Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tutorial on Hierarchical Classification with Applications in Bioinformatics.

In Machine Learning and Data Mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between the classes. There are, however, more complex classification problems where the classes to be predicted are hierarchically related. This chapter presents a tutorial on the hierarchical classification techniques found in the literature. We also discuss how hierarchical classification techniques have been applied to the area of Bioinformatics (particularly the prediction of protein function), where hierarchical classification problems are often found. INTRODUCTION Classification is one of the most important problems in Machine Learning (ML) and Data Mining (DM). In general, a classification problem can be formally defined as: Given a set of training examples composed of pairs {xi, yi}, find a function f(x) that maps each xi to its associated class yi, i = 1, 2, . , n, where n is the total number of training examples. After training, the predictive accuracy of the classification function induced is evaluated by using it to classify a set of unlabeled examples, unseen during training. This evaluation measures the generalization ability (predictive accuracy) of the classification function induced. The vast majority of classification problems addressed in the literature involves flat classification, where each example is assigned to a class out of a finite (and usually small) set of classes. By contrast, in hierarchical classification problems, the classes are disposed in a hierarchical structure, such as a tree or a Directed Acyclic Graph (DAG). In these structures, the nodes represent classes. Figure 1 illustrates the difference between flat and hierarchical classification problems. To keep the example simple, Figure 1(b) shows a tree-structured class hierarchy. The more complex case of DAG-structured class hierarchies will be discussed later. In Figure 1, each node – except the root nodes – is labeled with the number of a class. In Figure 1(b), class 1 is divided into two sub-classes, 1.1 and 1.2, and class 3 is divided into three sub-classes. The root nodes are labeled “any class”, to denote the case where the class of an example is unknown. Figure 1 clearly shows that flat classification problems are actually a particular case of hierarchical classification problems where there is a single level of classes, i.e., where no class is divided into sub-classes.
Enhancing Constitutive Models for Soils: Adding the Capability to Model Nonlinear Small Strain in Shear

The deviatoric stress-deviatoric strain relationship in soils is highly nonlinear, especially in the small strain range. However, the constitutive models which aim to replicate the small strain nonlinearity are often complex and rarely used in geotechnical engineering practice. The goal of this study is to offer a simple way for updating the existing constitutive models, widely used in geotechnical practice, to take into account the small strain shear modulus changes. The study uses an existing small strain relationship to derive a yield surface. When the yield surface is introduced to an existing soil model, it enhances the model with the nonlinear deviatoric stress-deviatoric strain relationship in the small strain range. The paper also gives an example of such a model enhancement by combining the new yield surface with the Modified Cam Clay constitutive model. The validation simulations of the undrained triaxial tests on London Clay and Ham River sand with the upgraded constitutive models replicate the experiments clearly better than the base models, without any changes to existing model parameters and the core source code associated with the base model.
Modeling the gas reservoir of circumstellar disks around young G-type stars

Interpretation of molecular line observations in tenuous circumstellar disks around young G-type stars in terms of a disk mass is difficult without a model that describes the chemical structure of these disks. This paper discusses the chemistry in tenuous disks around young solar-type stars based on disk models that take into account the presence of a stellar chromosphere. The example of the disk around a 70 Myr old solar-type star shows that the dissociating radiation from the chromosphere is stronger than the interstellar ultraviolet radiation field up to a distance of ∼400 AU from the star. Similar to other studies in this research field, it is found that, due to photodissociation, the CO-to-H 2 ratio is far from the canonical value of 10 -4 for molecular clouds. Moreover, the dust-to-gas mass ratio as well as the dust grain size plays an important role for the H 2 abundance in these disks.
Invasion by nonnative brook trout in Panther Creek, Idaho: Roles of local habitat quality, biotic resistance, and connectivity to source habitats

Abstract Theoretical models and empirical evidence suggest that the invasion of nonnative species in freshwaters is facilitated through the interaction of three factors: habitat quality, biotic resistance, and connectivity. We measured variables that represented each factor to determine which were associated with the occurrence of nonnative brook trout Salvelinus fontinalis in Panther Creek, a tributary to the Salmon River, Idaho. Habitat variables included measures of summer and winter temperature, instream cover, and channel size. The abundance of native rainbow trout Oncorhynchus mykiss within sampled sites was used as a measure of biotic resistance. We also considered the connectivity of sample sites to unconfined valley bottoms, which were considered habitats that may serve as sources for the spread of established populations of brook trout. We analyzed the occurrence of small (<150-mm [fork length]) and large (≥150-mm) brook trout separately, assuming that the former represents an established invasi.
On the Measurement of Implicative Dilemmas

The implicative dilemma (ID) is gradually receiving attention of clinical psychologists and researchers. Although research on its clinical relevance is promising, there are still some issues regarding its measurement that need further consideration. This article explores the adequacy of the different thresholds used to determine construct association, the efficacy of the different indices commonly used to measure IDs, and a verification of the ID not being a mere subproduct of the self-ideal distance. It also introduces a new index. Finally, guidelines are provided for practitioners on how to interpret the indices' numerical results in relation to major depression.
Finding Valuable Yelp Comments by Personality, Content, Geo, and Anomaly Analysis

User reported experiences and opinions are used by peers to make decisions about where to go and what to buy. Unfortunately, not all users or opinions are honest. Many opinions are fabricated and may be submitted by automated systems or by people who are recruited by businesses and search engine optimizers to write good reviews. Such reviews and ratings are called spam reviews. These are misleading for users and troublesome for honest businesses. While most current efforts to tackle this problem are focused on spam review detection, in this paper we focused on detecting authentic and valuable reviews for a front-end application that reorders the reviews. In this manner, we have identified several features based upon the content of the reviews as well as identifying behavioural features of reviewers to pinpoint useful reviews with 80% accuracy.
Emotionally Focused Therapy in the Context of Addictions

Substance addictions represent a serious social problem in North America, negatively impacting family relationships and couple functioning. Research is increasingly considering the potential for couple therapy as a model within this context. Issues presented by an addiction can be exacerbated by other issues present in a couple relationship. Using a case study design, this research study explored a proposed theoretical extension of emotionally focused couple therapy (EFT) in the context of substance addictions. Four couples were recruited, and an analysis of the therapeutic process and their experiences is presented. Comparisons between the normative EFT treatment model and the theoretical extension are made and recommendations are provided for further adaptations to the model. Results from this study indicate the important place of couple therapy in addiction treatment.
Immobilizing a Shape

Let shape P be any simply-connected set in the plane, bounded by a Jordan curve, that is not a circular disk. We say that a set of points I on the boundary of P immobilize the shape if any rigid motion of P in the plane causes at least one point of I to penetrate the interior of P. We prove that four points always suffice to immobilize any shape. For a large class of shapes, which includes polygons without parallel edges, three points are sufficient to immobilize. An O(n log n) algorithm is given that finds a set 3 points that immobilize a given polygon without parallel edges. The algorithm becomes linear for convex polygons. Some results are generalized for d-dimensional polytopes, where 2d points are always sufficient and sometimes necessary to immobilize.
Trace-Driven Simulation of Document Caching Strategies for Internet Web Servers

Given the continued growth of the World-Wide Web, performance of Web sewers is becoming increasingly important. File caching can be used to reduce the time that it takes a Web server to respond to client requests, by storing the most popular files in the main memory of the Web sewer, and by reducing the volume of data that must be transferred between secondary storage and the Web server. In this paper, we use trace-driven simulation to evaluate the effects of various replacement, threshold, and partitioning policies on the performance of a Web sewer. The workload traces for the simulations come from Web server access logs, from six different Internet Web sewers. The traces represent three different orders of magnitude in sewer activity and two different orders of magnitude in time duration. The results from our simulation study show that frequency-based caching strategies, using a variation of the Least Frequently Used (LFU) replacement policy, perform the best for the Web sewer workload traces considered. Thresholding policies and cache partitioning policies for Internet Web servers do not appear to be effective.
Fabrication of large area 100 nm pitch grating by spatial frequency doubling and nanoimprint lithography for subwavelength optical applications

In this article we report on the fabrication of 100 nm pitch gratings over a large area (∼10 cm2) using a simple, low-cost, fast process. This method includes (1) generation of the grating pattern using interferometric lithography and spatial frequency doubling and (2) pattern replication using nanoimprint lithography. The form birefringence of a 100 nm pitch Si grating was studied using ellipsometry. Measurements show an index difference of Δn>0.9 at a wavelength of 632.8 nm. The experimental data are in good agreement with effective medium theory. This indicates the possibility of using these structures for wave plates and other subwavelength optical devices operating in the visible.
Risk-Limiting Economic Dispatch for Electricity Markets With Flexible Ramping Products

The expected increase in the penetration of renewables in the approaching decade urges the electricity market to introduce new products-in particular, flexible ramping products-to accommodate the renewables' variability and intermittency. A risk-limiting economic dispatch scheme provides the means to optimize the dispatch and provision of these products. In this paper, we adopt the extended loss-of-load probability as the definition of risk. We first assess how the new products distort the optimal economic dispatch by comparing to the case without such products. Specifically, using parametric analysis, we establish the relationship between the minimal generation cost and the two key parameters of the new products: the up- and down-flexible ramping requirements. Such relationship yields a novel routine to efficiently solve the non-convex risk-limiting economic dispatch problem. Both theoretical analysis and simulation results suggest that our approach may substantially reduce the cost for incorporating the new products. We believe our approach can assist the ISOs with utilizing the ramping capacities in the system at the minimal cost.
CD147 modulates autophagy through the PI3K/Akt/mTOR pathway in human prostate cancer PC-3 cells

The multifunctional glycoprotein cluster of differentiation (CD)147 is highly expressed on the cell surface of the majority of cancer cells, and promotes tumor invasion, metastasis and growth. However, the role of CD147 in autophagy has not yet been explored in prostrate cancer cells. In the present study, prostate cancer PC-3 cells were cultured under starvation conditions, and the expression level of CD147 gradually increased. Therefore, RNA interference was used to inhibit CD147 expression, in order to investigate the biological role of this glycoprotein in autophagy progression. Autophagic activity was monitored by the changes in green fluorescent protein-light chain 3 (GFP-LC3) location and the expression of the autophagy-associated protein LC3-II. It was found that downregulation of CD147 significantly promoted GFP-LC3 puncta formation and the expression of LC3-II. Furthermore, the levels of phosphorylated serine/threonine protein kinase B (p-Akt) and phosphorylated mammalian target of rapamycin (p-mTOR) were significantly decreased, and the level of LC3-II was inversely associated with levels of p-Akt and p-mTOR in cells with downregulated expression of CD147. The results of a trypan blue exclusion assay revealed that starvation-induced cell death was increased in PC-3/shCD147 cells compared with control PC-3/Scramble cells (37.7±6.4 vs. 21.7±5.5%). Together, these results indicate that CD147 may be important in the inhibition of autophagy via the PI3K/Akt/mTOR pathway, which prevents cell death from unrestrained autophagy.
COMPONENTS OF COGNITIVE IMPULSIVITY AND THE ASSOCIATION BETWEEN IMPULSIVITY AND SOCIAL NETWORKING SITE USE

Elevated impulsivity is associated with numerous negative outcomes and clinical disorders. This thesis develops novel testing methods for use in assessing impulsivity, identifies cognitive processes associated with impulsive behaviour, and examines the relationship between different types of impulsivity and social network site use. It shows that current tasks used to measure impulsivity do not conform to a clear theoretical structure and that unique types of impulsivity are risk factors for developing problematic social networking use.
Current-Based Slippage Detection and Odometry Correction for Mobile Robots and Planetary Rovers

This paper introduces a novel method for wheel-slippage detection and correction based on motor current measurements. Our proposed method estimates wheel slippage from motor current measurements, and adjusts encoder readings affected by wheel slippage accordingly. The correction of wheel slippage based on motor currents works only in the direction of motion, but not laterally, and it requires some knowledge of the terrain. However, this knowledge does not have to be provided ahead of time by human operators. Rather, we propose three tuning techniques for determining relevant terrain parameters automatically, in real time, and during motion over unknown terrain. Two of the tuning techniques require position ground truth (i.e., GPS) to be available either continuously or sporadically. The third technique does not require any position ground truth, but is less accurate than the two other methods. A comprehensive set of experimental results have been included to validate this approach
Non-Linear Stochastic Control in Continuous State Spaces by Exact Integration in Bellman's Equations

We present an algorithm for sequential control of tasks with non-linear stochastic dynamics in continuous state spaces, characterized by inhomogeneous noise. The algorithm performs approximate value iteration steps on a select set of prototypical states whose cost-to-go is approximated by means of a radial-basis function network. This allows the resulting Bellman’s equations to be integrated exactly with respect to the transition densities of a large class of stochastic dynamical systems, resulting in a fast and efficient modified value-iteration procedure.
A Life-Sized Desk Display for Peripheral Awareness and Remote Collaboration

We present a horizontal life-sized desk display called the Escritoire. It fills a much larger visual angle than a conventional monitor, and thus exploits the user’s peripheral vision. Two-handed input over the extent of the user’s reach allows a style of working more like a real desk, and, using two desks, participants in a video conference can share a virtual space for remote collaboration.
Bayesian analysis and classification of two enzyme‐linked immunosorbent assay tests without a gold standard

Reconciling two quantitative enzyme‐linked immunosorbent assay tests for an antibody to an RNA virus, in a situation without a gold standard and where false negatives may occur, is the motivation for this work. False negatives occur when access of the antibody to the binding site is blocked. On the basis of the mechanism of the assay, a mixture of four bivariate normal distributions is proposed with the mixture probabilities depending on a two‐stage latent variable model including the prevalence of the antibody in the population and the probabilities of blocking on each test. There is prior information on the prevalence of the antibody, and also on the probability of false negatives, and so a Bayesian analysis is used. The dependence between the two tests is modeled to be consistent with the biological mechanism. Bayesian decision theory is utilized for classification.The proposed method is applied to the motivating data set to classify the data into two groups: those with and those without the antibody. Simulation studies describe the properties of the estimation and the classification. Sensitivity to the choice of the prior distribution is also addressed by simulation. The same model with two levels of latent variables is applicable in other testing procedures such as quantitative polymerase chain reaction tests, where false negatives occur when there is a mutation in the primer sequence. Copyright © 2013 John Wiley & Sons, Ltd.
Achievable Information Rate in Nonlinear WDM Fiber-Optic Systems With Arbitrary Modulation Formats and Dispersion Maps

The problem of analytical evaluation of the maximum rate at which information can be reliably transmitted on a nonlinear wavelength division multiplexing fiber-optic channel with a given modulation format and detection strategy is addressed. An approximate solution of the nonlinear Schrödinger equation is adopted to obtain an accurate analytical discrete-time channel model, valid for arbitrary link configurations and modulation formats. By exploiting the concept of mismatched decoding, considering a sub-optimum detection strategy that accounts for intra-channel nonlinearities, the proposed model is employed to derive closed-form expressions of the achievable information rate with various modulation formats. All the analytical results are verified through comparison with numerical simulations in different scenarios.
Automated Health Business Process Modelling and Analysis for E-Health System Requirements Elicitation

e-Health is a rising research area in the intersection of public health and business sectors which refers to health and information services that are delivered or improved by using the Internet and other related information and communication technologies. Various aspects of e-Health are discussed in the literature; however, there is a research gap in the areas of system requirements engineering and health business process. It is widely accepted that health business process can play a pivotal role in capturing the details of e-Health system requirements. Therefore, understanding health business process is important to obtain suitable e-Health system requirements. A previous work proposed a methodology that manually models and analyses health business process for the aim of eliciting valid e-Health system requirements. However, the manual modeling and analysis technique is time consuming and may not be effective in the rapidly changing e-Health environment. This paper aims to overcome this limitation. The paper presents the automated approach of health business process modeling and analysis for e-Health system requirements elicitation. A case study on routine patient consultation visits in a healthcare organization has been used to validate the approach.
The Effects of Cooperative Learning on Mathematics Achievement in Turkey: A Meta-Analysis Study

In this research, the effects of cooperative learning on mathematics achievement in Turkey were examined by meta-analysis method. For this purpose, the average effect size value and the average effect size values of the moderator variables (cooperative learning technique, education level, learning domain and implementation period) were calculated. MetaWin and Comprehensive MetaAnalysis (CMA) statistical programs were used for the analysis. Based on the inclusion criteria, 59 effect size values for 47 studies were calculated. Hedges's g coefficient was used when the effect sizes were calculated and the confidence level was accepted as 95%. The average effect size value was 0,840 with 0,077 standard error which was calculated by the random-effects model. As a result, the effects of cooperative learning on mathematics achievement is moderate and positive.
A new hybrid heuristic for driver scheduling

This paper describes a new hybrid method based on the application of the Population Training Algorithm (PTA) and linear programming (LP) for generation of schedules for drivers in a public transport system. These methods are applied in an iterative way, where PTA is responsible for the generation of good columns (low cost and good covering of the tasks), and LP for solving a set partitioning problem formed by these columns. The PTA employs heuristics in fitness definition, guiding the population to settle down in search areas where the individuals cannot be improved by such heuristics. The driver schedules are represented by columns in a large-scale set partitioning problem, which are formed when solving the linear programming relaxation. The computational results are compared against a Simulated Annealing metaheuristic using randomly formed instances based on real problems.
Estimation of user's hand motion based on EMG and EEG signals

A surface EMG signal is one of the most widely used signals as input signals to wearable robots. However, EMG signals that are used to estimate motions are not always available to all users. On the other hand, an EEG signal has drawn attention as input signals for those robots in recent years. The EEG signals can be measured even with amputees and paralyzed patients who are not able to generate some EMG signals. However, the measured EEG signal does not have one-to-one relationships with the corresponding brain part. Therefore, it is more difficult to find the required signals for the control of the robot in accordance with the intention of the user's motion using the EEG signals compared with that using the EMG signals. In this paper, both the EMG and EEG signals are used to estimate the user's motion intention. In the proposed method, the EMG signals are used as main input signals because the EMG signals have higher relative to the motion of a user in comparison with the EEG signals. The EEG signals are used as sub signals in order to cover the estimation of the intention of the user's motion when all required EMG signals cannot be measured. The effectiveness of the proposed method has been evaluated by performing experiments.