Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Wildlife Sentinel (SWS): Preventing Wildlife-Vehicle Collisions and Monitoring Road Ecology with Embedded IoT Systems and Machine Learning

Every year in the US, millions of animals are hit by vehicles, making wildlife vehicle collisions (WVCs) a real danger to both animal and human life. In addition, road networks are abiotic barriers to wildlife migration between regions, creating ripple effects on ecosystems. In this paper, a smart wildlife sentinel sign system is demonstrated, utilizing the technologies of Internet of Things (IoT), image recognition, data processing and visualization. This smart system is intended to prevent WVCs by warning driver early once sensors are triggered. Simultaneously, animal images are captured via infrared camera. Data processing and collection are conducted through computer vision filtering, convolutional neural network (CNN) based image classification, and animal activity metadata generation. Wildlife activity data can be exported wirelessly to cloud databases to assist ecologists and government road agencies to further analyze wildlife activity hotspots and potential migration patterns over time.
Hierarchical Categorical Generative Modeling for Multi-omics Cancer Subtyping

Identifying a specific cancer subtype from a variety of candidates is vital for precise and effective treatment. However, cancer subtyping is highly non-trivial as a result of cancer heterogeneity. While significant efforts have been put into understanding the mechanism of cancer subtypes via studying the omics data, existing methods run the risk of presenting biased analyses resulted from overfitting the high-dimensional and scarce omics data. In this paper, we propose a novel generative model that directly models the cancer data distribution by which downstream tasks can circumvent the curse of overfitting and achieve better performance. Unlike conventional generative modeling schemes, the proposed method underlines hierarchical categorical latent spaces to extract global features and local details respectively from transcriptomics and genomics profiles, which is the first to be considered in the cancer subtyping literature. By extensive experiments we verify that the proposed architecture achieves more clearly separated subtypes, as well as medically significant insights into real subtyping.
Sense and Control of Oscillating MEMS Mirrors

Manufacturing advances in the field of micro-electro-mechanical systems (MEMS) enabled the realization of MEMS mirrors. These MEMS mirrors, which are as small as a few square millimeter, are promising candidates for various types of automotive applications requiring laser beam steering. Prime application examples are Head-Up Displays (HUD) or Light Detection and Ranging (LiDAR) sensors. In this work, we present the latest advancements and concepts for sensing, actuating, and controlling oscillating comb-drive based MEMS mirrors. Also an efficient approach towards high-voltage generation and actuation is presented which is capable to reduce the MEMS mirror's power dissipation by factors. Furthermore, the performance of the presented control system is depicted by means of an automotive LiDAR use-case which was integrated into an automated demo vehicle.
Numerical Simulation Analysis of Flow and Temperature Fields in Large U-Shaped Heat Exchanger

Based on the comprehensive analysis of the complex structure and material characteristics of a large U-shaped heat exchanger, combined with the thin-shell heat transfer model and the similarity principle, the 3D geometric model of the heat exchanger of the original stabilizing device is constructed. Then, the grid size is greatly reduced by using boundary layer meshing technology. Finally, the temperature field and flow field distribution of the fluid in the heat exchanger are obtained by finite element calculation. The results show that, on the one hand, the condensate temperature is in a local overheating state and the oil flow rate is too low, and there is a retention zone; on the other hand, the temperature in the overheating region decreases with the increase of the shell flow rate. Therefore, the coupling effect of condensate oil retention and local overheating may accelerate the corrosion rate of heat exchanger tube bundle. It is suggested to adjust the operation parameters of the heat exchanger and optimize the internal structure to make the internal flow rate and temperature distribution more reasonable, so as to improve the service safety and reliability of the heat exchanger.
A Ship Anti-missile Effectiveness Assessment Model Based on LSSA-BP Neural Network

In the context of surface ship air defense and anti-missile operations, the design of effectiveness assessment algorithms and evaluation models based on the constructed standardized evaluation index system can better analyze and evaluate the effectiveness of ship anti-missile operations and guide the optimization of ship combat system design. In this end, an effectiveness assessment model based on backpropagation neural network (BP) optimized by a learning sparrow search algorithm (SSA), referred to as LSSA-BP, is proposed. In LSSA, good point set initialization is used to boost the initial population diversity of the algorithm, then the sorted pairing learning strategy is introduced to improve learning efficiency. A boundary processing method is also used to retain information carried by the sparrow individuals out of bounds. Then LSSA is applied to optimize the initial parameters of BP neural network to improve the model fitting accuracy, to precisely evaluate the effectiveness of surface ship anti-missile operations according to the established evaluation index system. The experimental results show that the algorithm has high effectiveness assessment accuracy and effectively improves the shortcomings of other effectiveness assessment methods and models.
Probabilistic Electricity Demand Forecasting with Transformer-Guided State Space Model

Forecasting the electricity demand has many critical applications for power station generation plans, energy resource allocations, power transmissions, and related decision makings. Existing electricity demand forecasting methods are mainly based on deterministic time-series prediction techniques and recurrent neural networks, ignoring the intrinsic uncertainties of electricity demands and the long-range temporal dependencies between electricity volumes and various external features. In this work, we proposed a Transformer-guided probabilistic electricity demand forecasting framework (TPEDF) that learns both the global-local electricity demand dependencies and the complex correlations of external features entangled with the electricity demands. Our proposed framework predicts electricity demand distributions with a probabilistic state space model, which can effectively eliminate the forecasting errors and enhance the expressiveness of the model by taking the electricity uncertainty into account. Extensive experiments on real-world datasets demonstrate that TPEDF significantly outperforms existing models on electricity demand forecasting.
A High-Efficiency 3D-Printed E-Band Dielectric Transmitarray For Integrated Space and Terrestrial Networks

A highly efficient wideband transmitarray working at E band is developed in this paper. This transmitarray is composed by 10 10 dielectric cells and fabricated through three-dimensional (3D) printing technology. To obtain the required phase distribution of the array, the height of dielectric cells is adjusted. To reduce the phase error under oblique incident angles, multiple-angle phase distribution is analyzed. This transmitarray can work within a wideband from 60 to 90GHz. The peak gain can reach 23.4 dBi at 74 GHz with the maximum aperture efficiency of 60.9%.
A continuous glucose monitoring measurements forecasting approach via sporadic blood glucose monitoring

Continuous glucose monitoring prediction is a crucial yet challenging task in precision medicine. This paper presents a novel neural ODE based approach for predicting continuous glucose monitoring (CGM) levels purely based on sporadic self-monitoring signals. We integrate the expert knowledge from physiological model into our model to improve the accuracy. Experiments on the real-world data demonstrate that our method outperforms other state-of-the-art methods on NRMSE metrics.
Thrust Vectored Rocket Landing Integrated Guidance and Control with Proximal Policy Optimization

This paper presents a 3 Degrees-of-Freedom (DoF) rocket landing model environment, controlled by an agent trained with the Proximal Policy Optimization (PPO) reinforcement learning algorithm. The objectives of this work are to model the dynamics of a rocket and its environment, convert into a simulated environment adequate to reinforcement learning, and evaluate PPO training results. This work contributes by implementing realistic models, and by contrasting basic implementations of PPO and another advanced reinforcement learning technique. The proposed model is a 3-DoF longitudinal rocket with mass-varying properties, landing gear, and stochastic wind disturbances. The environment is modeled with an observation space composed of kinematic and contact properties only, being a subset of all time-varying properties. The action space is composed of three elements: main thruster effort, nozzle angle, and side thruster effort. The reward computation is based on state, fuel consumption, action transitions, and termination status. Simple control techniques are generally not able to stabilize such complex systems. Reinforcement learning is chosen to tackle the complexity of the problem, and PPO for its theoretical training stability and continuous space treatment, in both observation and action space. Training and policy deployment assessments are presented to verify the algorithm efficacy and controllability of the proposed problem.
Radiating properties of solar plasmas

Using a series of 14 previously obtained empirical emission measure distributions and a number of spectral lines observed by the SMM and P78-1 instruments, the total power radiated by a hot plasma is compared to that radiated by individual spectrum lines. Results are presented for different choices of ionization balance and power loss functions. The results indicate that for some lines such as the C IV resonance doublet at 1548 A and 1550 A, the ratio of the line intensity to the total radiated power varied only over a factor of 2, suggesting that well-calibrated measurements of a single line intensity may provide a fairly good estimation of the total radiated power output from the solar plasma. 21 references.
Predicting Other’s Minds Using Model-based Reinforcement Learning

Most of the studies on model-based reinforcement learning (RL) have been confined to single agent learning scenarios. This impedes us from fully exploring the nature and potential of model-based RL. In this study, we design a simple model-based RL agent capable of performing multi-agent tasks and evaluate the ability of model-based RL using various types of iterative Keynesian beauty contests. We examined its characteristics from various perspectives, including dominance in the competition as a performance measure, evolutionary process as an adaptation measure, the rate of convergence to the Nash equilibrium as a basic measure of effectiveness, and the degree of cooperation as an alternative measure of it. Simulations showed that the model-based RL agent outperforms other types of agents, including rule-based methods and model-free RL, by all measures.
Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets

Tumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them.
Running Cooperative Tasks in a Multi-Robot System with Cyber-Physical Features

This paper develops a system based on cyber-physical concepts to support robots in tasks that require cooperative work and information sharing. The work's inspiration is the need of the 4.0 industry for cobots visiting different workstations to assist human workers, for tools and raw material transportation, and other tasks. As in any industrial environment, unexpected events may occur, and the cobots must face them, reducing their effects on the production line. In this work, multiple robots (MR) managed by a system with cyber-physical (CP) features cooperate in an experimental dynamic environment to safely accomplish several tasks. For this, the robots must visit targets (analogous to workstations) in an orderly way, while one support robot must deal with oil stains on the environment's floor detected along the robots' navigation (unexpected event). The proposed CP-MR system is evaluated through experimental results allowing a performance analysis.
Suitability of ISAs for Data Paths Based on Redundant Number Systems: Is RISC-V the best?

It has been known for a long time that in processor design, delay in arithmetic circuits can be reduced by using redundant number representations (RNS). This advantage is currently only exploited to a limited extent since various aspects complicate its use. For this reason the redundant representation is abandoned at the boundaries of the AL U and the values are reconverted back to the traditional binary representation. In particular, some operations that are traditionally considered fast are now subject to a higher delay. Among other concerns, this complicates comparison operations (e.g. equal to, greater than) and thus affects the timing behavior of conditional jumps. There is some initial research promising speedups using RNS in register files and the data path, but there are still some open questions. In particular it is important to evaluate how the instruction set is designed. Such a study is necessary to estimate whether it is worthwhile to develop the entire data path beyond the AL U in redundant representation. If no reconversion from redundant to traditional binary number system takes place, then e.g. the evaluation of condition codes or flags is problematic, since all speed advantages are lost again. In this work a qualitative and quantitative analysis of common ISAs in processors with redundant data paths is presented. All relevant properties of an ISA are identified and an evaluation of several common ISAs according to these criteria. A performance comparison of three common RISC ISAs (MIPS, A64 (ARM), RISC- V) is given based on a simulation of the Embench bench-mark suite using an adapted version of QEMU. This comparison estimates the speedup of processors with redundant versus binary data paths. It was found, that RISC- V was overall outperforming the other ISA with a maximum speedup of 1.41.
Path-Loss Prediction of Millimeter-wave using Machine Learning Techniques

Millimeter-wave communication systems design require accurate path-loss prediction, critical to determining coverage area and system capacity. In this work, four machine learning algorithms are proposed for path-loss prediction in an indoor environment for 5G millimeter-wave frequencies, from 26.5 to 40 GHz. They are artificial neural network, support vector regression, random forest, and gradient tree boosting. We compare their performances, including extensions of the empirical path-loss models alpha-beta-gamma and close-in frequency-dependent exponent incorporating the number of crossed walls. The results show that the ML techniques improve the prediction accuracy of empirical models.
AUTOMED: Automated Medical Risk Predictive Modeling on Electronic Health Records

Electronic health records (EHR) have been widely applied to various tasks in the medical domain such as risk predictive modeling, which aims to predict further health conditions by analyzing patients’ historical EHR. Existing work mainly focuses on modeling the sequential and temporal characteristics of EHR data with advanced deep learning techniques. However, the network architectures of these models are all manually designed based on experts’ prior knowledge, which largely impedes non-experts from exploring this task. To address this issue, in this paper, we propose a novel automated risk prediction model named AUTOMED to automatically search the optimal model architecture for modeling the complex EHR data and improving the performance of the risk prediction task. In particular, we follow the idea of neural architecture search to design a search space that contains three separate searchable modules. Two of them are used for analyzing sequential and temporal features of EHR data, respectively. The third is to automatically fuse both features together. Besides these three modules, AUTOMED contains an embedding module and a prediction module. All the three searchable modules are jointly optimized in the search stage to derive the optimal model architecture. In such a way, the model design can be automatically achieved with few human interventions. Experimental results on three real-world datasets show that AUTOMED outperforms state-of-the-art baselines in terms of PR-AUC, F1, and Cohen’s Kappa. Moreover, the ablation study shows that AUTOMED can obtain reasonable model architectures and offer useful insights to the future risk prediction model design.
ClassAphasia: An Ensemble Machine Learning Network to Improve Aphasia Diagnosis and Determine Severity

In light of medical biases and variability of Aphasia classes, determining the severity of the Aphasia disorder warrants the use of machine learning to properly leverage patient data for inferences. Machine learning solutions are also inherently efficient with regards to time, allowing for medical professionals to create timely treatment plans to mitigate further damage. Neural networks, however, are extremely computationally expensive, and require resources on scale in order to achieve an acceptable level of accuracy for the medical field. Computationally efficient alternatives, namely XGBoosting and Decision Trees, are equipped with drawbacks themselves. However, working to combine these algorithms to create an ensemble machine learning network effectively eliminates these drawbacks. Thus, the ensemble network highlighted within this paper, ClassAphasia, works to create comprehensive algorithm to accurately measure the severity of Aphasia within individuals by combining the XGBoosting and decision tree algorithms. The ClassAphasia algorithm utilizes data drawn from the Moss Aphasia Psycholinguistics Project Database (MAPPD), which contains 87,673 biometric voice data samples [5]. 60% of the data was split for training the algorithm, while 20% was used for validation and testing each. ClassAphasia achieved an accuracy of 100% on its testing set. Leveraging this algorithm for use in the medical field could assist in speech therapy of millions.
Variable-Length Instruction Set: Feature or Bug?

With the increasing complexity of digital applications, the use of variable-length instruction sets became essential, in order to achieve higher code density and thus better performance. However, security aspects must always be considered, in particular with the significant improvement of attack techniques and equipment. Fault injection, in particular, is among the most interesting and promising attack techniques thanks to the recent advancements. In this article, we provide proper characterization, at the instruction set architecture (ISA) level, for several faulty behaviors that can be obtained when targeting a variable-length instruction set. We take into account the binary encoding of instructions, and show how the obtained behaviors depend on the alignment of the instructions in the memory. Moreover, we are also able to give a better insight on previous results from the literature, that were still partially unexplained. We also show how the observed behaviors can be exploited in various security contexts.
Analysis of Discharge Parameters Influenced by Electrode Velocity Based on LSTM Prediction Model

Relationships were discussed in this work between discharge current and electrode moving speed, ionization coefficient, field strength, gas pressure, temperature, humidity and other factors. Gas flow distribution around electrode, according to Bernoulli's law in aerodynamics, was analyzed during the electrode moving toward the target. Mechanism was explained on forming of local part low vacuum in the discharge gap. Two sub-processes of gas ionization avalanche process and surface electronic emission process were used to describe small gap discharge. With Paschen's law, analysis of influence was performed on discharge current by electrode moving speed toward the target. Experiments were conducted many times based on test platform of electrostatic discharge electrode moving speed effect, while experiment data were recorded. Long Short-Term Memory network (LSTM) model was used to train and learn experimental data, predicted peak value of discharge current at different electrode moving speeds and rise time. The experimental results show that there is a strong correlation between the electrode moving speed and the peak discharge current, while there is a certain correlation between the electrode moving speed and the current rise time, but it is relatively weak. The research results maybe have some reference significance for the proposal and formulation of non-contact electrostatic discharge standards.
Predicting the Fare of a Flight Ticket with Machine Learning Algorithms *

The paper for the project is written as, Authors would be going to present the different suggestion framework projects. Authors will utilize a few AI approaches for this venture Someone who buys flight tickets every now and again would have the option to anticipate the ideal opportunity to get a pass to acquire the best arrangement. The number of flights have this dynamic change of prices based on the situation. The flight prices decrease only when there is a chance to expand their franchise. The airfare of flights is predicted based on the various features of airlines and flights like duration of journey, based on the number of stops between arrival and destination place. The datasets are trained to get the results through different machine learning techniques. Flight tickets changes dynamically from time to time regarding any airline be it any festival season or holiday. It can change trends by morning and evening based on the flight trends. Valuing in the web-based world is exceptionally straightforward and can be an essential to buy the online tickets. Airlines takes a lot of advantage in booking a ticket because it works dynamically. The regression and classification techniques used in the model predicts the 80 % accuracy results. Appropriate methods is used to AI calculations to upgrade cost buy (least expensive cost) by clients on web based business stage.