Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Light Alignment for Multi‐Scale Shape Depiction

Motivated by recent findings in the field of visual perception, we present a novel approach for enhancing shape depiction and perception of surface details. We propose a shading‐based technique that relies on locally adjusting the direction of light to account for the different components of materials. Our approach ensures congruence between shape and shading flows, leading to an effective enhancement of the perception of shape and details while impairing neither the lighting nor the appearance of materials. It is formulated in a general way allowing its use for multiple scales enhancement in real‐time on the GPU, as well as in global illumination contexts. We also provide artists with fine control over the enhancement at each scale.
SnakeBinning: Efficient Temporally Coherent Triangle Packing for Shading Streaming

Streaming rendering, e.g., rendering in the cloud and streaming via a mobile connection, suffers from increased latency and unreliable connections. High quality framerate upsampling can hide these issues, especially when capturing shading into an atlas and transmitting it alongside geometric information. The captured shading information must consider triangle footprints and temporal stability to ensure efficient video encoding. Previous approaches only consider either temporal stability or sample distributions, but none focuses on both. With SnakeBinning, we present an efficient triangle packing approach that adjusts sample distributions and caters for temporal coherence. Using a multi‐dimensional binning approach, we enforce tight packing among triangles while creating optimal sample distributions. Our binning is built on top of hardware supported real‐time rendering where bins are mapped to individual pixels in a virtual framebuffer. Fragment shader interlock and atomic operations enforce global ordering of triangles within each bin, and thus temporal coherence according to the primitive order is achieved. Resampling the bin distribution guarantees high occupancy among all bins and a dense atlas packing. Shading samples are directly captured into the atlas using a rasterization pass, adjusting samples for perspective effects and creating a tight packing. Comparison to previous atlas packing approaches shows that our approach is faster than previous work and achieves the best sample distributions while maintaining temporal coherence. In this way, SnakeBinning achieves the highest rendering quality under equal atlas memory requirements. At the same time, its temporal coherence ensures that we require equal or less bandwidth than previous state‐of‐the‐art. As SnakeBinning outperforms previous approach in all relevant aspects, it is the preferred choice for texture‐based streaming rendering.
Simulating brain signals with predeﬁned mutual correlations

This technical note describes a simple linear algorithm which allows generating arbitrary number of signal time courses whose mutual correlations are deﬁned by a given mathematically consistent correlation matrix. This is achieved by mixing some seed set of waveforms typically reﬂecting desired speciﬁc features of the target brain signals. If needed the resulting signals can be properly tapered to be neurophysiologically plausible. For multi-epoch designs the generated waveforms may also include a ”phase-locked” component which does not vary between the epochs.
Functional human genes typically exhibit epigenetic conservation

Recent DepMap CRISPR-Cas9 single gene disruptions have identified genes more essential to proliferation in tissue culture. It would be valuable to translate these finding with measurements more practical for human tissues. Here we show that DepMap essential genes and other literature curated functional genes exhibit cell-specific preferential epigenetic conservation when DNA methylation measurements are compared between replicate cell lines and between intestinal crypts from the same individual. Culture experiments indicate that epigenetic drift accumulates through time with smaller differences in more functional genes. In NCI-60 cell lines, greater targeted gene conservation correlated with greater drug sensitivity. These studies indicate that two measurements separated in time allow normal or neoplastic cells to signal through conservation which human genes are more essential to their survival in vitro or in vivo.
Persistent DNA damage promotes microglial dysfunction in Ataxia-telangiectasia

The autosomal recessive genome instability disorder Ataxia-telangiectasia, caused by mutations in ATM kinase, is characterised by the progressive loss of cerebellar neurons. We find that DNA damage associated with ATM loss results in dysfunctional behaviour of human microglia, immune cells of the central nervous system. Microglial dysfunction is mediated by the pro-inflammatory RELB/p52 non-canonical NF-κB transcriptional pathway and leads to excessive phagocytic clearance of neurites. Pathological phagocytosis of neuronal processes by microglia has also been observed in multiple sclerosis, Alzheimer’s and progranulin deficiency, suggesting a common mechanism that promotes neuronal damage. Activation of the RELB/p52 pathway in ATM-deficient microglia is driven by persistent DNA damage and is dependent on the NIK kinase. These results provide insights into the underlying mechanisms of aberrant microglial behaviour in Ataxia-telangiectasia, potentially contributing to neurodegeneration.
Why Blood Pressure and Body Mass Should be Controlled for in Resting-State Functional Magnetic Resonance Imaging Studies

Replicability has become an increasing focus within the scientific communities with the ongoing “replication crisis”. One area that appears to struggle with unreliable results is resting-state functional magnetic resonance imaging (rs-fMRI). Therefore, the current study aimed to improve the knowledge of endogenous factors that contribute to inter-individual variability. Arterial blood pressure, body mass, hematocrit, and glycated hemoglobin were investigated as potential sources of between-subject variability in rs-fMRI, in healthy individuals. Whether changes in resting state-networks (rs-networks) could be attributed to variability in the BOLD-signal, changes in neuronal activity, or both, was of special interest. Within-subject parameters were estimated utilizing Dynamic Causal Modelling (DCM) as it allows to make inferences on the estimated hemodynamic (BOLD-signal dynamics) and neuronal parameters (effective connectivity) separately. The results of the analyses imply that blood pressure and body mass can cause between-subject and between-group variability in the BOLD-signal and that all the included factors can affect the underlying connectivity. Given the results of the current and previous studies, rs-fMRI results appear to be susceptible to a range of factors, which is likely to contribute to the low degree of replicability of these studies. Interestingly, the highest degree of variability seems to appear within the much-studied Default Mode Network and its connections to other networks.
Modelling brain representations of abstract concepts

Abstract conceptual representations are critical for human cognition. Despite their importance, key properties of these representations remain poorly understood. Here, we used computational models of distributional semantics to predict multivariate fMRI activity patterns during the activation and contextualization of abstract concepts. We devised a task in which participants had to embed abstract nouns into a story that they developed around a given background context. We found that representations in inferior parietal cortex were predicted by concept similarities emerging in models of distributional semantics. By constructing different model families, we reveal the models’ learning trajectories and delineate how abstract and concrete training materials contribute to the formation of brain-like representations. These results inform theories about the format and emergence of abstract conceptual representations in the human brain.
Learning to represent continuous variables in heterogeneous neural networks

Animals must monitor continuous variables such as position or head direction. Manifold attractor networks—which enable a continuum of persistent neuronal states—provide a key framework to explain this monitoring ability. Neural networks with symmetric synaptic connectivity dominate this framework, but are inconsistent with the diverse synaptic connectivity and neuronal representations observed in experiments. Here, we developed a theory for manifold attractors in trained neural networks, which approximate a continuum of persistent states, without assuming unrealistic symmetry. We exploit the theory to predict how asymmetries in the representation and heterogeneity in the connectivity affect the formation of the manifold via training, shape network response to stimulus, and govern mechanisms that possibly lead to destabilization of the manifold. Our work suggests that the functional properties of manifold attractors in the brain can be inferred from the overlooked asymmetries in connectivity and in the low-dimensional representation of the encoded variable.
OBO Foundry in 2021: operationalizing open data principles to evaluate ontologies

Biological ontologies are used to organize, curate, and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application, and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology’s compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data FAIR.
Resource and seasonality drive interspecific variability in a Dynamic Energy Budget model

Animals show a vast array of phenotypic traits in time and space. These variation patterns have traditionally been described as ecogeographical rules; for example, the tendency of size and clutch size to increase with latitude (Bergman’s and Lack’s rules, respectively). Despite considerable research into these patterns, the processes behind trait variation remain controversial. Here, we show how food variability, which determines individual energy input and allocation trade-offs, can drive interspecific trait variation. Using a dynamic energy budget (DEB) model, we simulated different food environments as well as interspecific variability in the parameters for energy assimilation, mobilization, and allocation to soma. We found that interspecific variability is greater when the resource is non-limiting in both constant and seasonal environments. Our findings further show that individuals can reach larger biomass and greater reproductive output in a seasonal environment than in a constant environment of equal average resource due to the peaks of food surplus. Our results agree with the classical patterns of interspecific trait variation and provide a mechanistic understanding that supports recent hypotheses which explain them: the resource and the eNPP (net primary production during the growing season) rules. Due to the current alterations to ecosystems and communities, disentangling trait variation is increasingly important to understand and predict biodiversity dynamics under environmental change.
Nanostructure Absorption: A Comparative Study of Nanowire and Colloidal Quantum Dot Absorption Cross Sections

There has recently been much work on using nanowires for various applications. Examples include photodetectors and photovoltaics. One of the main reasons for this stems from the nanowire’s asymmetric shape, which, in principle, facilitates the ready band transport of carriers. As a consequence, effective mobilities should be larger than those of comparable quantum dot devices. At the same time, a less recognized advantage of nanowires over nanocrystals is their sizable absorption cross sections. Namely, circularly polarized nanowire cross sections in the visible are routinely on the order of 10−12 cm2 μm−1 to 10−10 cm2 μm−1 whereas those of dots range from 10−15 cm2 to 10−18 cm2. When scaled for volume differences, best case nanowire cross sections are still larger than those of nanocrystals by roughly an order of magnitude. This could, in turn, offer additional benefits for nanowire-based devices.
Classification of Digital Dental X-ray Images Using Machine Learning

Dental diseases like dental anomalies, periapical and dental caries is increasing day by day in children and adults. Artificial intelligence and neural network with its application in medical imaging is influencing the healthcare industry. X –ray imaging is the commonly employed technique to diagnose diseases of the teeth. Segmentation and classification of differing dental anomalies using neural network is proving to be a boon to the dental field. Application of neural algorithms aids in obtaining images with better detection accuracy. Automated detection reduces the workload of a dentist with classification being accurate. A better penetration of machine learning into these processes highlights its advantages to classify dental X ray images. Different machine learning techniques are deployed to identify and classify the dental abnormalities.
Constructing Optimal Sparse Decision Tree for Analying I-SIG System Attack

The Intelligent Traffic Signal System(I-SIG), has been deployed in states including New York, California and Florida by the US Department of Transportation, improving the traffic mobility, connectivity and efficiency in real-world intersections. Unfortunately, a new vulnerability to cause a congestion attack was revealed in 2018, which requires emerging security analysis and reinforcement. From high-dimension traffic features, to further evaluate attack consequences and analyze explainable feature influences, we propose an optimal sparse decision tree(OSDT)-based approach. Through massive feature engineering around attack performing, we finally define and determine 29 features, and collect 195 high-quality samples from VISSIM platform. This is the first standard dataset for statistical I-SIG analysis. Experiment shows that, facing random-deleted sparse traffic feature values, the OSDT can better explain the feature influence with a 78.6% prediction accuracy, compared to other state-of-art decision tree models.
Stacked Bi-LSTM with Attention and Contextual BERT Embeddings for Fake News Analysis

With the evolution of digital technology and media, there has been a surge in the number of internet users worldwide. Due to this growth, it has become necessary to stop fake news from being shared on social media platforms as it can mislead large sections of society by spreading unverified information. Therefore, it becomes vital to create a framework for the automatic verification of content shared online. In this work, we propose a deep learning framework using stacked Bi-LSTM with self-attention to detect fake news articles. The proposed model uses contextual embeddings pooled from BERT large model by fine-tuning its last ten layers on the training dataset. The proposed framework's effectiveness is being tested on four different open-source datasets, namely, News Articles Dataset, Kaggle Dataset, Gossipcop Dataset, and Politifact Dataset, through extensive experimentation and evaluation. The highest accuracy achieved by the proposed approach is 0.99 on the News Articles Dataset and Kaggle Dataset. State of the art comparison with contemporary methods shows that our proposed method achieves better performance than peers.
Development of Reconfigurable Control Schemes for Epileptic Seizures

Advanced research in neuroscience recognized electrical stimulation as a well-accepted treatment to suppress neurological disorders like Epilepsy, Parkinson’s, mental depression and schizophrenia. Among these disorders, epilepsy is one of the most common neurological disorders. When epileptic seizures occur, the nervous system generates abnormal action potentials with repetitive firing spikes which cause permanent brain damage and unexpected death. Hence, it must be controlled by either medication or surgical treatment. If it is severe, it cannot be controlled by medication. One of the alternate choices to control epilepsy is electrical nerve stimulation. However, development of a control scheme for the nervous systems to control neurological disorder is a challenging task due to complex biochemical reactions associated with electrical action potential. In this paper, Reconfigurable Control schemes are proposed to control epileptic seizure using Proportional Integral (PI) controller and Model Predictive Controller. Based on Hodgkin-Huxley (HH) model, the transfer function and state-space models are developed for a neuron under normal and Epileptic/abnormal conditions. It is assumed that the epileptic seizure is bifurcations in the neural system due to a change in sodium transconductance. When an epileptic seizure is detected, a reconfigurable controller will take control action to recover the nervous system by suppressing the repetitive firing. In addition, the stability and performances of reconfigurable control schemes are evaluated and compared. The results show the feasibility of proposed reconfigurable control schemes.
An improved variable neighborhood search algorithm for the solid waste collection and transportation problem with split deliveries

This study addresses the solid waste collection and transportation problem with split deliveries (SWCTPSD). To minimize the total fleet costs, a mixed-integer programming model is proposed. An improved variable neighborhood search (VNS) algorithm is proposed to obtain a high-quality collection and transportation plan quickly. To enhance the local exploitive ability, the VNS algorithm is hybridized with simulated annealing. The experimental results show that the proposed VNS algorithm is effective.
Frame Work For EEG Based Emotion Recognition Based On Hybrid Neural Network

In recent years, there were many attempts to classify human emotions based on corporeal signals including ECG, EEG, EMG. EEG based emotion classification is more accurate because it cannot be tainted by subjects' will. The recent development in CNNs has made it easier to systematically extract features from EEG easily. But again, the traditional CNNs fail to comprehend the multi-channel aspect of EEG. In this work, a simple and efficient pre-processing method by considering baseline signals is proposed to enhance the accuracy of recognition and we proposed a hybrid neural network which combines Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) to identify human emotions by extracting spatial and temporal features from raw EEG stream effectively. In CNN, the 1D EEG sequence is then efficiently converted into a 2D frame structure. In order to extract the inter-channel connection between physically adjacent EEG signals, the CNN module is used, and to extract the contextual information, the LSTM module is used. Using this logic, we were able to create a deep learning model which predicts arousal and valence emotions with 86.98% and 85.82% accuracy respectively.
SHS-GAN: Synthetic Enhancement of a Natural Hyperspectral Database

Deep Learning frameworks are gaining increased popularity in image processing tasks such as computational hyperspectral imaging. While these frameworks achieve state-of-the-art results in terms of reconstruction quality and run time, they often require massive databases of hyperspectral cubes for training the reconstruction algorithms. Unfortunately, such databases are usually hard to acquire due to complexity and cost considerations. To mitigate these challenges, we propose a method for generating a synthetic database of hyperspectral cubes in the visible range using a limited number of natural hyperspectral cubes, an unlimited number of RGB images, and a Generative Adversarial Network model. The suggested algorithm, dubbed SHS-GAN, is trained to get a query RGB image and to output a synthetic hyperspectral cube. While the spectral domain of the synthetic hyperspectral cube shares similar statistical properties as the natural hyperspectral cubes used in the training process, the SHS-GAN is trained to preserve the spatial characteristics of the query RGB image, whereas the R, G, B values provide an additional constraint along with the spectral domain. Our suggested framework was utilized for performing Snapshot Spectral Imaging (SSI) from a single monochromatic dispersed and diffused snapshot using the DD-Net reconstruction neural network. We demonstrate, by simulations and lab experiments, that enhancing the training database with synthetic data from the SHS-GAN improves the reconstruction quality of the hyperspectral cube. In addition, we share a new original database of more than 10,000 hyperspectral cubes of real objects of size 256x256x29 in the 420-700 nm visible range.
Optical Imaging Instruments and Main Science Results of Small Body Exploration: A Review

Compared with planets and their moons, small bodies retain the characteristics of their early formation. Therefore small bodies, especially Near Earth Asteroids (NEA), have become one of the most valuable targets in space exploration. This paper presents a review of the current status of small body exploration, including the science objectives, main optical imaging instruments and the main scientific results obtained from the relevant exploration missions in the past two decades. Then, it outlines and discusses future missions to small bodies by different space agencies, including those already planned and under development. Finally, it discusses the trend of optical imaging instrument development for exploring small bodies.
Determinant and Pattern Classification of Knitted Garment Exports in Certain Demographics

This study attempts to determine level of satisfaction towards knitted garment exports in Tirupur. This study carried out in Tirupur city, Tamil Nadu. A sample of 625 knitted garment exporters randomly chosen in Tirupur city for assortment of information and investigation. Simple tabular and percentage analysis have employed to contemplate the social and economic status of knitted garment export units in the area. In order to determine the level of satisfaction of the exporters towards the knitted garment exports, it is decided to use multiple regression analysis. The study concluded that the socio economic factors like age, educational qualification, location of the company, type of unit, nature of the company, type of ownership, year of experience, employee strength and yearly turn over, to determinant of the exporters satisfaction level in Tirupur city of Tamil Nadu.