Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of a Longitudinal and Lateral Lane-Changing Motion Planning Model for Intelligent Vehicles in Dynamical Driving Environments

This paper describes a lane-changing motion planning model for intelligent vehicles under constraints of collision avoidance in dynamical driving environments. The key innovation is decoupling the longitudinal and lateral motion planning to realize trajectory re-planning in a normal lane-changing process to prevent collisions. The longitudinal planning model decides a collision-free termination point of motion through planning the longitudinal acceleration and velocity. Taking the termination time as input, the lateral planning model plans the optimal reference trajectory for normal lane-changing maneuvers or re-plans the lane-changing trajectory to eliminate potential accidents. When traffic states have variations that may bring about the collisions, the termination point can be updated through the longitudinal planning model, based on which the lateral planning model makes adjustments to the pre-planned trajectory to complete the lane-changing successfully or return its original lane. The simulation results show that the proposed model not only can handle the general motion planning problem but also can re-plan trajectories in emergent conditions to ensure safety, while vehicle dynamics retain in a stable state during the lane-changing or returning maneuvers.
SAFE AREA FOR RESIDENTIAL POPULATION TO RESIDE NEAR LIMESTONE MINING: A RISK MANAGEMENT APPROACH

Di Sukabumi, batu kapur ditambang oleh penduduk setempat tanpa pengendalian bahaya yang memadai. Untuk mengestimasi risiko kesehatan akibat pajanan penambangan kapur dan menentukan lokasi aman untuk penduduk di sekitarnya, telah dilakukan analisis risiko kesehatan lingkungan untuk partikulat tersuspensi total (TSP) dan PM10 di Desa Padabeunghar, Kecamatan Jampang Tengah, Kabupaten Sukabumi. TSP dan PM10 diukur di lokasi penambangan dan di 10 tempat pemukiman dengan interval koaksial sekitar 500 m. Berat badan dan waktu kontak pemajanan diukur dari 110 orang penduduk lelaki dan perempuan dewasa yang dipilih secara acak dari 6.523 rumah tangga di sekitar lokasi penambangan. Estimasi risiko kesehatan, yang dinyatakan sebagai risk quotient (RQ), dihitung dari rata-rata asupan harian TSP dan PM10 dan dosis referensinya (RfC). Risiko kesehatan dianggap ada dan perlu dikendalikan jika RQg1. Hasil estimasi menunjukkan bahwa hanya sekitar 9% penduduk Desa Padabeunghar yang aman dari risiko kesehatan oleh pajanan debu partikulat sepanjang hidup mereka. Dengan RQ gabungan yang berkisar 0,67 sampai 13, lokasi yang aman untuk dihuni berada mulai dari 4 km dari pusat pertambangan ke luar, sedangkan menurut baku mutu lingkungan udara ambien (PP 41/1999) lokasi aman mulai dari 3 km. Konsentrasi TSP dan PM10 yang terukur masing-masing 23-1.606 dan 10-175 μg/M3, sedangkan menurut rumusan manajemen risiko masing-masing 81 μg/M3 dan 57 μg/M3. Angka tingkat aman ini, yang lebih rendah dari ketentuan PP 41/1999 sebesar 90 μg/M3 untuk TSP, dapat dicapai dengan menurunkan laju penambangan dari 25 ton/hari menjadi 6,3 ton/hari, atau dengan memindahkan tungku pembakaran kapur ke lokasi yang lebih jauh dari pemukiman
The Effectiveness of an Interactive Digital Map for Visually Impaired People

Individuals with visual impairment or blindness often experience problems with unfamiliar surroundings, especially when they want to know about the existence of an area where they live or a place they want to visit. With a map application that can help people with visual impairments, the application is developed unlike ordinary maps, which have sound and vibration features to support visually impaired users. The map application was developed based on interviews and literature studies of respondents during the research. The test scenario used 12 respondents divided into 3 experimental groups, each given 20 minutes, 10 minutes, and 5 minutes. Furthermore, the use of questions for spatial knowledge of an area is given to test the success of the maps that have been made during the research. The test scenario results show an increase in spatial knowledge when using a map developed with sound and vibration features for visually impaired users. With the technology created, it can help individuals with visual impairments in understanding an area and with the help of sound and vibration support features in the map application for users with visual impairments.
Monitoring of Black Pepper Growth at Different Elevation using Ground Data and NDVI Time Series

Implementation of remote sensing in agriculture helps to enhance crop growth monitoring especially during the Covid-19 pandemic. To enhance black pepper growth condition, a study was conducted at two study sites in Bintulu, Sarawak. Hence, this study aims (i) to construct a black pepper growth monitoring at different levels of elevation in Suka Farm (SF) and Taime Farm (TF); and (ii) to integrate limited ground data and NDVI time series from Landsat 8OLI for black pepper growth monitoring. Elevation maps were generated using Natural Neighbor (NN) based on the ground data analysed using ArcGIS 10.4 Software. Three elevation levels were classified into the lower, middle, and upper levels. Observational ground data and NDVI time series of Landsat 8 OLI were calculated using SAS 9.4 software. All parameters then correlating with the elevation levels using Pearson Correlation Coefficient. Optimum growth of black pepper growth in SF and TF was identified at an elevation range between 39m–50m. The NDVI time series also indicated equivalent results as the ground data. This study proposed that the elevation of an area gives a significant impact on black pepper growth. Besides, the NDVI time series of Landsat 8 OLI was feasible for monitoring black pepper growth.
Review on Power System Reliability Indices and Evaluation Techniques

Now a day’s most accountable issue in the power industry is Reliability. power systems reliability using Quantitative analysis can be examined with the help of reliability indices. For a long period of time different methods introduced and developed to assess the reliability indices of the power system. In modern society Uninterrupted and Reliable electric power supply is necessary. The wide use of electricity has been lead to a high vulnerability to electric power failures. This is the reason, reliability of electric power has gained spotlight and it’s considered significant role while planning for a electric power system operation. In this paper, a summary is given of the technique of finding out the reliability of a power system. Readers will get an idea about the possibilities of measuring Reliability indices. The inspiration at the back of this paper is to set up a comprehensive general idea about Analytical and simulation technique for reliability assessment and serving for future research and development in the area of electrical power system.
Interferometric synthetic aperture radar coherence constraints in heavily vegetated tropics

Success of Interferometry Synthetic Aperture Radar (InSAR) technique for deriving digital elevation model (DEM) in the heavily vegetated humid tropic using Sentinel-1 SAR has been controversial, owing to the difficulty in achieving good coherence. Yet achievable coherence with Sentinel-1 SAR over the heavily vegetated humid tropic has rarely been reported. Consequently, this paper evaluates coherence achievable with Sentinel-1 SAR over the heavily vegetated humid tropic. Twelve Sentinel-1 SAR over part of Johor selected based on perpendicular and temporal baselines considerations, were used as pairs of six reference and secondary images respectively and processed using Sentinel Application Platform (SNAP) to derive coherence maps and analysed accordingly. The result shows that regardless of image pairs baseline characteristics, coherence of above 0.6 over the heavily vegetated humid tropic with Sentinel-1 SAR can only be achieved in barely 10% of the study area. This result shall serve as eye-opener to geoscience community, especially SAR enthusiast looking forward to leveraging on SAR clouds penetrating ability; and Sentinel-1 SAR open access and short revisit circle to apply Sentinel-1 SAR in deriving DEM over heavily vegetated humid tropical environment. However, future studies shall focus on assessing consistency of good coherence patches and their usability in acquiring elevation.
Multi-Agent Reinforcement Learning Based Unlicensed Resource Sharing for LTE-U Networks

Unlicensed resource sharing is a promising means to increase the capacity of cellular networks through extending LTE systems to the unlicensed spectrum. In this paper, we formulate the unlicensed resource sharing problem in LTE-WiFi coexistence scenarios as a cooperative game. Then, a multi-agent deep Q-network reinforcement learning algorithm is developed to solve the game, which is based on a proposed special Q-learning update rule. The deep Q-network can resolve dimension explosion problem and generalize to the new states for which the model has not ever seen before. Based on our proposals, each LTE small cell base station can optimally determine the occupied time-slot fraction when sharing the unlicensed channels with neighboring WiFi access points under the condition of a specific fairness metric. Numerical results show that the proposed algorithm can converge to the optimal pure strategy Nash equilibrium and achieve exactly the same performance of the optimal centralized algorithm. In addition, our proposals can achieve better performance and faster convergence rate as compared with the traditional Q-learning algorithm.
Joint Delay and Energy Management for Cache-Enabled Ultra-Dense Cellular Networks: A Game-Theoretic Learning

Users enjoy various kinds of services on the cost of huge bandwidth and energy consumption. The user quality of experience can be improved and transmission delay can be reduced by applying cache on the ultra-dense radio access network side. Most of the previous works aim at improving the throughput or the delay performance by using many caching strategies while ignoring energy consumption. In this paper, unlike conventional caching strategies, we focus on the joint optimization of delay and energy consumption by using the Nash bargaining game, which can ensure good fairness. Game-theoretic learning is introduced to determine the convergence of the algorithm. With a unique caching strategy, a new utility function is designed in consideration of the delay cost and energy consumption. Finally, simulation results verify a good convergence of the cache placement algorithm and the efficiency of the existing caching strategy.
Evolutionary Prediction of Information Propagation Dynamics Based on Time Series Characteristics

Using online social network has become increasingly popular among the general public for sharing information and rapid spreading information in recent years. Evolution prediction of information propagation has started to post its significance for enterprise marketing, public opinion monitoring, and personalized recommendation. This paper proposes a time series characteristics-based prediction method of information propagation (TSPIP), which combines the structural characteristics of microblog community and the data association mining rules of multiple time series. Firstly, non-stationary time series of microblog hot events are detected and transformed into event propagation sequences and stationary event propagation sequences. Secondly, a linear regression model is used to establish the quantitative relationship between the quantity of data and the explanatory variables. The best explanatory variables are selected by analysing correlation coefficient and the hypothesis test. Finally, a time series prediction model of microblog hot events is constructed according to the state space model of Local Linear Trend and Regression. Time series analysis is conducted to get the characteristics of event development trend and cycle. Experiments on real datasets show that our prediction method is effective for the propagation of microblog hot events with respect to burst traffic.
Drone Classification and Localization Using Micro-Doppler Signature with Low-Frequency Signal

Security issues, such as unsafe operation and terrorist activity have become more critical due to the rising popularization of drones in recent years. To address these issues, drone classification and localization techniques become more desirable. In this paper, we propose a new approach for drone classification and localization using micro-Doppler signature. After obtaining the micro-Doppler signature generated from drone propeller rotation, dimension reduction is carried out for feature extraction. Next, extracted features are fed into four different classifiers. Finally, classification and localization are accomplished by discriminating features corresponding to different types of drones and different locations of interest. We carry out the practical experiment with radio frequency signal at low-frequency band. The experimental results show that the proposed approach can effectively classify and localize drones with good robustness.
Progressively Decomposing Graph Matching

Existing approaches to graph matching mainly include two types, i.e., the Koopmans-Beckmann’s QAP formulation (KB-QAP) and Lawler’s QAP formulation (L-QAP). The former is advantageous in scalability but disadvantageous in generality, while the latter is exactly the opposite. In this paper, we present a novel graph matching method, called progressively decomposing graph matching (PDGM), which can simultaneously possess the merits of the scalability of KB-QAP and the generality of L-QAP. Our method is motivated by a key observation that, the matching accuracy of KB-QAP can be dramatically boosted by properly introducing a guidance term into the formulation. Based on this observation, the proposed PDGM method progressively incorporates edge affinity information into the optimization procedure of KB-QAP through a guidance term, which mainly involves two iterative steps, i.e., solving the guided KB-QAP and updating the guidance matrix. The extensive experiments on both synthetic data and real image datasets demonstrate that our method can outperform the state-of-the-art in terms of the robustness to noise/deformation and outliers, and the good balance between effectiveness and computational efficiency.
Positioning Pedestrians and Cyclists in Urban Intersections Using VLF Magnetic Fields

In urban canyons with many highrise buildings, conventional global navigation satellite systems (e.g. GPS) are unreliable because of multipath signal reflection and limited sky coverage. We propose an alternative positioning system based on very low frequency magnetic fields for the localization of pedestrians and cyclists in such urban canyons as an element of an Intelligent Transportation System (ITS). While similar systems based on low frequency magnetic fields have been proposed, they can only achieve submetre accuracy over relatively small areas and in the absence of conducting structures such as buildings. We demonstrate the results from a positioning system based on three transmitters for 2-D localization whose positioning algorithm incorporates information from finite-difference time-domain (FDTD) modelling of the intersection of interest. Finally, results for miniaturized receiver sensors are also presented, as a fully implemented positioning system for pedestrians would ideally need to be scaled down so that it can be accommodated within a smartphone's form factor.
Quantum for

The quantum non-cloning theorem is discussed for Gar(cid:12)nkle{Horne dilaton black holes. It is found that if the black hole complementarity principle is correct, then it will be questioned whether the quantum non-cloning theorem is well established inside the inner horizon. It is also found that another complementarity principle may be needed inside the inner horizon of the Gar(cid:12)nkle{Horne dilaton black hole.
EVALUATION OF AGRO-MORPHOLOGICAL DIVERSITY IN SOME SEGREGATING LINES OF COWPEA ( VIGNA UNGUICULATA L . WALP )

A study was conducted to evaluate agro-morphological characteristics of some segregating lines of eight accessions of cowpea (Vigna unguiculata L. Walp.) using seeds from a first cycle generation progenies. Eight cowpea accessions [GH36841 (red), GH3684B4 (mottle brown), GH 3684B5 (mottle brown), GH3684A2 (red), GH3684C2 (red), GHUCO1BL (black), GH3684BL, (black) and GH7184BL (black with spots)] and an out group IT97K-499-35 (white) were cultivated under field conditions. Twenty three agro-morphological traits were assessed for thirty three lines of cowpea. A dendrogram generated using 23 agro-morphological characters distinguished the cowpea accessions into two lineages, with the out-group being the most diverse. Generally, morphological polymorphism revealed discriminations among the cowpea accessions studied. Seed weight, plant height, number of branches, length of branches, number of leaves, number of peduncles, length of peduncles and number of seeds per pod as well as number of pods per plant differed significantly (p  0.05) among the cowpea accessions. The evidence showed that there were variations in morphological characteristics expressed by the cowpea accessions and these were segregations in one trait or the other. The cowpea accession GH UCO1BL is possibly a cross between IT97K-499-35 x SARC-LO2. The segregation lines of GH 3684 had the greatest diversity probably due to genotype environment interactions. INTRODUCTION Provision of food security is a challenge, especially in many developing countries like Ghana. Cowpea production may be one of the most promising solutions that can be exploited to meet this challenge, because the value of cowpea lies in its high protein content (23-29%) and drought tolerance. It has the ability to fix atmospheric nitrogen, which allows it to survive on poor soils (Steele, 1972). In Africa, it is estimated that over 200 million people consume cowpea daily (Obembe, 2009). Worldwide, cowpea is cultivated, over 10.5 million hectares, with a total production of 3.9 million tons (Obembe, 2009). Despite the benefits of cowpea as a food legume in farming systems and its importance to curb food crisis, abiotic and biotic factors remain the major constraints to cowpea production and storage. Cowpea production in Ghana is constrained by low and variable grain yields, grain quality and susceptibility to diseases and pests. Genetic Journal of Ghana Science Association, Vol. 13 No. 2 . Dec., 2011 1 Evaluation of agro-morphological diversity in some segregating lines. Asare et al.
Vision Transformer with Deformable Attention Supplementary Materials 1

In this section, we provide a detailed comparision between our proposed deformable attention and the direct adaptation from the deformable convolution [4], which is also known as the multiscale deformable attention in Deformable DETR [20]. First, our deformable attention serves as a feature extractor in the vision backbones while the one in Deformable DETR which replaces the vanilla attention in DETR [1] with a linear deformable attention, plays the role of the detection head. Second, the m-th head of query q in the attention in Deformable DETR with M heads in a single scale is formulated as
Title: Residual Convolutional Neural Network for Determination of IDH Status in Low-and High-grade Gliomas from MR Imaging

Purpose: Isocitrate dehydrogenase ( IDH ) mutations in glioma patients confer longer survival and may guide treatment decision-making. We aimed to predict the IDH status of gliomas from MR imaging by applying a residual convolutional neural network to pre-operative radiographic data. Experimental Design: Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women’s Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 pre-contrast, and T1 post-contrast) and built a predictive model from the outputs. To increase the size of training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming. Results: With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively. Conclusion: We developed a deep learning technique to non-invasively predict IDH genotype in grade II-IV glioma using conventional MR imaging using a multi-institutional dataset. Statement of Significance: Our model may have the potential to serve as a noninvasive tool that complements direct tissue sampling, guiding patient management at an earlier stage of disease and in follow-up. Translational Relevance: Deep learning algorithms can be trained to recognize patterns directly from imaging. In our study, we use a residual convolutional neural network to non-invasively predict IDH status from MR imaging. IDH status is of clinical importance as patients with IDH-mutated tumors have longer overall survival than their IDH-wild-type counterparts. In addition, knowledge of IDH status may guide surgical planning. By using a large, multi-institutional patient dataset with a diversity of acquisition parameters, we show the potential of the approach in clinical practice. Furthermore, this algorithm offers broad applicability by utilizing conventional MR imaging sequences. Our model offers the potential to complement surgical biopsy and histopathological analysis. More generally, our results (i) show that artificial intelligence can robustly recognize genomic patterns within imaging, (ii) advance non-invasive characterization of gliomas, and (iii) demonstrate the potential of algorithmic tools within the clinic to aid clinical decision-making.
The Evolutionary Biology of Human Body Fatness: Thrift and Control

This comprehensive synthesis of current medical and evolutionary literature addresses key questions about the role body fat plays in human biology. It explores how body energy stores are regulated, how they develop over the life-course, what biological functions they serve, and how they may have evolved. There is now substantial evidence that human adiposity is not merely a buffer against the threat of starvation, but is also a resource for meeting the energy costs of growth, reproduction and immune function. As such it may be considered as important in our species evolution as other traits such as bipedalism, large brains, and long life spans and developmental periods. Indeed, adiposity is integrally linked with these other traits, and with our capacity to colonise and inhabit diverse ecosystems. It is because human metabolism is so sensitive to environmental cues that manipulative economic forces are now generating the current obesity epidemic.