Miracle-dz/UniFAR-FTUs huggingface dataset:
Estradiol and luteinizing hormone reverse memory loss in phencyclidine model of schizophrenia: Evidence for hippocampal GABA action

The cognitive symptoms of schizophrenia are poorly understood and difficult to treat. Estrogens may mitigate these symptoms via unknown mechanisms. To examine these mechanisms, we tested whether increasing estradiol (E) or decreasing luteinizing hormone (LH) could rescue declarative memory in a phencyclidine (PCP) model of schizophrenia. We then assessed whether changes in cortical or hippocampal GABA may underlie these effects. Female rats were ovariectomized and injected subchronically with PCP. To modulate E and LH, animals received hormone capsules or Antide injections. Short-term episodic memory was assessed using the novel object recognition task. Brain expression of GAD67 was analyzed via western blot, and parvalbumin-containing cells were counted using immunohistochemistry. Some rats received hippocampal infusions of a GABAA agonist, GABAA antagonist, or GAD inhibitor before behavioral testing. We found that PCP reduced hippocampal GAD67 and abolished object recognition. Antide restored hippocampal GAD67 and rescued recognition memory in PCP-treated animals. Estradiol reversed PCP’s amnesic effect but failed to restore hippocampal GAD67. PCP did not cause significant differences in number of parvalbumin-expressing cells or cortical expression of GAD67. Hippocampal infusions of a GABAA agonist restored memory in PCP-treated rats. Blocking hippocampal GAD or GABAA receptors in ovx animals reproduced memory loss similar to PCP and inhibited estradiol’s memory rescue in PCP-treated animals. In summary, decreasing LH or increasing E can reverse memory loss in a PCP model of schizophrenia. Alterations in hippocampal GABA may contribute to both PCP’s effects on declarative memory and the hormones’ ability to reverse them.
Computational Intelligence for Corrosion Rate Prediction of Refinery Cooling Water Plant

In order to predict the corrosion rate of refinery cooling water plant effectively, this paper proposes a novel model, which combines the Restricted Boltzmann Machines (RBM) and Support Vector Regression (SVR). The SVR has been confirmed the effectiveness for corrosion rate prediction, but it has not been used to predict the corrosion rate of the refinery cooling water plant. Moreover, by the aid of Deep Learning (DL) approaches that can model high-level abstractions in the data, the hybrid model is implemented by integrating the RBM with SVR to predict the corrosion rate of the refinery cooling water plant. The proposed model can reduce the dimensionality of data space and preserve the effective features of refinery cooling water plant. Compared with only using SVR model, the combination of the RBM and SVR model can achieve higher prediction accuracy and more efficient and effective. Prediction accuracy is evaluated using the actual Industrial data. According to the comparison results, the hybrid model proposed was better than the previous models in predicting the corrosion rate of the refinery cooling water plant. The hybrid model proposed is a promising and practical methodology for realtime tracking of corrosion in refinery cooling water plant system. The factory can use the hybrid model to schedule maintenance process that leads to risk reduction of structure failure and maintenance cost.
An Integrated DEMATEL-ANP Approach for Mobile Banking Adoptions in China Market

With the rapid development of the mobile device and Internet usage, the mobile banking has seen remarkable growth across the world, especially in the past few years, as one of the emerging financial innovations. This paper attempts to explore the mobile banking services offered by selected commercial Chinese banks. The main data of this study were obtained through the comprehensive evaluation of mobile banking services. The DEMATEL-ANP method was used to evaluate the main factors influencing the adoption of mobile banking. The results show that there is still big space for the improvement of the mobile banking services for the selected Chinese banks. The management significance and suggestions for these banks are also discussed.
An Extension of Learning Object Model Focusing on Learners’ Perspective

Instructional designer must identify specific objectives with meaningful teaching and learning strategies from learners’ perspective. Underlying models of preceding researches do not have sufficient pedagogical components such as instructional strategy classification to describe our unit of learning. To solve this problem we considered additional elements with existing meta-model. The purpose of this study is to describe an extension for learning object meta-model specific to learning activities, to help instructional designers select strategy in various situations where they are working with existing assets.
Effect of High Pressure Homogenization Treatment on Structure and Properties of Soybean Residue Cellulose Nanofibers

To reduce the adverse effects of non-cellulose materials on subsequent homogenization, the effects of a high-pressure homogenization treatment on the structure and properties of cellulose nanofibers (CNF) prepared by acid treatment of soybean residue were studied. The effects of the number of homogenization step on the microfibrillation degree, crystalline structure and mechanical properties of the soybean residue were analyzed by SEM, FT-IR, XRD, TG and DTG. The results showed that an increase in the number of homogenization steps led to an increase in the degree of microfibrillation, a more uniform distribution of the CNF diameter, and an increase in the crystallinity of CNF. However, but when the number of homogenization steps exceeded 15, the rate of change decreased, and the crystallinity of CNF decreased. As the number of homogenization steps increased, the average degree of polymerization and average molecular weight of CNF decreased continuously, and after 15 homogenization steps, their rate of change also decreased. Therefore, 15 steps of high-pressure homogenization represented a suitable number of steps to prepare the soybean residue CNF with an average diameter of 15 nm.
Hilbert Curve based Flexible Dynamic Partitioning Scheme for Adaptive Scientiﬁc Computations

Space Filling Curves (SFC) are commonly used by the HPC community for partitioning data[3, 4, 6] and for resource allocations[2, 5]. Amongst the various SFCs, Hilbert curves have been found to be in general superior to the more ubiquitous Morton or Z-Curve [3]. However, their adoption in large-scale HPC codes, especially for partitioning applications, has not been as common as Morton curves due to the computational complexity associated with the Hilbert ordering. In this work, we present an alternative algorithm for computing the Hilbert ordering that can be implemented almost as eﬃciently as the Morton ordering. We also pro-pose a modiﬁcation to the standard SFC based partitioning algorithm that tolerates small (user-speciﬁed) work-load imbalance to obtain larger savings in communication costs. This allows us to obtain signiﬁcant improvements in partition quality while using Hilbert ordering.
The Schatten p-norm on Rn

It is well known that the Schatten p-norm defined on the space of matrices is useful and possesses nice properties. In this paper, we explore the concept of Schatten p-norm on R via the structure of Euclidean Jordan algebra. Two types of Schatten p-norm on R are defined and the relationship between these two norms is also investigated.
Characterization of the Spatial and Parameter Variability in a Subtropical Wetland

Abstract The eutrophication of subtropical wetland ecosystems has profound effects on biogeochemical patterns, pedoand biodiversity, and ecological function. We investigated a nutrient-enriched subtropical wetland, which is undergoing natural succession. We collected 20 biogeochemical soil properties at 267 sites to characterize the current ecological status. This exhaustive dataset served as a reference. Our goal was to identify properties which accounted for much of the spatial and the parameter variability. We used Conditional Sequential Gaussian Simulation (CSGS) to generate realizations of biogeochemical properties to characterize spatial patterns and to assess explicitly the uncertainty of predictions. We used Principal Component (PC) Analysis to transform a number of possibly correlated variables into a smaller number of uncorrelated variables. CSGS was used to generate realizations of PCs. Each biogeochemical property was mapped into a PC indicating its significance to explain variability in the dataset. A spatial sensitivity analysis using subsets drawn randomly from the exhaustive dataset suggested that dynamic biogeochemical properties grouped into PC1 require sampling densities of > 3.75 sites/100 ha whereas properties grouped into PC2 require a sample density of > 2.52 sites/100 ha to reproduce the spatial patterns and variability across the wetland. Our results are valuable to document the current ecological spatial patterns in this wetland and optimize future sampling designs in subtropical wetlands explicitly considering parameter and spatial variability.
Use of CFCC Tendons and Reinforcements in Concrete Structures for Durability

This paper presents a brief description of the development, usage, and application of non-corrosive Carbon fiber composite cable (CFCC). In addition to that, case studies of the use of CFCC in bridges are discussed to share the technical information about the successful deployment and various applications of the CFCC tendons and reinforcements. CFCC has already been used as reinforcing materials for more than 140 structures. So far, it is a proven fact that durability of prestressed concrete bridge using CFCC tendons is more than 23 years through harsh environmental conditions, without any deterioration.
States and rates: Complementary approaches to developing flow‐ecology relationships

In recognition of the influence of flow on riverine habitats and organisms, stream ecologists have devoted considerable effort to the development of quantitative predictive relationships describing ecological responses to flow variability, i.e. flow-ecology relationships. Methods used to generate flow-ecology relationships can be thought of as a continuum bookended by pure states approaches on one end and by rates approaches on the other. In pure states approaches, the ecological response is a snapshot of a condition or property (i.e. a state) derived from a single measurement in time. In contrast, ecological responses in rates approaches reflect temporal change (i.e. a rate) and are thus reliant on repeated measurements made over time. Here, we elaborate on the fundamental characteristics of different approaches (pure states, rates and an intermediate approach we call repeated states) for generating flow-ecology relationships, examine how commonly the different approaches are used in the flow-ecology literature, conduct an independent analysis to illustrate the different outcomes achieved by applying repeated-states and rates approaches using a dataset for stream fish diversity in relation to flow magnitude, and identify some of the different ways ecologists are applying rates approaches in flow ecology. Our literature review revealed that repeated-states approaches (53% of reviewed studies) were used far more commonly than either pure states (19%) or rates (12%) approaches to generate flow-ecology relationships. The remaining hybrid studies (17%) used both state and rate responses, and thus also relied on repeated measurements over time. Despite frequent collection of data suitable for rates approaches, flow-ecology relationships have generally been developed using states approaches that relate changes in ecological states to different long-term average flow conditions, rather than to specific flow sequences over much shorter time intervals. Such flow-ecology relationships cannot generate temporally specific predictions of ecological responses to changing flow conditions (i.e. the expected change in state following a specific flow sequence), nor can they describe demographic processes underlying observed changes. While there are different scenarios in which a pure or repeated-states approach would be useful, more frequent use of rates approaches would increase our ability to test flow-ecology hypotheses and our mechanistic understanding of flow-ecology relationships.
Detecting contacts in protein folds by solving the inverse Potts problem – a pseudolikelihood approach

Spatially proximate amino acid positions in a protein tend to coevolve, so a protein’s 3D-structure leaves an echo of correlations in the evolutionary record. Reverse engineering 3D-structures from such correlations is an open problem in structural biology, pursued with increasing vigor as new protein sequences continue to fill the data banks. Within this task lies a statistical stumbling block, rooted in the following: correlation between two amino acid positions can arise from firsthand interaction, but also be network-propagated via intermediate positions; observed correlation is not enough to guarantee proximity. The remedy, and the focus of this thesis, is to mathematically untangle the crisscross of correlations and extract direct interactions, which enables a clean depiction of coevolution among the positions. Recently, analysts have used maximum-entropy modeling to recast this causeand-effect puzzle as parameter learning in a Potts model (a kind of Markov random field). Unfortunately, a computationally expensive partition function puts this out of reach of straightforward maximum-likelihood estimation. Mean-field approximations have been used, but an arsenal of other approximate schemes exists. In this work, we reimplement an existing contact-detection procedure and replace its mean-field calculations with pseudolikelihood maximization. We then feed both routines real protein data and highlight differences between their respective outputs. Our new program seems to offer a systematic boost in detection accuracy. Acknowledgements My work was carried out at the Department of Computational Biology at the Royal Institute of Technology in Stockholm. I wish to thank my supervisor Erik Aurell for encouragement, guidance, and access to the community (which turned out essential to my results). Also, big thanks to Martin Weigt for generously providing data, code, and invaluable help with the biological side of the effort. Finally, thanks to my family and friends for support and for listening to me go on about the subject. A note on language Throughout this report, ’we’ is written even if the reader is excluded and the reference is to me, the one author.
Learning a Decision Module by Imitating Driver's Control Behaviors

Autonomous driving systems have a pipeline of perception, decision, planning, and control. The decision module processes information from the perception module and directs the execution of downstream planning and control modules. On the other hand, the recent success of deep learning suggests that this pipeline could be replaced by end-to-end neural control policies, however, safety cannot be well guaranteed for the data-driven neural networks. In this work, we propose a hybrid framework to learn neural decisions in the classical modular pipeline through end-to-end imitation learning. This hybrid framework can preserve the merits of the classical pipeline such as the strict enforcement of physical and logical constraints while learning complex driving decisions from data. To circumvent the ambiguous annotation of human driving decisions, our method learns high-level driving decisions by imitating low-level control behaviors. We show in the simulation experiments that our modular driving agent can generalize its driving decision and control to various complex scenarios where the rule-based programs fail. It can also generate smoother and safer driving trajectories than end-to-end neural policies.
Extraction of Cellulose and Microcrystalline Cellulose from Kenaf

Bast and core of kenaf which categorized as agriculture residues were used in the extraction of cellulose due to its lignocellulosic nature. In this study, cellulose was extracted from the raw kenaf by using 10% alkaline peroxide pre-treatment followed by extraction process at 85 °C using the combination of 20% (v/v) formic acid and 10% (v/v) hydrogen peroxide that formed peroxy acid. The extracted cellulose was further hydrolyzed by 1.5 M of hydrochloric acid at 80 °C to yield microcrystalline cellulose (MCC). Relatively high yield of 71.81% (w/w) and 94.8% (w/w) were achieved for extracted cellulose and MCC, respectively. Based on FESEM, the average diameter of kenaf cellulose and MCC were 12.43 μm and 11.64 μm, respectively with the separated fibrils and rod-like structural. FT-IR spectral peaks observed in the cellulose and MCC revealed that most hemicellulose and lignin were eliminated during the chemical treatments. XRD analysis results exhibited that kenaf MCC gives the sharpest and highest peak s compared to others. The results also showed that the crystallinity index of extracted cellulose and MCC were increased to 60.5% and 62.3%, respectively.
Study of the surface integrity in a super duplex stainless steel after turning

The purpose of this paper was to study the main effects of the turning in the surface integrity of the duplex stainless steel ASTM A890-Gr6A.The focus of the work was the finishing operations and a complete factorial planning was used, with 2 levels and 5 factors. The tests were conducted on a turning center with carbide tools and the main entrances variables were: tool material class, feed rate, cutting depth, and cutting speed. The analyzed answers were: microstructure analysis, surface roughness, cutting force measurements and the micro-hardness variation. The results showed that was not possible to find microstructural changes even when the bigger cutting parameters were used. The other answers were correlated with the cutting parameters and a good selection was founded to the best surface integrity generation, that means, that the smaller feed rate, the smaller cutting speed and the bigger cutting depth provides the best combination of the best roughness and the micro-hardness values.
A nonparametric estimation of the overflow probability with long-range dependent input Stima Non Parametrica Della Probabilità Di Overflow Con Ingressi A Memoria Lunga

Riassunto: in questo lavoro si considera un sistema a coda in tempo discreto G/G/1 (General arrivals/General service times/1 server), utile per la valutazione di prestazioni di sistemi di trasmissione asincroni, in particolare sistemi ATM (Asynchronous Transfer Mode). L'attenzione è rivolta alla probabilità di coda del ”lavoro residuo” (unfinished work), in quanto equivalente alla probabilità di overflow. Si introduce una stima non parametrica, sia puntuale che intervallare, di un'approssimazione della probabilità di coda del lavoro residuo in equilibrio. Contrariamente alle ipotesi usuali, si assume una forma del tutto generale per la funzione di correlazione del processo di arrivi. Le stime ottenute sono applicate a dati reali di telecomunicazioni.
Correspondence chaining for enhanced dense 3D reconstruction

Within the computer vision community, the reconstruction of rigid 3D objects is a well known task in current research. Many existing algorithms provide a dense 3D reconstruction of a rigid object from sequences of 2D images. Commonly, an iterative registration approach is applied for these images, relying on pairwise dense matches between images, which are then triangulated. To minimize redundant and imprecisely reconstructed 3D points, we present and evaluate a new approach, called Correspondence Chaining, to fuse existing dense twoview 3D reconstruction algorithms to a multi-view reconstruction, where each 3D point is estimated from multiple images. This leads to an enhanced precision and reduced redundancy. The algorithm is evaluated with three different representative datasets. With Correspondence Chaining the mean error of the reconstructed pointclouds related to ground truth data, acquired with a laser scanner, can be reduced by up to 40%, whereas the root mean square error is even reduced by up to 56%. The reconstructed 3D models contain much less 3D points, while keeping details like fine structures, the file size is reduced by up to 78% and the computation time of the involved parts is decreased by up to 42%.
Piecewise Anisotropic Scaling Results

In this Supplementary Material, we provide additional results for the experiments on piecewise anisotropic scaling and non-rigid deformation on the chair, airplane, and human body datasets (described in the main manuscript). Sec. 1 and 2 demonstrate qualitative semantic parameter editing results on rigid and non-rigid shape categories, respectively, while Sec. 3 shows results on out-of-distribution examples. Sec. 4 provides additional comparisons to Neural Cages [4], a recent technique for source-to-target matching with a learned deformation, and DualSDF [2], a recent deformation method with learned re-synthesis.
Characterization of Resistance to the Green Rice Leafhopper (Nephotettix cincticeps Uhler) in a Core Collection of Landraces in Rice (Oryza sativa L.)

The green rice leafhopper (GRH; Nephotettix cincticeps Uhler) is one of the most devastating insect pests of cultivated rice (Oryza sativa L.) in temperate regions in Asia. Using the rice germplasms with biotic stress resistance is the most effective and environmentally-friendly way to control the insect pests in the paddy. Sixty accessions from a core set of worldwide collection of rice were characterized for resistance to the GRH by antibiosis test both at the seedling and at the booting stages. The positive correlations of average nymph mortality (ANM) were observed between at the seedling stage and at the booting stage on 3 days after infestation (DAI) (r = 0.684**), 5DAI (r = 0.680**), and 7DAI (r = 0.652**), respectively. This result will give us the opportunity to screen resistance to the GRH with the cost-efficient way using rice seedlings in a growth chamber. To classify the 60 accessions evaluated, the ANM of the GRH of each accession was compared to the respective ANM of resistant and susceptible controls with the least significant difference (LSD) value. Based on the statistical difference or similarity of the ANMs to the resistant and the susceptible controls, we proposed the four groups of resistance to the GRH, (I) high level of resistance, (II) considerable level of resistance, (III) moderate level of resistance, and (IV) susceptibility. At the seedling stage, a total of 26 accessions were highly resistant in addition to other 6 for considerable level of resistance and other 10 for moderate level of resistance. At the booting stage, on the other hand, a total of 18 accessions were highly resistant in addition to other 3 for considerable level of resistance and other 5 for moderate level of resistance. A total of 42 accessions with high to moderate level of resistance were distributed across 16 countries in Asia in addition to each one for Madagascar and USA. The classification of landraces based on the present protocol for screening resistance to the insect provided fundamental information for genetics and breeding on resistance to the GRH in rice.