwater are site-specific. These necessitate modeling based decision making for safe well location. Secondly, all the DF values are negative, which means that arsenic in sediments is depleted with respect to their world average values. To locate safe drinking water wells in the Yamuna sub-basin, the modeler should incorporate depth specified hydro-stratigraphy and field constraints. This will be followed by calibration of other hydrological parameters to simulate observed arsenic spreading responsible for its sporadic nature.
Graphite-on-Paper Based Capacitive Touch Arrays for Wireless Mouse Curser Control Pad

In the era of wireless technology, the demand for cost-effective and versatile solutions has grown exponentially. This study presents a design for a wireless mouse pad that leverages graphite-sensing arrays on paper to create a capacitive sensor-based touchpad. By integrating the Espressif Systems ESP32 microcontroller, we established wireless cursor control for both laptops and mobile devices. The innovation introduced here extends the application possibilities of paper-based sensors, particularly those involving graphite-on-paper (GoP) technology. This advancement holds great promise for diverse fields, including soft robotics, healthcare, and biomedicine.
Multimodal Image Registration Using a Viscous Fluid Model with the Bhattacharyya Distance

Image registration is an elementary task in medical image processing and analysis, which can be divided into monomodal and multimodal. Direct 3D multimodal registration in volumetric medical images can provide more insight into the interpretation of subsequent image processing applications than 2D methods. This paper is dedicated to the development of a 3D multimodal image registration algorithm based on a viscous fluid model associated with the Bhattacharyya distance. In our approach, a modified Navier-Stoke's equation is exploited as the foundation of the multimodal image registration framework. The hopscotch method is numerically implemented to solve the velocity field, whose values at the explicit locations are first computed and the values at the implicit positions are solved by transposition. The differential of the Bhattacharyya distance is incorporated into the body force function, which is the main driving force for deformation, to enable multimodal registration. A variety of simulated and real brain MR images were utilized to assess the proposed 3D multimodal image registration system. Preliminary experimental results indicated that our algorithm produced high registration accuracy in various registration scenarios and outperformed other competing methods in many multimodal image registration tasks.Clinical Relevance— This facilitates the disease diagnosis and treatment planning that requires accurate 3D multimodal image registration without massive image data and extensive training regardless of the imaging modality.
Cross-Subcarrier Precoder Design for Massive MIMO-OFDM Downlink

We propose a cost efficient cross-subcarrier pre-coder design (CSPD) for massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) downlink with imperfect channel state information (CSI). To reduce the high computational complexity caused by individual precoder design for each subcarrier, we design transform domain precoding vectors (TDPVs), from which the precoders for a set of subcarriers can be obtained through a transform. The number of TDPVs is much less than that of subcarriers, and the number of total parameters to be designed can be reduced significantly. The main objective is to maximize an upper bound of the ergodic sum-rate by exploiting the a posteriori beam-based statistical channel model. We provide a concave minorizing function of the upper bound of the ergodic sum-rate and then derive the stationary points of a concave quadratic optimization problem with this minorizing function. To reduce the dimension of the matrix inversion in the stationary points, we propose an algorithm by using block coordinate descent (BCD) method with power allocation.
Concurrent Prediction of Dexterous Finger Flexion and Extension Force via Deep Forest

Neuromuscular injuries can impair hand function and profoundly impacting the quality of life. This has motivated the development of advanced assistive robotic hands. However, the current neural decoder systems are limited in their ability to provide dexterous control of these robotic hands. In this study, we propose a novel method for predicting the extension and flexion force of three individual fingers concurrently using high-density electromyogram (HD-EMG) signals. Our method employs two deep forest models, the flexor decoder and the extensor decoder, to extract relevant representations from the EMG amplitude features. The outputs of the two decoders are integrated through linear regression to predict the forces of the three fingers. The proposed method was evaluated on data from three subjects and the results showed that it consistently outperforms the conventional EMG amplitude-based approach in terms of prediction error and robustness across both target and non-target fingers. This work presents a promising neural decoding approach for intuitive and dexterous control of the fingertip forces of assistive robotic hands.
Partial Label Multi-organ Segmentation based on Local Feature Enhancement*

The automatic segmentation of abdominal organs from CT images is essential for surgical planning of abdominal diseases. However, each medical institution only annotates some organs according to its own clinical practice. This brings the partial annotation problem to multi-center abdominal multi-organ segmentation. To address this issue, we introduce a 3D local feature enhanced multi-head segmentation network for multi-organ segmentation of abdominal regions in multiple partially labeled datasets. More specifically, our proposed architecture consists of two branches, the global branch with 3D Transformer and U-Net fusion named 3D TransUNet as the backbone, and the local 3D U-Net branch that provides additional abdominal organ structure information to the global branch to generate more accurate segmentation results. We evaluate our method on four publicly available CT datasets with four different partial label. Our experiments show that the proposed approach provides better accuracy and robustness, with 93.01% average Dice-score-coefficient (DSC) and 3.489 mm Hausdorff Distance (HD) outperforming three existing state-of-the-art methods.
Comprehensive Analysis of Barcode Localization Methods

Barcode Localization is an extremely useful in many application areas like banks, supermarkets, trade etc. Different types of barcode types such as UPC, EAN13 etc along with speed and accuracy requirements makes the task of barcode localization a challenging process. In this paper, we will be analyzing two existing barcode localization methods: one of which is based on dilation and erosion while the other is based on bottomhat filtering. These two methods are implemented in MATLAB and are compared with each other for different type of barcodes like skewed, blurry barcodes along with multiple barcodes in an image. It is observed that the first method based on dilation and erosion is faster whereas the second based on bottomhat filtering is more accurate and provides orientation details. Results of first method are also better for blurry as well as skewed image whereas none of the two methods provides accurate results for complex images.
Identification of Mild Cognitive Impairment Subtypes using an Interpretable Neural Network based Clustering of Gene Expression Data and Neuroimaging Markers

In this study, an attempt is made to cluster the gene expression data and neuroimaging markers using an interpretable neural network model to identify Mild Cognitive Impairment (MCI) subtypes. For this, structural Magnetic Resonance (MR) brain images and gene expression data of early and late MCI subjects are considered from a public database. A neural network model is employed to cluster the gene expression data and regional MR volumes. To evaluate the performance of model, clustering metrics are employed and model is explained using perturbation-based method. Results indicate that the developed model is able to identify MCI subtypes. The network learns latent embeddings of disease-specific genes and MR images markers. The clustering metrics are found to be highest when both the imaging and genetic markers are employed. Volumes of lateral ventricles, hippocampus, amygdala and thalamus are found to be associated with late MCI. Significant scores suggest that genes such as StAR, CCDC108, APOO, TRMT13, RASAL2 and ZNF43 play a key role in identifying the MCI subtypes.Clinical Relevance—Identifying distinct MCI subtypes offer potential for precision diagnostics and targeted clinical recruitment.
Research of Failure Modes and Mechanisms of Space Reactor Reactivity Control System

The reactivity control system of space reactors plays a crucial role in enabling long-life missions under challenging environmental stresses, including large temperature differences, high vacuum, vibration shocks, and radiation fields. Maintaining the system during such missions is nearly impossible, demanding extremely high system reliability. In this paper, we explore the design characteristics of reactivity control systems used in major space reactors from the United States and the Soviet Union. Additionally, we analyze the four main environmental stresses encountered throughout the full mission life of space reactors, namely temperature, vacuum, mechanical, and irradiation stresses. These different environmental stresses impose various independent or interrelated damage mechanisms on the reactivity control system. We further investigate the failure mechanisms, such as thermal effects, cold welding in vacuum, mechanical impacts, and particle irradiation, organizing the potential failure modes of the reactivity control system. The findings presented here serve as a reference for future studies aimed at enhancing the overall reliability of the space reactors.
Relative Depth-Keeping of an Underactuated, Near-Surface Underwater Vehicle in Waves

This paper describes a control method for near-surface depth-keeping by an underactuated autonomous underwater vehicle (AUV). Specifically, we address the problem of maintaining depth relative to the slow variations in free surface elevation for an AUV that is fully submerged, but is operating near the free surface in waves. The nominal depth of the vehicle relative to the undisturbed free surface is five body diameters, which is shallow enough that the added mass and inertia vary with depth, Froude- Krylov forces and diffraction due to passing waves are important, and memory effects cannot be ignored. The slender AUV has independent control over the yaw, pitch, roll, and surge degrees of freedom. A nonlinear control law based on the idea of potential energy shaping is applied to the dynamic model and is shown to be effective at tracking the slow variations in free surface elevation while rejecting faster variations due to waves of shorter wavelength. The control law is applied to a Lumped Parameter Maneuvering (LPM) model of the Virginia Tech (VT) 690 AUV that incorporates the hydrodynamic effects described above.
Evaluation of Landslide Susceptibility in Helan Mountain by Fusion of Cluster Analysis and Remote Sensing Images

In order to assess the degree of influence of each landslide influence factor on landslides, this paper takes Helan Mountain as the research object, and collects data from 56 historical landslide sites in Helan Mountain through field surveys and remote sensing images. Through cluster analysis, the landslide influencing factors were categorized into eight main major categories, including slope, slope direction, curvature, planar curvature, profile curvature, terrain undulation, surface roughness, and terrain humidity index. Then, a comprehensive evaluation was carried out using the principal component analysis integrated evaluation method, and the evaluation results showed that terrain undulation and surface roughness had high indices in the evaluation, while the curvature and terrain humidity indices were relatively low. This means that terrain undulation and surface roughness are the main factors leading to landslides, and mountain terrain with high terrain undulation and surface roughness tends to be more likely to cause landslides, while landslides with low terrain undulation and surface roughness have a lower probability of landslide potential. The comprehensive analysis concluded that terrain relief and surface roughness were identified as the main factors in the assessment indicators, which are closely related to the degree of landslide damage. Therefore, special attention needs to be paid to the changes in these geomorphic features. This study provides a scientific basis for landslide prevention and ecological restoration in the Helan Mountain area.
Integration of Solar Thermal Energy and Heat Pump in a Fish Canning Process Combining Pinch and Exergy Analysis

Many industrial processes work in temperature intervals where solar thermal technology would be able to provide a main amount of the needed energy at a reasonable cost. However solar technology presents some difficulties for its implementation in productive scale: intermittence of the source, low density and some heat integration handicaps (Philibert, 2006). The main objective of this investigation was to evaluate the potentiality of a thermosolar-heat pump set in an industrial process, using a systematic methodology which combines the pinch analysis and the exergy analysis for heat integration and optimization. The methodology was validated in the context of a tuna fish canning industrial plant, located in the seaside of the Basque Country of Spain, a zone subjected to nonfavourable climatic conditions for thermosolar system implementation on industrial scale, due to diffuse irradiance is mainly relevant than direct irradiance (Basque Government Energy Entity, 2002). The study was focussed on heat improvement, starting with the establishment of the mass and energy balances of the process, and continued investigating and proposing feasible improvement. Then, it was evaluated the contribution of a solar thermal subsystem in combination with an adequate heat pump, and reinforced by a conventional fossil fuel resource (a natural gas boiler). Finally, the optimum heat integration of the overall system was determined through pinch and exergy analysis, taking into account main environmental and economical parameters.
A seismological overview of the induced earthquakes in the Duvernay play near Fox Creek, Alberta

This paper summarizes the current state of understanding regarding the induced seismicity in connection with hydraulic fracturing operations targeting the Duvernay Formation in central Alberta, near the town of Fox Creek. We demonstrate that earthquakes in this region cluster into distinct sequences in time, space, and focal mechanism using (i) cross‐correlation detection methods to delineate transient temporal relationships, (ii) double‐difference relocations to confirm spatial clustering, and (iii) moment tensor solutions to assess fault motion consistency. The spatiotemporal clustering of the earthquake sequences is strongly related to the nearby hydraulic fracturing operations. In addition, we identify a preference for strike‐slip motions on subvertical faults with an approximate 45° P axis orientation, consistent with expectation from the ambient stress field. The hypocentral geometries for two of the largest‐magnitude (M ~4) sequences that are robustly constrained by local array data provide compelling evidence for planar features starting at Duvernay Formation depths and extending into the shallow Precambrian basement. We interpret these lineaments as subvertical faults orientated approximately north‐south, consistent with the regional moment tensor solutions. Finally, we conclude that the sequences were triggered by pore pressure increases in response to hydraulic fracturing stimulations along previously existing faults.