Miracle-dz/UniFAR-FTUs huggingface dataset:
Camera-Based Road Snow Coverage Estimation

The current road condition is a crucial factor regarding road safety of the ego-vehicle and other road users. Road condition estimation provides essential input data for friction estimation which is used for autonomous and automated driving systems. Camera-based approaches are still far from being practical and other sensors dominate the field of friction estimation. This is due to the limited performance of current approaches and the lack of datasets for the incorporation of learning-based methods.We propose a novel dataset for a special scenario of road condition, the coverage with snow. It is the first large-scale dataset for camera-based road classification of snow-covered roads with different types of snow coverage. The dataset consists of road patches in bird’s eye view perspective and ground truth annotation for the current snow coverage type. It is combinable with RoadSaW [4], a dataset for road surface and wetness estimation, leading to a holistic road condition dataset with 15 categories. The baseline evaluation employs state-of-the-art, real-time capable approaches for classification and uncertainty estimation with RBF (Radial Basis Function) networks. Our experiments demonstrate that the proposed data opens new challenges in the field of camera-based road condition estimation.
Reproductive Performance, Milk Yield Parameters, and Some Postpartum Diseases That Are Associated with Clinical And Subclinical Ketosis in Dairy Cows

Negative energy balance (NEB) is a physiological state that arises during the transition from pregnancy to lactation in dairy cows. This transition period is associated with a decline in dry matter intake and an increased demand for energy sources for milk production. Ketosis, a metabolic disorder, often emerges shortly after calving and is characterized by reduced glucose levels, depleted liver glycogen, and elevated ketone body concentrations. The study aimed to investigate the impacts of ketosis on milk yield, reproductive parameters, and postpartum metabolic diseases in cows with subclinical and clinical ketosis. The study involved 267 healthy pregnant Holstein dairy cows in the second and third lactation periods. Groups were formed based on postpartum third-day beta-hydroxybutyric acid (BHBA) levels. The groups included control (BHBA < 1.2 mmol/dL; n=101), subclinical ketosis (BHBA = 1.2-2.4 mmol/dL; n=90), and clinical ketosis (BHBA > 2.4 mmol/dL; n=76) groups. Results indicated that higher body condition scores (BCS) during calving were associated with an increased risk of postpartum ketosis (p<0.001). Subclinical ketosis led to a higher number of inseminations per pregnancy (p<0.01), extended service periods (p<0.001), and decreased pregnancy rates at first insemination (p>0.05). It was observed that the clinical ketosis group had a longer time to reach peak milk yield (p<0.001) and a higher peak milk yield (p<0.01). The incidence of septic metritis was found to be higher in cows with clinical ketosis (p<0.01). Consequently, the study revealed that ketosis has negative effects on reproductive parameters and the incidence of metabolic diseases. Additionally, the importance of BCS in assessing and managing the risk of ketosis in dairy cows during the transition period was emphasized. It is believed that further evaluation of the study with more animal material and additional parameters could yield more meaningful results.
C H A P T E R Pantethine rescues a Drosophila model for pantothenate kinase– associated neurodegeneration

Pantothenate kinase–associated neurodegeneration (PKAN), a progressive neurodegenerative disorder, is associated with impairment of pantothenate kinase function. Pantothenate kinase is the first enzyme required for de novo synthesis of CoA, an essential metabolic cofactor. The pathophysiology of PKAN is not understood, and there is no cure to halt or reverse the symptoms of this devastating disease. Recently, we and others presented a PKAN Drosophila model, and we demonstrated that impaired function of pantothenate kinase induces a neurodegenerative phenotype and a reduced lifespan. We have explored this Drosophila model further and have demonstrated that impairment of pantothenate kinase is associated with decreased levels of CoA, mitochondrial dysfunction, and increased protein oxidation. Furthermore, we searched for compounds that can rescue pertinent phenotypes of the Drosophila PKAN model and identified pantethine. Pantethine feeding restores CoA levels, improves mitochondrial function, rescues brain degeneration, enhances locomotor abilities, and increases lifespan. We show evidence for the presence of a de novo CoA biosynthesis pathway in which pantethine is used as a precursor compound. Importantly, this pathway is effective in the presence of disrupted pantothenate kinase function. Our data suggest that pantethine may serve as a starting point to develop a possible treatment for PKAN.
OFFPRINT Optimal active particle navigation meets machine learning

– The question of how “smart” active agents, like insects, microorganisms, or future colloidal robots need to steer to optimally reach or discover a target, such as an odor source, food, or a cancer cell in a complex environment has recently attracted great interest. Here, we provide an overview of recent developments, regarding such optimal navigation problems, from the micro-to the macroscale
A New Algorithm for Earthquake Prediction Using Machine Learning Methods

: Seismic tremors are among the foremost perilous normal fiascos individuals confront due to their event without earlier caution and their effect on their lives and properties. In expansion, to consider future disaster prevention measures for major earthquakes, it is necessary to predict earthquakes using Neural Networks (NN). A machine learning technique has developed a technology to predict earthquakes from ground controller data by measuring ground vibration and transmitting data by a sensor network. Devices to process this data and record it in a catalog of seismic data from 1900-2019 for Iraq and neighboring regions, then divide this data into 80% training data and 20% test data. It gave better results than other prediction algorithms, where the NN model performs better Seismic prediction than other machine learning methods.
A GPU Parallel Algorithm for Finding a Negative Subset Disjoint Cycle in a Graph

Many problems in combinatorial optimization rely on local search heuristics attempting to swap elements between subsets. The best results are achieved by combining multiple exchanges, where the multi-subset move can be described as a subset disjoint cycle. Finding a valid cycle becomes the core part of the search routine, and the valid cycle of minimum weight corresponds to the best possible move. The problem complexity has been growing at a rapid pace in recent years, hitting the limits of both exact and heuristic approaches. We propose a parallel algorithm for finding subset disjoint negative cycles of minimum weight and a GPU accelerated implementation. The approach is exact if the memory limit is sufficient and heuristic otherwise. A parallel hashmap structure is provided to meet the specific needs of the algorithm. The computational experiments demonstrate up to 60x speedup with respect to the sequential alternative. We showcase the practical benefits of our algorithm for a time sensitive variant of the vehicle routing problem.
Pruning Binarized Neural Networks Enables Low-Latency, Low-Power FPGA-Based Handwritten Digit Classification

As neural networks are increasingly deployed on mobile and distributed computing platforms, there is a need to lower latency and increase computational speed while decreasing power and memory usage. Rather than using FPGAs as accelerators in tandem with CPUs or GPUs, we directly encode individual neural network layers as combinational logic within FPGA hardware. Utilizing binarized neural networks minimizes the arithmetic computation required, shrinking latency to only the signal propagation delay. We evaluate size-optimization strategies and demonstrate network compression via weight quantization and weight-model unification, achieving 96% of the accuracy of baseline MNIST digit classification models while using only 3% of the memory. We further achieve 86% decrease in model footprint, 8mW dynamic power consumption, and $< 9\text{ns}$ latency, validating the versatility and capability of feature-strength-based pruning approaches for binarized neural networks to flexibly meet performance requirements amid application resource constraints.
Pro-Arrhythmic Effects of Gaseous Pollutants Under Healthy Conditions: An In-Silico Study

Air pollution is responsible for millions of global deaths annually. The most dangerous gaseous pollutants are sulfur dioxide ($SO_{2}$), carbon monoxide ($CO$), and nitrogen oxides ($NO_{x}$), which have been linked to an increased risk of cardiac arrhythmias. However, the underlying mechanisms have not been fully established in humans. This study uses multiscale atrial models to assess the effects of individual gaseous pollutants at low, medium, and high concentrations. For this, mathematical equations describing the effects of these pollutants were included in an atrial cell model to evaluate the electrophysiological characteristics. Then, the modified cell model was incorporated into a 3D model of human atria to evaluate the propagation dynamics. The results indicate pro-arrhythmic effects in a concentration-dependent manner. $SO_{2}$ was the pollutant with the highest effects, achieving an action potential duration decrease and triggering the most chaotic and disordered propagation characterized by several re-entries. In conclusion, gaseous air pollutants, particularly $SO_{2}$ and $CO$ at high concentrations, show pro-arrhythmic effects in a concentration-dependent manner.
Novel Methods Inspired by Reinforcement Learning Actor-Critic Mechanism for Eye-in-Hand Calibration in Robotics

Eye-in-hand camera calibration is an essential step in robotic vision systems. Traditional methods perform the calibration via geometry computation methods where the calibration points are acquired by moving the robot arm while capturing images of a static object (often a checkerboard) of known geometry. Our work aims to explore deep reinforcement learning (DRL) techniques for solving common calibration optimization problems. Inspired by the actor-critic mechanism of DRL, we propose a novel approach to perform eye-in-hand camera calibration on the robot arm. Numerical and real-world experiments prove the feasibility of the proposed method as such here we show the potential of the reinforcement learning method to solve the camera calibration problem.
A Novel Substrate Cylindrical Cavity 4×4 Fractal inspired MIMO Antenna for 5G - n258 Satellite Communication

Modern and next-generation communication requires compact and low-profile antennae that are fabricated easily with multi-purpose applications function. This paper uses five congruent circular ring fractal slots with a novel substrate divider cylindrical cavity to enhance the performance of the MIMO (Multi-input multi-output) antenna. Here four port MIMO antenna is proposed with an Inversiory mirror imposed placement of ground with fr4 substrate of 35 mm2. The proposed antenna covers the 5G - n258 band with a bandwidth range of 3.25 GHz. The proposed MIMO antenna is inspired by five congruent circular ring fractal slots with its element antenna that has a peak gain and front back to ratio of 5.6 dBi and 26.8 dB with a compact size, respectively. Further, the MIMO configuration parameters, ECC (envelope correlation coefficient) is found to be less than 0.0012, Diversity gain is found to be greater than 9.994dB, and isolation of antenna is enhanced with greater than 20dB with a wide bandwidth of 12.25% for n258 5G applications respectively and the proposed designs are implemented using CST software.
Recent researches of GaN-based materials and devices in NU-AIST

NU-AIST is the cooperation site between Advanced Industrial Science and Technology (AIST) and Nagoya University, and its aim is bridging the gap between academic research and industry, and promoting research and developments on GaN and related compounds through all the stages from material science to their device applications. In this paper, our development of GaN-based epitaxial growth technologies, light emitting devices, and high-power electric devices and their switching circuit applications at a frequency of over MHz.
Estimation of Quiet Sleep in Preterm and Full-Term Newborns Using Machine Learning Algorithms Based on Cardio-Respiratory and Motion Signals

Monitoring sleep states of newborns, especially those born prematurely, before 37 weeks of gestation, is essential for tracking their development. This study presents an automated method for estimating the state of Quiet Sleep (QS). Given that QS is characterized by regular cardiorespiratory rhythm and non-movement, this approach combines machine learning algorithms trained on cardiorespiratory features with body motion segmentation. It was evaluated on manually annotated recordings from 10 preterm and 5 full-term newborns. Each newborn was recorded for eight hours during their first week of life, and preterm newborns were recorded again at 37 weeks Post-Menstrual Age (PMA). The results achieved an average balanced accuracy of 78% and a Cohen's kappa of 0.51 across all recordings. For neonates with a PMA greater than 33 weeks, these values increased to 82% and 0.58, respectively. This approach proves effective and holds promise for continuously monitoring QS in newborns with a PMA greater than 33 weeks using non-invasive signals.
General Matrix Multiplication (GEMM) Evaluation on Cyclone-V SoC FPGA Using OpenCL

Trends in AI implementations, such as machine learning and deep learning, have been widely used in various studies. At the heart of AI computations such as speech recognition, image recognition, and processing computer graphics is general matrix multiplication, abbreviated as GEMM. AI implementation is currently attracting programmers to implement it on an FPGA. On the other hand, the latest FPGA is integrated with a Hard Processor System (HPS) to increase the productivity of system development. Here, we performed the matrix multiplication using a Cyclone V SoC FPGA integrated with ARM processors. To reduce programming complexity and speed up the process, the kernel for the FPGA was built using the OpenCL programming language. In the experiment, we conducted the matrix multiplication by employing global and local memory. We also proposed a method for estimating the peak performance based on the compilation report. According to the experimental results, the actual performance was approximately identical to the estimation. The peak performance for matrix multiplication using local memory access was 17.86 GFlops and utilized 14.2 watts of power.
Contactless Breathing Waveform Detection Through RF Sensing: Radar vs. Wi-Fi Techniques

Human breathing detection plays a vital role in healthcare, safety, and various other applications. This research paper explores the use of radio-frequency (RF) sensing technologies, specifically radar and Wi-Fi, for detecting human breathing patterns. Abnormal breathing patterns can indicate respiratory or cardiovascular diseases, and early detection is crucial for timely diagnosis and treatment. Radar-based systems utilize low-power RF pulses to capture subtle chest movements associated with breathing, while software-defined radio (SDR)-based systems analyze Wi-Fi signals to detect variations caused by human chest motion. Deep learning algorithms, namely residual neural network (ResNet) and deep multilayer perceptron (DMLP), are employed to classify breathing patterns based on the collected data. ResNet attained classification accuracy up to 90% on radar-based spectrogram images data, while DMLP attained classification accuracy up to 99% on SDR-based channel state information data. The proposed approaches offer non-intrusive, remote-operable, and cost-effective solutions for breathing detection. The research demonstrates the potential of RF sensing technologies in healthcare, eldercare, sleep monitoring, and emergency response systems, paving the way for enhanced well-being and safety.
Relevance of Pre-Training in the Development of a Light Convolutional Neural Network for ECG Quality Assessment

Long-term cardiac monitoring with conventional technology requires huge resources. This problem can be addressed using portable devices. However, they acquire ECGs hardly disturbed by noise, and accurate quality assessment (QA) of these recordings is crucial. Recently, pre-trained convolutional neural networks (CNNs) have reported promising performance in that context, but they demand a lot of computational resources, limiting their use in portable systems. Hence, this work aims to explore the ability of a lightweight CNN, with far fewer parameters than well-known pre-trained CNNs. Applying transfer learning is common in many classification tasks, so the performance of the network was compared when trained from scratch and when pre-trained on a freely available set of natural images. Moreover, the well-known GoogLeNet model was also considered for comparison. All CNNs were fine-tuned with a balanced set of 20,000 5 second-length ECG segments and validated externally. Results show that GoogLeNet performed slightly better than the pre-trained lightweight CNN. However, proposed models were about 12 times faster to classify each ECG interval. These findings highlight the suitability of pre-training a CNN using natural images, retaining a comparable performance in ECG QA than much deeper networks but reducing notably the computational cost.
Deep-learning based detection model of small pancreatic cystic lesions on contrast-enhanced CT

With the advancement of radiological technology, the detection rate of pancreatic cystic lesions (PCLs) has been increasingly augmented, particularly the small cysts (<1cm) are being observed more frequently during examinations. In clinical practice, small cysts are prone to being overlooked, which poses a significant challenge for radiologists. Consequently, we propose a deep learning model capable of automatically detecting cysts and segmenting both the pancreas and cysts. The model incorporates dual-phase imaging information and includes a dataset consisting of pancreatic images with diverse characteristics, demonstrating proficient and robust performance in both detection and segmentation.
Autonomic Regulation During Acute Mental Stress Is Characterized by Dynamic Interactions

The human organism can be understood as a complex system of dynamic interactions regulating physiological functions to maintain a state of homeostasis. Acute mental stress disrupts homeostasis and triggers a cardiovascular response controlled by the autonomic nervous system. We investigated the effects of acute mental stress on dynamic interactions between 20 vital parameters of haemodynamics, heart rate variability, QT variability, respiration, and skin conductance in 35 healthy subjects. To characterize dynamic interactions, we calculated symbolic transfer entropy between all vital parameters during baseline and acute mental stress. Significant changes were found in the dynamic interactions for 206 of 400 parameter combinations between baseline and acute mental stress (p < 0.05, Bonferoni-Holm corrected). Overall, dynamic interactions increased significantly by 9.3 % (p < 0.001) compared to baseline. Specifically, acute mental stress caused a 22 % increase of interactions between vital parameters that are dominated by sympathetic and parasympathetic nervous system. Our results indicate that acute mental stress leads to increased autonomic regulation. The characterization of dynamic interactions during acute mental stress provides insights for a better understanding of autonomic regulation processes.
Understanding Bugs in Rust Compilers

Rust compilers play a foundational role in the Rust language. Like any complex system, they are susceptible to bugs, which can impact the correctness and reliability of the compiled Rust programs. To gain a deeper understanding of these bugs, this paper presents the first comprehensive analysis of historical bugs in two widely used Rust compilers: Rustc and Rust-GCC. The analysis delves into the bugs’ characteristics, bug-proneness locations, bug root causes, and bug-fixing efforts. The findings reveal that the majority of bugs in Rustc are associated with the compiler’s kernel, while Rust-GCC experiences most bugs related to the cleanup process. Among all modules, the ‘src/librustc’ module exhibits the highest bug-proneness in the Rustc compiler, whereas the ‘gcc/rust’ modules demonstrate the highest bug-proneness in the Rust-GCC compiler. Furthermore, the study reveals that the bug-fixing process is accelerated when test cases utilize Rust’s concurrency features.
MAD: One-Shot Machine Activity Detector for Physics-Based Cyber Security

Side channel analysis offers several advantages over traditional machine monitoring methods. The low intrusiveness, independence with the host, data reliability and difficulty to bypass are compelling arguments for using involuntary emissions as input for enforcing security policies. However, side-channel information often comes in the form of unlabeled time series of a proxy variable of the activity. Enabling the definition and enforcement of high-level security policies requires extracting the state or activity of the system from the input data. We present in this paper a novel time series, one-shot pattern locator and classifier called Machine Activity Detector (MAD) specifically designed and evaluated for side-channel analysis. We evaluate MAD in two case studies on a variety of machines and datasets where it outperforms other traditional state detection solutions and presents formidable performances for security rules enforcement. Results of state detection with MAD enable the definition and verification of high-level security rules to detect various attacks without any interaction with the monitored machine.
Graph Neural Networks in Emergency Management: A Case Study on Fire Call Assignment

This paper introduces a Graph Neural Network (GNN) approach to tackle the problem of emergency fire call assignment. The dataset utilized comprises emergency fire calls received by the 911 service in San Francisco. Each call is processed and assigned to a specific fire station. To capture the geographical nuances, we preprocess the dataset into a bipartite graph representation. The right nodes of the graph represent the calls with their geographical coordinates (latitude and longitude), while the left nodes signify the stations and their current occupancy. The graph edges represent the distances between calls and stations. Our GNN model predicts the optimal assignment of calls to stations based on unit availability. Instead of the conventional method of routing calls to the nearest station, our model bypasses this system to directly forecast the station with available units for each call. Through rigorous experimentation, we showcase the superiority of our method in enhancing the call assignment process. By leveraging the intrinsic graph structure, the GNN model taps into the rich information within the dataset. This study advances the field of emergency management by offering a groundbreaking optimization framework that can markedly improve resource allocation in emergency response systems.
Design of Arrayed Waveguide Grating (AWG) for Indoor Free-space Optical (FSO) Pointing System

In this paper, we propose a free space optical communication (FSO) pointing system based on arrayed waveguide grating (AWG), which mainly uses the wavelength division multiplexing (WDM) function of the AWG. The demultiplexing of AWG is achieved based on different emission wavelength of the laser. By changing the direction of the laser through AWG at different wavelengths, the beam steering of the FSO system is achieved.