Miracle-dz/UniFAR-FTUs huggingface dataset:
Training Module for Marine Microplastics Monitoring 1 Introduction 1.1 Plastic litter as a global ocean concern

The light weight, high durability, resistance to chemicals, plasticity, and high buoyancy of foamed and resin products and the cost-effectiveness of plastics (Thompson et al., 2009) make them so-called “essential” materials in our daily life. A world without plastics, or synthetic organic polymers, seems unimaginable today, yet their large-scale production and use only dates back to ~1950. Plastics have outgrown most man-made materials. World plastic production of 1.7×10 ton in 1950 increased 189 times to 3.2×10 ton in 2015 (Plastics Europe, 2013, 2016). Moreover, the global production of polyethylene (PE) and polypropylene (PP) (the most common marine microplastics) grew at a rate of 8.7% per year from 1950 to 2012 (Andrady, 2017). By identifying and synthesizing dispersed data on production, use, and end-of-life management of polymer resins, synthetic fibers, and additives, researchers present the first global analysis of all mass-produced plastics ever manufactured. It is estimated that 8300 million metric tons (Mt) as of virgin plastics have been produced to date. As of 2015, approximately 6300 Mt of plastic waste had been generated, around 9% of which had been recycled, 12% was incinerated, and 79% was accumulated in landfills or the natural environment. If current production and waste management trends continue, roughly 12,000 Mt of plastic waste will be in landfills or in the natural environment by 2050(Geyer et al., 2017).
The Effectiveness of Acceptance and Commitment Therapy (ACT) in Reducing Pain Intensity and Enhancing the Sense of Coherence and Psychological Well-being among the Patients with Chronic Low Back Pain

Chronic pain is a common, multifactorial problem that requires medical and psychological interventions to be managed. On the other hand, Acceptance and commitment therapy (ACT) is one of the third-wave cognitive-behavioral therapies, which has recently been used to treat the certain psychiatric disorders and to enhance the patients’ psychological status. Therefore, the present study aimed to investigate the effectiveness of acceptance and commitment therapy on reducing pain intensity and improving the sense of coherence and psychological well-being among the patients with chronic low back pain. This quasi-experimental study was performed by a pretest-posttest design with two groups. Also, it had a statistical population including all the patients with chronic low back pain and were present at the neuropsychiatry clinic of Ali ibn Abi Talib Hospital, who were referred to Red Crescent Physiotherapy Clinic in Zahedan from March 2016 to May 2017. Moreover, its sample consisted of 30 patients with chronic low back pain who were selected from all the patients referred to Red Crescent Physiotherapy Clinic in Zahedan, using the targeted sampling method in terms of the inclusion and exclusion criteria. These patients were then assigned into two groups as experimental and control, each one included 15 patients. In addition, the McGill Pain Questionnaire (1997), the Ryff Psychological Well-being Scale (1989), and the Sense of Coherence Scale designed by Flensborg-Madson et al. (2006) were used as data collection tools. To analyze the data, descriptive statistics such as means, standard deviations, and univariate and multivariate covariance analyses were used. The data analysis indicated that, the acceptance and commitment therapy could significantly decrease the pain intensity and its subscales among the patients in the experimental group compared to the control group (p < 0.01). Furthermore, acceptance and commitment therapy significantly increased the sense of coherence and psychological well-being as well as their subscales in the experimental group's patients compared to the control group (p < 0.01). It can be concluded that, acceptance and commitment therapy was effective on reducing the pain intensity and boosting the sense of coherence and psychological well-being among the patients with chronic low back pain. Therefore, the findings represent new horizons in clinical interventions and can be used as an effective intervention method.
Temporal and Spatial Vegetation Index Variability
and Response to Temperature and Precipitation in
the Qinghai-Tibet Plateau Using GIMMS NDVI

The vegetation of the Qinghai-Tibet Plateau (QTP) is fragile and vulnerable to global and regional climate change. This paper analyzed the temporal and spatial variability of NDVI and response to temperature and precipitation in the QTP from 1982 to 2015. The results showed that: (1) there was a nonsignificant increase temporally, while there was a significant spatial increase that accounted for 63.26% of the total area and was distributed in the north, west, and south of the QTP. A significant decrease was concentrated southeast of the QTP. (2) Average NDVI values for the main vegetation types in the QTP were broadleaf forest>coniferous forest>shrub>meadow>alpine vegetation>grassland>desert. NDVI values of grassland, alpine vegetation, and desert vegetation had a significant linear increase, while NDVI for shrub, coniferous, and broadleaf forest decreased non-significantly. (3) The correlation coefficients between NDVI and temperature were spatially distributed in a north-south direction with latitudinal zonal characteristics. The correlation coefficients between NDVI and precipitation were distributed in an east-west direction with dry-humidity zonal characteristics. The study concluded that with the lack of hydrothermal conditions in the northern part of the QTP, NDVI increased significantly, while there was a significant decrease in areas with sufficient hydrothermal conditions.
UNSUPERVISED SEGMENTATION OF INDOOR 3D POINT CLOUD: APPLICATION TO OBJECT-BASED CLASSIFICATION

Abstract. Point cloud data of indoor scenes is primarily composed of planar-dominant elements. Automatic shape segmentation is thus valuable to avoid labour intensive labelling. This paper provides a fully unsupervised region growing segmentation approach for efficient clustering of massive 3D point clouds. Our contribution targets a low-level grouping beneficial to object-based classification. We argue that the use of relevant segments for object-based classification has the potential to perform better in terms of recognition accuracy, computing time and lowers the manual labelling time needed. However, fully unsupervised approaches are rare due to a lack of proper generalisation of user-defined parameters. We propose a self-learning heuristic process to define optimal parameters, and we validate our method on a large and richly annotated dataset (S3DIS) yielding 88.1% average F1-score for object-based classification. It permits to automatically segment indoor point clouds with no prior knowledge at commercially viable performance and is the foundation for efficient indoor 3D modelling in cluttered point clouds.
Optimal guidance and estimation of a 1D diffusion process by a team of mobile sensors

This paper describes a framework to design guidance for a team of mobile sensors to estimate a distributed parameter system modeled by a diffusion process. The diffusion process has an abstract linear system representation with a linear observation equation, so an infinite-dimensional version of the Kalman filter is applied for estimation. We propose an optimization problem that minimizes the weighted sum of the trace of the covariance operator of the Kalman filter and the guidance effort of the mobile sensors, whose motion is modeled by linear dynamics. This formulation is well-suited for limited endurance mobile sensor platforms. We provide a solution method to solve for the optimal guidance. A finite-dimensional approximation is applied to a simulation in which we analyze how the performance of a single mobile sensor depends on mobility penalty and sensor noise. We also illustrate the application of the framework to a team of heterogeneous sensors.
A Study of Hot and Cold Metaphors in Iraqi Arabic

The present study is an attempt to explore the ways of an Iraqi Arabic speaker conceptualises his/her understanding of various abstract domains of emotions or feeling and attitudes through his/her experiences of concrete domains of Cold and Hot metaphors. Lakoff & Johnson’s (1980) Conceptual Metaphor Theory is adopted to analyse the collected data. Idiomatic expressions and proverbs are chosen specifically from the southern dialect where they are widely used in daily interaction. The data analysis classified the results according to their abstract and concrete domains of the sampled data. The findings of this study showed different conceptualisations of the Hot and Cold metaphor in Iraqi Arabic. The study concluded that these metaphors are used to conceive more of the abstract concepts of emotions, feelings, and attitudes. Cold domain is used to conceptualise the bad habits of people, and it is also used to conceptualise some good habits, on the other hand. Whereas Hot domain is used to conceptualise the more intensive of emotions and attitudes. The study concluded that the sharp emotions, feelings and attitudes that are understood from the conceptualisation of Hot and Cold conceptual metaphors, are experienced from the more concrete domains of hard things.
Fbxo2 mediates clearance of damaged lysosomes and modifies neurodegeneration in the Niemann-Pick C brain

A critical response to lysosomal membrane permeabilization (LMP) is the clearance of damaged lysosomes through a selective form of macroautophagy known as lysophagy. Although regulators of this process are emerging, whether organ- and cell-specific components contribute to the control of lysophagy remains incompletely understood. Here, we examined LMP and lysophagy in Niemann-Pick type C (NPC) disease, an autosomal recessive disorder characterized by the accumulation of unesterified cholesterol within late endosomes and lysosomes, leading to neurodegeneration and early death. We demonstrated that NPC human fibroblasts show enhanced sensitivity to lysosomal damage as a consequence of lipid storage. Moreover, we described a role for the glycan-binding F-box protein 2 (Fbxo2) in CNS lysophagy. Fbxo2 functions as a component of the S phase kinase-associated protein 1–cullin 1–F-box protein (SKP1-CUL1-SCF) ubiquitin ligase complex. Loss of Fbxo2 in mouse primary cortical cultures delayed clearance of damaged lysosomes and decreased viability after lysosomal damage. Moreover, Fbxo2 deficiency in a mouse model of NPC exacerbated deficits in motor function, enhanced neurodegeneration, and reduced survival. Collectively, our data identified a role for Fbxo2 in CNS lysophagy and establish its functional importance in NPC.
3D OBJECT DETECTION BY FEATURE AGGREGATION USING POINT CLOUD INFORMATION FOR FACTORY OF THE FUTURE

Abstract. Nowadays, object detection is considered as an unavoidable aspect that needs to be addressed in any robotic application, especially in industrial settings where robots and vehicles interact closely with humans and objects and therefore a high level of safety for workers and machines is required. This paper proposes an object detection framework suitable for automated vehicles in the factory of the future. It utilizes only point cloud information captured by LiDAR sensors. The system divides the point cloud into voxels and learns features from the calculated local patches. The aggregated feature samples are then used to iteratively train a classifier to recognize object classes. The framework is evaluated using a new synthetic 3D LiDAR dataset of objects that simulates large indoor point cloud scans of a factory model. It is also compared with other methods by evaluating on SUN RGB-D benchmark dataset. The evaluations reveal that the framework can achieve promising object recognition and detection results that we report as a baseline.
Combining reinforcement learning with supervised deep learning for neural active scene understanding

—While vision in living beings is an active process where image acquisition and classiﬁcation are intertwined to gradually reﬁne perception, much of today’s computer vision is build on the inferior paradigm of episodic classiﬁcation of i.i. d. samples. We aim at improved scene understanding for robots by taking the sequential nature of seeing over time into account. We present a supervised multi-task approach to answer questions about different aspects of a scene such as the relationship between objects, their quantity or the their relative positions to the camera. For each question, we train a different output head which operates on input from one shared recurrent convolutional neural network that accumulates information over time steps. In parallel, we train an additional output head using reinforcement learning (RL) that uses the reduction in cumulative loss from the supervised heads as reward signal. It thereby learns to gradually improve the prediction conﬁdence of e.g. partially occluded objects by moving the camera to a more favourable angle with respect to these objects. We present preliminary results on simulated RGB-D image sequences that show superior performance of our RL-based approach in answering questions quicker and more accurately than using static or random camera movement.
Fine mapping of high-temperature adult-plant resistance to stripe rust in wheat cultivar Louise

Bread wheat is a major food crop on a global scale. Stripe rust, caused by Puccinia striiformis f. sp. tritici , has become one of the largest biotic stresses and limitations for wheat production in the 21st century. Post 2000 races of the pathogen are more virulent and able to overcome the defense of previously resistant cultivars. Despite the availability of effective fungicides, genetic resistance is the most economical, effective, and environmentally friendly way to control the disease. There are two major types of resistance to stripe rust: all-stage seedling resistance (ASR) and adult-plant resistance (APR). Although both resistance types have negative and positive attributes, ASR generally is race-specific and frequently is defeated by new races, while APR has been shown to be race non-specific and durable over time. Finding genes with high levels of APR has been a major goal for wheat improvement over the past few decades. Recent advancements in molecular mapping and sequencing technologies provide a valuable framework for the discovery and validation of new sources of resistance. Here we report the discovery of a precise molecular marker for a highly durable type of APR – high-temperature adult-plant (HTAP) resistance locus in the wheat cultivar Louise. Using a Louise × Penawawa mapping population, coupled with data from survey sequences of the wheat genome, linkage mapping, and synteny analysis techniques, we developed an amplified polymorphic sequence (CAPS) marker LPHTAP2B on the short arm of wheat chromosome 2B, which cosegregates with the resistant phenotype. LPHTAP2B accounted for 62 and 58% of phenotypic variance of disease severity and infection type data, respectively. Although cloning of the LPHTAP2B region is needed to further understand its role in durable resistance, this marker will greatly facilitate incorporation of the HTAP gene into new wheat cultivars with durable resistance to stripe rust.
Homocysteine aggravates DNA damage by impairing the FA/Brca1 Pathway in NE4C murine neural stem cells

There is existing evidence that elevated homocysteine (Hcy) levels are risk factors for some neurodegenerative disorders. The pathogenesis of neurological diseases could be contributed to excessive cell dysfunction and death caused by defective DNA damage response (DDR) and accumulated DNA damage. Hcy is a neurotoxic amino acid and acts as a DNA damage inducer. However, it is not clear whether Hcy participates in the DDR. To investigate the effects of Hcy on DNA damage and the DDR, we employed mitomycin C (MMC) to cause DNA damage in NE4C murine neural stem cells (NSCs). Compared to treatment with MMC alone, we found that co-treatment with MMC and Hcy worsened DNA damage and increased death in NE4C cells. Intriguingly, in this DNA damage model mimicked by MMC, immunoblotting results showed that the monoubiquitination levels of Fanconi anemia complementation group I (Fanci) and Fanconi anemia complementation group D2 (Fancd2) were decreased to about 60.3% and 55.7% by supplementing cell culture medium with Hcy, indicating Hcy inactivates the function of Fanci and Fancd2 in DNA damage conditions. Given Breast Cancer 1 (BRCA1) is an important downstream of FANCD2, we next detected the interaction between Fancd2 and Brca1 in NE4C cells. Compared to treatment with MMC alone, the Fancd2-Brca1 interaction and the amount of Brca1 on chromatin were decreased when cells were co-exposed to MMC and Hcy, suggesting Hcy could impair the Fanconi anemia (FA)/Brca1 pathway. Taken together, our study demonstrates that Hcy may enhance cell death, which contributes to the accumulation of DNA damage and promotion of hypersensitivity to cytotoxicity by impairing the FA/Brca1 pathway in murine NSCs in the presence of DNA damage.
DIAMONDNET: SHIP DETECTION IN REMOTE SENSING IMAGES BY EXTRACTING AND CLUSTERING KEYPOINTS IN A DIAMOND

Abstract. Ship detection plays an important role in military and civil fields. Despite it has been studied for decays, ship detection in remote sensing images is still a challenging topic. In this work, we come up with a novel ship detection framework based on the keypoint extraction technique. We use a convolutional neural network to detect ship keypoints and then cluster the keypoints into groups, where each group is composed of keypoints belonging to the same ship. The choice of the keypoints is specifically considered to derive an effective ship representation. One keypoint is located at the center of the ship and the rest four keypoints are located at the head, the tail, the midpoint of the left side and the midpoint of the right side, respectively. Since these keypoints are distributed in a diamond, we name our network DiamondNet. In addition, a corresponding clustering algorithm based on the geometric characteristics of the ships is proposed to cluster keypoints into groups. We demonstrate that our method provides a more flexible and effective way to represent ships than the popular anchor-based methods, since either the rectangular bounding box or the rotated bounding box of each ship instance can be easily derived from the ship keypoints. Experiments on two datasets reveal that our DiamondNet reaches the state-of-the-art results.
Herschel-PACS photometry of the five major moons of Uranus

Aims. We aim to determine far-infrared fluxes at 70, 100, and 160 μm for the five major Uranus satellites, Titania, Oberon, Umbriel, Ariel, and Miranda. Our study is based on the available calibration observations at wavelengths taken with the PACS photometer aboard the Herschel Space Observatory. Methods. The bright image of Uranus was subtracted using a scaled Uranus point spread function (PSF) reference established from all maps of each wavelength in an iterative process removing the superimposed moons. The photometry of the satellites was performed using PSF photometry. Thermophysical models of the icy moons were fitted to the photometry of each measurement epoch and auxiliary data at shorter wavelengths. Results. The best-fit thermophysical models provide constraints for important properties of the moons, such as surface roughness and thermal inertia. We present the first thermal infrared radiometry longward of 50 μm for the four largest Uranian moons, Titania, Oberon, Umbriel, and Ariel, at epochs with equator-on illumination. Due to this inclination geometry, heat transport took place to the night side so that thermal inertia played a role, allowing us to constrain that parameter. Also, we found some indication for differences in the thermal properties of leading and trailing hemispheres. The total combined flux contribution of the four major moons relative to Uranus is 5.7 × 10−3, 4.8 × 10−3, and 3.4 × 10−3 at 70, 100, and 160 μm, respectively. We therefore precisely specify the systematic error of the Uranus flux by its moons when Uranus is used as a far-infrared prime flux calibrator. Miranda is considerably fainter and always close to Uranus, impeding reliable photometry. Conclusions. We successfully demonstrate an image processing technique for PACS photometer data that allows us to remove a bright central source and reconstruct point source fluxes on the order of 10−3 of the central source as close as ≈3 × the half width at half maximum of the PSF. We established improved thermophysical models of the five major Uranus satellites. Our derived thermal inertia values resemble those of trans-neptunian object (TNO) dwarf planets, Pluto and Haumea, more than those of smaller TNOs and Centaurs at heliocentric distances of about 30 AU.
A Novel Transceiver Architecture for Making Polar Codes Work Over Multipath Fading Channels as They Do Over AWGN Channels

Polar codes, recently accepted for adoption in 5G standard due to their excellent performance at a very low complexity compared to other competitive schemes in the literature, are deemed to be a strong candidate for the Internet of Things (IoT) applications as well due to meeting their requirements. However, since polar codes construction is naturally channel-dependent, there has recently been an increasing interest in addressing the challenge of making polar codes work in realistic fading environments as they do in a binary symmetric channel (BSC). Recent studies on polar codes for fading channels have mainly focused on constructing new speciﬁc polar codes suitable to particular fading channels. This results in a non-universal code structure, leading to continuous changes in the code structure based on the channel, which is not desirable in practice. To address this problem, we develop and propose a novel transceiver architecture which enables using the polar coding design of a binary input additive white Gaussian noise (BI-AWGN) channel for multi-path fading channels without causing any changes in the structure of the encoder and decoder sides. This is made possible by eliminating the channel fading effect so that a net AWGN channel can be seen at the input of a simple successive cancellation decoder (SCD). The novelty of the proposed solution lies in using a channel-based orthonormal transformation with optimal power allocation at the transmitter and another transformation at the receiver to make the net, effective channel seen by the SCD very similar to an AWGN channel. The obtained results show that the proposed design makes the bit error rate (BER) performance of polar codes over a frequency selective fading channel the same as that over an AWGN channel. As a plus, great advantage, the proposed design is found to be capable of providing conﬁdentiality against eavesdropping receivers (i.e., unintended users) at the physical layer. Particularly,
CAN FINANCIAL ASSISTANCE MEDIATE THE TRAINING AND HUMAN CAPITAL RELATIONSHIP FOR PAKISTANI WOMEN MICRO ENTREPRENEURS?

Women participation in the economy can help accumulate capital formation and ideation which helps them in becoming a tool for socio-economic uplift for poor people working in the informal sector of the economy. The current research has examined the impact of financial assistance programs provided to micro and small women entrepreneurs, on the economic capital formation. Assistance programs include training and financial assistance. This research also aimed to investigate the mediating role of financial assistance between training assistance and economic capital. The methodology included empirical study, collection of data from 350 women micro-entrepreneurs from Southern Punjab in Pakistan, and analysis is conducted with the help of SPSS. Findings revealed that training assistance program has a significant impact on capital formation. While the mediation test confirmed the mediation of financial assistance between the training and economic capital.