Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparticle Spectroscopy at LHC-Run3 and LSP Dark Matter in light of Muon g-2

Inspired by the latest measurement of muon g-2 by the Fermilab Experiment, we revisit a class of supersymmetric models in which non-universality at MGUT allows us to realize relatively light sleptons in the few hundred GeV range. These sleptons provide additional contributions to muon g-2 that can be arranged to reconcile theory and experiment. The solutions compatible with the muon g − 2 resolution typically predict light sleptons, charginos and neutralinos. We show how these solutions can be probed during LHC-Run3. A direct impact on the chargino mass is observed such that the chargino can be probed up to about 800 GeV during the Run3 experiments. Despite such a direct impact, it is still possible to realize lighter chargino masses which can escape detection due to the chirality mixture of the lighter slepton states. The colored squarks as well as the gluino turn out to be heavier than about 3-4 TeV if the LSP neutralino satisfies the Planck bound on the dark matter relic abundance. We highlight a variety of benchmark points and, in particular, coannihilation scenarios with dark matter candidates that will be tested in the ongoing and planned direct and indirect detection experiments. By relaxing the requirement that the LSP neutralino saturates the relic dark matter abundance, we are able to find solutions with gluino and squark masses in a range that may be accessible at LHC-Run3.
A Multi-modal and Multi-task Learning Method for Action Unit and Expression Recognition

Analyzing human affect is vital for human-computer interaction systems. Most methods are developed in restricted scenarios which are not practical for in-the-wild settings. The Affective Behavior Analysis in-the-wild (ABAW) 2021 Contest provides a benchmark for this in-the-wild problem. In this paper, we introduce a multi-modal and multi-task learning method by using both visual and audio information. We use both AU and expression annotations to train the model and apply a sequence model to further extract associations between video frames. We achieve an AU score of 0.712 and an expression score of 0.477 on the validation set. These results demonstrate the effectiveness of our approach in improving model performance.
Personalized Federated Learning over non-IID Data for Indoor Localization

Localization and tracking of objects using data-driven methods is a popular topic due to the complexity in characterizing the physics of wireless channel propagation models. In these modeling approaches, data needs to be gathered to accurately train models, at the same time that user’s privacy is maintained. An appealing scheme to cooperatively achieve these goals is known as Federated Learning (FL). A challenge in FL schemes is the presence of non-independent and identically distributed (non-IID) data, caused by unevenly exploration of different areas. In this paper, we consider the use of recent FL schemes to train a set of personalized models that are then optimally fused through Bayesian rules, which makes it appropriate in the context of indoor localization.
Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention

This paper seeks to tackle the bin packing problem (BPP) through a learning perspective. Building on self-attention-based encoding and deep reinforcement learning algorithms, we propose a new end-to-end learning model for this task of interest. By decomposing the combinatorial action space, as well as utilizing a new training technique denoted as prioritized oversampling, which is a general scheme to speed up on-policy learning, we achieve state-of-the-art performance in a range of experimental settings. Moreover, although the proposed approach attend2pack targets offline-BPP, we strip our method down to the strict online-BPP setting where it is also able to achieve state-of-the-art performance. With a set of ablation studies as well as comparisons against a range of previous works, we hope to offer as a valid baseline approach to this field of study.
$P$-strict promotion and $B$-bounded rowmotion, with applications to tableaux of many flavors

We define P-strict labelings for a finite poset P as a generalization of semistandard Young tableaux and show that promotion on these objects is in equivariant bijection with a toggle action on B-bounded Q-partitions of an associated poset Q. In many nice cases, this toggle action is conjugate to rowmotion. We apply this result to flagged tableaux, Gelfand-Tsetlin patterns, and symplectic tableaux, obtaining new cyclic sieving and homomesy conjectures. We also show P-strict promotion can be equivalently defined using Bender-Knuth and jeu de taquin perspectives.
Photoproduction of possible pentaquark states 
Λb0(5912)
 and 
Λb0(5920)
 in the 
γp→Λb0(*)B+
 reactions

In this work, we report on a theoretical study of possible pentaquark states Λ b (5912) and Λ b (5920) in the γp → Λ b B reactions within an effective Lagrangian approach. In addition to the contributions from the schannel nucleon pole and t-channel B̄ exchange, the contact term contribution are also included. Our theoretical approach is based on the chiral unitary theory where the Λ b (5912) and Λ b (5920) resonances are dynamically generated. Within the coupling constants of the Λ b (5912) and Λ b (5920) to B̄p and B̄p channels obtained from chiral unitary theory, the total and differential cross sections of the γp → Λ b B are evaluated. Our calculation indicates that the cross section for γp → Λ b (5912)B and γp → Λ b (5920)B reactions are of the order of 0.0164 nb and 0.00527 nb,respectively. If measured in future experiments, such as Electron-Ion Collider in China (EicC) or US(US-EIC), the predicted total cross sections and specific features of the angular distributions can be used to test the (molecular) nature of the Λ b (5912) and Λ b (5920) that they may be pentaquark states.
Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation

With the increase in the digitization efforts of herbarium collections worldwide, dataset repositories such as iDigBio and GBIF now have hundreds of thousands of herbarium sheet images ready for exploration. Although this serves as a new source of plant leaves data, herbarium datasets have an inherent challenge to deal with the sheets containing other non-plant objects such as color charts, barcodes, and labels. Even for the plant part itself, a combination of different overlapping, damaged, and intact individual leaves exist together with other plant organs such as stems and fruits, which increases the complexity of leaf trait extraction and analysis. Focusing on segmentation and trait extraction on individual intact herbarium leaves, this study proposes a pipeline consisting of deep learning semantic segmentation model (DeepLabv3+), connected component analysis, and a single-leaf classifier trained on binary images to automate the extraction of an intact individual leaf with phenotypic traits. The proposed method achieved a higher F1-score for both the in-house dataset (96%) and on a publicly available herbarium dataset (93%) compared to object detection-based approaches including Faster R-CNN and YOLOv5. Furthermore, using the proposed approach, the phenotypic measurements extracted from the segmented individual leaves were closer to the ground truth measurements, which suggests the importance of the segmentation process in handling background noise. Compared to the object detection-based approaches, the proposed method showed a promising direction toward an autonomous tool for the extraction of individual leaves together with their trait data directly from herbarium specimen images.
Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

We present a smartphone-based indoor localisation system, able to track pedestrians over multiple floors. The system uses Pedestrian Dead Reckoning (PDR), which exploits data from the smartphone’s inertial measurement unit to estimate the trajectory. The PDR output is matched to a scaled floor plan and fused with model-based WiFi received signal strength fingerprinting by a Backtracking Particle Filter (BPF). We proposed a new Viterbi-based floor detection algorithm, which fuses data from the smartphone’s accelerometer, barometer and WiFi RSS measurements to detect stairs and elevator usage and to estimate the correct floor number. We also proposed a clustering algorithm on top of the BPF to solve multimodality, a known problem with particle filters. The proposed system relies on only a few pre-existing access points, whereas most systems assume or require the presence of a dedicated localisation infrastructure. In most public buildings and offices, access points are often available at smaller densities than used for localisation. Our system was extensively tested in a real office environment with seven 41 m × 27 m floors, each of which had two WiFi access points. Our system was evaluated in real-time and batch mode, since the system was able to correct past states. The clustering algorithm reduced the median position error by 17% in real-time and 13% in batch mode, while the floor detection algorithm achieved a 99.1% and 99.7% floor number accuracy in real-time and batch mode, respectively.
Filtering Biomechanical Signals in Movement Analysis

Biomechanical analysis of human movement is based on dynamic measurements of reference points on the subject’s body and orientation measurements of body segments. Collected data include positions’ measurement, in a three-dimensional space. Signal enhancement by proper filtering is often recommended. Velocity and acceleration signal must be obtained from position/angular measurement records, needing numerical processing effort. In this paper, we propose a comparative filtering method study procedure, based on measurement uncertainty related parameters’ set, based upon simulated and experimental signals. The final aim is to propose guidelines to optimize dynamic biomechanical measurement, considering the measurement uncertainty contribution due to the processing method. Performance of the considered methods are examined and compared with an analytical signal, considering both stationary and transient conditions. Finally, four experimental test cases are evaluated at best filtering conditions for measurement uncertainty contributions.
Deep-Learning-Driven Full-Waveform Inversion for Ultrasound Breast Imaging

Ultrasound breast imaging is a promising alternative to conventional mammography because it does not expose women to harmful ionising radiation and it can successfully image dense breast tissue. However, conventional ultrasound imaging only provides morphological information with limited diagnostic value. Ultrasound computed tomography (USCT) uses energy in both transmission and reflection when imaging the breast to provide more diagnostically relevant quantitative tissue properties, but it is often based on time-of-flight tomography or similar ray approximations of the wave equation, resulting in reconstructed images with low resolution. Full-waveform inversion (FWI) is based on a more accurate approximation of wave-propagation phenomena and can consequently produce very high resolution images using frequencies below 1 megahertz. These low frequencies, however, are not available in most USCT acquisition systems, as they use transducers with central frequencies well above those required in FWI. To circumvent this problem, we designed, trained, and implemented a two-dimensional convolutional neural network to artificially generate missing low frequencies in USCT data. Our results show that FWI reconstructions using experiment data after the application of the proposed method successfully converged, showing good agreement with X-ray CT and reflection ultrasound-tomography images.
Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

The evolution of low power electronics and the availability of new smart materials are opening new frontiers to develop wearable systems for medical applications, lifestyle monitoring, and performance detection. This paper presents the development and realization of a novel smart insole for monitoring the plantar pressure distribution and gait parameters; indeed, it includes a piezoresistive sensing matrix based on a Velostat layer for transducing applied pressure into an electric signal. At first, an accurate and complete characterization of Velostat-based pressure sensors is reported as a function of sizes, support material, and pressure trend. The realization and testing of a low-cost and reliable piezoresistive sensing matrix based on a sandwich structure are discussed. This last is interfaced with a low power conditioning and processing section based on an Arduino Lilypad board and an analog multiplexer for acquiring the pressure data. The insole includes a 3-axis capacitive accelerometer for detecting the gait parameters (swing time and stance phase time) featuring the walking. A Bluetooth Low Energy (BLE) 5.0 module is included for transmitting in real-time the acquired data toward a PC, tablet or smartphone, for displaying and processing them using a custom Processing® application. Moreover, the smart insole is equipped with a piezoelectric harvesting section for scavenging energy from walking. The onfield tests indicate that for a walking speed higher than 1 ms−1, the device’s power requirements (i.e., P¯=5.84 mW) was fulfilled. However, more than 9 days of autonomy are guaranteed by the integrated 380-mAh Lipo battery in the total absence of energy contributions from the harvesting section.
Ambient Healthcare Approach with Hybrid Whale Optimization Algorithm and Naïve Bayes Classifier

There is a crucial need to process patient’s data immediately to make a sound decision rapidly; this data has a very large size and excessive features. Recently, many cloud-based IoT healthcare systems are proposed in the literature. However, there are still several challenges associated with the processing time and overall system efficiency concerning big healthcare data. This paper introduces a novel approach for processing healthcare data and predicts useful information with the support of the use of minimum computational cost. The main objective is to accept several types of data and improve accuracy and reduce the processing time. The proposed approach uses a hybrid algorithm which will consist of two phases. The first phase aims to minimize the number of features for big data by using the Whale Optimization Algorithm as a feature selection technique. After that, the second phase performs real-time data classification by using Naïve Bayes Classifier. The proposed approach is based on fog Computing for better business agility, better security, deeper insights with privacy, and reduced operation cost. The experimental results demonstrate that the proposed approach can reduce the number of datasets features, improve the accuracy and reduce the processing time. Accuracy enhanced by average rate: 3.6% (3.34 for Diabetes, 2.94 for Heart disease, 3.77 for Heart attack prediction, and 4.15 for Sonar). Besides, it enhances the processing speed by reducing the processing time by an average rate: 8.7% (28.96 for Diabetes, 1.07 for Heart disease, 3.31 for Heart attack prediction, and 1.4 for Sonar).
Photoplethysmography behind the Ear Outperforms Electrocardiogram for Cardiovascular Monitoring in Dynamic Environments

An increasing proportion of occupational mishaps in dynamic, high-risk operational environments have been attributed to human error, yet there are currently no devices to routinely provide accurate physiological data for insights into underlying contributing factors. This is most commonly due to limitations of commercial and clinical devices for collecting physiological data in environments of high motion. Herein, a novel Photoplethysmography (PPG) sensor device was tested, called SPYDR (Standalone Performance Yielding Deliberate Risk), reading from a behind-the-ear location, specifically designed for high-fidelity data collection in highly dynamic high-motion, high-pressure, low-oxygen, and high-G-force environments. For this study, SPYDR was installed as a functional ear-cup replacement in flight helmets worn by rated US Navy aircrew. Subjects were exposed to reduced atmospheric pressure using a hypobaric chamber to simulated altitudes of 25,000 feet and high G-forces in a human-rated centrifuge up to 9 G acceleration. Data were compared to control devices, finger and forehead PPG sensors, and a chest-mounted 12-lead ECG. SPYDR produced high-fidelity data compared to controls with little motion-artifact controls in the no-motion environment of the hypobaric chamber. However, in the high-motion, high-force environment of the centrifuge, SPYDR recorded consistent, accurate data, whereas PPG controls and ECG data were unusable due to a high-degree-motion artifacts. The data demonstrate that SPYDR provides an accurate and reliable system for continuous physiological monitoring in high-motion, high-risk environments, yielding a novel method for collecting low-artifact cardiovascular assessment data important for investigating currently inaccessible parameters of human physiology.
Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms

The forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have used Artificial Neural Networks (ANN) to forecast electricity demand, aiming to help newcomers and experienced researchers to appraise the common practices and to detect areas where there is room for improvement in the face of the current widespread deployment of smart meters and sensors, which yields an unprecedented amount of data to work with. The review looks at the specific problems tackled by each one of the selected papers, the results attained by their algorithms, and the strategies followed to validate and compare the results. This way, it is possible to highlight some peculiarities and algorithm configurations that seem to consistently outperform others in specific settings.
Topic-Emotion Propagation Mechanism of Public Emergencies in Social Networks

The information propagation of emergencies in social networks is often accompanied by the dissemination of the topic and emotion. As a virtual sensor of public emergencies, social networks have been widely used in data mining, knowledge discovery, and machine learning. From the perspective of network, this study aims to explore the topic and emotion propagation mechanism, as well as the interaction and communication relations of the public in social networks under four types of emergencies, including public health events, accidents and disasters, social security events, and natural disasters. Event topics were identified by Word2vec and K-means clustering. The biLSTM model was used to identify emotion in posts. The propagation maps of topic and emotion were presented visually on the network, and the synergistic relationship between topic and emotion propagation as well as the communication characteristics of multiple subjects were analyzed. The results show that there were similarities and differences in the propagation mechanism of topic and emotion in different types of emergencies. There was a positive correlation between topic and emotion of different types of users in social networks in emergencies. Users with a high level of topic influence were often accompanied by a high level of emotion appeal.
Cyberattack on Flight Safety: Detection and Mitigation Using LoRa

Automatic Dependent Surveillance-Broadcast (ADS-B) is the main communication system currently being used in Air Traffic Control (ATC) around the world. The ADS-B system is planned to be a key component of the Federal Aviation Administration (FAA) NextGen plan, which will manage the increasingly congested airspace in the coming decades. While the benefits of ADS-B are widely known, its lack of security measures and its vulnerability to cyberattacks such as jamming and spoofing is a great concern for flight safety experts. In this paper, we first summarize the cyberattacks and challenges related to ADS-B’s vulnerabilities. Thereafter, we present theoretical and practical methods for implementing an Internet of Things (IoT)-based system as a possible additional safety layer to mitigate the presented cyber-vulnerabilities. Finally, a set of simulations and field experiments is presented to test the expected performance of the suggested IoT flight safety system. We conjecture that the presented system can be implemented in a wide range of civilian airplanes, leading to an improvement in flight safety in cases of cyberattacks or the absence of reliable ADS-B communication.
Breast and cervical cancer screening attendance among Czech women.

OBJECTIVES The aim of the study was to investigate the variation in breast and cervical cancer screening attendance among Czech women by age and in regions in 2009-2017. METHODS The data from the health insurance company that covers around 50% of the Czech population were used to calculate age-specific attendance rates and standardized attendance rates by year and region. RESULTS In 2017, the attendance of all eligible women was 52% in breast cancer screening and 46% in cervical cancer screening. There were differences in attendance among groups of women. Women aged 45-49 had attendance rates in both screenings around 60%, while 39% of women aged 75-79 attended breast cancer screening, and 23% attended cervical cancer screening. In regions, attendance ranged from 38% to 70% in breast cancer screening and from 32% to 55% in cervical cancer screening. CONCLUSIONS We identified the age-specific differences and regional variation in both breast and cervical cancer screening attendance among Czech women. Those with lower attendance may have a higher risk of dying from breast and cervical cancers. Mitigating this risk should be a priority of public health policies.
CHEMICAL AND BIOACTIVE CHARACTERIZATION OF POT-POLLEN PRODUCED BY MELIPONA AND SCAPTOTRIGONA STINGLESS BEES FROM PARIA GRANDE, AMAZONAS STATE, VENEZUELA

The chemical composition and bioactivity indicators (antioxidant activity, flavonoids, polyphenols) of bee pollen produced by two Meliponini bee genera (Melipona and Scaptotrigona) in Paria Grande, Amazonas state, Venezuela, were compared. Proximal analyses were performed in triplicate following standard methods. Moisture, ash, and crude fat were done by volumetric methods, proteins by the microKjeldahl method (digestion with sulfuric acid and catalysts, distillation of ammonia by steam with sodium hydroxide and sodium thiosulphate, collection in boric acid and titration with hydrochloric acid), and carbohydrates by difference. Bioactivity was measured with spectrophotometry: The antioxidant activity by the ABTS method, flavonoids with a modified aluminum chloride method, and polyphenols with Folin-Ciocalteu. The following compositional and bioactivity variations were observed for the contents of moisture (43.49- 48.54) g/100g pollen, ash (1.94-2.33) g/100g pollen, fat (3.19-6.72) g/100g pollen, proteins (16.80-18.32) g/100g pollen, carbohydrates (27.62-31.03) g /100g pollen, antioxidant activity (373.5 – 493.6) TEAC/100g pollen, flavonoids (1,110.7 – 3,905.6) QE/100 g pollen, and polyphenols (1,576.9 – 1,644.9) GAE/100 g pollen. This study will benefit further nutrional proposals on this proteinaceous food of bee origin, and supports Venezuelan meliponiculture in the Amazone.