Miracle-dz/UniFAR-FTUs huggingface dataset:
LGNet: Location-Guided Network for Road Extraction From Satellite Images

Road connectivity is vital in road extraction for accurate vehicle navigation. However, the segmentation-based methods fail to model the connectivity resulting in broken road segments. Therefore, we propose a location-guided network (LGNet) for promoting connectivity performance in a very effective and efficient way. Specifically, an auxiliary road location prediction (RLP) task is designed to obtain global road connectivity information, which improves the performance of road segmentation. The RLP can predict the location coordinates of the whole road with row anchors and column anchors. By aggregating the global location context to the segmentation branch with a location-guided decoder (LG-Decoder), the features can finally capture the connectivity of each road segment. Overall, LGNet has the following advantages: 1) the proposed RLP and location context guidance (LCG) can plug into any encoder–decoder network and achieve an impressive performance; 2) high computational efficiency. In comparison with the multi-branch method, our proposed LGNet requires about $6\times $ fewer GFLOPs; and 3) superior road connectivity performance. A series of experiments are conducted on two road extraction datasets (SpaceNet and DeepGlobe), confirming the effectiveness of the LGNet.
Research progress of biomaterials and innovative technologies in urinary tissue engineering

Substantial interests have been attracted to multiple bioactive and biomimetic biomaterials in recent decades because of their ability in presenting a structural and functional reconstruction of urinary tissues. Some innovative technologies have also been surging in urinary tissue engineering and urological regeneration by providing insights into the physiological behavior of the urinary system. As such, the hierarchical structure and tissue function of the bladder, urethra, and ureter can be reproduced similarly to the native urinary tissues. This review aims to summarize recent advances in functional biomaterials and biomimetic technologies toward urological reconstruction. Various nanofirous biomaterials derived from decellularized natural tissues, synthetic biopolymers, and hybrid scaffolds were developed with desired microstructure, surface chemistry, and mechanical properties. Some growth factors, drugs, as well as inorganic nanomaterials were also utilized to enhance the biological activity and functionality of scaffolds. Notably, it is emphasized that advanced approaches, such as 3D (bio) printing and organoids, have also been developed to facilitate structural and functional regeneration of the urological system. So in this review, we discussed the fabrication strategies, physiochemical properties, and biofunctional modification of regenerative biomaterials and their potential clinical application of fast-evolving technologies. In addition, future prospective and commercial products are further proposed and discussed.
Towards Sim-to-Real Industrial Parts Classification with Synthetic Dataset

This paper is about effectively utilizing synthetic data for training deep neural networks for industrial parts classification, in particular, by taking into account the domain gap against real-world images. To this end, we introduce a synthetic dataset that may serve as a preliminary testbed for the Sim-to-Real challenge; it contains 17 objects of six industrial use cases, including isolated and assembled parts. A few subsets of objects exhibit large similarities in shape and albedo for reflecting challenging cases of industrial parts. All the sample images come with and without random backgrounds and post-processing for evaluating the importance of domain randomization. We call it Synthetic Industrial Parts dataset (SIP-17). We study the usefulness of SIP-17 through benchmarking the performance of five state-of-the-art deep network models, supervised and self-supervised, trained only on the synthetic data while testing them on real data. By analyzing the results, we deduce some insights on the feasibility and challenges of using synthetic data for industrial parts classification and for further developing larger-scale synthetic datasets. Our dataset † and code ‡ are publicly available.
Distilling knowledge from Gaussian process teacher to neural network student

Neural Networks (NN) and Gaussian Processes (GP) are different modelling approaches. The former stores characteristics of the training data in its many parameters, and then performs inference by parsing inputs through these parameters. The latter instead performs inference by computing a similarity between the test and training inputs, and then predicts test outputs that are correlated with the reference training outputs of similar inputs. These models may be combined to leverage upon their diversity. However, both combination and the matrix computations for GP inference are expensive. This paper investigates whether a NN student is able to effectively learn from the information distilled from a GP or ensemble teacher. It is computationally cheaper to infer using this student. Experiments on the speechocean762 spoken language assessment dataset suggest that learning is effective.
Fractional Delay-Doppler Channel Estimation in OTFS with Sparse Superimposed Pilots using RNNs

In this paper, we consider the problem of delay-Doppler (DD) channel estimation in orthogonal time frequency space (OTFS) modulation with fractional delays and Dopplers. Exclusive use of DD bins in a frame for pilot symbols causes rate loss. Superimposing pilot symbols over data symbols avoids this rate loss. Our contributions in this paper are two-fold. 1) We propose a sparse superimposed pilot (SSP) scheme where pilot and data symbols are superimposed in a few bins and the remaining bins carry data symbols only. This scheme offers the benefit of better inter-symbol leakage profile in a frame, while retaining full rate. 2) For the SSP scheme, we propose a recurrent neural network based learning architecture (referred to as SSPNet) trained to provide accurate channel estimates overcoming the leakage effects in channels with fractional DD. Simulation results show that the proposed SSP scheme along with fractional DD channel estimation using the proposed SSPNet performs better than a fully superimposed pilot scheme.
Analyzing the Characteristics of Cropping Intensity’s Change of Cultivated Land in China During 2010-2019

The sustained stability of the arable land replanting index is crucial to the national food security strategy. The exploration of the temporal and spatial changes in the arable land replanting index is of great significance for agricultural development and food security evaluation. This study investigates China’s arable land replanting index from 2010 to 2019 using MODIS NDVI image data, S-G filtering, and quadratic differentiation methods. The results show that there are significant spatial differences in China’s arable land replanting index, with the Huang-Huai-Hai region mainly producing double-cropping crops, the Northeast Plain and Loess Plateau mainly producing single-cropping crops, and the area south of the Yangtze River mainly producing multiple-cropping crops. Overall, China’s food production is mainly based on single-season crops. There is a gradual shift towards double-season crops from north to south, with lower replanting indices in the northwest and higher indices in the eastern provinces. The south has significantly higher indices than the north. During the study period, the arable land replanting index showed an overall upward trend. There were significant increases in the replanting index in the northeast, the Loess Plateau, and the northern Huang-Huai-Hai region. However, there was a downward trend in the southern Huang-Huai-Hai region and the middle and lower reaches of the Yangtze River. It is crucial to maintain the effective planting area of arable land in the Loess Plateau and the double-season planting area of arable land in the Huang-Huai-Hai region while also addressing the downward trend of the arable land replanting index in the middle and lower reaches of the Yangtze River to ensure food security by stabilizing the index.
Optical nonlinearity enhancement in a silicon-organic hybrid slot microring resonator

Silicon photonic integrated devices that rely on optical nonlinear effects play a vital role in all-optical signal processing, optical computing, optical routing and so on. However, the practical use of these devices is limited due to the low Kerr nonlinear index of silicon and high nonlinear loss associated with two-photon absorption (TPA) and TPA-induced free carrier absorption (FCA) in the near-infrared band. In this paper, we propose and numerically demonstrate the optical nonlinear enhancement of silicon-organic hybrid slot microring, which consists of an ultra-narrow slot microring coated with highly nonlinear organic material MEH-PPV. The degenerate four-wave mixing (DFWM) spectra show that the FWM conversion efficiency is improved by 12 dB compared to a silicon microring of the same size. This improvement further indicates that the nonlinear coefficient of the proposed device is up to $1.45\times 10^{6}\mathrm{W}^{-1}k\mathrm{m}^{-1}$. We show that this device’s nonlinear enhancement capability has great potential for applications in all-optical signal processing, high-performance optical switching and optical computing.
Multiplex core of the human brain using structural, functional and metabolic connectivity derived from hybrid PET-MR imaging

With the increasing success of mapping brain networks and availability of multiple MR- and PET-based connectivity measures, the need for novel methodologies to unravel the structure and function of the brain at multiple spatial and temporal scales is emerging. Therefore, in this work, we used hybrid PET-MR data of healthy volunteers (n = 67) to identify multiplex core nodes in the human brain. First, monoplex networks of structural, functional and metabolic connectivity were constructed, and consequently combined into a multiplex SC-FC-MC network by linking the same nodes categorically across layers. Taking into account the multiplex nature using a tensorial approach, we identified a set of core nodes in this multiplex network based on a combination of eigentensor centrality and overlapping degree. We introduced a coreness coefficient, which mitigates the effect of modeling parameters to obtain robust results. The proposed methodology was applied onto young and elderly healthy volunteers, where differences observed in the monoplex networks persisted in the multiplex as well. The multiplex core showed a decreased contribution to the default mode and salience network, while an increased contribution to the dorsal attention and somatosensory network was observed in the elderly population. Moreover, a clear distinction in eigentensor centrality was found between young and elderly healthy volunteers.
LoRa Communication Using TVWS Frequencies: Range and Data Rate

Low power wide area network (LPWAN) is a wireless communication technology that offers large coverage, low data rates, and low power consumption, making it a suitable choice for the growing Internet of Things and machine-to-machine communication applications. Long range (LoRa), an LPWAN technology, has recently been used in the industrial, scientific and medical (ISM) band for various low-power wireless applications. The coverage and data rate supported by these devices in the ISM band is well-studied in the literature. In this paper, we study the usage of TV white spaces (TVWS) for LoRa transmissions to address the growing spectrum demand. Additionally, the range and data rate of TVWS-based LoRa, for different transmission parameter values using different path-loss models and for various scenarios such as free space, outdoor and indoor are investigated. A path-loss model for TVWS-based LoRa is also proposed and explored, and the evaluations show that TVWS offers a longer range. This range and data rate study would be useful for efficient network planning and system design for TVWS-based LoRa LPWANs.
A Fast Decentralized Scheduling Method of Cooperative Localization Based on Actor-Critic Deep Reinforcement Learning

With the emergence of more and more automated vehicles, localization of vehicles has attracted a lot of attention. Among multiple localization methods, cooperative localization is very attractive due to its high coverage and accuracy. However, it is costly and leads to larger delays to exhaustively measure and exchange information between all adjacent vehicles. Thus, it is a challenge to schedule the transmissions for the cooperative localization. In this paper, we describe the cooperative localization as a partially observable Markov process and propose an actorcritic deep reinforcement learning algorithm to bring the vehicles to a given localization accuracy threshold as quickly as possible. The proposed algorithm allows the transmissions to be optimally scheduled in a distributed manner. Simulation results show that, compared with random, greedy, and two existing deep reinforcement learning algorithms, the proposed algorithm has better performance and is more adaptable to large-scale complex networks.
Resveratrol and Other Natural Oligomeric Stilbenoid Compounds and Their Therapeutic Applications

The use of natural compounds as an alternative to synthetic molecules has become a significant subject of interest in recent decades. Stilbenoids are a group of phenolic compounds found in many plant species and they have recently gained the focus of a multitude of studies in medicine and chemistry, resveratrol being the most representative molecule. In this review, we focused on the research that illustrates the therapeutic potential of this class of natural molecules considering various diseases with higher incidence rates. PubChem database was searched for bioactivities of natural stilbenoids, while several keywords (i.e., “stilbenoids”, “stilbenoid anticancer”) were used to query PubMed database for relevant studies. The diversity and the simplicity of stilbenes’ chemical structures together with the numerous biological sources are key elements that can simplify both the isolation of these compounds and the drug design of novel bioactive molecules. Resveratrol and other related compounds are heterogeneously distributed in plants and are mainly found in grapes and wine. Natural stilbenes were shown to possess a wide range of biological activities, such as antioxidant, anti-inflammatory, antihyperglycemic, cardioprotective, neuroprotective, and antineoplastic properties. While resveratrol is widely investigated for its benefits in various disorders, further studies are warranted to properly harness the therapeutic potential of less popular stilbenoid compounds.
Message Generation Algorithm for Maneuver Coordination Based on Value of Information

The Maneuver Coordination Service (MCS) is currently undergoing standardization by the European Telecommunications Standards Institute (ETSI). This service will give connected vehicles the opportunity to share their planned, desired, and suggested trajectories while traveling on the road. While MCS will share channel bandwidth with other services and road users, it is crucial to conserve the sent data to the most important ones for the channel’s economy. In this work, we present a message generation technique based on the Value of Information of the vehicle’s planned trajectory. We implement the proposed approach in a simulator and compare it to other rule-based and fixed-frequency message generation techniques. We test these approaches in an urban scenario and the results show that our proposed algorithm delivers a better channel busy ratio compared to other message generation techniques without sacrificing precision in object tracking.
Antibacterial Activity of some Antibiotics and Disinfectants against Airborne Bacteria Isolated from Restaurants in Yola

The aim of this study was to determine the antibacterial activity of some antibiotic and disinfectant against airborne bacteria from restaurants. Airborne bacteria were isolated from five different restaurants using open plate method and were characterized using standard microbiological techniques. The susceptibility of the isolates to some antibiotics and disinfectants was determined using Kirby-Bauer disc diffusion method and well diffusion. The predominant bacteria identified in the air of the restaurants were S. aureus 9 (45%), Micrococcus spp. 5 (25%), Bacillus subtilis 4 (20 %) and P. aeruginosa 2 (10 %). The results showed that the highest and lowest average densities of bacteria for both morning and afternoon release were for 71 and 86 CFU/ m3 and 37 and 46 CFU / m3 respectively. Antimicrobial susceptibility test results revealed that S. aureus was susceptible to Ciprofloxaxin (88.9%), Ampiclox (66.7%), Amoxicillin (66.7%), Rocephin (55.6%) and Gentamycin (55.6%), but resistant to streptomycin and erythromycin. Micrococcus spp was susceptible to Pefloxacin (60%), Erythromycin (100%), Ciprofloxacin (100%), and Streptomycin (80%), but were resistance to Gentamycin, Zinnacet, and Co-trimoxazole. Bacillus subtillis were susceptible to Amoxicillin 4 (100 %), Pefloxacin, Gentamycin, Streptomycin and Ampiclox (50 %), and Zinnacet (75 %), but were resistance to Rocephin, Erythromycin and Co-trimoxazole. P. aeruginosa were susceptible to Ciprofloxacin (100%) and Augmentin (50%) but resistant to Gentamycin, Pefloxacin, ofloxacin, streptomycin, Chloramphenicol, Co-trimoxazole, Sparfloxacin, Amoxicilin and Rocephin. All the bacteria species showed multiple drug resistance. Disinfectants (Hypo and Dettol) showed antibacterial activity with varying magnitudes (50-100% concentrations but showed no efficacy at concentrations lower than 25%. The study identifies the presence of potentially pathogenic bacteria in the air of restaurants with varying degree of antimicrobial susceptibilities which may pose a serious health hazard to both students and workers.
Towards Paralinguistic-Only Speech Representations for End-to-End Speech Emotion Recognition

We propose a methodology for information aggregation from the various transformer layer outputs of a generic speech En-coder (e.g. WavLM, HuBERT) for the downstream task of Speech Emotion Recognition (SER). The proposed methodology significantly reduces the dependency of model predictions on linguistic content, while leading to competitive performance without requiring costly Encoder re-training. The proposed paradigm is evaluated via Accuracy, Positive Pointwise Mutual Information, and visualization of the learned attention weights. This methodology generalizes well to a multi-language SER setting in addition to single-language SER, suggesting existing cultural commonalities in the paralinguistic domain between different languages. Experimental results demonstrate this ability by testing our model on unseen languages in a zero-shot fashion, suggesting our proposed method is inclusive in the context of speech and language, thus, making it applicable to a wide audience of speakers.
Deep Learning Video Classification of Lung Ultrasound Features Associated with Pneumonia

Ultrasound (US) imaging holds promise as a low-cost versatile, non-invasive point-of-care diagnostic modality in low-and middle-income countries (LMICs). Still, lung US can be challenging to interpret because air bronchograms are anechoic and the US images mostly contain artifacts rather than lung anatomy. To help overcome these barriers, advances in computer vision and machine learning (ML) provide tools to automatically recognize abnormal US lung features, offering valuable information to healthcare workers for point-of-care diagnosis. This paper describes deep learning algorithms that target three key US features associated with lung pathology: pleural effusion, lung consolidation, and B-lines. The algorithms were developed and validated using a large and varied dataset of 22,400 US lung scans (videos) from 762 patients of all ages (newborn to adult) in Nigeria and China. The architectures include effective methods for leveraging frame-level and video-level annotations, are light enough to deploy on mobile or embedded devices and have high accuracy (e.g., AUCs ≈ 0.9). Coupled with portable US devices, we demonstrate that they can provide expert-level clinical assistance for diagnosis of pneumonia, which is the leading cause of both childhood mortality and adult hospitalization in LMICs. We also discuss some of the challenges associated with determining ground truth for pneumonia, which impact the question of how to leverage ML models for lung US to support clinical diagnosis of pneumonia.
Synthetic Voice Spoofing Detection based on Feature Pyramid Conformer

In speech anti-spoofing, artefacts used to detect spoofed speech are often located in specific sub-bands. Previous works often use Convolution Neural Networks (CNNs) as backbone which are good at capturing local features. However, if artefacts simultaneously exist in different sub-bands, CNNs cannot model this kind of information. Thus, we propose to use Feature Pyramid Conformer to solve this issue. Conformer can capture both lo-cal and global features. We aggregate the outputs of each Con-former block with Feature Pyramid Module. Through addition and lateral connection, the aggregation can be better integrated. Besides, to improve generalization of detecting unknown attacks, we propose to adopt Elastic penalty Margin Softmax. It can enhance intra-class compactness and inter-class discrepancy flexibly. Without data augmentaion, our system achieve an Equal Error Rate (EER) of 1.65% on the evaluation set of ASVspooof 2019 logical access, outperforming most existing systems.
Traditional Buildings: A Global Survey of Structural Forms and Cultural Functions

Based on a lifelong professional and personal interest, "Traditional Buildings" presents a unique survey of vernacular architecture across the globe. The reader is taken on a fascinating tour of traditional building around the world, which includes the loess cave homes of central China, the stilt houses on the shores of Dahomey, the housebarns of Europe and North America, the wind towers of Iran, the Bohio houses of the Arawak Indians of the Caribbean, and much more. Professor's Noble's extensive travels have allowed him to examine many of the building at close quarters and the richly illustrated text includes photographs from his personal collection. With its comprehensive and detailed bibliography, the work will be welcomed by experts and non-specialists alike.
The portfolio theory of inflation and policy (in)effectiveness revisited: Corroborating evidence

This study revisits and tests empirically the Portfolio Theory of Inflation (PTI), which analyzes how the effectiveness of macroeconomic policy in open and globally financially integrated economies is influenced by global investor decisions (Bossone, The portfolio theory of inflation and policy (in)effectiveness, 2019). The PTI shows that when an economy is heavily indebted and is perceived by the market to be poorly credible, investors hold it to a tighter intertemporal budget constraint and policies aimed to stimulate output growth dissipate into domestic currency depreciation and higher inflation, with limited or no impact on output, or with lower output and lower inflation. On the other hand, markets afford highly credible economies much greater space for effective and noninflationary macro policies. The study leads to a very basic advice: policymakers of an internationally highly integrated economy should keep public liabilities (the stock of both central bank money and public debt) at low levels: the larger the liabilities, the higher the degree of surrender of the country's national policy sovereignty to external forces and interests.