Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation and Validation of CryoSat-2-Derived Water Levels Using In Situ Lake Data from China

CryoSat-2 altimetry has become a valuable tool for monitoring the water level of lakes. In this study, a concentrated probability density function (PDF) method was proposed for preprocessing CryoSat-2 Geophysical Data Record (GDR) data. CryoSat-2 altimetry water levels were preprocessed and evaluated by in situ gauge data from 12 lakes in China. Results showed that the accuracy of the raw GDR data was limited due to outliers in most of the along-track segments. The outliers were generally significantly lower than the in situ values by several meters, and some by more than 30 m. Outlier detection, therefore, improves upon the accuracy of CryoSat-2 measurements. The concentrated PDF method was able to greatly improve the accuracy of CryoSat-2 measurements. The preprocessed CryoSat-2 measurements were able to observe lake levels with a high accuracy at nine of the twelve lakes, with an absolute mean difference of 0.09 m, an absolute standard deviation difference of 0.04 m, a mean root mean square error of 0.27 m, and a mean correlation coefficient of 0.84. Overall, the accuracy of CryoSat-2-derived lake levels was validated in China. In addition, the accuracy of Database for Hydrological Time Series of Inland Waters (DAHITI) and HYDROWEB water level products was also validated by in situ gauge data.
Neural Image Decompression: Learning to Render Better Image Previews

A rapidly increasing portion of Internet traffic is dominated by requests from mobile devices with limited- and metered-bandwidth constraints. To satisfy these requests, it has become standard practice for websites to transmit small and extremely compressed image previews as part of the initial page-load process. Recent work, based on an adaptive triangulation of the target image, has shown the ability to generate thumbnails of full images at extreme compression rates: 200 bytes or less with impressive gains (in terms of PSNR and SSIM) over both JPEG and WebP standards. However, qualitative assessments and preservation of semantic content can be less favorable. We present a novel method to significantly improve the reconstruction quality of the original image with no changes to the encoded information. Our neural-based decoding not only achieves higher PSNR and SSIM scores than the original methods, but also yields a substantial increase in semantic-level content preservation. In addition, by keeping the same encoding stream, our solution is completely inter-operable with the original decoder. The end result is suitable for a range of small-device deployments, as it involves only a single forward-pass through a small, scalable network.
Tri-axial Self-Attention for Concurrent Activity Recognition

We present a system for concurrent activity recognition. To extract features associated with different activities, we propose a feature-to-activity attention that maps the extracted global features to sub-features associated with individual activities. To model the temporal associations of individual activities, we propose a transformer-network encoder that models independent temporal associations for each activity. To make the concurrent activity prediction aware of the potential associations between activities, we propose self-attention with an association mask. Our system achieved state-of-the-art or comparable performance on three commonly used concurrent activity detection datasets. Our visualizations demonstrate that our system is able to locate the important spatial-temporal features for final decision making. We also showed that our system can be applied to general multilabel classification problems.
Antimicrobial Effects of Egyptian Local Chicory, Cichorium endivia subsp. pumilum

The discovery of novel and more efficient antimicrobial agents from natural sources like plants is one of the most important ways through which the growing threat of antibiotic-resistant pathogens can be overcome. Herein, we report the potential antimicrobial activity of Cichorium endivia L. subsp. pumilum. Different concentrations of various solvent extracts prepared from several parts of chicory were tested for their antimicrobial effect against a panel of microorganisms. The antimicrobial activity was analyzed using the well diffusion method, where zones of inhibition were used as indicators of antimicrobial activity. The results indicated the superiority of seed extracts over both leaf and root extracts. Methanol extracts showed higher activity compared with chloroform and water extracts. Increased solvent extract concentration was accompanied by a parallel increase in the diameter of the inhibition zone. Gram-positive bacteria were found to be more sensitive than Gram-negative bacteria and fungi. On a whole, the highest observed inhibition zones (21.3 ± 0.6 and 20.1 ± 0.4 mm) were recorded with the methanolic extract of chicory seeds against S. aureus and B. cereus, respectively. These results offer insights into the antimicrobial potency of this Egyptian local plant and provide a basis for further phytochemical and pharmacological research.
Inhibiting Bacterial Drug Efflux Pumps via Phyto-Therapeutics to Combat Threatening Antimicrobial Resistance

Antibiotics, once considered the lifeline for treating bacterial infections, are under threat due to the emergence of threatening antimicrobial resistance (AMR). These drug-resistant microbes (or superbugs) are non-responsive to most of the commonly used antibiotics leaving us with few treatment options and escalating mortality-rates and treatment costs. The problem is further aggravated by the drying-pipeline of new and potent antibiotics effective particularly against the drug-resistant strains. Multidrug efflux pumps (EPs) are established as principal determinants of AMR, extruding multiple antibiotics out of the cell, mostly in non-specific manner and have therefore emerged as potent drug-targets for combating AMR. Plants being the reservoir of bioactive compounds can serve as a source of potent EP inhibitors (EPIs). The phyto-therapeutics with noteworthy drug-resistance-reversal or re-sensitizing activities may prove significant for reviving the otherwise fading antibiotics arsenal and making this combination-therapy effective. Contemporary attempts to potentiate the antibiotics with plant extracts and pure phytomolecules have gained momentum though with relatively less success against Gram-negative bacteria. Plant-based EPIs hold promise as potent drug-leads to combat the EPI-mediated AMR. This review presents an account of major bacterial multidrug EPs, their roles in imparting AMR, effective strategies for inhibiting drug EPs with phytomolecules, and current account of research on developing novel and potent plant-based EPIs for reversing their AMR characteristics. Recent developments including emergence of in silico tools, major success stories, challenges and future prospects are also discussed.
A Container-based DoS Attack-Resilient Control Framework for Real-Time UAV Systems

The Unmanned aerial vehicles (UAVs) sector is fast-expanding. Protection of real-time UAV applications against malicious attacks has become an urgent problem that needs to be solved. Denial-of-service (DoS) attack aims to exhaust system resources and cause important tasks to miss deadlines. DoS attack may be one of the common problems of UAV systems, due to its simple implementation. In this paper, we present a software framework that offers DoS attack-resilient control for real-time UAV systems using containers: ContainerDrone. The framework provides defense mechanisms for three critical system resources: CPU, memory, and communication channel. We restrict attacker’s access to CPU core set and utilization. Memory bandwidth throttling limits attacker’s memory usage. By simulating sensors and drivers in the container, a security monitor constantly checks DoS attacks over communication channels. Upon the detection of a security rule violation, the framework switches to the safety controller to mitigate the attack. We implemented a prototype quadcopter with commercially off-the-shelf (COTS) hardware and open-source software. Our experimental results demonstrated the effectiveness of the proposed framework defending against various DoS attacks.
Lack of Hypothalamus Polysialylation Inducibility Correlates With Maladaptive Eating Behaviors and Predisposition to Obesity

High variability exists in individual susceptibility to develop overweight in an obesogenic environment and the biological underpinnings of this heterogeneity are poorly understood. In this brief report, we show in mice that the vulnerability to diet-induced obesity is associated with low level of polysialic acid-neural cell adhesion molecule (PSA-NCAM), a factor of neural plasticity, in the hypothalamus. As we previously shown that reduction of hypothalamic PSA-NCAM is sufficient to alter energy homeostasis and promote fat storage under hypercaloric pressure, inter-individual variability in hypothalamic PSA-NCAM might account for the vulnerability to diet-induced obesity. These data support the concept that reduced plasticity in brain circuits that control appetite, metabolism and body weight confers risk for eating disorders and obesity.
Applications of Blockchain in Healthcare: Current Landscape & Challenges

Several problems in healthcare stem from the complex network of intermediaries and the lack of traceability of transactions. To mention a few: healthcare data is fragmented across several silos negatively affecting research and services, about half of the clinical trials are never reported, the cost of drug discovery is ever increasing, and substandard and fake medicines are still a huge problem. Blockchain has the potential to solve these problems as it provides trust without any intermediaries, has traceability as a default feature, and promises new business models by enabling novel incentive structures. Due to its potential, blockchain has gathered significant interest in the healthcare industry. In this paper, we review major use cases of blockchain in healthcare: patient data management, pharmaceutical research, supply chain management of medical goods, prescription management, billing claims management, analytics, and telemedicine alongside the related projects. We found that most of the blockchain projects are limited as white-papers, proof of concepts, and products with a limited user base. However, we observed that the quantity, quality, and maturity of the projects are increasing. We also discuss technical, regulatory, and business challenges to the adoption of blockchain in the healthcare industry
Protecting Life While Preserving Liberty: Ethical Recommendations for Suicide Prevention With Artificial Intelligence

In the United States, suicide increased by 24% in the past 20 years, and suicide risk identification at point-of-care remains a cornerstone of the effort to curb this epidemic (1). As risk identification is difficult because of symptom under-reporting, timing, or lack of screening, healthcare systems rely increasingly on risk scoring and now artificial intelligence (AI) to assess risk. AI remains the science of solving problems and accomplishing tasks, through automated or computational means, that normally require human intelligence. This science is decades-old and includes traditional predictive statistics and machine learning. Only in the last few years has it been applied rigorously in suicide risk prediction and prevention. Applying AI in this context raises significant ethical concern, particularly in balancing beneficence and respecting personal autonomy. To navigate the ethical issues raised by suicide risk prediction, we provide recommendations in three areas—communication, consent, and controls—for both providers and researchers (2).
A Survey of Malicious Accounts Detection in Large-Scale Online Social Networks

As a virtual community and online platform, people can share different views, ideas, and experiences freely on social networks. Due to popularity of online social networks (OSNs) such as Facebook, Twitter etc., malicious accounts gain improper benefits through abuse of social network services, and malicious accounts detection is increasingly becoming a focus in the field of OSNs security research. In this paper, we give a detailed review in various types of malicious accounts detection methods in large-scale OSNs. We present most common methods based on user behavior analysis and OSNs structure. It also discusses major challenge relating malicious accounts detection.
Population responses of common ravens to reintroduced gray wolves

Abstract Top predators have cascading effects throughout the food web, but their impacts on scavenger abundance are largely unknown. Gray wolves (Canis lupus) provide carrion to a suite of scavenger species, including the common raven (Corvus corax). Ravens are wide‐ranging and intelligent omnivores that commonly take advantage of anthropogenic food resources. In areas where they overlap with wolves, however, ravens are numerous and ubiquitous scavengers of wolf‐acquired carrion. We aimed to determine whether subsidies provided through wolves are a limiting factor for raven populations in general and how the wolf reintroduction to Yellowstone National Park in 1995–1997 affected raven population abundance and distribution on the Yellowstone's Northern Range specifically. We counted ravens throughout Yellowstone's Northern Range in March from 2009 to 2017 in both human‐use areas and wolf habitat. We then used statistics related to the local wolf population and the winter weather conditions to model raven abundance during our study period and predict raven abundance on the Northern Range both before and after the wolf reintroduction. In relatively severe winters with greater snowpack, raven abundance increased in areas of human use and decreased in wolf habitat. When wolves were able to acquire more carrion, however, ravens increased in wolf habitat and decreased in areas with anthropogenic resources. Raven populations prior to the wolf reintroduction were likely more variable and heavily dependent on ungulate winter‐kill and hunter‐provided carcasses. The wolf recovery in Yellowstone helped stabilize raven populations by providing a regular food supply, regardless of winter severity. This stabilization has important implications for effective land management as wolves recolonize the west and global climate patterns change.
BrLAS, a GRAS Transcription Factor From Brassica rapa, Is Involved in Drought Stress Tolerance in Transgenic Arabidopsis

GRAS proteins belong to a plant-specific transcription factor family and play roles in diverse physiological processes and environmental signals. In this study, we identified and characterized a GRAS transcription factor gene in Brassica rapa, BrLAS, an ortholog of Arabidopsis AtLAS. BrLAS was primarily expressed in the roots and axillary meristems, and localized exclusively in the nucleus of B. rapa protoplast cells. qRT-PCR analysis indicated that BrLAS was upregulated by exogenous abscisic acid (ABA) and abiotic stress treatment [polyethylene glycol (PEG), NaCl, and H2O2]. BrLAS-overexpressing Arabidopsis plants exhibited pleiotropic characteristics, including morphological changes, delayed bolting and flowering time, reduced fertility and delayed senescence. Transgenic plants also displayed significantly enhanced drought resistance with decreased accumulation of ROS and increased antioxidant enzyme activity under drought treatment compared with the wild-type. Increased sensitivity to exogenous ABA was also observed in the transgenic plants. qRT-PCR analysis further showed that expression of several genes involved in stress responses and associated with leaf senescence were also modified. These findings suggest that BrLAS encodes a stress-responsive GRASs transcription factor that positively regulates drought stress tolerance, suggesting a role in breeding programs aimed at improving drought tolerance in plants.
Admittance control for physical human–robot interaction

This paper presents an overview of admittance control as a method of physical interaction control between machines and humans. We present an admittance controller framework and elaborate control scheme that can be used for controller design and development. Within this framework, we analyze the influence of feed-forward control, post-sensor inertia compensation, force signal filtering, additional phase lead on the motion reference, internal robot flexibility, which also relates to series elastic control, motion loop bandwidth, and the addition of virtual damping on the stability, passivity, and performance of minimal inertia rendering admittance control. We present seven design guidelines for achieving high-performance admittance controlled devices that can render low inertia, while aspiring coupled stability and proper disturbance rejection.
Walsh Hadamard kernel‐based texture feature for multimodal MRI brain tumour segmentation

The automated brain tumor segmentation methods are challenging due to the diverse nature of tumors. Recently, the graph based Spectral Clustering (SC) method is utilized for brain tumor segmentation to create high‐quality clusters. In this article, a new superpixel based SC using the Walsh Hadamard texture feature for multimodal brain tumor segmentation from Magnetic Resonance Image is proposed. The selected kernels of Walsh Hadamard Transform (WHT) are projected on equal size blocks of the image for texture feature extraction. The texture feature strength of each block is considered as superpixels, and these superpixels become nodes in the graph of SC. Finally, the original members of superpixels are recovered to represent Complete Tumor (CT), Tumor Core (TC), and Enhancing Tumor (ET) tissues. The observational results are brought out on BRATS 2015 data sets and evaluated using the Dice Score (DS), Hausdorff Distance, and Volumetric Difference metrics. The proposed method has produced competitive results with a DS of 0.83 for CT, 0.75 for TC, and 0.73 for ET, respectively, for high‐grade images. In case of low‐grade images, the proposed method achieves DS of 0.78 for CT, 0.68 for TC, and 0.60 for ET, respectively. The proposed method produces results better than other existing clustering methods.
OrigamiSpeaker: Handcrafted Paper Speaker with Silver Nano-Particle Ink

In this study, we present an OrigamiSpeaker which can be handcrafted with silver nano-particle ink on a paper substrate. The OrigamiSpeaker is based on the "electrostatic loudspeaker" technique. The audio signal is amplified to a high voltage and applied to an electrode that vibrates to generate sound. By using Origami techniques, users are able to design various shapes of OrigamiSpeaker.
FabAuth: Printed Objects Identification Using Resonant Properties of Their Inner Structures

We present a method we propose called FabAuth for identifying 3D-printed objects, which utilizes the differences in the resonant properties of such objects. We focus on changing the internal structures of each object made through a 3D printing process to assign a unique resonant property to it even if multiple objects have the same appearance. To identify the objects, the method identifies resonant property differences by using vibration that can pass through 3D-printed objects. The method can be applied even to low-filled 3D-printed objects as long as an acoustic wave can travel through the objects from one sensor to another. To validate the method's feasibility, we conducted a preliminary experiment to confirm whether it can be applied to low-filled 3D-printed objects and found that its average classification accuracy reached 92.2%.
Human paths have positive impacts on plant richness and diversity: A meta‐analysis

Abstract We assess the impacts of human paths, trails, and roads on plant species richness and Shannon diversity. Most reviews of this topic have not considered community‐level measures and have focused on excessive tourism impacts. We found significant positive effects of paths on plant richness and diversity. The effect size for richness was highest when studies included roads (paved) or trails (unpaved). The effect size found for diversity was highest when studies were in grasslands. We also found experimental designs comparing high levels of path use to low levels of path use, near‐to‐path versus far‐from‐path and path‐presence versus path‐absence comparisons obtained the largest effect sizes. There was no evidence that non‐native species explained most increases in species richness or diversity. The effect sizes of human paths on plant communities are comparable in magnitude to those reported for other mammals’ disturbance and ecosystem engineering activities.
Improving Similarity Search with High-dimensional Locality-sensitive Hashing

We propose a new class of data-independent locality-sensitive hashing (LSH) algorithms based on the fruit fly olfactory circuit. The fundamental difference of this approach is that, instead of assigning hashes as dense points in a low dimensional space, hashes are assigned in a high dimensional space, which enhances their separability. We show theoretically and empirically that this new family of hash functions is locality-sensitive and preserves rank similarity for inputs in any `p space. We then analyze different variations on this strategy and show empirically that they outperform existing LSH methods for nearest-neighbors search on six benchmark datasets. Finally, we propose a multi-probe version of our algorithm that achieves higher performance for the same query time, or conversely, that maintains performance of prior approaches while taking significantly less indexing time and memory. Overall, our approach leverages the advantages of separability provided by high-dimensional spaces, while still remaining computationally efficient
Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets

Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area. Existing studies carried out for review spam detection have exploited feature engineering approach; however limited number of features are considered. This paper proposes a Feature-Centric Model for Review Spam Detection (FMRSD) to detect spam reviews. The proposed model examines a wide range of feature sets including ratings, sentiments, content, and users. The experimentation reveals that the proposed technique outperforms the baseline and provides better results.
Reduction of the Retransmission Delay for Heterogeneous Devices in Dynamic Opportunistic Device-to-device Network

The dynamic opportunistic device-to-device (DO-D2D) network will frequently emerge in the fifth generation (5G) wireless communication due to high-density and fast-moving mobile devices. In order to improve the Quality of Experience (QoE) of users with different computing capacity devices in the DO-D2D network, in this paper, we focus on the study of how to reduce the packets retransmission delay and satisfy heterogeneous devices. To select as many devices as possible to transmit simultaneously without interference, the concurrent transmitters-selecting algorithm is firstly put forward. It jointly considers the number of packets successfully received by each device and the device’s connectivity. Then, to satisfy different devices’ demands while primarily ensuring the base-layer packets successfully received by all the devices, the layer-cooperation instantly decodable network coding is presented, which is used to select transmission packets combination for each transmitter. Simulation results illustrate that there is an appreciable retransmission delay gain especially in the poor channel quality network compared to the traditional base-station (BS) retransmission algorithm. In addition, our proposed algorithms perform well to satisfy the different demands of users with heterogeneous devices