Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Manipulation via Neuro-Symbolic Networks

Image manipulation via natural language text – an extremely useful task for multiple AI applications but requires complex reasoning over multi-modal spaces. Neuro-symbolic approaches has been quite effective in solving such tasks as they offer better modularity , interpretability , and generalizability . A noteworthy such approach is NSCL [10] developed for the task of Visual Question Answering (VQA). We extend NSCL for the image manipulation task and propose a solution referred to as N EURO SIM. Unlike previous works, which either require supervised data training or can only deal with simple reasoning instructions over single object scenes; N EURO SIM can perform complex multi-hop reasoning over multi-object scenes and requires only weak supervision in the form of annotated data for the VQA task. On the language side, N EURO SIM contains neural modules that parse an instruction into a symbolic program over a Domain Specific Language (DSL) comprising manipulation operations that guide the manipulation. On the perceptual side, N EURO SIM contains neural modules which first generate a scene graph of the input image and then change the scene graph representation following the parsed instruction. To train these modules, we design novel loss functions capable of testing the correctness of manipulated object and scene graph representations via query networks. An image decoder is trained to render the final image from the manipulated scene graph representation. Extensive experiments demonstrate that N EURO SIM is highly competitive with state-of-the-art supervised baselines.
Evaluating the Alterations Induced by Virtual Reality in Cerebral Small-World Networks Using Graph Theory Analysis with Electroencephalography

Virtual reality (VR), a rapidly evolving technology that simulates three-dimensional virtual environments for users, has been proven to activate brain functions. However, the continuous alteration pattern of the functional small-world network in response to comprehensive three-dimensional stimulation rather than realistic two-dimensional media stimuli requires further exploration. Here, we aimed to validate the effect of VR on the pathways and network parameters of a small-world organization and interpret its mechanism of action. Fourteen healthy volunteers were selected to complete missions in an immersive VR game. The changes in the functional network in six different frequency categories were analyzed using graph theory with electroencephalography data measured during the pre-, VR, and post-VR stages. The mutual information matrix revealed that interactions between the frontal and posterior areas and those within the frontal and occipital lobes were strengthened. Subsequently, the betweenness centrality (BC) analysis indicated more robust and extensive pathways among hubs. Furthermore, a specific lateralized channel (O1 or O2) increment in the BC was observed. Moreover, the network parameters improved simultaneously in local segregation, global segregation, and global integration. The overall topological improvements of small-world organizations were in high-frequency bands and exhibited some degree of sustainability.
Protein language model rescue mutations highlight variant effects and structure in clinically relevant genes

Despite being self-supervised, protein language models have shown remarkable performance in fundamental biological tasks such as predicting impact of genetic variation on protein structure and function. The effectiveness of these models on diverse set of tasks suggests that they learn meaningful representations of fitness landscape that can be useful for downstream clinical applications. Here, we interrogate the use of these language models in characterizing known pathogenic mutations in curated, medically actionable genes through an exhaustive search of putative compensatory mutations on each variant's genetic background. Systematic analysis of the predicted effects of these compensatory mutations reveal unappreciated structural features of proteins that are missed by other structure predictors like AlphaFold. While deep mutational scan experiments provide an unbiased estimate of the mutational landscape, we encourage the community to generate and curate rescue mutation experiments to inform the design of more sophisticated co-masking strategies and leverage large language models more effectively for downstream clinical prediction tasks.
Low-Dimensional Compounds Containing Bioactive Ligands. Part XIX: Crystal Structures and Biological Properties of Copper Complexes with Halogen and Nitro Derivatives of 8-Hydroxyquinoline

Six new copper(II) complexes were prepared: [Cu(ClBrQ)2] (1a, 1b), [Cu(ClBrQ)2]·1/2 diox (2) (diox = 1,4-dioxane), [Cu(BrQ)2] (3), [Cu(dNQ)2] (4), [Cu(dNQ)2(DMF)2] (5) and [Cu(ClNQ)2] (6), where HClBrQ is 5-chloro-7-bromo-8-hydroxyquinoline, HBrQ is 7-bromo-8-hydroxyquinoline, HClNQ is 5-chloro-7-nitro-8-hydroxyquinoline and HdNQ is 5,7-dinitro-8-hydroxyquinoline. Prepared compounds were characterised by infrared spectroscopy, elemental analysis and by X-ray structural analysis. Structural analysis revealed that all complexes are molecular. Square planar coordination of copper atoms in [Cu(XQ)2] (XQ = ClBrQ (1a, 1b), BrQ (3) and ClNQ (6)) and tetragonal bipyramidal coordination in [Cu(dNQ)2(DMF)2] (5) complexes were observed. In these four complexes, bidentate chelate coordination of XQ ligands via oxygen and nitrogen atoms was found. Hydrogen bonds stabilizing the structure were observed in [Cu(dNQ)2(DMF)2] (5) and [Cu(ClNQ)2] (6), no other nonbonding interactions were noticed in all five structures. The stability of the complexes in DMSO and DMSO/water was evaluated by UV-Vis spectroscopy. Cytotoxic activity of the complexes and ligands was tested against MCF-7, MDA-MB-231, HCT116, CaCo2, HeLa, A549 and Jurkat cancer cell lines. The selectivity of the complexes was verified on a noncancerous Cos-7 cell line. Antiproliferative activity of the prepared complexes was very low in comparison with cisplatin, except complex 3; however, its activity was not selective and was similar to the activity of its ligand HBrQ. Antibacterial potential was observed only with ligand HClNQ. Radical scavenging experiments revealed relatively high antioxidant activity of complex 3 against ABTS radical.
Recent Developments on Bioinspired Cellulose Containing Polymer Nanocomposite Cation and Anion Exchange Membranes for Fuel Cells (PEMFC and AFC)

Hydrogen fuel cell (FC) technologies are being worked on as a possible replacement for fossil fuels because they produce a lot of energy and do not pollute the air. In FC, ion-exchange membranes (IEMs) are the vital components for ion transport between two porous electrodes. However, the high production cost of commercialized membranes limits their benefits. Various research has focused on cellulose-based membranes such as IEM with high proton conductivity, and mechanical, chemical, and thermal stabilities to replace the high cost of synthetic polymer materials. In this review, we focus on and explain the recent progress (from 2018 to 2022) of cellulose-containing hybrid membranes as cation exchange membranes (CEM) and anion exchange membranes (AEM) for proton exchange membrane fuel cells (PEMFC) and alkaline fuel cells (AFC). In this account, we focused primarily on the effect of cellulose materials in various membranes on the functional properties of various polymer membranes. The development of hybrid membranes with cellulose for PEMFC and AFC has been classified based on the combination of other polymers and materials. For PEMFC, the sections are associated with cellulose with Nafion, polyaryletherketone, various polymeric materials, ionic liquid, inorganic fillers, and natural materials. Moreover, the cellulose-containing AEM for AFC has been summarized in detail. Furthermore, this review explains the significance of cellulose and cellulose derivative-modified membranes during fuel cell performance. Notably, this review shows the vital information needed to improve the ion exchange membrane in PEMFC and AFC technologies.
Over-training with Mixup May Hurt Generalization

Mixup, which creates synthetic training instances by linearly interpolating random sample pairs, is a simple and yet effective regularization technique to boost the performance of deep models trained with SGD. In this work, we report a previously unobserved phenomenon in Mixup training: on a number of standard datasets, the performance of Mixup-trained models starts to decay after training for a large number of epochs, giving rise to a U-shaped generalization curve. This behavior is further aggravated when the size of original dataset is reduced. To help understand such a behavior of Mixup, we show theoretically that Mixup training may introduce undesired data-dependent label noises to the synthesized data. Via analyzing a least-square regression problem with a random feature model, we explain why noisy labels may cause the U-shaped curve to occur: Mixup improves generalization through fitting the clean patterns at the early training stage, but as training progresses, Mixup becomes over-fitting to the noise in the synthetic data. Extensive experiments are performed on a variety of benchmark datasets, validating this explanation.
Detection of Aircrafts Uusing RCNN in Remote Sensing Images

Modern remote sensing images have developed, and the resultant of the sophisticated technology makes the detection of objects a tedious task. Detection of aircraft from remote sensing imagery has some challenges that are often not addressed. The image captured by remote sensing technology would have more information about the objects in the scene. Smaller objects around the aircraft and similar unknown objects could easily affect the detection model. And there will be numerous objects in the wide-open space of the airfield and airports. Although several research studies showed aircraft detection from remote sensing images, the models didn't solve the overlap problem. And therefore, the present study proposed a method to resolve the overlap problem using the Region-based convolutional neural network architecture from the YOLOv3 model to detect the aircraft from the remote sensing images. The model would use the Interaction of unit and the ground truth data from the images to select the appropriate bounding boxes for the aircraft. The model was trained, and the results produced the model could perform at an accuracy of 98.5 %. The RCNN model could detect the aircraft appropriately, and the metrics analysis shows the model had less error while detecting the aircraft. The performance comparison with other aircraft detection models showed that the proposed model performed better.
Towards a Conditional and Multi-preferential Approach to Explainability of Neural Network Models in Computational Logic (Extended Abstract)

This short paper reports on a line of research exploiting a conditional logic of commonsense reasoning to provide a semantic interpretation to neural network models. A “concept-wise" multi-preferential semantics for conditionals is exploited to build a preferential interpretation of a trained neural network starting from its input-output behavior. The approach is a general one; it has first been proposed for Self-Organising Maps (SOMs), and exploited for MultiLayer Perceptrons (MLPs) in the verification of properties of a network by model-checking. An MLPs can be regarded as a (fuzzy) conditional knowledge base (KB), in which the synaptic connections correspond to weighted conditionals. Reasoners for many-valued weighted conditional KBs are under development based on Answer Set solving to deal with entailment and model-checking.
CE-BART: Cause-and-Effect BART for Visual Commonsense Generation

“A Picture is worth a thousand words”. Given an image, humans are able to deduce various cause-and-effect captions of past, current, and future events beyond the image. The task of visual commonsense generation has the aim of generating three cause-and-effect captions for a given image: (1) what needed to happen before, (2) what is the current intent, and (3) what will happen after. However, this task is challenging for machines, owing to two limitations: existing approaches (1) directly utilize conventional vision–language transformers to learn relationships between input modalities and (2) ignore relations among target cause-and-effect captions, but consider each caption independently. Herein, we propose Cause-and-Effect BART (CE-BART), which is based on (1) a structured graph reasoner that captures intra- and inter-modality relationships among visual and textual representations and (2) a cause-and-effect generator that generates cause-and-effect captions by considering the causal relations among inferences. We demonstrate the validity of CE-BART on the VisualCOMET and AVSD benchmarks. CE-BART achieved SOTA performance on both benchmarks, while an extensive ablation study and qualitative analysis demonstrated the performance gain and improved interpretability.
Effects of Differences in Fibre Composition and Maturity of Forage-Based Diets on the Fluid Balance, Water-Holding Capacity and Viscosity in Equine Caecum and Colon Digesta

Simple Summary Horses are large herbivorous hindgut fermenters, and the equine hindgut has been suggested to function as a fluid reservoir when dietary fibre holds water in the intestinal content. Forage-based diets and plant-fibre contributes the most to this hindgut fluid reservoir. However, the fibre content and fibre composition of forage can differ greatly and there is limited research published on which fibre source and what type of fibre that would be the most appropriate. We investigated the effect of a grass forage diet, a legume forage diet and the more conventional forage and concentrate diet, on horses’ fluid balance and function of the hindgut fluid reservoir. The three diets implied differences in water intake, body weight and digesta water-holding capacity. Total water intake and body weight were higher when the horses were fed the lucerne forage diet but digesta water-holding capacity was higher when the horses were fed the young grass forage diet. It can be concluded that early harvested forage might benefit fluid balance of athletic horses without increasing body weight. In addition, further studies on plant-fibre and forage diets for horses are of great importance for horse feeding, for advisors, veterinarians and for the diet formulations industry. Abstract Horses are herbivores, and their hindgut functions as a fluid reservoir as forage fibre properties have great impact on the water content of digesta and the milieu in the ecosystem. Our objective was to compare the effect of grass fibre maturity and legume forage on the water-holding capacity (WHC) and viscosity of the equine hindgut and the body weight (BW) and fluid balance of horses. Three diets: concentrate and late harvested grass haylage (35:65 energy ratio) (C); early and late harvested grass haylage (80:20) (G); lucerne and late harvested grass haylage (80:20) (L) were fed to six caecum and colon fistulated horses for 28 days in a Latin-square design. Total water intake and BW were higher when the horses were fed Diet L, but the digesta WHC was higher when fed Diet G. Total water excretion (via faeces + urine) and the difference in total water intake—output was higher when fed Diet L. Viscosity, measured on centrifuged digesta fluid, did not differ between diets, but the individual colon data of one horse were higher. In conclusion, early harvested forage might be beneficial for the fluid balance of athletic horses providing a higher WHC of hindgut digesta without increasing BW. The importance of digesta viscosity in relation to equine diets needs further investigations.
A Balanced Design of Time Series Experiments

Time series experiments are a family of experimental designs on a time series. One 1 experimental unit is sequentially exposed to some version of treatment, stays in the 2 version of treatment for a duration of time, and gets exposed to another version of 3 treatment. While this type of experimental designs could handle population inter- 4 ference between units, it typically still needs to account for temporal interference, 5 i.e
Assessing multi-objective optimization of molecules with genetic algorithms against relevant baselines

Chemical design is often complex, requiring the optimal trade-off between several competing objectives. Multi-objective optimization algorithms are designed to optimally balance multiple objectives, but many chemical design approaches use the naïve weighted sum method, which is not guaranteed to give desired solutions. Here, we rigorously assess the performance of genetic algorithms for inverse molecular design using more advanced multi-objective methods. CHIMERA and Hy-pervolume are assessed against relevant baselines for the optimization of molecules with high logP and high QED score. As a more realistic task, we also simulate a drug design campaign, optimizing for synthetically accessible molecules which bind to the 1OYT protein. Additionally, we include a three-objective task of optimizing logP, QED and SAS to investigate scalability to more than two objectives. We show that both methods achieve better formal optimality than the baselines and generate molecules closer to a user-specified Utopian point in property space, mimicking typical materials design objectives.
The Impact of Extreme Temperature Shocks on the Health Status of the Elderly in China

With the accelerating process of climate change, long-term exposure to extreme temperatures could threaten individuals’ physical health, especially for the vulnerable population. This paper aims to investigate the long-term effects of extreme temperature exposure on the health of the elderly in the context of climate change and aging. Different from most of the existing literature in environmental economics, we define the relative extreme temperature exposure based on the local temperature pattern. By combining a large national household survey and nationwide meteorologic historical data, this study provides empirical evidence that heat exposure days and cold exposure days during the past year both significantly affect the physical health of middle-aged and elderly groups, controlling for city, year, and individual fixed effects. The effect on individual physical health has certain seasonal characteristics and is heterogeneous across populations. Additionally, cooling and heating equipment are effective in alleviating the reverse impact of heat and cold exposure. The estimation is robust and consistent across a variety of temperature measurements and model modifications. Our findings provide evidence of the long-term and accumulative cost of extreme temperature to middle-aged and elderly human capital, contributing to helping the public to better understand the full impact of climate change.
Toward Fair and Robust Optimal Treatment Regimes

We propose a new framework for robust nonparametric estimation of optimal treatment regimes under flexible fairness constraints. Under standard regularity conditions we show that the resulting estimators possess the double robustness property. We use this framework to characterize the trade-off between fairness and the maximum utility that is achievable by the optimal treatment policy.
Ex Vivo Evaluation of Ethosomes and Transethosomes Applied on Human Skin: A Comparative Study

In this study, the transdermal fate of vesicular nanosystems was investigated. Particularly, ethosomes based on phosphatidylcholine 0.9% w/w and transethosomes based on phosphatidylcholine 0.9 or 2.7% w/w plus polysorbate 80 0.3% w/w as an edge activator were prepared and characterized. The vesicle mean size, morphology and deformability were influenced by both phosphatidylcholine and polysorbate 80. Indeed, the mean diameters of ethosome were around 200 nm, while transethosome’s mean diameters were 146 or 350 nm in the case of phosphatidylcholine 0.9 or 2.7%, w/w, respectively. The highest deformability was achieved by transethosomes based on phosphatidylcholine 0.9%, w/w. The three types of vesicular nanosystems were applied on explanted human skin maintained in a bioreactor. Transmission electron microscopy demonstrated that all vesicles were able to enter the skin, keeping their structural integrity. Notably, the vesicle penetration capability was influenced by their physical-chemical features. Indeed, ethosomes reached keratinocytes and even the dermis, phosphatidylcholine 0.9% transethosomes were found in keratinocytes and phosphatidylcholine 2.7% transethosomes were found only in corneocytes of the outer layer. These findings open interesting perspectives for a differentiated application of these vesicles for transdermal drug delivery as a function of the cutaneous pathology to be addressed.
Mini-batch k-means terminates within O(d/ε) iterations

We answer the question: "Does local progress (on batches) imply global progress (on the entire dataset) for mini-batch k-means? ". Specifically, we consider minibatch k-means which terminates only when the improvement in the quality of the clustering on the sampled batch is below some threshold. Although at first glance it appears that this algorithm might execute forever, we answer the above question in the affirmative and show that if the batch is of size Ω̃((d/ε)), it must terminate within O(d/ε) iterations with high probability, where d is the dimension of the input, and ε is a threshold parameter for termination. This is true regardless of how the centers are initialized. When the algorithm is initialized with the k-means++ initialization scheme, it achieves an approximation ratio of O(log k) (the same as the full-batch version). Finally, we show the applicability of our results to the mini-batch k-means algorithm implemented in the scikit-learn (sklearn) python library.
A fictitious crack XFEM with two new solution algorithms for cohesive crack growth modeling in concrete structures

Purpose – The purpose of this paper is to achieve numerical simulation of cohesive crack growth in concrete structures. Design/methodology/approach – The extended finite element method (XFEM) using four-node quadrilateral element associated with the fictitious cohesive crack model is used. A mixed-mode traction-separation law is assumed for the cohesive crack in the fracture process zone (FPZ). Enrichments are considered for both partly and fully cracked elements, and it thus makes the evolution of crack to any location inside the element possible. In all. two new solution procedures based on Newton-Raphson method, which differ from the approach suggested by Zi and Belytschko (2003), are presented to solve the nonlinear system of equations. The present formulation results in a symmetric tangent matrix, conveniently in finite element implementation and programming. Findings – The inconvenience in solving the inversion of an unsymmetrical Jacobian matrix encountered in the existing approach is avoided. Nume.