Miracle-dz/UniFAR-FTUs huggingface dataset:
Long Term Time Series Prediction with Multi-Input Multi-Output Local Learning

Existing approaches to long term time series forecasting are based either on iterated one-step-ahead predictors or direct predictors. In both cases the modeling techniques which are used to implement these predictors are multi-input single-output techniques. This paper discusses the limits of single-output approaches when the predictor is expected to return a long series of future values and presents a multi-output approach to long term prediction. The motivation for this work is the fact that, when predicting multiple steps ahead of a time series, it could be interesting to exploit the information that a future series value could have on another future value. We propose a multi-output extension of our previous work on Lazy Learning, called LL-MIMO, and we introduce an averaging strategy of several long term predictors to improve the final accuracy. In order to show the effectiveness of the method, we present the results obtained on the three training time series of the ESTSP’08 competition.
A Theory on the Completeness of the DN Logic Capability

While the brain develops through interactions with the external environment, it almost never experiences an exact physical event twice. Furthermore, the attended objects and events do not appear exactly the same as before and the background settings are not the exactly same either. However, the brain can abstract and generalize to deal with those environmental variations. This paper discusses a theory on the completeness of the logic capability of the Developmental Networks (DN) as a simplified brain-like model from such environmental variations. Various abstractions and generalizations are emergent properties of a DN through its incremental lifetime learning experience. While this process takes place, the network appears to have an increasing amount of logic capability in the eyes of human observers. Since it seems impossible to explain all possible kinds of logic capabilities that a human can acquire through his lifetime, I propose a general task-nonspecific formulation about logic capability in a DN. I prove that a DN incrementally generates and updates an internal, emergent, Finite Automaton (FA), whose complexity becomes increasingly high under the teacher’s scaffolding scheme. It seems that such a highly complex FA can implement any practical logic. This is a theoretical paper but it discusses and cites supporting experimental results.
Promoting Physical Activity through Priming the Content of Motivation

Non-conscious processes are important in influencing the performance of a number of behaviors, such as physical activity. One way that such processes can be influenced is through priming. Despite this, approaches within health psychology have predominantly focused on reflective processes with a number of psychological theories dedicated to identifying the predictors of intention. In doing so, critical beliefs and thoughts are first identified and then altered within interventions. Such work has shown limited effectiveness, however, with a gap apparent between what one intends to do and what subsequently ensues. Although there have been attempts to bridge this gap, such as theoretical integration, recent efforts include priming implicit processes. The aim of this commentary is to demonstrate the potential effectiveness of priming non-conscious processes and to suggest that the content of motivation should also succumb to priming influences. This brief review suggests that priming one of the most influential conscious processes, that of self-efficacy, could demonstrate particular effectiveness in promoting physical activity. Thus, the main purpose of the article is to suggest that the content of implicit processes as well their more traditional conscious counterparts may provide useful intervention targets. To achieve this, the article will first introduce the role of non-conscious processes and behavioral priming. Following this, the more common reflective processes will be outlined as well as attempts at theoretical integration. Finally, the article will identify studies priming non-conscious processes and will then suggest priming self-efficacy.
Mixed-Cell-Height Standard Cell Placement Legalization

A traditional standard cell library consists of various functional cells with the same height, which could speed up VLSI design flow since designers could align cells to placement sites in rows. However, in advanced nodes, cells are designed with different heights. With mixed cell heights, we can design simple cells (e.g. inverters) as single-row-height cells, and complex cells (e.g. flip-flops) as multiple-row-height cells. Nevertheless, placement legalization becomes more difficult with mixed-cell-height libraries. In this paper, we propose a parallel legalization method for mixed-cell-height standard cell libraries to minimize total displacement. Experimental results show that our method has a 19\% improvement on average in displacement compared with the state-of-the-art work [9].
Probabilistic View-based 3D Curve Skeleton Computation on the GPU

Computing curve skeletons of 3D shapes is a challenging task. Recently, a high-potential technique for this task was proposed, based on integrating medial information obtained from several 2D projections of a 3D shape. However effective, this technique is strongly influenced in terms of complexity by the quality of a so-called skeleton probability volume, which encodes potential 3D curve-skeleton locations. In this paper, we extend the above method to deliver a highly accurate and discriminative curve-skeleton probability volume. For this, we analyze the error sources of the original technique, and propose improvements in terms of accuracy, culling false positives, and speed. We show that our technique can deliver point-cloud curve-skeletons which are close to the desired locations, even in the absence of complex postprocessing. We demonstrate our technique on several 3D models.
Assessing the Hydrologic Performance of a Green Roof Retrofitting Scenario for a Small Urban Catchment

In an existing urban environment, retrofitting low impact development (LID) solutions can provide an opportunity to address flooding and water quality problems. Taking into account the need to effectively estimate the impact of vegetated LIDs, particular attention has recently been given on the evapotranspiration (ET) process that is responsible for the restoring of green roof water-holding capacity. The present study aims to develop a methodological approach to estimate the actual ET as climate input data in the United States Environmental Protection Agency (EPA) Storm Water Management Model (SWMM) continuous simulation. The proposed approach is calibrated on a single green roof installation based on one-minute continuous simulations over 26 years of climate records. Then the calibrated methodological approach has been implemented to perform continuous simulation of a small urban catchment retrofitted with green roofs. Based on simulation results, the peak and volume reduction rate evaluated for the 1433 rainfall events are equal to 0.3 on average (with maximum values of 0.96 for peak and 0.86 for volume). In general, the adopted methodology indicates that the actual ET estimate is needed to suitably assess the hydrologic performance of vegetated LIDs mainly concerning the volume reduction index; furthermore, the methodology can be easily replicated for other vegetated LID applications.
Expression, Polymorphism, and Potential Functional Sites of the BMPR1A Gene in the Sheep Horn

Simple Summary This study investigated the relationship between the BMPR1A gene and horn type in sheep. The study found that the expression of the BMPR1A gene was significantly higher in the normal horn sheep compared to scurred sheep. This trend was observed in multiple sheep breeds. It was also discovered that there is high expression in the skin of three species: cattle, sheep, and pigs. Importantly, it was found that certain nucleotides in the BMPR1A gene are likely to be crucial in horn size and horn type. Abstract Sheep horns are composed of bone and sheaths, and the BMPR1A gene is required for cartilage and osteogenic differentiation. Therefore, the BMPR1A gene may have a function related to the sheep horn, but its relationship with the sheep horn remains unclear. In this study, we first utilized RNA sequencing (RNA-seq) data to investigate the expression of the BMPR1A gene in different tissues and breeds of sheep. Second, whole-genome sequencing (WGS) data were used to explore the functional sites of the BMPR1A gene. Lastly, the allele-specific expression of the BMPR1A gene was explored. Our results indicate that BMPR1A gene expression is significantly higher in the normal horn groups than in the scurred groups. Importantly, this trend is consistent across several sheep breeds. Therefore, this finding suggests that the BMPR1A gene may be related to horn type. A total of 43 Single-Nucleotide Polymorphisms (SNPs) (F-statistics > 0.15) and 10 allele-specific expressions (ASEs) exhibited difference between the large and small horn populations. It is probable that these sites significantly impact the size of sheep horns. Compared to other polled species, we discovered ten amino acid sites that could influence horn presence. By combining RNA-seq and WGS functional loci results, we identified a functional site at position 40574836 on chromosome 25 that is both an SNP and exhibits allele-specific expression. In conclusion, we demonstrated that the BMPR1A gene is associated with horn type and identified some important functional sites which can be used as molecular markers in the breeding of sheep horns.
Principles for Examining Predictive Validity: The Case of Information Systems Spending Forecasts

Research over two decades has advanced the knowledge of how to assess predictive validity. We believe this has value to information systems (IS) researchers. To demonstrate, we used a widely cited study of IS spending. In that study, price-adjusted diffusion models were proposed to explain and to forecast aggregate U.S. information systems spending. That study concluded that such models would produce more accurate forecasts than would simple linear trend extrapolation. However, one can argue that the validation procedure provided an advantage to the diffusion models. We reexamined the results using an alternative validation procedure based on three principles extracted from forecasting research: (1) use ex ante (out-of-sample) performance rather than the fit to the historical data, (2) use well-accepted models as a basis for comparison, and (3) use an adequate sample of forecasts. Validation using this alternative procedure did confirm the importance of the price-adjustment, but simple trend extrapolations were found to be more accurate than the price-adjusted diffusion models.
Neural Changes Associated with Training Exercise , cognition , and the aging brain

Kramer, Arthur F. , Kirk I. Erickson, and Stanley J. Colcombe. Exercise, cognition, and the aging brain. J Appl Physiol 101: 1237–1242, 2006. First published June 15, 2006; doi:10.1152/japplphysiol.000500. 2006. —We provide a brief review of the literature on exercise effects on brain and cognition. To this end, we focus on both prospective and retrospective human epidemiological studies that have examined the influence of exercise and physical activity on cognition and dementia. We then examine the relatively small set of human randomized clinical trials that have, for the most part, focused on exercise training effects on cognition. Next, we discuss animal research that has examined the molecular, cellular, and behavioral effects of exercise training. Finally, we conclude with a summary and brief discussion of important future directions of research on fitness cognition and brain.
Temporary abdominal closure techniques: a prospective randomized trial comparing polyglactin 910 mesh and vacuum-assisted closure.

OBJECTIVE The options for abdominal coverage after damage control laparotomy or abdominal compartment syndrome vary by institution, surgeon preference, and type of patient. Some advocate polyglactin mesh (MESH), while others favor vacuum-assisted closure (VAC). We performed a single institution prospective randomized trial comparing morbidity and mortality differences between MESH and VAC. METHODS Patients expected to survive and requiring open abdomen management were prospectively randomized to either MESH or VAC. After randomization, an enteral feeding tube was inserted and the closure device placed. VAC patients returned to the operating room every 3 days for a total of three changes at which time polyglactin mesh was placed if closure was not possible. The MESH group had twice daily assessments for the possibility of bedside mesh cinching and closure. Both groups underwent split thickness skin grafting when granulation tissue was evident, if delayed primary closure was not possible. RESULTS Fifty-one patients were randomized. Both cohorts were matched for Injury Severity Scale score, gender, blunt/penetrating/abdominal compartment syndrome and age. Three patients died within 7 days and were excluded from closure rate calculation. There were no differences between delayed primary fascial closure rates in the VAC (31%) or MESH (26%) groups. The fistula rate in the VAC group was 21% but not statistically different from the 5% rate for MESH. Intraabdominal rates were not statistically different. All VAC fistulas were related to feeding tubes and suture line areas; the MESH fistula followed a retroperitoneal colon leak remote from the mesh. CONCLUSIONS MESH and VAC are both useful methods for abdominal coverage, and are equally likely to produce delayed primary closure. The fistula rate for VAC is most likely due to continued bowel manipulation with VAC changes with a feeding tube in place-enteral feeds should be administered via nasojejunal tube. Neither method precludes secondary abdominal wall reconstruction.
Robust Linear Regression via $\ell_0$ Regularization

Linear regression in the presence of outliers is an important problem and is challenging as the support of outliers is not known beforehand. Many robust estimators solve this problem via explicitly or implicitly assuming that outliers are sparse and result in large observation errors. We propose an algorithm for robust outlier support identification (AROSI) utilizing a novel objective function with <inline-formula><tex-math notation="LaTeX">$\ell _{0}$</tex-math> </inline-formula>-“norm” regularization which models the sparsity of outliers. The optimization procedure naturally utilizes the large observation error assumption of outliers and directly operates on the <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$</tex-math></inline-formula>-“norm” and is guaranteed to converge. When only sparse outliers are present (no dense inlier noise), we show that, under certain model and algorithm parameter settings, AROSI can recover the solution exactly. In the case, where both dense inlier noise and sparse outliers are present, we prove that the estimation error is bounded. Extensive empirical comparisons with state-of-the-art methods demonstrate the advantage of the proposed method.
Decomposition of mixed pixels using Bayesian Self- Organizing Map (BSOM) neural networks

How to decompose mixed pixels effectively and precisely for remote sensing images is a critical issue for the quantitative remote sensing research. In this paper we propose a new method for decomposition of mixed pixels of multispectral or hyperspectral remote sensing images. The proposed method introduces the algorithm of Bayesian Self-Organizing Map (BSOM) into the problem of the decomposition of mixed pixels. It estimates Gaussian parameters by minimizing the Kullback-Leibler information metric, and finishes the unmixing with Gaussian Mixture Model (GMM). In order to obtain a high unmixing precision, we need to extend the range of Gaussian distributions, and thus propose the 3 sigma variances adjustment method to solve this problem. In addition, the used unmixing model satisfies two constraints which are demanded for the problem of the decomposition of mixed pixels automatically: abundances non-negative constraint (ANC) and abundances summed-to-one constraint (ASC). Experimental results on simulated and practical remote sensing images demonstrate that the proposed method can get good unmixing results for the decomposition of mixed pixels, and is more robust to noise than other methods.
Antioxidant and α-glucosidase inhibitory activities of the leaf and stem of selected traditional medicinal plants

The study was aimed to determine the antioxidant and α-glucosidase inhibition activities of the stem and leaf of five different traditional medicinal plants. The studied plants exhibited varied antioxidant and α-glucosidase inhibition activities. The antioxidant activities of the plants were determined through their free radical scavenging capabilities using DPPH assay. The most potent antioxidant activity was demonstrated by Neptunia oleracea with an IC 50 of 35.45 and 29.72 µg/mL for leaf and stem, respectively. For α-glucosidase inhibition activity, Neptunia oleracea exhibited potential α-glucosidase inhibition activity with IC 50 value of 19.09 and 19.74 µg/mL for leaf and stem, respectively. The highest total phenolic content (TPC) was also marked in Neptunia oleracea leaf and stem with value of 40.88 and 21.21 mg GAE/g dry weight, respectively. The results also showed that Strobilanthes crispus collected from two different locations possessed different levels of phenolic content, antioxidant and α-glucosidase inhibition activities. The study revealed that phenolic compounds could be the main contributors to the antioxidant and α-glucosidase inhibition activities with R values of 78.9 and 67.4%, respectively. In addition, antioxidant and α-glucosidase were positively correlated (R = 81.9%). Neptunia oleracea could be suggested as a potential natural source of antioxidant and antidiabetic compounds that can be used for the prevention or treatment of diabetes.
Emotion Classification Using Facial Expression

Human emotional facial expressions play an important role in interpersonal relations. This is because humans demonstrate and convey a lot of evident information visually rather than verbally. Although humans recognize facial expressions virtually without effort or delay, reliable expression recognition by machine remains a challenge as of today. To automate recognition of emotional state, machines must be taught to understand facial gestures. In this paper we developed an algorithm which is used to identify the person's emotional state through facial expression such as angry, disgust, happy. This can be done with different age group of people with different situation. We Used a Radial Basis Function network (RBFN) for classification and Fisher's Linear Discriminant (FLD), Singular Value Decomposition (SVD) for feature selection.
Quasi-linear controllable inductor

A magnetic structure consisting of two ferromagnetic cores and three windings is shown to behave as a quasi-linear electronically controllable inductor. Together with the controllable resistor and capacitor it constitutes a complete set of components with electronically variable fundamental electrical properties: resistance, capacitance, and inductance. In contrast to the other two components, the power-handling capability of a controllable inductor is not limited a priori. Because of this, the spectrum of its potential applications is extremely wide.
Non-negative matrix factorization considering given vectors in a basis

Recently, a novel matrix factorization, named non-negative matrix factorization (NMF), attracts much attention in the field of signal processing. A matrix with non-negative elements can be decomposed into a product of two matrices with non-negative elements by the NMF. One resulting matrix can be regarded as a basis matrix; and the other can be regarded as a coefficient matrix giving linear combinations of the basis vectors. In practical problems, there exists a case where an ideal basis is partially known. In this paper, we propose a novel method for NMF considering given vectors in an ideal basis. We introduce a criterion for the method and construct an algorithm to optimize the criterion. Moreover, we prove that the proposed algorithm surely converges. Some results of computer simulations are also given to verify the efficacy of the proposed method.
LIN28 cooperates with WNT signaling to drive invasive intestinal and colorectal adenocarcinoma in mice and humans

LIN28A and LIN28B are highly related RNA-binding paralogs that influence development, tissue regeneration, and oncogenesis. Tu et al. demonstrate that overexpression of either paralog drives invasive intestinal adenocarcinoma and cooperates with the Wnt pathway to promote tumor initiation and progression in murine models.
Development and Application of an Oxidation Flow Reactor to Study Secondary Organic Aerosol Formation from Ambient Air

Secondary organic aerosols (SOA) in the atmosphere play an important role in air quality, human health, and climate. However, the sources, formation pathways, and fate of SOA are poorly constrained. In this dissertation, I present development and application of the oxidation flow reactor (OFR) technique for studying SOA formation from OH, O3, and NO3 oxidation of ambient air. With a several-minute residence time and a portable design with no inlet, OFRs are particularly well-suited for this purpose. I first introduce the OFR concept, and discuss several advances I have made in performing and interpreting OFR experiments. This includes estimating oxidant exposures, modeling the fate of lowvolatility gases in the OFR (wall loss, condensation, and oxidation), and comparing SOA yields of single precursors in the OFR with yields measured in environmental chambers. When these experimental details are carefully considered, SOA formation in an OFR can be more reliably compared with ambient SOA formation processes. I then present an overview of what OFR measurements have taught us about SOA formation in the atmosphere. I provide a comparison of SOA formation from OH, O3, and NO3 oxidation of ambient air in a wide variety of environments, from rural forests to urban air. In a rural forest, the SOA formation correlated with biogenic precursors (e.g., monoterpenes). In urban air, it correlated instead with reactive anthropogenic tracers (e.g., trimethylbenzene). In mixed-source regions, the SOA formation did not correlate well with any single precursor, but could be predicted by multilinear regression from several precursors. Despite these correlations, the concentrations of speciated ambient VOCs could only
Building Detection from High Resolution Digital Elevation Models in Urban Areas

This paper describes an algorithm which aims at deriving digital terrain models (DTM) from dense digital elevation models (DEM). This algorithm takes place in a processing chain for building detection and modelling in urban areas from high resolution aerial imagery. DEM either come from stereophotogrammetry, or from direct measures such as airborne laser data. DTM are computed as low order frequency models, which exclude “high frequencies” such as buildings and other man-made structures. Above-ground regions are then detected by differences between DEM and DTM. Our processing chain ends with the 3D building modelling.