Miracle-dz/UniFAR-FTUs huggingface dataset:
Vanishing corrections on the intermediate scale and implications for unification of forces

In two–step breaking of a class of grand uniﬁed theories including SO (10), we prove a theorem showing that the scale ( M I ) where the Pati–Salam gauge symmetry with parity breaks down to the standard gauge group, has vanishing corrections due to all sources emerging from higher scales ( µ > M I ) such as the one–loop and all higher–loop eﬀects, the GUT–threshold, gravitational smearing, and string threshold eﬀects. Implications of such a scale for the uniﬁcation of gauge couplings with small Majorana neutrino masses are dis-cussed. In string inspired SO (10), we show that M I ≃ 5 × 10 12 GeV, needed for neutrino masses, with the GUT scale M U ≃ M str can be realized provided certain particle states in the predicted spectrum are light.
Roll-to-roll fabricated self-filling polydimethylsiloxane diagnostic platforms for multiplexed pathogen nucleic acid detection

Polydimethylsiloxane (PDMS) is a commonly used material for prototyping microfluidics due to its biocompatibility, transparency, elasticity, low auto-fluorescence, and low cost in comparison to similar materials. PDMS-based devices provide alternatives for conventional diagnostic materials, such as polymerase chain reaction (PCR) tubes, that are limited in supply due to high demand in pandemics or similar situations. Roll-to-roll (R2R) production enables mass production of PDMS-based devices for point-of-care (PoC) diagnostics of pathogen nucleic acids. Mass manufacturing and end user points-of-view must be considered when designing a new assay. Isothermal nucleic acid amplification techniques enable less complex heating solutions. Self-filling of the device without external equipment, such as vacuum pumps, is also necessary for user-friendly and portable PoC devices. Here we describe a R2R-manufacturable, user-friendly (easy-to-operate and has a shelf-life of at least 6 months), self-filling and low-cost (<1 €, materials only) PDMS assay with LAMP-based and multiplexed fluorescence detection of nucleic acids from difficult sample matrixes, such as blood, for PoC diagnostics. The assay can detect up to 3 different sepsis-causing bacteria simultaneously (limit of detection, LOD, is 500 fg) from low-volume blood sample (2 µL) without pre-treatment in less than 1 h.
For The Better Future: The Green Movement and Indonesia Manufacturing Performance

The concept of a sustainable business places environmental aspects as part of a company's success. Manufacturing companies must pay attention to environmental cost management, which is expected to be fully disclosed in financial reports to provide helpful information for external parties in forming environmentally friendly business process strategies. This study aimed to examine and analyze the effect of green accounting, environmental performance, and disclosure of SR on the manufacturing companies performance in Indonesia listed on IDX in 2018–2020 using multiple regression analysis. The results show that only environmental performance has a positive effect on profitability, while green accounting and SR disclosure do not affect profitability. Environmental performance can increase profitability by being voluntarily assessed by the company. The assessment results from PROPER show that the company is committed to environmental management and the effective use of natural resources. It proves that PROPER is considered capable of being a branding medium for companies to show company compliance with the government, and it can improve company performance.
Multiclass Categorisation of Respiratory Sound Signals Using Neural Network

Respiratory diseases have seriously impacted human life in the last couple of years; as Covid 19 arrived, many lost their beloved ones. Since respiratory diseases directly attack the patient’s lungs, it is becoming risky day by day for human life and doctors because a confined number of resources are available in hospitals to detect these respiratory diseases, and detection of these diseases is a difficult job to the doctors. Therefore early-stage diagnosis can help the doctor in saving human lives. Researchers are continuously trying to help doctors by designing efficient and more accurate tools for detecting different types of respiratory diseases. This paper uses a convolution-based deep learning model to classify these respiratory diseases using patient respiratory sound signals with Mel frequency cepstral coefficients (MFFCs) as a feature vector. In this paper, we have tried to keep our neural network model as simple as possible with less trainable parameters and good classification accuracy. The model performance is measured in terms of sensitivity, specificity, average score, and harmonic score.
Deep Learning-based receiver for Uplink in LoRa Networks with Sigfox Interference

The Internet of Things faces a significant scaling issue due to the rapid growth of the number of devices and asynchronous communications. Different technologies in the license-free industrial, scientific, and medical (ISM) band have been widely deployed to fill this gap. LoRa and Sigfox are the most common. Many devices can use the ISM band if they obey the regulations and cope with internal and external interference. However, when there is massive connectivity, the effect of the inter and intra-network interference between multiple networks is significant. This study uses a deep learning-based technique to decode signals and deal with the interference in the uplink of a LoRa network. Two classification-based symbol detection methods are proposed using a deep feedforward neural net-work (DFNN) and a convolutional neural network (CNN). The proposed receivers can decode the signals of a selected user when many LoRa users transmit simultaneously using the same spreading factor over the same frequency band (intra-spreading factor interference), and multiple Sigfox users interfere (inter-network interference). Simulation results show that both receivers outperform the conventional LoRa receiver in the presence of interference. For a target symbol error rate (SER) of 10−3, the proposed DFNN and CNN-based receivers attain around 2 dB and 3.5 dB gain, respectively.
CHARACTERIZING THE POPULATION OF BRIGHT INFRARED SOURCES IN THE SMALL MAGELLANIC CLOUD

We have used the Infrared Spectrograph (IRS) on the Spitzer Space Telescope to observe stars in the Small Magellanic Cloud (SMC) selected from the Point Source Catalog of the Midcourse Space Experiment (MSX). We concentrate on the dust properties of the oxygen-rich evolved stars. The dust composition has smaller contributions from alumina compared to the Galaxy. This difference may arise from the lower metallicity in the SMC, but it could be a selection effect, as the SMC sample includes more stars that are brighter and thus more massive. The distribution of the SMC stars along the silicate sequence looks more like the Galactic sample of red supergiants than asymptotic giant branch stars (AGBs). While many of the SMC stars are definitively on the AGB, several also show evidence of hot bottom burning. Three of the supergiants show PAH emission at 11.3 μm. Two other sources show mixed chemistry, with both carbon-rich and oxygen-rich spectral features. One, MSX SMC 134, may be the first confirmed silicate/carbon star in the SMC. The other, MSX SMC 049, is a candidate post-AGB star. MSX SMC 145, previously considered a candidate OH/IR star, is actually an AGB star with a background galaxy at z = 0.16 along the same line of sight. We consider the overall characteristics of all the MSX sources, the most infrared-bright objects in the SMC, in light of the higher sensitivity and resolution of Spitzer, and compare them with the object types expected from the original selection criteria. This population represents what will be seen in more distant galaxies by the upcoming James Webb Space Telescope (JWST). Color–color diagrams generated from the IRS spectra and the mid-infrared filters on JWST show how one can separate evolved stars from young stellar objects (YSOs) and distinguish among different classes of YSOs.
A chip-based NMR relaxometry system for point-of-care analysis

In this paper, we present a miniaturized NMR relaxometry system for the point-of-care analysis of body liquids, centered on an NMR-on-a-chip transceiver ASIC and a DDS-based reference frequency generator. The NMR ASIC is fabricated in a 0.13-µm SiGe BiCMOS technology. It co-integrates a low-IF receiver, a power amplifier, and a phase-locked loop on a small footprint of 1100 × 900 µm2. The presented system provides an excellent low-field concentration sensitivity of ${v_{{\text{c}},\min }} = 5.4{\text{mM}}/\sqrt {{\text{Hz}}} . $ The reference frequency generator provides an output signal with arbitrarily adjustable phase, enabling classical CPMG experiments as well as its modern adaptations. The system also includes an automatic frequency lock to prevent temperature-induced frequency drifts. Proof-of-concept NMR measurements on reference and biomedical samples demonstrate the excellent performance of the presented system as well as its applicability for future point-of-care analysis.
PATTERNS OF INTERACTION: MAKING SENSE OF DIGITALIZATION IN INCUMBENT FIRMS

Current research on digitalization often takes a strategic or change management perspective. More recently, research has pointed to continuous development as essential in digitally enabled change. However, less is known about how continuous development evolves, and how organizational actors interact during continuous development. Our research questions are: (i) which patterns of interaction can we identify in the continuous development of digitalization, and (ii) how can incumbent firms succeed in digitalization through continuous development? We conduct a longitudinal case study of a large grid company, and explore their approach to digitali-zation through continuous development. Applying a sense-making and sense-giving lens, we identify a generic pattern of interaction in continuous digitalization in incumbent firms. Key actors in the pattern are the middle management. We identify two interlinked cycles, one for the top management and one for the operational level. Our model highlights new and surprising insight on sense-making and sense-giving in relation to change and digitalization in incumbent firms.
A path-following guidance algorithm for fixed-wing UAV swarms on a decentralized network

This paper proposes a decentralized path-following guidance algorithm for the tracking formation of multiple fixed-wing UAVs. In particular, our proposal adopts the decoupled control technique to separate the lateral and longitudinal channels. Using the well-known vector-field law, the former is in charge of making UAV track a defined path with wind disturbances. The latter guarantees that a group of fixed-wing UAVs reaches the desired formation by modifying the commanded airspeed of UAVs. The decentralized communication network handles the shared information between a ground control station (GCS) and UAVs. Finally, numerical simulation results for a five-UAV group verify the feasibility and benefits of our proposed approach.
Improving PPG-based Heart-Rate Monitoring with Synthetically Generated Data

Improving the quality of heart-rate monitoring is the basis for a full-time assessment of people’s daily care. Recent state-of-the-art heart-rate monitoring algorithms exploit PPG and inertial data to efficiently estimate subjects’ beats-per-minute (BPM) directly on wearable devices. Despite the easy-recording of these signals (e.g., through commercial smartwatches), which makes this approach appealing, new challenges are arising. The first problem is fitting these algorithms into low-power memory-constrained MCUs. Further, the PPG signal usually has a low signal-to-noise ratio due to the presence of motion artifacts (MAs) arising from movements of subjects’ arms. In this work, we propose using synthetically generated data to improve the accuracy of PPG-based heart-rate tracking using deep neural networks without increasing the algorithm’s complexity. Using the TEMPONet network as baseline, we show that the HR tracking Mean Absolute Error (MAE) can be reduced from 5.28 to 4.86 BPM on PPGDalia dataset. Noteworthy, to do so, we only increase the training time, keeping the inference step unchanged. Consequently, the new and more accurate network can still fit the small memory of the GAP8 MCU, occupying 429 KB when quantized to 8bits.
Efficient Driver Drunk Detection by Sensors: A Manifold Learning-Based Anomaly Detector

This study presents an effective data-driven anomaly detection scheme for drunk driving detection. Specifically, the proposed anomaly detection approach amalgamates the desirable features of the t-distributed stochastic neighbor embedding (t-SNE) as a feature extractor with the Isolation Forest (iF) scheme to detect drivers’ drunkenness status. We used the t-SNE model to exploit its capacity in reducing the dimensionality of nonlinear data by preserving the local and global structures of the input data in the feature space to obtain good detection. At the same time, the iF scheme is an effective and unsupervised tree-based approach to achieving good detection of anomalies in multivariate data. This approach only employs normal events data to train the detection model, making them more attractive for detecting drunk drivers in practice. To verify the detection capacity of the proposed t-SNE-iF approach in reliably detecting drivers with excess alcohol, we used publically available data collected using a gas sensor, temperature sensor, and a digital camera. The overall detection system proved a high detection performance with AUC around 95%, demonstrating the proposed approach’s robustness and reliability. Furthermore, compared to the Principal Component Analysis (PCA), Incremental PCA (IPCA), Independent component analysis (ICA), Kernel PCA (kPCA), and Multi-dimensional scaling (MDS)-based iForest, EE, and LOF detection schemes, the proposed t-SNE-based iF scheme offers superior detection performance of drunk driver status.
Real-Time Deep Learning based Road Deterioration Detection for Smart Cities

Timely road condition inspection and maintenance are key components of infrastructure management for smart cities, as they reduce traffic congestion, accidents and repairing costs. Traditional road inspection methods that employ vibrations and/or laser scanning for detecting road deterioration use expensive equipment and dedicated municipality vehicles. Recently, computer vision techniques and artificial intelligence are emerging as alternative solutions to traditional approaches for road condition detection, offering more flexibility, higher accuracy, and overall lower cost. In this paper, we utilize convolutional neural network-based and vision transformer-based object detection models to accurately identify road deteriorations namely, potholes, cracks, and alligators. We compare four different state-of-the-art models in terms of detection accuracy and speed. Performance evaluations have shown that, on the same dataset the Swin Transformer model outperformed the other state-of-the-art methods by a substantial margin. With 74% detection accuracy, and 42 frames per second processing speed Swin Transformer exceled over EfficentDet, YOLOv4, and YOLOX. We also present a new comprehensive and balanced large-scale road condition dataset of 27,298 annotated images, captured by ordinary car cameras.
Perfect state transfer : Beyond nearest-neighbor couplings

In this paper we build on the ideas presented in previous works for perfectly transferring a quantum state between opposite ends of a spin chain using a fixed Hamiltonian. While all previous studies have concentrated on nearest-neighbor couplings, we demonstrate how to incorporate additional terms in the Hamiltonian by solving an inverse eigenvalue problem. We also explore issues relating to the choice of the eigenvalue spectrum of the Hamiltonian, such as the tolerance to errors and the rate of information transfer.
Evaluating the impact of Permanent Faults in a GPU running a Deep Neural Network

Currently, Deep Neural Networks (DNNs) are fun-damental computational structures deployed in a wide range of modern application domains (e.g., data analysis, healthcare, automotive, robotics). The computational complexity is inherent in these cognitive models, which demand high-performance devices like Graphics Processing Units (GPUs). Therefore, the implementation of DNNs on GPU devices is becoming increasingly frequent, even for cutting-edge safety-critical applications (e.g., autonomous and semi-autonomous cars). Thus, the reliability evaluation of these applications is mandatory because several phenomena (including aging) may produce permanent defects in the GPU, thus inducing the DNN to produce wrong results. Until now, the effects of permanent faults on DNNs have been mainly investigated at the application level, only, e.g., acting on the parameters of the network. This paper presents an environment allowing for the first time a more detailed experimental evaluation of the impact of permanent faults in a GPU on the reliability of a DNN running on it, based on considering faults at the architectural level. The results of the fault injection campaigns we performed on the GPU register files are compared with those at the application level, proving that the latter ones are generally optimistic.
On the Trade-off Between Privacy Protection and Data Utility for Chest X-ray Images

The rising advancement in deep learning (DL) techniques has enabled machine learning (ML) models to assist practitioners in performing medical tasks with high accuracy. However, it also poses privacy concerns regarding how such models will proceed with medical data containing protected patient health information. Therefore, some efforts have been made to anonymize medical data to preserve data privacy while keeping the model performance high enough to avoid wrong decisions in the medical field. Nevertheless, the adversary can develop an ML model to re-identify a patient's identity by matching an arbitrary chest X-ray image with a public or leaked image dataset with high accuracy. This paper aims to find a trade-off between our privacy protection method and data utility for medical images. Specifically, we propose a solution to anonymize chest X-ray images by directly adding noise to the images to prevent verification attacks and evaluate how well those images can maintain good performance in the lung disease classification task. Simulation results on real-world datasets show that the proposed solution achieved a good trade-off between privacy protection and data utility.
THE ROTATION OF PRINCIPAL COMPONENTS

Doubt has been expressed as to the validity and desirability of rotating principal components. In the present case a battery of tests, which was expected to give clear-cut factors, was analysed by the method of principal components, and the components were rotated orthogonally. It is shown that the results thus obtained were in keeping with those that were expected. It is concluded that, whatever may be the weaknesses of rotation as a technique, it is one which can be applied to principal components quite as well as to factors obtained by other procedures.
Prediction of the extubation outcome through Electrical Impedance Tomography measurements

Extubation failure can occur in 10 to 25% of patients who were successfully separated from their ventilator. In which case, patients need to be re-intubated. To reduce the extubation failure rate, we monitor patients during 48h after extubation using Electrical Impedance Tomography (EIT). In total, we recruited 34 patients from which 8 failed their extubation in the ICU ward. Prediction of extubation success or failure using only non-invasive EIT data show a sensitivity of 0.80 and a specificity of 0.73. The prediction for the 8 extubation failures was accurate for most right after the extubation and achieved 100% accuracy for the measurement set preceding the failure.
Finiteness theorems for K3 surfaces and abelian varieties of CM type

We study abelian varieties and K3 surfaces with complex multiplication defined over number fields of fixed degree. We show that these varieties fall into finitely many isomorphism classes over an algebraic closure of the field of rational numbers. As an application we confirm finiteness conjectures of Shafarevich and Coleman in the CM case. In addition we prove the uniform boundedness of the Galois invariant subgroup of the geometric Brauer group for forms of a smooth projective variety satisfying the integral Mumford–Tate conjecture. When applied to K3 surfaces, this affirms a conjecture of Várilly-Alvarado in the CM case.
Time-dependent Occurrence Rate of Electromagnetic Cyclotron Waves in the Solar Wind: Evidence for the Effect of Alpha Particles?

Previous studies revealed that electromagnetic cyclotron waves (ECWs) near the proton cyclotron frequency exist widely in the solar wind, and the majority of ECWs are left-handed (LH) polarized waves. Using the magnetic field data from the STEREO mission, this Letter carries out a survey of ECWs over a long period of 7 years and calculates the occurrence rates of ECWs with different polarization senses. Results show that the occurrence rate is nearly a constant for the ECWs with right-handed polarization, but it varies significantly for the ECWs with LH polarization. Further investigation of plasma conditions reveals that the LH ECWs take place preferentially in a plasma characterized by higher temperature, lower density, and larger velocity. Some considerable correlations between the occurrence rate of LH ECWs and the properties of ambient plasmas are discussed. The present research may provide evidence for the effect of alpha particles on the generation of ECWs.
The Unusual Apparition of Comet 252P/2000 G1 (LINEAR) and Comparison with Comet P/2016 BA14 (PanSTARRS)

We imaged Comet 252P/2000 G1 (LINEAR; hereafter 252P) with the Hubble Space Telescope and both 252P and P/2016 BA14 (PanSTARRS; hereafter BA14) with the Discovery Channel Telescope in 2016 March and April, surrounding its close encounter to Earth. The r′-band Afρ of 252P in a 0. ″2-radius aperture were 16.8 ± 0.3 and 57 ± 1 cm on March 14 and April 4, respectively, and its gas production rates were Q(OH) = (5.8 ± 0.1) × 1027 s−1, and Q(CN) = (1.25 ± 0.01) × 1025 s−1 on April 17. The r′-band upper limit Afρ of BA14 was 0.19 ± 0.01 cm in a 19. ″2-radius aperture, and Q(CN) = (1.4 ± 0.1) × 1022 s−1 on 2017 April 17. 252P shows a bright and narrow jet of a few hundred kilometers long in the sunward direction, changing its projected position angle in the sky with a periodicity consistent with 7.24 hr. However, its photometric light curve is consistent with a periodicity of 5.41 hr. We suggest that the nucleus of 252P is likely in a non-principal axis rotation. The nucleus radius of 252P is estimated to be about 0.3 ± 0.03 km, indicating an active fraction of 40% to >100% in its 2016 apparition. Evidence implies a possible cloud of slow-moving grains surrounding the nucleus. The activity level of 252P in the 2016 apparition increased by two orders of magnitude from its previous apparitions, making this apparition unusual. On the other hand, the activity level of BA14 appears to be at least three orders of magnitude lower than that of 252P, despite its 10 times or larger surface area.