Miracle-dz/UniFAR-FTUs huggingface dataset:
Boosted Regression Trees for ecological modeling

This is a brief tutorial to accompany a set of functions that we have written to facilitate fitting BRT (boosted regression tree) models in R . This tutorial is a modified version of the tutorial accompaniying Elith, Leathwick and Hastie’s article in Journal of Animal Ecology. It has been adjusted to match the implementation of these functions in the ’dismo’ package. The gbm* functions in the dismo package extend functions in the ’gbm’ package by Greg Ridgeway. The goal of our functions is to make the functions in the ’gbm’ package easier to apply to ecological data, and to enhance interpretation. The tutorial is aimed at helping you to learn the mechanics of how to use the functions and to develop a BRT model in R . It does not explain what a BRT model is for that, see the references at the end of the tutorial, and the documentation of the gbm package. For an example application with similar data as in this tutorial, see Elith et al., 2008. The gbm functions in ’dismo’ are as follows: 1. gbm.step Fits a gbm model to one or more response variables, using cross-validation to estimate the optimal number of trees. This requires use of the utility functions roc, calibration and calc.deviance. 2. gbm.fixed, gbm.holdout Alternative functions for fitting gbm models, implementing options provided in the gbm package. 3. gbm.simplify Code to perform backwards elimination of variables, to drop those that give no evidence of improving predictive performance. 4. gbm.plot Plots the partial dependence of the response on one or more predictors. 5. gbm.plot. fits Plots the fitted values from a gbm object returned by any of the model fitting options. This can give a more reliable guide to the shape of the fitted surface than can be obtained from the individual functions, particularly when predictor variables are correlated and/or samples are unevenly distributed in environmental space. 6. gbm.interactions Tests whether interactions have been detected and modelled, and reports the relative strength of these. Results can be visualised with gbm.perspec
Cliques in K-partite Graphs and Their Application in Textile Engineering

In many practical cases one has to choose an arrangement of di erent objects so that they are compatible. Whenever the compatibility of the objects can be checked by a pair-wise comparison the problem can be modelled using the graph-theoretic notion of cliques. We consider a special case of the problem where the objects can be grouped so that exactly one object in every group has to be chosen. This object has to be compatible to every other object selected from the other groups. The problem was motivated by a braiding application from textile technology. The task is to route a set of thread-spools (bobbins) on a machine from their origins to their destinations so that collisions are avoided. We present a new model and algorithm in order to solve this important practical problem.
CSCF: A Mashup Service Recommendation Approach based on Content Similarity and Collaborative Filtering

Lightweight Mashup service become very prevalent now since there are lots of advantages for them, such as easy use, short development time, and strong scalability. It is a challenge problem how to recommend user-interested, high-quality Mashup services to user with the rapid development of more and more Mashup service. In this paper, we propose CSCF (a Mashup service recommendation approach based on Content Similarity and Collaborative Filtering). CSCF firstly computes the content similarity between user history records and Mashup services and gets user interest value. Secondly, according to Mashup QoS(Quality of Service) invocation records of user, user similarity model and service similarity model are designed, and then get the QoS prediction value of active user to target service by using collaborative filtering. Finally, combining user interest value and QoS predictive value of Mashup service, CSCF ranks and recommends Mashup services to user. The experiments are performed with real Mashup services dataset, and the results of experiments show that CSCF can effectively recommends Mashup services to user with well-interesting, high-quality, better prediction precision.
AGATHA: automated construction of case law theories through heuristic search

In this paper we describe AGATHA, a program designed to automate the process of theory construction in case based domains. Given a seed case and a number of precedent cases, the program uses a set of argument moves to generate a search space for a dialogue between the parties to the dispute. Each move is associated with a set of theory constructors, and thus each point in the space can be associated with a theory intended to explain the seed case and the other cases in the domain. The space is large, and so a heuristic search method based on A* is used to guide the selection of moves and precedent cases. The paper describes a series of experiments designed to explore the appropriateness of different evaluation functions, the most useful precedents and the quality of the resulting theories.
Shear strength prediction using dimensional analysis and functional networks

This paper presents a three steps methodology for predicting the failure shear effort in concrete beams. In the first step, dimensional analysis is applied to obtain several sets of dimensionless variables; in the second step, functional and neural networks are used to estimate a rela- tion between those variables and, in the last step, the failure shear effort is recovered from the relations learnt. Finally, the performance of the methodology was validated using data from shear strength experiments.
On the feasibility of fully wireless remote control for underwater vehicles

In this paper, we explore the possibility of controlling a Remotely Operated Vehicle (ROV) via a fully wireless control channel. As a first step, we review the expected bit rate offered by optical, acoustic as well as radio-frequency underwater communication technologies, as a function of the distance between the transmitter and the receiver. We then discuss the ROV data transfer requirements and discuss which ones can be supported by a given technology at a given distance. Finally, we simulate the performance of the system during missions of interest, and conclude by discussing the effectiveness of wireless control methods for ROVs.
Expressions of Tight Junction Proteins Occludin and Claudin-1 Are under the Circadian Control in the Mouse Large Intestine: Implications in Intestinal Permeability and Susceptibility to Colitis

Background & Aims The circadian clock drives daily rhythms in behavior and physiology. A recent study suggests that intestinal permeability is also under control of the circadian clock. However, the precise mechanisms remain largely unknown. Because intestinal permeability depends on tight junction (TJ) that regulates the epithelial paracellular pathway, this study investigated whether the circadian clock regulates the expression levels of TJ proteins in the intestine. Methods The expression levels of TJ proteins in the large intestinal epithelium and colonic permeability were analyzed every 4, 6, or 12 hours between wild-type mice and mice with a mutation of a key clock gene Period2 (Per2; mPer2m/m). In addition, the susceptibility to dextran sodium sulfate (DSS)-induced colitis was compared between wild-type mice and mPer2m/m mice. Results The mRNA and protein expression levels of Occludin and Claudin-1 exhibited daily variations in the colonic epithelium in wild-type mice, whereas they were constitutively high in mPer2m/m mice. Colonic permeability in wild-type mice exhibited daily variations, which was inversely associated with the expression levels of Occludin and Claudin-1 proteins, whereas it was constitutively low in mPer2m/m mice. mPer2m/m mice were more resistant to the colonic injury induced by DSS than wild-type mice. Conclusions Occludin and Claudin-1 expressions in the large intestine are under the circadian control, which is associated with temporal regulation of colonic permeability and also susceptibility to colitis.
Use of the HR index to predict maximal oxygen uptake during different exercise protocols

This study examined the ability of the HRindex model to accurately predict maximal oxygen uptake ( V˙O2max) across a variety of incremental exercise protocols. Ten men completed five incremental protocols to volitional exhaustion. Protocols included three treadmill (Bruce, UCLA running, Wellness Fitness Initiative [WFI]), one cycle, and one field (shuttle) test. The HRindex prediction equation (METs = 6 × HRindex − 5, where HRindex = HRmax/HRrest) was used to generate estimates of energy expenditure, which were converted to body mass‐specific estimates of V˙O2max. Estimated V˙O2max was compared with measured V˙O2max. Across all protocols, the HRindex model significantly underestimated V˙O2max by 5.1 mL·kg−1·min−1 (95% CI: −7.4, −2.7) and the standard error of the estimate (SEE) was 6.7 mL·kg−1·min−1. Accuracy of the model was protocol‐dependent, with V˙O2max significantly underestimated for the Bruce and WFI protocols but not the UCLA, Cycle, or Shuttle protocols. Although no significant differences in V˙O2max estimates were identified for these three protocols, predictive accuracy among them was not high, with root mean squared errors and SEEs ranging from 7.6 to 10.3 mL·kg−1·min−1 and from 4.5 to 8.0 mL·kg−1·min−1, respectively. Correlations between measured and predicted V˙O2max were between 0.27 and 0.53. Individual prediction errors indicated that prediction accuracy varied considerably within protocols and among participants. In conclusion, across various protocols the HRindex model significantly underestimated V˙O2max in a group of aerobically fit young men. Estimates generated using the model did not differ from measured V˙O2max for three of the five protocols studied; nevertheless, some individual prediction errors were large. The lack of precision among estimates may limit the utility of the HRindex model; however, further investigation to establish the model's predictive accuracy is warranted.
Bananas, oil, and development: examining the resource curse and its transmission channels by resource type

This paper examines the resource curse and its transmission channels by resource type. We review and synthesize existing theories of the transmission channels of the curse. This synthesis suggests that (1) relating the transmission channels to the characteristics of different types of resources, and (2) considering how other country characteristics, such as institutional quality and policy outcomes, affect the impact of natural resource wealth on development, would improve our understanding of the functioning of the curse. We then assess these two aspects empirically and find different transmission channels to be relevant for different types of resources. Furthermore, we illustrate the interaction between other country characteristics and the curse.
Error control codes for parallel channel with correlated errors

This paper introduces two channel models of correlated parallel channels. Then we analyze structure of error correcting codes over these correlated parallel channels. We derive necessary and sufficient conditions for these codes and some code construction is presented. We also show some upper and lower bounds on the coding rate of the error correcting codes for correlated parallel channels. The introduced channel models are related to burst error channels and the codes analyzed in this paper can be used as asymmetric interleaving codes for burst error channels.
Measuring the Natural Interest Rate for the Peruvian Economy

Since the adoption of the fully-fledged inflation targeting (IT) regime by an important group of central banks, a measure of both the potential output and the natural interest rate have become one of the main concerns of the research agenda. Estimation of the natural interest rate (NIR) is crucial to capture the stance of the monetary policy. In particular, the gap between the instrument rate of the Central Bank and the NIR can be a useful guideline for the position of the monetary policy and can also help to rationalize policy decisions. In this paper we estimate the NIR for the Peruvian Economy. We do so by applying the Kalman Filter to a semi-structural small open economy model with Peruvian data during he sample 1994-2005. Overall, our findings show a persistent reduction on the Peruvian NIR since 1999, which is related to an improvement on the terms of trade and a reduction on the international interest rate. Moreover, the estimated gap shows a loose monetary impulse between 1994 and 1997, tight between 1998 and 2001, and slightly loose between 2002-2005. Finally, the variance decomposition shows that 25 percent of fluctuations in the gap are explained by fluctuations in the NIR. According to this, a time-fixed NIR would give a quite imprecise measure of monetary policy stance.
Design of an Encryption-Decryption Module Oriented for Internet Information Security SOC Design

In order to protect the security of network data, a high speed chip module for encrypting and decrypting of network data packet is designed. The chip module is oriented for internet information security SOC (System on Chip) design. During the design process, AES (Advanced Encryption Standard) and 3DES (Data Encryption Standard) encryption algorithm are adopted to protect the security of network data. The following points are focused: (1) The SOC (System on Chip) design methodology based on IP (Intellectual Property) core is used. AES (Advanced Encryption Standard) and 3DES (Data Encryption Standard) IP (Intellectual Property) cores are embedded in the chip module, peripheral control sub-modules are designed to control the encryption-decryption module, which is capable of shortening the design period of the chip module. (2) The implementation of encryption-decryption with hardware was presented, which improves the safety of data through the encryption-decryption chip and reduce the load of CPU. (3) In our hardware solution, two AES (Advanced Encryption Standard) cores are used to work in parallel, which improves the speed of the encryption module. Moreover, the key length of AES (Advanced Encryption Standard) encryption algorithm is designed with three optional configurations at 128 bits, 256 bits and 192 bits respectively and six optional encryption algorithm modes: CBC (Cipher Block Chaining) mode, ECB (Electronic Code Book) mode, GCM (Galois/Counter Mode) mode, XTS(cipherteXT Stealing) mode, CTR (CounTeR) mode and 3DES respectively, which adds the flexibility to its applications.
Sparsity Averaging Reweighted Analysis (SARA): a novel algorithm for radio‐interferometric imaging

We propose a novel algorithm for image reconstruction in radio interferometry. The ill-posed inverse problem associated with the incomplete Fourier sampling identified by the visibility measurements is regularized by the assumption of average signal sparsity over representations in multiple wavelet bases. The algorithm, defined in the versatile framework of convex optimization, is dubbed Sparsity Averaging Reweighted Analysis (SARA). We show through simulations that the proposed approach outperforms state-of-the-art imaging methods in the field, which are based on the assumption of signal sparsity in a single basis only.
An adaptive search ordering for rate-constrained successive elimination algorithms

This paper proposes a solution for the problem of unnecessary cost function evaluations, found when combining the successive elimination algorithm with a spiral scan search ordering. Our experiments show that the implementation of such a combination inside the HEVC reference software leads to unnecessary cost function evaluations. On the tested video sequences, an average of 3.46% unnecessary cost function evaluations was measured. Considering only small block sizes (e.g., 4×8 and 8×4), this average rises to 8.06%. To solve this problem, we propose an adaptive scan ordering of block matching candidates within the search area. When used with our early termination threshold, the proposed approach will only evaluate necessary cost functions, without impacting rate-distortion.
Peptide secondary structure modulates single-walled carbon nanotube fluorescence as a chaperone sensor for nitroaromatics

A class of peptides from the bombolitin family, not previously identified for nitroaromatic recognition, allows near-infrared fluorescent single-walled carbon nanotubes to transduce specific changes in their conformation. In response to the binding of specific nitroaromatic species, such peptide–nanotube complexes form a virtual “chaperone sensor,” which reports modulation of the peptide secondary structure via changes in single-walled carbon nanotubes, near-infrared photoluminescence. A split-channel microscope constructed to image quantized spectral wavelength shifts in real time, in response to nitroaromatic adsorption, results in the first single-nanotube imaging of solvatochromic events. The described indirect detection mechanism, as well as an additional exciton quenching-based optical nitroaromatic detection method, illustrate that functionalization of the carbon nanotube surface can result in completely unique sites for recognition, resolvable at the single-molecule level.
An Empirical Study of Class Noise Impacts on Supervised Learning Algorithms and Measures

Noise in data is an effective cause of concern for many machine learning techniques. Researchers have studied the noise impacts only on some particular learning algorithm. We empirically study the noise impacts on four different representative learning algorithms and the two popular measures (accuracy and AUC) under different intensities of noise, particularly decision tree, naïve bayes, support vector machine, and logistic regression. Our empirical results show that AUC is more tolerant to noise. Among the four algorithms, naïve bayes is the most resistant to noise, but it performs the worst in accuracy. The other algorithms perform much better than naïve bayes especially after the noisy level is lower than 40%. When we develop approaches to improve the data quality (reduce the noise level) and build model with higher accuracy, decision tree is the most preferred one, followed by logistic regression and support vector machine. However, logistic regression performs the best in AUC.
Genome Sequence of Rhodococcus sp. 4J2A2, a Desiccation-Tolerant Bacterium Involved in Biodegradation of Aromatic Hydrocarbons

ABSTRACT The genome sequence for Rhodococcus sp. 4J2A2, a newly described desiccation-tolerant strain that removes aromatic hydrocarbons, is reported here. The genome is estimated to be around 7.5 Mb in size, with an average G+C content of 60.77% and a predicted number of protein-coding sequences of 6,354.
Multi-objective optimization in service systems

AbstrAct: Muti-objective optimization deals with the simultaneous optimization of two or more conflicting objective functions in real-life systems. This paper deals with the multi-objective optimization in service systems. The goal of service systems is to provide cost-efficient service to customers, while at the same time, reducing the customer waiting time for service. In general, a low cost in system operation leads to longer waiting times, while a higher cost in system operation leads to shorter waiting times. The two objectives – service cost (operational cost) and waiting time (customer satisfaction) are, therefore, conflicting in nature. We use the novel Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to optimize the two conflicting objective functions simultaneously. MOPSO is a fairly recent swarm intelligence meta-heuristic algorithm known for its simplicity in programming and its rapid convergence. The multi-objective optimization procedure is illustrated with the example of a practical service system. MOPSO produces a family of well-spread Pareto fronts for the two objective functions in the practical service system.
Parameter identification for a one-dimensional blood flow model

The purpose of this work is to use a variational method to identify some of the parameters of one-dimensional models for blood flow in arteries. These parameters can be fit to approach as much as possible some data coming from experimental measurements or from numerical simulations performed using more complex models. A nonlinear least squares approach to parameter estimation was taken, based on the optimization of a cost function. The resolution of such an optimization problem generally requires the efficient and accurate computation of the gradient of the cost function with respect to the parameters. This gradient is computed analytically when the one-dimensional hyperbolic model is discretized with a second order Taylor-Galerkin scheme. An adjoint approach was used. Some preliminary numerical tests are shown. In these simulations, we mainly focused on determining a parameter that is linked to the mechanical properties of the arterial walls, the compliance. The synthetic data we used to estimate the parameter were obtained from a numerical computation performed with a more accurate model: a three-dimensional fluid-structure interaction model. The first results seem to be promising. In particular, it is worth noticing that the estimated compliance which gives the best fit is quite different from the values that are commonly used in practice.
Performance Analysis of Sub-Band nLMS, APA and RLS in fMRI ANC with a Non-Minimum Phase Secondary Path

We present the performance comparison of sub-band FXLMS algorithm for fMRI acoustic noise cancellation when the secondary path is non-minimum phase. Three types of least square adaptive filtering methods (nLMS, APA, RLS) are used in sub-bands. A series of simulations have been done using recorded fMRI acoustic noise and the results are given and compared based on the noise attenuation level, convergence rate and the quality of primary path estimation. It is verified that the spectrum structure of the fMRI acoustic noise has the main role in the performance of the active noise control especially when sub-band filtering is used.
Application of single neuron model to motion planning and control of under-actuated robot by MDP framework

Motion planning of robots based on the Markov decision process (MDP) is now one of the most important topics of robotics research. In motion planning of robots by the MDP, an efficient way to divide robot motion is essential because discrete expressions of states and transitions are used in the calculation. In preparing the discrete expressions of robot motion, it is important that the motion follows the MDP, second, that the time and the amount of memory for the planning calculation are reduced, and that the maneuverability and smoothness of the robot motion are not lost. This paper proposes a new discrete expression of robot motion that can meet these requirements considering the effect of robot dynamics to robot kinematics. The method uses a continuous-time continuous-variable neuron model, which has the stimulation and adaptation properties. The performance of the proposed method was examined by simulation of the motion planning of an under actuated underwater robot and under actuated manipulator with a free joint. The results show the high reliability and flexibility of the method.