Miracle-dz/UniFAR-FTUs huggingface dataset:
Chitosan and its Derivatives

Chitosan (CS) is a hemi-synthetic cationic linear polysaccharide produced by the deacetylation of chitin. CS is non-toxic, highly biocompatible, and biodegradable, and it has a low immunogenicity. Additionally, CS has inherent antibacterial properties and a mucoadhesive character and can disrupt epithelial tight junctions, thus acting as a permeability enhancer. As such, CS and its derivatives are well-suited for the challenging field of ocular drug delivery. In the present review article, we will discuss the properties of CS that contribute to its successful application in ocular delivery before reviewing the latest advances in the use of CS for the development of novel ophthalmic delivery systems. Colloidal nanocarriers (nanoparticles, micelles, liposomes) will be presented, followed by CS gels and lenses and ocular inserts. Finally, presented. the latest advances in the use of CS for the development of novel ophthalmic delivery systems. In the first part, colloidal nanocarriers will be presented, followed by CS gels and finally lenses and ocular implants. CS has also been used as a coating, to confer mucoadhesiveness to other matrixes, and such examples will be discussed as well. of against coli and and the scope was then further extended to PLA and PLGA NPs. The NPs coated with F68 exhibited the highest hydrophilicity and mucoadhesivity. For PCL NPs, F68 was the best coating, but for PLGA NPs, CS-coated NPs demonstrated a higher bioavailability in conjunctiva, sclera, choroid, and retina. Nasr and Khoee reported the formation of a crosslinked CS shell, coating poly(butylene adipate) (PBA) micelles for the improved ocular delivery of loteprednol etabonate . PBA was modified in order to prepare a dendrimerized structure with hydrophilic amine-functionalized chain ends that could form micelles. The amine-functionalized micelles were further reacted with N-succinyl modified CS, 2 in Scheme 1, in the presence of EDC and N-hydroxysuccinimide. CS was grafted to the micelles via the reaction of the amine groups of the micelles with the carboxylic acid groups of modified CS. Additionally, a crosslinking reaction among amine and carboxylic groups within CS chains took also place. Core–shell spherical NPs were obtained with sizes from 40 to 82 nm depending on the molecular weight of PBA. Commercial eye drops released their drug content within 10 h. In contrast, loteprednol etabonate release from the crosslinked core–shell NPs was extended over 50 h.
Discovering Disrupted Connectivities in Neuroimaging Studies: A Meta Analytic Approach

Aberrant activities in the complex human brain network can lead to various neurological disorders such as multiple sclerosis, Parkinson’s disease, Alzheimer’s disease and Autism Spectrum Disorder (ASD). Functional Magnetic Resonance Imaging (fMRI) has emerged as an important tool to delineate the neural networks affected by such diseases, particularly ASD. In seeking for earlier diagnosis, we aimed to find biomarkers through the analysis of resting state fMRI images. In this article, we present a holistic approach to detect disrupted connectivities in whole brain studies. Our meta analytic approach addresses multidimensional heterogeneities in the context of multiple ROIs borrowing strength from all sites. Results are illustrated with a large data set known as Autism Brain Imaging Data Exchange (ABIDE), which includes 361 subjects from eight medical centers. Our results are consistent with previous studies in Autism research. These interrupted regions are involved in language processing, social cognition, auditory effect on social communication which are associated with ASD. We believe that our findings have addressed the variations due to different hierarchies and thus lead to more reliable identification of therapeutic targets for intervention. Our disciplined study can be used for early detection of subjects who are at a higher risk of developing neurological disorders.
Perceptions And Acceptance Of Islamic Banking in Pakistan

The paper adds to the body of knowledge in the behavioral study by the geographical context of questionnaire, the behavior of individuals towards Islamic banking was examined. Initially, demographic characteristics together with the perceived attributes and behavioral intention of Islamic banks versus commercial banks were determined. The findings suggest that culture and services quality factors stand out as being the significant compared with many other factors based on personal beliefs. The evidence generated in the study did not find an important consideration of the specific income group in increasing the utilization of services provided by the Islamic bank. The study also explores the implications of the regulatory services performance of Pakistani banks specifically when observed for conventional banks and Islamic banks. The result shows that there is a high degree of awareness on the part of respondents on the advantage of the best regulatory services and role of the Islamic banking system in social and economic growth of Pakistan. The study also shows that respondent orientation is a significant predictor of institutional role in the banking industry. In addition, focus on practices associated with marketing management (market characteristics) is critical in improving business growth. However, Future research should replicate this finding with a larger sample size.
IMPROVING BUSINESS VALUE BY IMPLEMENTATION OF LEAN MANUFACTURING: AN EMPIRICAL RESEARCH IN VIETNAM

Nowadays, in globalization industries are adopting new techniques and tools to produce goods for sustainability. However, manufacturing industries are facing challenges to minimize operations costs, market inconsistency, competition, and variance demands. Lean practice helps sustainability of the industries by doing more with less resources through elimination of non-value-added activities to maintain effectiveness and profitability. A study uses the method of survey to identify factors for effective implementation of the lean manufacturing. Two industries producing plastics, metals, textiles, woods, biscuits, and bread products were selected for the study. The results revealed that manufacturing planning and control, new product development, process and equipment, concurrent engineering, workforce management, customer relationship, and supplier relationship are positive factors for the adoption of lean practice in Vietnam. However, a lean model indicates that concurrent engineering has the highest correlation to lean practice whereas new product development has the lowest significance. The new product development is not common because most industries have a lack of innovation, limited technology, less desire of customers on changes of product features, dearth of research and development culture, lack of desire from top management, inadequate resources, and a fear of failure for new products. It was advised that effective application of lean factors enable companies to map risk of non-value-added activities and improve business value.
Distributed Algorithm Over Time-Varying Unbalanced Topologies for Optimization Problem Subject to Multiple Local Constraints

This paper studies the distributed optimization problem with possibly nonidentical local constraints, where its global objective function is composed of N convex functions. The aim is to solve the considered optimization problem in a distributed manner over time-varying unbalanced directed topologies by using only local information and performing only local computations. Towards this end, a new distributed discrete-time algorithm is developed by synthesizing the row stochastic matrices sequence and column stochastic matrices sequence analysis technique. Furthermore, for the developed distributed discrete-time algorithm, its convergence property to the optimal solution as well as its convergence rate are established under some mild assumptions. Numerical simulations are ﬁnally presented to verify the theoretical results.
Extremal statistics of quadratic forms of GOE/GUE eigenvectors

We consider quadratic forms of deterministic matrices $A$ evaluated at the random eigenvectors of a large $N \times N$ GOE or GUE matrix, or equivalently evaluated at the columns of a Haar-orthogonal or Haar-unitary random matrix. We prove that, as long as the deterministic matrix has rank much smaller than $\sqrt{N}$, the distributions of the extrema of these quadratic forms are asymptotically the same as if the eigenvectors were independent Gaussians. This reduces the problem to Gaussian computations, which we carry out in several cases to illustrate our result, finding Gumbel or Weibull limiting distributions depending on the signature of $A$. Our result also naturally applies to the eigenvectors of any invariant ensemble.
No $((n,K,d< 127))$ Code Can Violate the Quantum Hamming Bound

It is well-known that nondegenerate quantum error correcting codes (QECCs) are constrained by a quantum version of the Hamming bound. Whether degenerate codes also obey such a bound, however, remains a long-standing question with practical implications for the efficacy of QECCs. We employ a combination of previously derived bounds on QECCs to demonstrate that a subset of all codes must obey the quantum Hamming bound. Specifically, we combine an analytical bound due to Rains with a numerical bound due to Li and Xing to show that no $((n,K,d))$((n,K,d)) code with $d< 127$d<127 can violate the quantum Hamming bound.
Large discrepancy between observations and simulations: Implications for urban air quality in China

Chemical transport models (CTMs) have been widely used to provide instructions for the control of ozone (O 3 ) pollution. However, we find large discrepancies between observation- and model-based urban O 3 chemical regimes: volatile organic compound (VOC)-limited regimes over N. China and weak nitrogen oxides (NO x )-limited regimes over S. China in observations, in contrast to simulations with widespread distributions of strong NO x -limited regimes. The conflicting O 3 evolutions are caused by underestimated urban NO x concentrations and the possible overestimation of biogenic VOC emissions. Reductions in NO x emissions, in
No Provisioned Concurrency: Fast RDMA-codesigned Remote Fork for Serverless Computing

Serverless platforms essentially face a tradeoff between container startup time and provisioned concurrency (i.e., cached instances), which is further exaggerated by the frequent need for remote container initialization. This paper presents MITOSIS, an operating system primitive that provides fast remote fork, which exploits a deep codesign of the OS kernel with RDMA. By leveraging the fast remote read capability of RDMA and partial state transfer across serverless containers, MITOSIS bridges the performance gap between local and remote container initialization. MITOSIS is the first to fork over 10,000 new containers from one instance across multiple machines within a second, while allowing the new containers to efficiently transfer the pre-materialized states of the forked one. We have implemented MITOSIS on Linux and integrated it with FN, a popular serverless platform. Under load spikes in real-world serverless workloads, MITOSIS reduces the function tail latency by 89% with orders of magnitude lower memory usage. For serverless workflow that requires state transfer, MITOSIS improves its execution time by 86%.
Design and Implementation of a Human-Robot Joint Action Framework using Augmented Reality and Eye Gaze

When humans work together to complete a joint task, each person builds an internal model of the situation and how it will evolve. Efficient collaboration is dependent on how these individual models overlap to form a shared mental model among team members, which is important for collaborative processes in human-robot teams. The development and maintenance of an accurate shared mental model requires bidirectional communication of individual intent and the ability to interpret the intent of other team members. To enable effective human-robot collaboration, this paper presents a design and implementation of a novel joint action framework in human-robot team collaboration, utilizing augmented reality (AR) technology and user eye gaze to enable bidirectional communication of intent. We tested our new framework through a user study with 37 participants, and found that our system improves task efficiency, trust, as well as task fluency. Therefore, using AR and eye gaze to enable bidirectional communication is a promising mean to improve core components that influence collaboration between humans and robots.
High-Fidelity Image Inpainting with GAN Inversion

Image inpainting seeks a semantically consistent way to recover the corrupted image in the light of its unmasked content. Previous approaches usually reuse the well-trained GAN as effective prior to generate realistic patches for missing holes with GAN inversion. Nevertheless, the ignorance of a hard constraint in these algorithms may yield the gap between GAN inversion and image inpainting. Addressing this problem, in this paper, we devise a novel GAN inversion model for image inpainting, dubbed InvertFill, mainly consisting of an encoder with a pre-modulation module and a GAN generator with F&W+ latent space. Within the encoder, the pre-modulation network leverages multi-scale structures to encode more discriminative semantics into style vectors. In order to bridge the gap between GAN inversion and image inpainting, F&W+ latent space is proposed to eliminate glaring color discrepancy and semantic inconsistency. To reconstruct faithful and photorealistic images, a simple yet effective Soft-update Mean Latent module is designed to capture more diverse in-domain patterns that synthesize high-fidelity textures for large corruptions. Comprehensive experiments on four challenging datasets, including Places2, CelebA-HQ, MetFaces, and Scenery, demonstrate that our InvertFill outperforms the advanced approaches qualitatively and quantitatively and supports the completion of out-of-domain images well.
Treatment Effect Quantiles in Stratified Randomized Experiments and Matched Observational Studies

Evaluating the treatment effect has become an important topic for many applications. However, most existing literature focuses mainly on average treatment effects. When the individual effects are heavy-tailed or have outlier values, not only may the average effect not be appropriate for summarizing treatment effects, but also the conventional inference for it can be sensitive and possibly invalid due to poor large-sample approximations. In this paper we focus on quantiles of individual treatment effects, which can be more robust in the presence of extreme individual effects. Moreover, our inference for them is purely randomization-based, avoiding any distributional assumptions on the units. We first consider inference in stratified randomized experiments, extending the recent work by? Caughey et al. (2021). We show that the computation of valid p-values for testing null hypotheses on quantiles of individual effects can be transformed into instances of the multiple-choice knapsack problem, which can be efficiently solved exactly or slightly conservatively. We then extend our approach to matched observational studies and propose a sensitivity analysis to investigate to what extent our inference on quantiles of individual effects is robust to unmeasured confounding. The proposed randomization inference and sensitivity analysis are simultaneously valid for all quantiles of individual effects, noting that the analysis for the maximum or minimum individual effect coincides with the conventional analysis assuming constant treatment effects.
Controlling collective dynamical states of mesoscale brain networks with local perturbations

Oscillatory synchrony is hypothesized to support the flow of information between brain regions, with different phase-locked configurations enabling activation of different effective interactions. Along these lines, past work has proposed multistable phase-locking as a means for hardwired brain networks to flexibly support multiple functional patterns, without having to reconfigure their anatomical connections. Given the potential link between interareal communication and phase-locked states, it is thus important to understand how those states might be controlled to achieve rapid alteration of functional connectivity in interareal circuits. Here, we study functional state control in small networks of coupled neural masses that display collective multistability under determinstic conditions, and that display more biologically-realistic irregular oscillations and transient phase-locking when conditions are stochastic. In particular, we investigate the global responses of these mesoscale circuits to external signals that target only a single subunit. Focusing mainly on the more realistic scenario wherein network dynamics are stochastic, we identify conditions under which local inputs (i) can trigger fast transitions to topologically distinct functional connectivity motifs that are temporarily stable (“state switching”), (ii) can smoothly adjust the spatial pattern of phase-relations for a particular set of lead-lag relationships (“state morphing”), and (iii) fail to regulate global phase-locking states. In total, our results add to a growing literature highlighting that the modulation of multistable, interareal coherence patterns could provide a basis for flexible brain network operation. 1 ar X iv :2 20 8. 12 23 1v 1 [ qbi o. N C ] 2 5 A ug 2 02 2
Distributed Optimization with Inexact Oracle

In this paper, we study the distributed optimization problem using approximate first-order information. We suppose the agent can repeatedly call an inexact first-order oracle of each individual objective function and exchange information with its time-varying neighbors. We revisit the distributed subgradient method in this circumstance and show its suboptimality under square summable but not summable step sizes. We also present several conditions on the inexactness of the local oracles to ensure an exact convergence of the iterative sequences towards the global optimal solution. A numerical example is given to verify the efficiency of our algorithm.
Practical Real Video Denoising with Realistic Degradation Model

Existing video denoising methods typically assume noisy videos are degraded from clean videos by adding Gaussian noise. However, deep models trained on such a degradation assumption will inevitably give rise to poor performance for real videos due to degradation mismatch. Although some studies attempt to train deep models on noisy and noise-free video pairs captured by cameras, such models can only work well for speciﬁc cameras and do not generalize well for other videos. In this paper, we propose to lift this limitation and focus on the problem of general real video denoising with the aim to generalize well on unseen real-world videos. We tackle this problem by ﬁrstly investigating the common behaviors of video noises and observing two important characteristics: 1) downscaling helps to reduce the noise level in spatial space and 2) the information from the adjacent frames help to remove the noise of current frame in temporal space. Motivated by theses two observations, we propose a multi-scale recurrent architecture by making full use of the above two characteristics. Secondly, we propose a synthetic real noise degradation model by randomly shufﬂing different noise types to train the denoising model. With a synthesized and enriched degradation space, our degradation model can help to bridge the distribution gap between training data and real-world data. Extensive experiments demonstrate that our proposed method achieves the state-of-the-art performance and better generalization ability than existing methods on both synthetic Gaussian denoising and practical real video denoising.
Bottom-Up 2D Pose Estimation via Dual Anatomical Centers for Small-Scale Persons

In multi-person 2D pose estimation, the bottom-up methods simultaneously predict poses for all persons, and unlike the top-down methods, do not rely on human detection. However, the SOTA bottom-up methods' accuracy is still inferior compared to the existing top-down methods. This is due to the predicted human poses being regressed based on the inconsistent human bounding box center and the lack of human-scale normalization, leading to the predicted human poses being inaccurate and small-scale persons being missed. To push the envelope of the bottom-up pose estimation, we firstly propose multi-scale training to enhance the network to handle scale variation with single-scale testing, particularly for small-scale persons. Secondly, we introduce dual anatomical centers (i.e., head and body), where we can predict the human poses more accurately and reliably, especially for small-scale persons. Moreover, existing bottom-up methods use multi-scale testing to boost the accuracy of pose estimation at the price of multiple additional forward passes, which weakens the efficiency of bottom-up methods, the core strength compared to top-down methods. By contrast, our multi-scale training enables the model to predict high-quality poses in a single forward pass (i.e., single-scale testing). Our method achieves 38.4\% improvement on bounding box precision and 39.1\% improvement on bounding box recall over the state of the art (SOTA) on the challenging small-scale persons subset of COCO. For the human pose AP evaluation, we achieve a new SOTA (71.0 AP) on the COCO test-dev set with the single-scale testing. We also achieve the top performance (40.3 AP) on OCHuman dataset in cross-dataset evaluation.
On confidence intervals for precision matrices and the eigendecomposition of covariance matrices

The eigendecomposition of a matrix is the central procedure in prob-abilistic models based on matrix factorization, for instance principal component analysis and topic models. Quantifying the uncertainty of such a decomposition based on a ﬁnite sample estimate is essential to reasoning under uncertainty when employing such models. This paper tackles the challenge of computing conﬁdence bounds on the individual entries of eigenvectors of a covariance matrix of ﬁxed dimension. Moreover, we derive a method to bound the entries of the inverse covariance matrix, the so-called precision matrix. The assumptions behind our method are minimal and require that the covariance matrix exists, and its empirical estimator converges to the true covariance. We make use of the theory of U-statistics to bound the L 2 perturbation of the empirical covariance matrix. From this result, we obtain bounds on the eigenvectors using Weyl’s theorem and the eigenvalue-eigenvector identity and we derive conﬁdence intervals on the entries of the precision matrix using matrix inversion perturbation bounds. As an application of these results, we demonstrate a new statistical test, which allows us to test for non-zero values of the precision matrix. We compare this test to the well-known Fisher-z test for partial correlations, and demonstrate the soundness and scalability of the proposed statistical test, as well as its application to real-world data from medical and physics domains.