Miracle-dz/UniFAR-FTUs huggingface dataset:
Semiparametric Autoregression Model with Exogenous Variables for Forecasting Chinese Population

In order to overcome the problems of forecasting Chinese population in the existing literature such as lacking the forecast function and the bigger error,semi-parametric model with exogenous variable using the data in 1952-2005 is constructed.The population total quantity and its lag are served as the explainary variables.The significant variable and the best bandwidth are chosen by the RMSE criterion.The result showed that the model is reasonable, and has better prediction than linear model.
Poaching sources and trade routes in Peru and Ecuador warn of the unsustainable rural demand for preferred parrot species

Illegal wildlife trade remains highly active in the Neotropics, as indicated by the thousands of parrots annually sold in illicit city markets. However, little is known about where parrots are poached, whether certain parrot species are selected among those available in the wild, their trade routes, and potential conservation impacts. We conducted a large‐scale survey in Peru and southern Ecuador to simultaneously estimate the relative abundance of parrots in the wild and as household pets in rural areas, determine their origin (locally poached or bought at city markets), and measured the shortest distances to their native ranges and markets through the existing grid of roads. Household‐poached native parrots were found in 96% of the rural localities surveyed. Most pets were locally poached, with only 14% of them bought at markets. Parrot poaching was highly selective, with preferred species (mainly Amazon parrots and large macaws) being collected much more than expected given their abundances in the wild and attaining higher prices than the other species. Individuals that were moved away from their native ranges or bought at distant markets were of those species most preferred by people, and covered large distances (up to 1010 km), even crossing country boundaries. Our results differ from those previously obtained from city markets and seizures of illegally traded parrots in Peru, where preferred species were underrepresented. Local poaching and rural trade activities act at very large spatial scales and negatively affect the population trends of preferred parrot species. This unsustainable scenario is a challenge to the application of effective conservation actions aimed at halting poaching and illegal trade. These actions should focus on very extensive and remote rural areas throughout the Neotropics rather than just on well‐known markets located in large cities.
Citrinin Exposure Induced Testicular Damage and Spermatogenesis Disorder by Triggering Endoplasmic Reticulum Stress

Damage to the reproductive system is the key factor leading to male infertility. Citrinin (CTN) is produced by Penicillium and Aspergillus in nature, and is definitely found in food and animal feed. Studies have revealed that CTN can cause damage to male reproductive organs and reduce fertility, but the mechanism of toxicity has not been revealed. In the present study, male Kunming mice were given different doses of CTN (0, 1.25, 5 or 20 mg/kg BW) by intragastric administration. The results demonstrated that CTN exposure caused disorder of androgen, a decline in sperm quality, and histopathological damage of testis. The inhibition of the expression of ZO-1, claudin-1 and occludin suggests that the blood-testis barrier (BTB) was damaged. Simultaneously, CTN inhibited the activity of antioxidant enzymes such as CAT and SOD, and promoted the production of MDA and ROS, resulting in oxidative damage of testis. Additionally, apoptotic cells were detected and the ratio of Bax/Bcl-2 was increased. Not only that, CTN activated the expression of endoplasmic reticulum stress (ERS)-related proteins IRE1, ATF6, CHOP, and GRP78. Interestingly, 4-Phenylbutyric Acid (4-PBA, an ERS inhibitor) treatment blocked the adverse effects of CTN exposure on male reproduction. In short, the findings suggested that CTN exposure can cause damage to mouse testis tissue, in which ERS exhibited an important regulatory role.
Automatic Generation of Interactive Nonlinear Video for Online Apparel Shopping Navigation

We present an automatic generation pipeline of interactive nonlinear video for online apparel shopping navigation. Our approach was inspired by Google's “Messy Middle” theory, which suggests that people mentally are faced with two tasks—exploration and evaluation—before purchasing online. Given a set of apparel product presentation videos, our navigation UI organizes them to optimize users' product exploration and automatically generates interactive videos for users' product evaluation. To support automatic methods, we proposed a video clustering similarity (<inline-formula><tex-math notation="LaTeX">$\operatorname{CSIM}$</tex-math></inline-formula>) and a camera movement similarity (<inline-formula><tex-math notation="LaTeX">$\operatorname{MSIM}$</tex-math></inline-formula>), as well as a comparative video generation algorithm for product recommendation, presentation, and comparison. To evaluate our pipeline's effectiveness, we conducted several user studies. The results showed that our pipeline can help users complete the consumption process more efficiently, making it easier for them to understand and choose a product.
Discussing on Image Restoration Technology for Constrained Least Squares Filtering

Image restoration is an imagerebuilding technology in which image was rebuilt with some knowledge for degradation phenomena(degradation model) according to counter-process of degradation.The article analyses the principle of Constrained Least Squares Filtering,and an optimal result for image restoration is produced by this method,which requires knowledge of only the mean and variance of the noise.Lastly an image with convolution blur and additive noise was restored,and its product was given in this paper.
RRP Nb3Sn Subelement Shear Dependence on Hexagonal Subelement Stack Orientation and the Strand's Position Within a Rutherford Cable

Superconducting strands built into Rutherford cables undergo high deformation at the cable edges. This deformation negatively impacts subelement integrity and subsequently the Residual Resistance Ratio (RRR) of the stabilization Cu in Nb3Sn strands after heat treatment due to compromised diffusion barriers (Sumption et al. 2008). Rutherford cables for the high luminosity upgrade of the LHC at CERN (HL-LHC) use Restack Rod Process (RRP) Nb3Sn wires with a 108/127 subelement stack. Numerous transverse cross-sectional samples from the cables made under the HL-LHC Accelerator Upgrade Project (“HL-LHC AUP”) (Apollinari et al. 2019) have been mounted, polished, and analyzed with light microscopy. The number of sheared subelements within a strand was seen to depend not only on a strand's position in the overall cable (i.e., center vs. edge), but also on its specific position within the cable edge. Moreover, the rotational angle of the subelement stack within a strand, relative to the cable's centerline plane, appears to play a role. There is a need to understand these dependencies, as they might impact the applicability of any criterion for a maximum number of sheared subelements (typically <15% of subelements) applied to series production quality assurance, often imposed to ensure acceptable RRR degradation. By gathering data across many HL-LHC AUP cables and utilizing a more statistical approach, we aim to establish more accurate success criteria for future R&D cables using RRP wire when the available cable runs (and thus samples) are limited.
Polymer-Based Nanostructures for Pancreatic Beta-Cell Imaging and Non-Invasive Treatment of Diabetes

Diabetes poses major economic, social, and public health challenges in all countries worldwide. Besides cardiovascular disease and microangiopathy, diabetes is a leading cause of foot ulcers and lower limb amputations. With the continued rise of diabetes prevalence, it is expected that the future burden of diabetes complications, early mortality, and disabilities will increase. The diabetes epidemic is partly caused by the current lack of clinical imaging diagnostic tools, the timely monitoring of insulin secretion and insulin-expressing cell mass (beta (β)-cells), and the lack of patients’ adherence to treatment, because some drugs are not tolerated or invasively administrated. In addition to this, there is a lack of efficient topical treatment capable of stopping the progression of disabilities, in particular for treating foot ulcers. In this context, polymer-based nanostructures garnered significant interest due to their tunable physicochemical characteristics, rich diversity, and biocompatibility. This review article emphasizes the last advances and discusses the prospects in the use of polymeric materials as nanocarriers for β-cell imaging and non-invasive drug delivery of insulin and antidiabetic drugs in the management of blood glucose and foot ulcers.
Image classification through scattering media using optronic convolutional neural networks

Object classification behind a complex inhomogenous medium remains a significant challenge in many fields. Valid information is hardly extracted from speckles owing to the distortion of scattering media. Recent years deep learning has shown powerful capability in classifying object from scattered speckle patterns. However, largescale computations and pure digital procedures set a challenging task for deep neural networks running in optics. Here, we present an optronic technique for object classification through random diffuser media. A group of Fourier lens and a programmable spatial light modulator, form an optronic convolutional neural networks(OPCNN) with optimized kernels. The CMOS camera not only works as an image detection sensor but a non-linear activation layer by the curve built-in. We demonstrated the technique classification result using the airy disk intensity of every candidate channel. The trained OPCNN shows high-quality object predictions on the speckle patterns. Our work paves the way to an all-optical approach for imaging through scattering media.
Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging

Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, with an increasing number of basis materials, the nonlinearity of measurements causes difficulty in decomposition. In addition, noise amplification and beam hardening further reduce image quality. Thus, improving the accuracy of material decomposition while suppressing noise is pivotal for spectral CT imaging. This paper proposes a one-step multi-material reconstruction model as well as an iterative proximal adaptive decent method. In this approach, a proximal step and a descent step with adaptive step size are designed under the forward–backward splitting framework. The convergence analysis of the algorithm is further discussed according to the convexity of the optimization objective function. For simulation experiments with different noise levels, the peak signal-to-noise ratio (PSNR) obtained by the proposed method increases approximately 23 dB, 14 dB, and 4 dB compared to those of other algorithms. Magnified areas of thorax data further demonstrated that the proposed method has a better ability to preserve details in tissues, bones, and lungs. Numerical experiments verify that the proposed method efficiently reconstructed the material maps, and reduced noise and beam hardening artifacts compared with the state-of-the-art methods.
Exploring the Impact of Nitrogen Doping on the Optical Properties of Carbon Dots Synthesized from Citric Acid

The differences between bare carbon dots (CDs) and nitrogen-doped CDs synthesized from citric acid as a precursor are investigated, aiming at understanding the mechanisms of emission and the role of the doping atoms in shaping the optical properties. Despite their appealing emissive features, the origin of the peculiar excitation-dependent luminescence in doped CDs is still debated and intensively being examined. This study focuses on the identification of intrinsic and extrinsic emissive centers by using a multi-technique experimental approach and computational chemistry simulations. As compared to bare CDs, nitrogen doping causes the decrease in the relative content of O-containing functional groups and the formation of both N-related molecular and surface centers that enhance the quantum yield of the material. The optical analysis suggests that the main emission in undoped nanoparticles comes from low-efficient blue centers bonded to the carbogenic core, eventually with surface-attached carbonyl groups, the contribution in the green range being possibly related to larger aromatic domains. On the other hand, the emission features of N-doped CDs are mainly due to the presence of N-related molecules, with the computed absorption transitions calling for imidic rings fused to the carbogenic core as the potential structures for the emission in the green range.
Deep Confidence Propagation Stereo Network

Stereo matching depth estimation based on rectified image pairs is of great importance to many computer vision tasks such as vehicle navigation and autonomous driving. Confidence measures are typically used to refine stereo matching results, which provides robustness and efficiency for disparity estimation. However, previous learning-based confidence methods for stereo matching usually use the middle results or composition as a post-processing step to refine the stereo matching results. This cannot be optimized end-to-end and the performance is limited by the quality of the tri-modal output. To handle this issue, in this paper, we pursue an end-to-end hierarchical architecture and propose a differentiable confidence propagation (DCP) model of a cost aggregation network for stereo matching. The DCP model is integrated into an end-to-end neural network hierarchical architecture to guide matching cost volume aggregation. More specifically, to better represent the similarity of left and right feature maps, we extract unary context feature maps with an effective attention mechanism for matching cost construction. Moreover, we aggregate the cost volume with the multiple stacked DCP cost aggregation (DCPCA) networks to generate a more reliable and finer cost volume. This network suppresses multi-level disparity maps. Each output disparity is supervised with different training weights to learn in a coarse-to-fine way. Our method outperforms previous methods on the Sceneflow dataset by achieving the $0.6735px$ EPE error, achieving 1.53% D1-all metric of Non-occluded pixels regions and 0.72% Non-occluded pixels of $5px$ metric on KITTI 2015 and 2012 dataset. Extensive experiments carried out on the KITTI Stereo benchmarks demonstrate that our DCPCA-Net can significantly minimize the trade-off between accuracy and efficiency for stereo matching.
Explicit Representation of Events, Actions and Plans for Assumption-Based Plan Reasoning

We propose a wide-ranging knowledge representation formalism designed expressly to support many different forms of reasoning about plans. We begin with an event-based language based on the interval temporal logic. The language supports reasoning about action attempts and composite actions, both of which are given axiomatic definitions. We then define a representation for plans viewed as arguments that a certain course of action under certain explicit conditions will achieve certain goals. We can represent both correct and incorrect plans, and reason about why they might or might not fail. An important aspect of this work is the formal characterization of plan reasoning as assumption-based reasoning, to make the non-deductive aspects of plan reasoning explicit. A preliminary implementation of these ideas has already been built as the plan reasoning component of the TRAINS system.
Association-based Outlier Detection for Mixed Data

Various methods have been developed to detect outliers which are significantly different from others. Most outlier detection methods assume the data lie in Euclidean space in which distances can be easily defined and computed. In reality, we meet many data with both numerical and categorical attributes together, so-called mixed-data, for which it is not easy to define widely-accepted distance metrics. This paper proposes an outlier detection method which can be applied to mixed data. The method focuses on the association among attribute values. It first selects the sets of potentially associated attributes, computes the degrees of outlierness for records with respect to the associated attributes, and then determines a collection of outliers using the degrees. In addition, this paper shows some experiment results of the proposed method and compares with some other methods.
Transcriptomics and Metabolomics Analysis Provides Insight into Leaf Color and Photosynthesis Variation of the Yellow-Green Leaf Mutant of Hami Melon (Cucumis melo L.)

Leaf color mutants are ideal materials for studying the regulatory mechanism of chloroplast development and photosynthesis. We isolated a cucumis melo spontaneous mutant (MT), which showed yellow-green leaf phenotype in the whole growing period and could be inherited stably. We compared its leaves with the wild type (WT) in terms of cytology, physiology, transcriptome and metabolism. The results showed that the thylakoid grana lamellae of MT were loosely arranged and fewer in number than WT. Physiological experiments also showed that MT had less chlorophyll content and more accumulation of reactive oxygen species (ROS) than WT. Furthermore, the activity of several key enzymes in C4 photosynthetic carbon assimilation pathway was more enhanced in MT than WT. Transcriptomic and metabolomic analyses showed that differential expression genes and differentially accumulated metabolites in MT were mainly co-enriched in the pathways related to photosystem-antenna proteins, central carbon metabolism, glutathione metabolism, phenylpropanoid biosynthesis and flavonoid metabolism. We also analyzed several key proteins in photosynthesis and chloroplast transport by Western blot. In summary, the results may provide a new insight into the understanding of how plants respond to the impaired photosynthesis by regulating chloroplast development and photosynthetic carbon assimilation pathways.
A Historically Driven Spinup Procedure for Soil Carbon Modeling

Soil process models such as RothC typically assume soil organic carbon (SOC) is in equilibrium at the beginning of each simulation run. This is not likely to be true in the real world, since recalcitrant SOC pools (notably, humified material) take many decades to re-stabilize after a land use change. The equilibrium assumption stems from a spinup method in which the model is run under a single land use until all SOC pools stabilize. To overcome this, we demonstrate an alternative spinup procedure that accounts for historical land use changes. The “steady-state” and “historical” spinup methods both impute unknown C inputs such that the modeled SOC matches empirical measurements at the beginning of the simulation and set initial SOC fractions. Holding all other parameters equal, we evaluated how each spinup affects SOC projections in simulations of agricultural land use change in the U.S. state of Vermont. We found that projected SOC trajectories for all land use scenarios are sensitive to the spinup procedure. These differences are due to disparities in imputed below-ground plant-derived carbon between the two procedures. Compared to the steady-state, imputed C in the historical spinup is higher for land uses that increase SOC (e.g., adoption of regenerative practices) and lower for land uses that decrease SOC (e.g., a transition from pasture to crops), due to the time window within which land use changes are assumed to have occurred. The novel historical spinup procedure captures important dynamics commonly missing in previous studies, representing an advancement in soil process modeling.
STEIN FILLINGS OF CONTACT STRUCTURES SUPPORTED BY PLANAR OPEN BOOKS.

ACKNOWLEDGEMENTS I would like to thank my advisor John Etnyre for help and encouragement throughout writing of this thesis. In particular, his help in improving the overall exposition and writing of this thesis. I would have never finished this thesis without his help. I would like to thank Dan Margalit for discussions and help regarding the mapping class groups. In particular, I would like to thank Dan for pointing me to relevant papers which form the basis for what is done in this thesis. I would like to thank my collaborator Youlin Li for letting me use parts of our joint work in this thesis. I would also like to thank Jeremy Van-Horn Morris for help and discussions which were helpful in my understanding of the material involved in this thesis. Thank you to Tom Mark and Laura Starkston for asking me a question during Tech Topology conference in 2013 whose answer led to Theorem 1.0. 4. I would also like to thank Patrick Massot for letting me use his pictures of contact structures in this thesis.
A Simple Dataset for Demonstrating Common Distributions

The baby boom dataset contains the time of birth, sex, and birth weight for 44 babies born in one 24-hour period at a hospital in Brisbane, Australia. The data can be used to demonstrate that some common distributions -- the normal, binomial, geometric, Poisson, and exponential -- can be used to model real situations. Because the dataset is small and easily understood, it provides a useful classroom example for discussing these distributions. ; ;
Highly nonlinear balanced Boolean functions satisfying high degree propagation criterion

Three of the most important nonlinearity criteria for cryptographically strong functions are balancedness, nonlinearity and propagation criterion. In this paper we study systematic methods for constructing highly nonlinear balanced Boolean functions satisfying the high degree propagation criterion. In particular, we present a method for constructing highly nonlinear balanced Boolean functions on V 2k+1 satisfying the propagation criterion with respect to all but one vectors in V 2k+1 , and a method for constructing highly nonlinear balanced Boolean functions on V 2k satisfying the propagation criterion with respect to all but three vectors in V 2k. We then show that the vectors where the propagation criterion is not satissed can be moved upward in such a way that the functions constructed on V 2k+1 satisfy the propagation criterion of degree 2k, while the function constructed on V 2k satisfy the propagation criterion of degree 4 3 k. The moving technique does not alter the balancedness and nonlinearity of the functions. The methods are illustrated by concrete constructions of such cryptographically strong functions. The algebraic degrees of the functions are also discussed.