Miracle-dz/UniFAR-FTUs huggingface dataset:
Learned Compression of High Dimensional MRI Datasets

In many applications, such as magnetic resonance imaging (MRI), multiple images are acquired to reduce the noise of the eventual reconstructed image. However, this leads to very high dimensional datasets which have redundant information across the various acquired images. In MRI, multiple images are acquired via multiple RF coil arrays in the scanner. Afterwards, coil compression is performed to convert the original set of coil images into a smaller set of virtual coil images to enable smaller datasets and faster computation time. However, traditional iterative coil compression methods are lossy and time-consuming. In this work, we propose a novel neural network-based coil compression method in pursuit of higher reconstruction accuracy and faster coil compression. Our learned compression method achieves up to 1.5x lower NRMSE and up to 10 times runtime speed compared to traditional methods on a benchmark test dataset. We also explore 2D Convolutional Encoder for larger dataset with 32 coils and 3D Convolu-tional Encoder for non-cartesian dataset, which both show better performances compared to traditional methods.
Evaluation of recovery configuration options by product utilisation value

Purpose The purpose of this paper is to propose an approach to evaluate product performance of returned products, using four key performance attributes as the basis for improving sustainability through product recovery. Design/methodology/approach A fuzzy logic approach is developed to account a trade-off scenario for a manufactured product with recovery options. This approach is demonstrated using a numerical example and is validated using a case study in the automotive parts and components industry. Findings Product utilisation value (PUV) is found to be a useful index that manufacturers can use to assess product recovery options, as it brings together a number of conflicting parameters into a rationalised value for decision making. In addition, PUV provides a rationalised approach for comparing and selecting the most appropriate recovery configuration option. Research limitations/implications The authors only utilise four key performance measures to derive PUV. Further research is needed to modify and incorporate other measures that are important to decision makers to improve sustainability in manufacturing supply chains. Practical implications The proposed approach may motivate decision makers to consider sustainable recovery options by comparing PUVs of products for primary and secondary markets. The case study demonstrated the conflict and complexity organisations face in a global supply chain of a competitive industry. Originality/value The authors propose an approach to optimise trade-off considerations of selected performance attributes through PUV. This PUV as a benchmark can help improve recovery of the returned products and reduce landfill.
Iwasawa nilpotency degree of non compact symmetric cosets in N−extended Supergravity

We analyze the polynomial part of the Iwasawa realization of the coset representative of non compact symmetric Riemannian spaces. We start by studying the role of Kostant’s principal SU(2)P subalgebra of simple Lie algebras, and how it determines the structure of the nilpotent subalgebras. This allows us to compute the maximal degree of the polynomials for all faithful representations of Lie algebras. In particular the metric coefficients are related to the scalar kinetic terms while the representation of electric and magnetic charges is related to the coupling of scalars to vector field strengths as they appear in the Lagrangian. We consider symmetric scalar manifolds in N−extended supergravity in various space-time dimensions, elucidating various relations with the underlying Jordan algebras and normed Hurwitz algebras. For magic supergravity theories, our results are consistent with the Tits-Satake projection of symmetric spaces and the nilpotency degree turns out to depend only on the space-time dimension of the theory. These results should be helpful within a deeper investigation of the corresponding supergravity theory, e.g. in studying ultraviolet properties of maximal supergravity in various dimensions.
Zombies Eat Brains, You are Safe : A Knowledge Infusion based Multitasking System for Sarcasm Detection in Meme

Sarcasm detection is, in itself, a challenging 001 task in the field of Natural Language Process- 002 ing (NLP), and the task even becomes more 003 complex when the target is a meme. In this pa- 004 per, we first hypothesize that sarcasm detection 005 is closely associated with emotions present in 006 the meme. We propose a deep learning-based 007 multitask model to perform these two tasks in 008 parallel, where sarcasm detection is the pri- 009 mary, whereas emotion recognition is consid- 010 ered as an auxiliary task. Furthermore, we pro- 011 pose a novel knowledge infusion (KI) method 012 to get a sentiment-aware knowledge represen- 013 tation on top of our multitasking model. This 014 sentiment-aware knowledge representation is 015 obtained from a pre-trained parent model and 016 subsequently this representation is used via a 017 novel Gating Mechanism to train our down- 018 stream multitasking model. For training and 019 evaluation purposes, we created a large-scale 020 dataset consisting of 7416 sample Hindi memes 021 as there was no readily available dataset for 022 building such multimodal systems. We col- 023 lect the Hindi memes from various domains, 024 such as politics , religious , racist , and sexist , 025 and manually annotate each instance with three 026 sarcasm categories, i.e., ( i) Not Sarcastic , ii) 027 Mildly Sarcastic or iii) Highly Sarcastic and 13 028 fine-grained emotion classes. We demonstrate 029 the effectiveness of our proposed work through 030 extensive experiments. The experimental re- 031 sults show that our proposed system achieves a 032 64 . 48% macro F1-score, outperforming all the 033 baseline models. Finally, we note that our pro- 034 posed system is model agnostic and can be used 035 with any downstream model in practice. We 036 will make the resources and codes available 1
Fusion 7×7 median filter and seeded region growing area extraction algorithms for effective detection of acute leukemia based on blood images

In the most medical image analysis tasks, image segmentation is a crucial step where one of the usual segmentation methods is based on clustering algorithms. Clustering algorithms have been employed as a digital image segmentation method in various fields, including engineering, computer and mathematics. This study aims to detect acute leukemia cells based on blood images with effective methods. Normally, although the segmentation process is completed, there is still the existence of unwanted noise and background in acute leukemia blood images. Thus, this study proposes a fusion of median filter and seeded region growing area extraction algorithm to overcome this problem. Finally, the results demonstrate that the proposed method has successfully been distinguished and segmented between acute leukemia cells from the unwanted noise or complicated background in blood images. The proposed method is able to yield an average of 98.26% based on the percentage of accuracy. Keywords: Acute leukemia, median filter, Seeded Region Growing Area Extraction, k-Means
Spatial Interpolation of Toxic Air Pollutants in Jeddah City, Saudia Arabia

This work aimed to study hazardous air pollutants in Jeddah city, between 2010 - 2018. The study of the spatial distribution of (NO X , SO 2 , PM 10 ) was done by the application of the geostatistical method IDW. Daily observation Data were collected from three field observation stations (Bani Malik, Stadium, Industrial activity), and treated in ArcGIS environment. According to the obtained results, in the year 2010, NO X varies between (25 – 62.99 ppb), with very important concentrations in the south of the study area, surrounding the industrial activities. While in the year 2018, the highest concentrations were between (25 – 30.99 ppb) in the north of the study area. the spatial distribution of SO 2 was very important in the south of the study area, compared to the highest concentrations in the north in 2018. The spatial distribution of PM 10 was between (153 – 173 µg/m 3 ) in the south of the study area, in 2018 we noticed a decrease of the PM 10 concentration, in the industrial area with values between (67 – 77 µg/m 3 ). Even though a lot of work must be done to improve air quality in the city of Jeddah to meet international air quality standards in modern cities.
Imitation Learning for Visual Robotic Manipulation

A milestone in robot learning is to learn policies that can manipulate objects precisely and reason about surrounding abstract concepts in the meanwhile. In this project, we step towards this goal by learning a language-conditioned policy for visual robotic manipulation through behavioural cloning. Concretely, conditioned on text description specifying target objects to manipulate, our model aligns RGB images input with learned object embedding and predicts movements of the end effector of a robot arm that can solve the task. Our learned model can solve the manipulation task of “put an object into another object” with a high success rate above 70% . We also identify the difficulty of recognizing the correct object to manipulate and propose the solution of aligning RGB image inputs with learned object embedding. Our experiment shows that this solution significantly improves the performance when there are distractor objects.
Finding Interpretable Word Embedding Subspaces using Covariance and Correlation Maximization

This paper proposes a new method for estimat-001 ing a direction in a word embedding space cor-002 responding to an interpretable semantic prop-003 erty such as gender, race, or religion. Our tech-004 nique assumes that words can be assigned nu-005 merical scores that quantify their association 006 with the target property. We estimate the sub-007 space by maximizing the covariance or corre-008 lation of these scores with the projection of 009 word embeddings along the subspace. Using 010 our technique, we show that word embedding 011 spaces in English, French, and Chinese contain 012 subspaces that encode gender, race, religion, 013 sentiment, word length, and national popula-014 tion. We then apply our technique to the mit-015 igation of gender and racial bias from word 016 embeddings. We ﬁnd that using our technique 017 to estimate a gender or race subspace improves 018 performance on several benchmarks. 019
PELVIC FLOOR MUSCLE TRAINING VERSUS NO TREATMENT FOR URINARY INCONTINENCE IN WOMEN

Pelvic floor muscle training is the most commonly used physical therapy treatment for women with urinary incontinence (UI). According to the World Health Organization bladder problems affect more than 200milion people worldwide. Urinary incontinence is a disorder that affects women far more frequently than men; 85% of people suffering from urinary incontinence are women. According to existing studies and literature few women go to consult a therapist who specializes in urinary incontinence in our country. Talking about UI, it is a taboo for most women, especially for women living in small cities or rural area. The purpose of the study was to determine the effects of pelvic floor muscle training for women with urinary incontinence in comparison to no treatment. This is a randomized control trial and to gather information was used a questionnaire( ICIQ-SF), and personal contact with patients. The patients were randomly allocated into two groups, the control group and the experimental group. The experimental group practiced PFME at home, the control group didn’t practice PFME at home. The study indicates that physiotherapy has a key role in the conservative treatment of UI and is less costly than other methods of treatment. PFME gives good results in controlling involuntary loss of urine. This treatment isn’t very recognized in Albania. One of the best ways to introduce this method will be from the family doctors and from maternity staff who treats the most effected patients, future mothers.
Transfer Learning for the ICU: Comparing the Performance of YOLOv5 and Faster R-CNN X101-FPN

The recent success of transfer learning in a variety of object detection tasks have proven particularly useful in fields such as medicine where training data is limited or difficult to access. Empirical studies on transfer learning has also found that the characteristics of the pre-training data itself has a very minor impact on the performance of transfer learning models themselves. But what about the fine-tuning dataset? What characteristics of the transfer learning dataset can we leverage to maximize transfer learning performance? This study proposed two such characteristics, and empirically investigated the effects of varying the fine-tuning dataset size, as well as varying whether the fine-tuning datasets were in-distribution, out-of-distribution, or very out-of-distribution, on the performance of transfer learning object detection tasks using the state-of-the-art YOLOv5 and Detectron2 Faster R-CNN models. Our over-all findings suggest that both models actually perform better on transfer learning tasks with images that are significantly out-of-distribution, and that reducing the size of the fine-tuning dataset generally did not have a significant impact on model performance. However, our results may be skewed by the homogeneous nature of the datasets we used, and further study would be required to validate our findings.
Altitudinal variation in life-history features of a Qinghai-Tibetan Plateau lizard

Abstract Environmental changes along an altitudinal gradient can facilitate the differentiation of life-history features in ectothermic species, but little attention has been devoted to the reciprocal influence of altitude and alpine slope directionality on life-history variation. According to life-history theory, increased environmental stress causes a change in reproductive allocation from number to quality of offspring, as well as a stronger trade-off between size and number of offspring. To clarify the influence of environmental pressures on the life-history features of the Qinghai toad-headed lizard Phrynocephalus vlangalii along an altitudinal cline, we surveyed late pregnant females from 3 populations of low (2,600 m), middle (3,400 m), and high (3,600 m) elevations in the Dangjin Mountain of Gansu, China from July to October 2019, and compared their inter-population differences in maternal body size, reproductive characteristics, offspring growth, and locomotor performance. Because of lower temperatures, higher humidity, and lower light intensity caused by slope aspect and altitude, the middle-altitude region experienced stronger environmental stress than the high- and low-altitude regions. Our results showed that females were larger at middle- and high-altitude sites and smaller at the low-altitude site, following Bergmann’s rule. We also found that females from low-altitude population gave birth earlier than those from the middle and high altitudes. Our results showed a shift in the offspring size-number trade-off of P. vlangalii in response to colder and harsher environments, with lizards from the alpine steppe (i.e. the middle- and high-altitude habitats) producing fewer but larger offspring than those from the warm steppe (i.e. the low-altitude habitat). Low-altitude juveniles grew faster than high-altitude ones, but at the same rates as middle-altitude juveniles. This result demonstrates that the growth of P. vlangalii was associated with temperature and light intensity. Our findings contribute to enhancing our understanding of the altitudinal variation in life-history features of plateau ectotherms and their phenotypic plasticity or local adaptation.
Light-induced charged slippery surfaces

Slippery lubricant-infused porous (SLIPS) and superhydrophobic surfaces have emerged as promising interfacial materials for various applications such as self-cleaning, anti-icing, and antifouling. Paradoxically, the coverage/screening of lubricant layer on underlying rough matrix endows functionalities impossible on superhydrophobic surfaces; however, the inherent flexibility in programming droplet manipulation through tailoring structure or surface charge gradient in underlying matrix is compromised. Here, we develop a class of slippery material that harnesses the dual advantages of both solid and lubricant. This is achieved by rationally constructing a photothermal-responsive composite matrix with real-time light-induced surface charge regeneration capability, enabling photocontrol of droplets in various working scenarios. We demonstrate that this light-induced charged slippery surface (LICS) exerts photocontrol of droplets with fast speed, long distance, antigravity motion, and directionally collective motion. We further extend the LICS to biomedical domains, ranging from specific morphological hydrogel bead formation in an open environment to biological diagnosis and analysis in closed-channel microfluidics.
Gating Hysteresis as an Indicator for Silicon Nanowire FET Biosensors

We present a biosensor chip with integrated large area silicon nanowire-based field effect transistors (FET) for human alpha-thrombin detection and propose to implement the hysteresis width of the FET transfer curve as a reliable parameter to quantify the concentration of biomolecules in the solution. We further compare our results to conventional surface potential based measurements and demonstrate that both parameters distinctly respond at a different analyte concentration range. A combination of the two approaches would provide broader possibilities for detecting biomolecules that are present in a sample with highly variable concentrations, or distinct biomolecules that can be found at very different levels. Finally, we qualitatively discuss the physical and chemical origin of the hysteresis signal and associate it with the polarization of thrombin molecules upon binding to the receptor at the nanowire surface.
Comparison of Accuracy in Calculation of Absorbed Dose to the Kidneys Following Radioligand Therapy with 177Lu-DKFZ-PSMA-617 by Two Different Background Correction Methods

Background: To improve the accuracy of activity image quality, scatter correction is a critical method. The aim of this study is to compare the accuracy in calculation of absorbed dose to patients following radioligand therapy (RLT) with 177Lu-DKFZ-PSMA-617 by two different methods of background correction in the conjugate view method. Materials and Methods: This study involved 10 patients. The individualized patient dosimetry calculations were based on whole-body planar scintigraphy images acquired in 10 patients with a mean age of 71.4 ± 6.07 years (range 63–85 years) at approximately 0–2 h, 4–6 h, 18–24 h, and 36–48 h after administration of the mean 6253 ± 826.4 MBq (range 5500–7400 MBq) of 177Lu-DKFZ-PSMA-617. Organ activities were calculated using the conjugate view method by Buijs and conventional background correction. Eventually, the absorbed dose of radiation was calculated using Medical Internal Radiation Dose formalism. Results: The dose per unit of injected activity (mGy/MBq) ± standard deviation for kidney using Buijs and conventional methods was 1.05 ± 0.11 and 0.63 ± 0.14, respectively. Conclusion: The Buijs background correction method was more accurate than the conventional method.
Brain Metabolic Connectivity Patterns in Patients with Prolonged Disorder of Consciousness after Hypoxic-Ischemic Injury: A Preliminary Study

Understanding the patterns of brain glucose metabolism and connectivity in hypoxic-ischemic encephalopathy (HIE) patients with prolonged disorders of consciousness (DOC) may be of positive significance to the accurate assessment of consciousness and the optimization of neuromodulation strategy. We retrospectively analyzed the brain glucose metabolism pattern and its correlation with clinical Coma Recovery Scale-Revised (CRS-R) score in six HIE patients with prolonged DOC who had undergone 18F-deoxyglucose brain positron emission tomography scanning (FDG-PET). We also compared the differences in global metabolic connectivity patterns and the characteristics of several brain networks between HIE patients and healthy controls (HC). The metabolism of multiple brain regions decreased significantly in HIE patients, and the degree of local metabolic preservation was correlated with CRS-R score. The internal metabolic connectivity of occipital lobe and limbic system in HIE patients decreased, and their metabolic connectivity with frontal lobe, parietal lobe and temporal lobe also decreased. The metabolic connectivity patterns of default mode network, dorsal attention network, salience network, executive control network and subcortex network of HIE also changed compared with HC. The present study suggested that pattern of cerebral glucose metabolism and network connectivity of HIE patients with prolonged DOC were significantly different from those of healthy people.
Soldering Data Classification with a Deep Clustering Approach: Case Study of an Academic-Industrial Cooperation

Modern industries still commonly use traditional methods to visually inspect products, even though automation has many advantages over the skills of human labour. The automation of redundant tasks is one of the greatest successes of Artificial Intelligence (AI). It employs human annotation and finds possible relationships between features within a particular dataset. However, until recently, this has always been the responsibility of AI specialists with a specific type of knowledge that is not available to the industrial domain experts. We documented the joint research of AI and domain experts as a case study on processing a soldering-related industrial dataset. Our image classification approach relies on the latent space representations of neural networks already trained on other databases. We perform dimensionality reduction of the representations of the new data and cluster the outputs in the lower dimension. This method requires little to no knowledge of the underlying architecture of neural networks by the domain experts, meaning it is easily manageable by them, supporting generalization to other use cases that can be investigated in future work. We also suggest a misclassification detecting method. We were able to achieve near-perfect test accuracy with minimal annotation work.
Polysaccharides, as biological macromolecule-based platforms in skeletal muscle tissue engineering: a systematic review

Abstract Traumatic injuries to skeletal muscles caused by accidents, surgeries, and strains result in a significant disability. Currently, the golden standard of treatment for skeletal muscle injuries is tissue grafting. However, this treatment strategy is associated with several challenges. Therefore, developing alternative strategies is highly demanded. In this context, skeletal muscle tissue engineering (SMTE) aims to regenerate defective muscle tissue using combinations of cells, growth factors, and biomaterials. Although significant progress has been made in SMTE, the inherent complexity of skeletal muscle tissue requires a versatile approach to develop scaffolds with high similarity to native muscle tissue. These scaffolds can be produced using synthetic or natural polymers. Among different biomaterial options, polysaccharides are ideal scaffolding platforms with potential applicability in different biomedical fields, such as drug delivery, guided tissue regeneration, and driving stem cells fate. Due to their versatile properties, biocompatibility, low cost, high availability, low immunogenicity, surface modification capabilities, and processability, the appeal of polysaccharides in SMTE is on the rise. Although different polysaccharides can be potentially used in SMTE, only alginate, chitosan, cellulose, and hyaluronic acid have been studied. In the current review, the recent progress, applications, and challenges of polysaccharides in SMTE will be highlighted. Graphical Abstract