Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Histological study of Normal human placentae with Hypertensive placentae in Western part of Tamilnadu state (Kongunadu region)

Corresponding Author: Dr. Vijaianand M, Associate Professor, Department of Anatomy, KMCH Institute of Health Sciences and Research, Coimbatore, Tamilnadu, India. E-Mail: dr.vijaitarun7474@gmail.com Background: Placenta forms an intimate bonding between the mother and the foetus. Apart from gaseous exchange, transport of nutrients from mother to the foetus, any disease or disorder affecting the mother also reflects the foetus. Hypertension during antenatal period produces maternal vasospasm and vascular damage due to luminal constriction of uterine spiral arterioles causing histological changes in the placenta and ultimately leads to foetal hypoxia and death. Aim of the study: To compare the histological features between the normal and hypertensive placenta. Materials and Methods: 30 normal and 30 hypertensive placentae were collected, processed in the Department of Anatomy, Karpagam Faculty of Medical Sciences and Research, Coimbatore, Tamilnadu, and visualised under light microscope and their histological features are noted. Results: Various histological features obtained are Syncytial knots, Fibrinoid necrosis, Calcification of Placenta, Placental infarction, Stromal fibrosis, Hypovascularity of Villous, Intervillous fibrin deposition and Basement membrane thickening. Conclusion: In our study, the histological features are more prominent and their areas in microscopic field is noted more in hypertensive placentae than normal placentae.Histtological features are mainly due to maternal vasospasm and vascular endothelial injury.The statistical analysis calculated for the histological features was found to be significant.
Crystal structure of diethyl 3,3′-(diazene-1,2-diyl)(E)-dibenzoate, C18H18N2O4

Abstract C18H18N2O4, monoclinic, P21/n (no. 14), a = 5.2291(2) Å, b = 10.5932(4) Å, c = 15.3775(6) Å, β = 96.619(2)°, V = 846.13(6) Å3, Z = 4, Rgt(F) = 0.0479, wRref(F2) = 0.1438, T = 293(2) K.
The Effects of Warm Air Heater on the Dispersion and Deposition of Particles in an Enclosed Environment

ABSTRACT The warm air heaters are now widely used in enclosed environments without central heating systems or as auxiliary heating facilities. However, the effect of warm air heaters on indoor air quality has received scant attention with insufficient available data. Therefore, the present work deals with an experimental investigation of the particle concentration, air velocity, temperature, and relative humidity in a storeroom with a warm air heater. The influences of the warm air heater on the dispersion and deposition of 0.3, 0.5, 1.0, 3.0, and 5.0 μm particles in eighteen cases with different heating powers and air blowing directions are discussed. The results indicate that the deposition rates of particles are higher in cases with the working warm air heater. We also found that the particle decay rate loss coefficients increase with rising output powers of the warm air heater, decrease when the distance from the air outlet increases and can be further influenced by air blowing directions.
S-Like Ribonuclease T2 Genes Are Induced during Mobilisation of Nutrients in Cotyledons from Common Bean

Germination and seedling development are crucial phases in a plant’s life cycle with economical and agronomical implications. The RNA quality in seeds is linked to seed viability, being an important agronomic trait since this leads to a loss in germination efficiency. In addition, RNA can be an important phosphorous reservoir in seeds, affecting the efficiency of the mobilisation of nutrients towards the seedlings. However, knowledge about the physiological function of ribonucleases during germination and seedling development is scarce. We analysed the ribonuclease activities of cotyledons during these processes and the expression of S-like ribonucleases T2. Ribonuclease activity was detected in cotyledons at 1 day after imbibition and the specific activity increased during germination and seedling development, reaching a maximal value at 10 days after imbibition. At this stage, the levels of proteins and RNA in cotyledons were very low. Using in-gel assays, three ribonucleases were detected with apparent molecular masses of 16, 17 and 19 kDa along cotyledon ontogeny. The S-like ribonucleases T2 family consists of four genes in common bean (PvRNS1 to PvRNS4). The expression of PvRNS1, PvRNS2 and PvRNS4 increased in the phase of nutrient mobilisation in cotyledons. The expression of PvRNS1 increased 1000 fold in cotyledons, from 1 to 6 days after imbibition. The suppression of the induction of ribonuclease activity and gene expression in decapitated seedlings suggests that the regulatory signal comes from the developing axes. These results clearly state that S-like ribonucleases T2 are involved in RNA turnover in cotyledons during seedling development.
MuBeFE: Multimodal Behavioural Features Extraction Method

The paper aims to provide a method to analyse and observe the characteristics that distinguish the individual communication style such as the voice intonation, the size and slant used in handwriting and the trait, pressure and dimension used for sketching. These features are referred to as Communication Extensional Features. Observing from the Communication Extensional Features, the user’s behavioural features, such as the communicative intention, the social style and personality traits can be extracted. These behavioural features are referred to as Communication Intentional Features. For the extraction of Communication Intentional Features, a method based on Hidden Markov Models is provided in the paper. The Communication Intentional Features have been extracted at the modal and multimodal level; this represents an important novelty provided by the paper. The accuracy of the method was tested both at modal and multimodal levels. The evaluation process results indicate an accuracy of 93.3% for the Modal layer (handwriting layer) and 95.3% for the Multimodal layer.
Effective removal of carcinogenic dye from aqueous solution by using alginate-based nanocomposites

Significant efforts are applied to prepare novel nanomaterials in which functionalized multi-walled carbon nanotubes (COOH-MWCNTs) are dispersed in alginate-chitosan network under ultrasonic waves. Instrumental techniques like scanning electron microscopy, thermal analysis (thermogravimetric analysis/differential scanning calorimetry), Fourier transform infrared spectroscopy, X-ray diffractometry, and Brunauer–Emmett–Teller are used for characterization of materials. Nanocomposite films are also evaluated based on swelling behavior. These techniques and measurements indicated the interacted and more stable nature of nanocomposites compared to the starting materials. The prepared films are used as sorbents to remove the Congo red dye from the aqueous solution. Various experimental parameters like contact time, temperature, pH, and dosage are varied to explore their effect on adsorption capacity. Nanocomposites films are analyzed for % desorption and recycled in the adsorption process successfully for 12 times. The adsorption mechanism involved is a pseudo-first-order mechanism. While isothermally, the Freundlich model appears to fit better than the Langmuir adsorption model. The high adsorption efficiency with more strength and regeneration suggests nanocomposite films as the best alternative for already existing petroleum-based membranes.
Dental Orthopedic Morbidity Among Drafted Men Residing in Ivano-Frankivsk Region

The objective of the research was to establish the level of dental orthopedic morbidity among draftees in the western region of Ukraine. Materials and Methods. A clinical dental examination of 294 drafted men residing in Ivano-Frankivsk region was carried out; among them, there were 185 urban residents and 109 rural residents. Results. There were established a quite high prevalence of dental orthopedic morbidity and intensity of its development, especially among rural residents - 275.7 and 522.9 people per 1,000 population among urban and rural residents, respectively. The amount of orthopedic care in terms of total number of dentition defects and the teeth with destroyed coronal portion requiring orthopedic treatment, was found to be 2.4 times greater among rural residents as compared to urban ones and reached 1899.1 versus 787.6 defects, respectively. There was determined the identical structure of dentition defects among both urban and rural residents; bounded edentulous spaces accounted for 100% of cases, the absence of one tooth was observed in 92.3% of cases. Conclusions. The urgent need for a radical reform of the system for providing comprehensive orthopedic care to drafted men with a mandatory active dispensary registration was substantiated.
A Generative Variational Model for Inverse Problems in Imaging

This paper is concerned with the development, analysis and numerical realization of a novel variational model for the regularization of inverse problems in imaging. The proposed model is inspired by the architecture of generative convolutional neural networks; it aims to generate the unknown from variables in a latent space via multi-layer convolutions and non-linear penalties, and penalizes an associated cost. In contrast to conventional neural-network-based approaches, however, the convolution kernels are learned directly from the measured data such that no training is required. The present work provides a mathematical analysis of the proposed model in a function space setting, including proofs for regularity and existence/stability of solutions, and convergence for vanishing noise. Moreover, in a discretized setting, a numerical algorithm for solving various types of inverse problems with the proposed model is derived. Numerical results are provided for applications in inpainting, denoising, deblurring under noise, super-resolution and JPEG decompression with multiple test images.
Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture

Abstract. With the growing interest in deep learning algorithms and computational design in the architectural field, the need for large, accessible and diverse architectural datasets increases. Due to the complexity of such 3D datasets, the most widespread techniques of 3D scanning and manual building modeling are very time-consuming, which does not allow to have a sufficiently large open-source dataset. We decided to tackle this problem by constructing a field-specific synthetic data generation pipeline that generates an arbitrary amount of 3D data along with the associated 2D and 3D annotations. The variety of annotations, the flexibility to customize the generated building and dataset parameters make this framework suitable for multiple deep learning tasks, including geometric deep learning that requires direct 3D supervision. Creating our building data generation pipeline we leveraged the experts’ architectural knowledge in order to construct a framework that would be modular, extendable and would provide a sufficient amount of class-balanced data samples. Moreover, we purposefully involve the researcher in the dataset customization allowing the introduction of additional building components, material textures, building classes, number and type of annotations as well as the number of views per 3D model sample. In this way, the framework would satisfy different research requirements and would be adaptable to a large variety of tasks. All code and data is made publicly available: https://cdinstitute.github. io/Building-Dataset-Generator/.
Predicting the self-lensing population in optical surveys

The vast majority of binaries containing a compact object and a regular star spend most of their time in a quiescent state where no strong interactions occur between components. Detection of these binaries is extremely challenging and only few candidates have been detected through optical spectroscopy. Self-lensing represents a new means of detecting compact objects in binaries, where gravitational lensing of the light from the visible component by the compact object produces periodic optical flares. Here we show that current and planned large-area optical surveys can detect a significant number (∼100–10 000s) of these self-lensing binaries and provide insights into the properties of the compact lenses. We show that many of the predicted population of observable self-lensing binaries will be observed with multiple self-lensing flares; this both improves the chances of detection and also immediately distinguishes them from chance-alignment micro-lensing events. Through self-lensing we can investigate long – but previously hidden – stages of binary evolution and consequently provide new constraints on evolutionary models that impact the number and nature of double compact object mergers.
Detecting multipartite quantum coherence of various coherence measure via spectrum estimation

Detecting multipartite quantum coherence usually requires quantum state reconstruction, which is quite inefficient for large-scale quantum systems. Here, we show that the spectrum-estimation-based method is capable of detecting multipartite quantum coherence of various coherence measures efficiently. With the spectrumestimation-based method, we observe that the lower bound of coherence is significantly enhanced than the fidelity-based estimation method in measures of the coherence of formation and the geometric measure of coherence, as well as the witness-based estimation method in the measure of the robustness of coherence. We test our scheme using the existing experimental data [Ding et al. arXiv:2010.02612 (2020)].
Reducing Write Amplification in Log-Structured Storage via Inferring Block Invalidation Time

Log-structured storage has been widely deployed in various do-mainsofstoragesystemsforhigh performance.However,itsgarbage collection (GC) incurs severe write amplification (WA) due to the frequent rewrites of live data. This motivates many research studies, particularly on data placement strategies, that mitigate WA in log-structured storage. We show how to design an optimal data placement scheme that leads to the minimum WA with the future knowledge of block invalidation time (BIT) of each written block. Guided by this observation, we propose SepBIT , a novel data placement algorithm that aims to minimize WA in log-structured storage. Its core idea is to infer the BITs of written blocks from the underlying storage workloads, so as to place the blocks with similar estimated BITs into the same group in a fine-grained manner. We show via both mathematical and trace analyses that SepBIT can infer the BITs by leveraging the write skewness property in real-world storage workloads. Evaluation on block-level I/O traces from real-world cloud block storage workloads shows that SepBIT achieves the lowest WA compared to eight state-of-the-art data placement schemes.
Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy

A practical long-term tracker typically contains three key properties, i.e. an efficient model design, an effective global re-detection strategy and a robust distractor awareness mechanism. However, most state-of-the-art long-term trackers (e.g., Pseudo and re-detecting based ones) do not take all three key properties into account and therefore may either be time-consuming or drift to distractors. To address the issues, we propose a two-task tracking framework (named DMTrack), which utilizes two core components (i.e., one-shot detection and re-identification (re-id) association) to achieve distractor-aware fast tracking via Dynamic convolutions (d-convs) and Multiple object tracking (MOT) philosophy. To achieve precise and fast global detection, we construct a lightweight one-shot detector using a novel dynamic convolutions generation method, which provides a unified and more flexible way for fusing target information into the search field. To distinguish the target from distractors, we resort to the philosophy of MOT to reason distractors explicitly by maintaining all potential similarities’ tracklets. Benefited from the strength of high recall detection and explicit object association, our tracker achieves state-of-the-art performance on the LaSOT, Ox-UvA, TLP, VOT2018LT and VOT2019LT benchmarks and runs in real-time (3x faster than comparisons)1.
Evaporation of dark matter from celestial bodies

Scatterings of galactic dark matter (DM) particles with the constituents of celestial bodies could result in their accumulation within these objects. Nevertheless, the finite temperature of the medium sets a minimum mass, the evaporation mass, that DM particles must have in order to remain trapped. DM particles below this mass are very likely to scatter to speeds higher than the escape velocity, so they would be kicked out of the capturing object and escape. Here, we compute the DM evaporation mass for all spherical celestial bodies in hydrostatic equilibrium, spanning the mass range [10-10 - 102] M⊙, for constant scattering cross sections and s-wave annihilations. We illustrate the critical importance of the exponential tail of the evaporation rate, which has not always been appreciated in recent literature, and obtain a robust result: for the geometric value of the scattering cross section and for interactions with nucleons, at the local galactic position, the DM evaporation mass for all spherical celestial bodies in hydrostatic equilibrium is approximately given by Ec/Tχ ∼ 30, where Ec is the escape energy of DM particles at the core of the object and Tχ is their temperature. In that case, the minimum value of the DM evaporation mass is obtained for super-Jupiters and brown dwarfs, m evap ≃ 0.7 GeV. For other values of the scattering cross section, the DM evaporation mass only varies by a factor smaller than three within the range 10-41 cm2 ≤ σp ≤ 10-31 cm2, where σp is the spin-independent DM-nucleon scattering cross section. Its dependence on parameters such as the galactic DM density and velocity, or the scattering and annihilation cross sections is only logarithmic, and details on the density and temperature profiles of celestial bodies have also a small impact.
UNIFY: Multi-Belief Bayesian Grid Framework based on Automotive Radar

Grid maps are widely established for the representation of static objects in robotics and automotive applications. Though, incorporating velocity information is still widely examined because of the increased complexity of dynamic grids concerning both velocity measurement models for radar sensors and the representation of velocity in a grid framework. In this paper, both issues are addressed: sensor models and an efficient grid framework, which are required to ensure efficient and robust environment perception with radar. To that, we introduce new inverse radar sensor models covering radar sensor artifacts such as measurement ambiguities to integrate automotive radar sensors for improved velocity estimation. Furthermore, we introduce UNIFY, a multiple belief Bayesian grid map framework for static occupancy and velocity estimation with independent layers. The proposed UNIFY framework utilizes a grid-cell-based layer to provide occupancy information and a particle-based velocity layer for motion state estimation in an autonomous vehicle's environment. Each UNIFY layer allows individual execution as well as simultaneous execution of both layers for optimal adaption to varying environments in autonomous driving applications. UNIFY was tested and evaluated in terms of plausibility and efficiency on a large real-world radar data-set in challenging traffic scenarios covering different densities in urban and rural sceneries.
Partitioning sparse deep neural networks for scalable training and inference

The state-of-the-art deep neural networks (DNNs) have significant computational and data management requirements. The size of both training data and models continue to increase. Sparsification and pruning methods are shown to be effective in removing a large fraction of connections in DNNs. The resulting sparse networks present unique challenges to further improve the computational efficiency of training and inference in deep learning. Both the feedforward (inference) and backpropagation steps in stochastic gradient descent (SGD) algorithm for training sparse DNNs involve consecutive sparse matrix-vector multiplications (SpMVs). We first introduce a distributed-memory parallel SpMV-based solution for the SGD algorithm to improve its scalability. The parallelization approach is based on row-wise partitioning of weight matrices that represent neuron connections between consecutive layers. We then propose a novel hypergraph model for partitioning weight matrices to reduce the total communication volume and ensure computational load-balance among processors. Experiments performed on sparse DNNs demonstrate that the proposed solution is highly efficient and scalable. By utilizing the proposed matrix partitioning scheme, the performance of our solution is further improved significantly.
Impact of Spatial Frequency Based Constraints on Adversarial Robustness

Adversarial examples mainly exploit changes to input pixels to which humans are not sensitive to, and arise from the fact that models make decisions based on uninterpretable features. Interestingly, cognitive science reports that the process of interpretability for human classification decision relies predominantly on low spatial frequency components. In this paper, we investigate the robustness to adversarial perturbations of models enforced during training to leverage information corresponding to different spatial frequency ranges. We show that it is tightly linked to the spatial frequency characteristics of the data at stake. Indeed, depending on the data set, the same constraint may results in very different level of robustness (up to 0.41 adversarial accuracy difference). To explain this phenomenon, we conduct several experiments to enlighten influential factors such as the level of sensitivity to high frequencies, and the transferability of adversarial perturbations between original and low-pass filtered inputs.
Hybrid Satellite-UAV-Terrestrial Networks for 6G Ubiquitous Coverage: A Maritime Communications Perspective

In the coming smart ocean era, reliable and efficient communications are crucial for promoting a variety of maritime activities. Current maritime communication networks (MCNs) mainly rely on marine satellites and on-shore base stations (BSs). The former generally provides limited transmission rate, while the latter lacks wide-area coverage capability. Due to these facts, the state-of-the-art MCN falls far behind terrestrial fifth-generation (5G) networks. To fill up the gap in the coming sixth-generation (6G) era, we explore the benefit of deployable BSs for maritime coverage enhancement. Both unmanned aerial vehicles (UAVs) and mobile vessels are used to configure deployable BSs. This leads to a hierarchical satellite-UAV-terrestrial network on the ocean. We address the joint link scheduling and rate adaptation problem for this hybrid network, to minimize the total energy consumption with quality of service (QoS) guarantees. Different from previous studies, we use only the large-scale channel state information (CSI), which is location-dependent and thus can be predicted through the position information of each UAV/vessel based on its specific trajectory/shipping lane. The problem is shown to be an NP-hard mixed integer nonlinear programming problem with a group of hidden non-linear equality constraints. We solve it suboptimally by using Min-Max transformation and iterative problem relaxation, leading to a process-oriented joint link scheduling and rate adaptation scheme. As observed by simulations, the scheme can provide agile on-demand coverage for all users with much reduced system overhead and a polynomial computation complexity. Moreover, it can achieve a prominent performance close to the optimal solution.