nils-herrmann/clean_scirepeval huggingface dataset:
Multiobjective Combinatorial Optimization by Using Decomposition and Ant Colony

Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes a multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving one subproblem. All the ants are divided into a few groups and each ant has several neighboring ants. An ant group maintains a pheromone matrix and an individual ant has a heuristic information matrix. During the search, each ant also records the best solution found so far for its subproblem. To construct a new solution, an ant combines information from its group’s pheromone matrix, its own heuristic information matrix and its current solution. An ant checks the new solutions constructed by itself and its neighbors, and updates its current solution if it has found a better one in terms of its own objective. Extensive experiments have been conducted in this paper to study and compare MOEA/D-ACO with other algorithms on two set of test problems. On the multiobjective 0-1 knapsack problem, MOEA/D-ACO outperforms MOEA/D-GA on all the nine test instances. We also demonstrate that the heuristic information matrices in MOEA/D-ACO are crucial to the good performance of MOEA/D-ACO for the knapsack problem. On the biobjective traveling salesman problem, MOEA/D-ACO performs much better than BicriterionAnt on all the 12 test instances. We also evaluate the effects of grouping, neighborhood and the location information of current solutions on the performance of MOEA/D-ACO. The work in this paper shows that reactive search optimization scheme, i.e., the “learning while optimizing” principle, is effective in improving multiobjective optimization algorithms.
THE J.P. MORGAN GUIDE TO CREDIT DERIVATIVES

Credit Derivatives are continuing to enjoy major growth in the financial markets, aided and abetted by sophisticated product development and the expansion of product applications beyond price management to the strategic management of portfolio risk. As Blythe Masters, global head of credit derivatives marketing at J.P. Morgan in New York points out: " In bypassing barriers between different classes, maturities, rating categories, debt seniority levels and so on, credit derivatives are creating enormous opportunities to exploit and profit from associated discontinuities in the pricing of credit risk ". With such intense and rapid product development Risk Publications is delighted to introduce the first Guide to Credit Derivatives, a joint project with J.P. Morgan, a pioneer in the use of credit derivatives, with contributions from the RiskMetrics Group, a leading provider of risk management research, data, software, and education. The guide will be of great value to risk managers addressing portfolio concentration risk, issuers seeking to minimise the cost of liquidity in the debt capital markets and investors pursuing assets that offer attractive relative value.
Giant congenital melanocytic nevus*


Giant congenital melanocytic nevus is usually defined as a melanocytic lesion present at birth that will reach a diameter ≥ 20 cm in adulthood. Its incidence is estimated in <1:20,000 newborns. Despite its rarity, this lesion is important because it may associate with severe complications such as malignant melanoma, affect the central nervous system (neurocutaneous melanosis), and have major psychosocial impact on the patient and his family due to its unsightly appearance. Giant congenital melanocytic nevus generally presents as a brown lesion, with flat or mammilated surface, well-demarcated borders and hypertrichosis. Congenital melanocytic nevus is primarily a clinical diagnosis. However, congenital nevi are histologically distinguished from acquired nevi mainly by their larger size, the spread of the nevus cells to the deep layers of the skin and by their more varied architecture and morphology. Although giant congenital melanocytic nevus is recognized as a risk factor for the development of melanoma, the precise magnitude of this risk is still controversial. The estimated lifetime risk of developing melanoma varies from 5 to 10%. On account of these uncertainties and the size of the lesions, the management of giant congenital melanocytic nevus needs individualization. Treatment may include surgical and non-surgical procedures, psychological intervention and/or clinical follow-up, with special attention to changes in color, texture or on the surface of the lesion. The only absolute indication for surgery in giant congenital melanocytic nevus is the development of a malignant neoplasm on the lesion.
User Identity Linkage across Online Social Networks: A Review

The increasing popularity and diversity of social media sites has encouraged more and more people to participate on multiple online social networks to enjoy their services. Each user may create a user identity, which can includes profile, content, or network information, to represent his or her unique public figure in every social network. Thus, a fundamental question arises -- can we link user identities across online social networks? User identity linkage across online social networks is an emerging task in social media and has attracted increasing attention in recent years. Advancements in user identity linkage could potentially impact various domains such as recommendation and link prediction. Due to the unique characteristics of social network data, this problem faces tremendous challenges. To tackle these challenges, recent approaches generally consist of (1) extracting features and (2) constructing predictive models from a variety of perspectives. In this paper, we review key achievements of user identity linkage across online social networks including stateof- the-art algorithms, evaluation metrics, and representative datasets. We also discuss related research areas, open problems, and future research directions for user identity linkage across online social networks.
A robust inverse kinematics algorithm for animating a joint chain

The cyclic coordinate descent (CCD) is a well-known algorithm used for inverse kinematics solutions in applications involving joint chains and moving targets. Even though a CCD algorithm can be easily implemented, it can take a series of iterations before converging to a solution and also generate undesirable joint rotations. This paper presents a novel single-pass algorithm that is fast and eliminates problems associated with improper and large angle rotations. Experimental results are presented to show the performance benefits of the proposed algorithm over CCD and the ‘triangulation’ methods, using different types of cost functions.
EFFECTS OF RADIOFREQUENCY RADIATION FROM WIFI DEVICES ON HUMAN EJACULATED SEMEN

This is an in-vitro pilot study which established the effect of radiofrequency radiation (RFR) from 2.4 GHz laptop antenna on human semen. Ten samples of the semen, collected from donors between the ages of 20 and 30 years were exposed when the source of the RFR was in active mode. Sequel to the exposure, both the exposed samples and another ten unexposed samples from same donors were analysed for sperm concentration, motility and morphology grading. A test of significance between results of these semen parameters using Mann-Whitney Utest at 0.05 level of significance showed a significant effect of RFR exposure on the semen parameters considered.
First- and Second-Order Optimality Conditions for a Class of Optimal Control Problems with Quasilinear Elliptic Equations

A class of optimal control problems for quasilinear elliptic equations is considered, where the coefficients of the elliptic differential operator depend on the state function. Firstand second-order optimality conditions are discussed for an associated control-constrained optimal control problem. In particular, the Pontryagin maximum principle and second-order sufficient optimality conditions are derived. One of the main difficulties is the non-monotone character of the state equation.
The heterochromatin protein 1 (HP1) family: put away a bias toward HP1.

Heterochromatin protein 1 (HP1) was first described in Drosophila melanogaster as a heterochromatin associated protein with dose-dependent effect on gene silencing. The HP1 family is evolutionarily highly conserved and there are multiple members within the same species. The multi-functionality of HP1 reflects its ability to interact with diverse nuclear proteins, ranging from histones and transcriptional co-repressors to cohesion and DNA replication factors. As its name suggests, HP1 is well-known as a silencing protein found at pericentromeres and telomeres. In contrast to previous views that heterochromatin is transcriptionally inactive; noncoding RNAs transcribed from heterochromatic DNA repeats regulates the assembly and function of heterochromatin ranging from fission yeast to animals. Moreover, more recent progress has shed light on the paradoxical properties of HP1 in the nucleus and has revealed, unexpectedly, its existence in the euchromatin. Therefore, HP1 proteins might participate in both transcription repression in heterochromatin and euchromatin.
Using Word Embeddings for Visual Data Exploration with Ontodia and Wikidata

One of the big challenges in Linked Data consumption is to create visual and natural language interfaces to the data usable for nontechnical users. Ontodia provides support for diagrammatic data exploration, showcased in this publication in combination with the Wikidata dataset. We present improvements to the natural language interface regarding exploring and querying Linked Data entities. The method uses models of distributional semantics to find and rank entity properties related to user input in Ontodia. Various word embedding types and model settings are evaluated, and the results show that user experience in visual data exploration benefits from the proposed approach.
Verbal forward digit span in Spanish population.

Older people complain of difficulties in recalling telephone numbers and being able to dial them in the correct order. This study examined the developmental trend of verbal forward digit span across adulthood and aging in a Spanish population, as an index of one of the components of Baddeley’s working memory model—the phonological loop—, which illustrates these two aspects.



A verbal digit span was administered to an incidental sample of 987 participants ranging from 35 to 90 years old. The maximum length was defined that participants could recall of at least two out of three series in the same order as presented with no errors. Demographic variables of gender and educational level were also examined.



The ANOVA showed that the three main factors (age group, gender and educational level) were significant, but none of the interactions was. Verbal forward digit span decreases during the lifespan, but gender and educational level affect it slightly.



Phonological loop is affected by age. The verbal forward digit span in this study is generally lower than the one reported in other studies.
Supervised and Unsupervised Transfer Learning for Activity Recognition from Simple In-home Sensors

In this paper, we propose an approach to improve the accuracy of home activity estimation using device-free sensors in the home. This is achieved by transferring existing training data to a new household considering the differences between households. We assumed two scenarios in which we only have training data from other households with labels and we also have training labels for our own household, and proposed the method to compose supervised transfer between labeled data and unsupervised transfer between labeled unlabeled data for each scenario. To evaluate in realistic settings, we developed the system which consists of an application for use on tablet terminals, which continuously collect light and any optional sensor data, and a Web-based server system that stores the sensor data, estimates activities, provides visualization on users’ Web browsers, and enables users to edit the activity labels. Using the system, we gathered subjects from open called households during a period of approximately four months, and obtained approximately 11,745 activity inputs, approximately 7.14GB of sensor data, and power consumption data of 237,280 hours from 35 households. As a result of evaluation, our method outperformed naive methods, both in the first and second scenarios.
A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios

Autonomous vehicle navigation is challenging since various types of road scenarios in real urban environments have to be considered, particularly when only perception sensors are used, without position information. This paper presents a novel real-time optimal-drivable-region and lane detection system for autonomous driving based on the fusion of light detection and ranging (LIDAR) and vision data. Our system uses a multisensory scheme to cover the most drivable areas in front of a vehicle. We propose a feature-level fusion method for the LIDAR and vision data and an optimal selection strategy for detecting the best drivable region. Then, a conditional lane detection algorithm is selectively executed depending on the automatic classification of the optimal drivable region. Our system successfully handles both structured and unstructured roads. The results of several experiments are provided to demonstrate the reliability, effectiveness, and robustness of the system.
Integrating online i-vector extractor with information bottleneck based speaker diarization system

Conventional approaches to speaker diarization use short-term features such as Mel Frequency Cepstral Co-efficients (MFCC). Features such as i-vectors have been used on longer segments (minimum 2.5 seconds of speech). Using i-vectors for speaker diarization has been shown to be beneficial as it models speaker information explicitly. In this paper, the i-vector modelling technique is adapted to be used as short term features for diarization by estimating i-vectors over a short window of MFCCs. The Information Bottleneck (IB) approach provides a convenient platform to integrate multiple features together for fast and accurate diarization of speech. Speaker models are estimated over a window of 10 frames of speech and used as features in the IB system. Experiments on the NIST RT datasets show absolute improvements of 3.9% in the best case when ivectors are used as auxiliary features to MFCC. Further, discriminative training algorithms such as LDA and PLDA are applied on the i-vectors. A best case performance improvement of 5% in absolute terms is obtained on the RT datasets.
Algorithmic Persuasion with No Externalities

We study the algorithmics of information structure design --- a.k.a. persuasion or signaling --- in a fundamental special case introduced by Arieli and Babichenko: multiple agents, binary actions, and no inter-agent externalities. Unlike prior work on this model, we allow many states of nature. We assume that the principal's objective is a monotone set function, and study the problem both in the public signal and private signal models, drawing a sharp contrast between the two in terms of both efficacy and computational complexity.
 When private signals are allowed, our results are largely positive and quite general. First, we use linear programming duality and the equivalence of separation and optimization to show polynomial-time equivalence between (exactly) optimal signaling and the problem of maximizing the objective function plus an additive function. This yields an efficient implementation of the optimal scheme when the objective is supermodular or anonymous. Second, we exhibit a (1-1/e)-approximation of the optimal private signaling scheme, modulo an additive loss of ε, when the objective function is submodular. These two results simplify, unify, and generalize results of [Arieli and Babichenko, 2016] and [Babichenko and Barman, 2016], extending them from a binary state of nature to many states (modulo the additive loss in the latter result). Third, we consider the binary-state case with a submodular objective, and simplify and slightly strengthen the result of [Babichenko and Barman, 2016] to obtain a (1-1/e)-approximation via a scheme which (i) signals independently to each receiver and (ii) is "oblivious" in that it does not depend on the objective function so long as it is monotone submodular.
 When only a public signal is allowed, our results are negative. First, we show that it is NP-hard to approximate the optimal public scheme, within any constant factor, even when the objective is additive. Second, we show that the optimal private scheme can outperform the optimal public scheme, in terms of maximizing the sender's objective, by a polynomial factor.
The entropy conjecture for diffeomorphisms away from tangencies

We prove that every C1 diffeomorphism away from homoclinic tangencies is entropy expansive, with locally uniform expansivity constant. Consequently, such diffeomorphisms satisfy Shub’s entropy conjecture: the entropy is bounded from below by the spectral radius in homology. Moreover, they admit principal symbolic extensions, and the topological entropy and metrical entropy vary upper semicontinuously with the map. In contrast, generic diffeomorphisms with persistent tangencies are not entropy expansive.
Unsupervised vocabulary selection for real-time speech recognition of lectures

In this work, we propose a novel method for vocabulary selection to automatically adapt automatic speech recognition systems to the diverse topics that occur in educational and scientific lectures. Utilizing materials that are available before the lecture begins, such as lecture slides, our proposed framework iteratively searches for related documents on the web and generates a lecture-specific vocabulary based on the resulting documents. In this paper, we propose a novel method for vocabulary selection where we first collect documents similar to an initial seed document and then rank the resulting vocabulary based on a score which is calculated using a combination of word features. This is a critical component for adaptation that has typically been overlooked in prior works. On the inter ACT German-English simultaneous lecture translation system our proposed approach significantly improved vocabulary coverage, reducing the out-of-vocabulary rate, on average by 57.0% and up to 84.9%, compared to a lecture-independent baseline. Furthermore, our approach reduced the word error rate, by 12.5% on average and up to 25.3%, compared to a lecture-independent baseline.
HEDGING YOUR BETS : L 2 LEARNERS ’ ACQUISITION OF PRAGMATIC DEVICES IN ACADEMIC WRITING AND COMPUTER-MEDIATED

This study had two purposes: The first was to investigate the effects of instruction on pragmatic acquisition in writing. In particular, the focus was on the use of hedging devices in the academic writing of learners of English as a second language. The second purpose was to discover whether this training transferred to a less-planned, less-formal, computer-mediated type of writing, namely a Daedalus interaction. Graduate students enrolled in an academic writing class for non-native Englishspeakers received treatment designed to increase their metapragmatic awareness and improve their ability to use hedging devices. Data were compared to a control group that did not receive the treatment. The treatment group showed statistically significant increases in the use of hedging devices in the research papers and in the computer-mediated discussion.
First-time mothers: social support, maternal parental self-efficacy and postnatal depression.

To examine the relationships between social support, maternal parental self-efficacy and postnatal depression in first-time mothers at 6 weeks post delivery.



Social support conceptualised and measured in different ways has been found to positively influence the mothering experience as has maternal parental self-efficacy. No research exists which has measured the relationships between social support, underpinned by social exchange theory and maternal parental self-efficacy using a domain-specific instrument, underpinned by self-efficacy theory and postnatal depression, with first-time mothers at 6 weeks post delivery.



A quantitative correlational descriptive design was used.



Data were collected using a five-part questionnaire package containing a researcher developed social support questionnaire, the Perceived Maternal Parental Self-Efficacy Scale and the Edinburgh Postnatal Depression Scale. Four hundred and ten mothers completed questionnaires at 6 weeks post delivery.



Significant relationships were found between functional social support and postnatal depression; informal social support and postnatal depression; maternal parental self-efficacy and postnatal depression and informal social support and maternal parental self-efficacy at 6 weeks post delivery.



Nurses and midwives need to be aware of and acknowledge the significant contribution of social support, particularly from family and friends in positively influencing first-time mothers' mental health and well-being in the postpartum period. The development of health care policy and clinical guidelines needs to define and operationalise social support to enhance maternal parental self-efficacy.


 TO CLINICAL PRACTICE
These findings suggest that nurses and midwives need to be cognisant of the importance of social support for first-time mothers in both enhancing maternal parental self-efficacy and reducing postnatal depressive symptomatology in the early postpartum period.
Speed Control of DC Motor Using Fuzzy Logic Application

Direct current (DC) motors are controlled easily and have very high performance. The speed of the motors could be adjusted within a wide range. Today, classical control techniques (such as Proportional Integral Differential PID) are very commonly used for speed control purposes. However, it is observed that the classical control techniques do not have an adequate performance in the case of nonlinear systems. Thus, instead, a modern technique is preferred: fuzzy logic. In this paper the control system is modelled using MATLAB/Simulink. Using both PID controller and fuzzy logic techniques, the results are compared for different speed values.
The Persistence and Transience of Memory

The predominant focus in the neurobiological study of memory has been on remembering (persistence). However, recent studies have considered the neurobiology of forgetting (transience). Here we draw parallels between neurobiological and computational mechanisms underlying transience. We propose that it is the interaction between persistence and transience that allows for intelligent decision-making in dynamic, noisy environments. Specifically, we argue that transience (1) enhances flexibility, by reducing the influence of outdated information on memory-guided decision-making, and (2) prevents overfitting to specific past events, thereby promoting generalization. According to this view, the goal of memory is not the transmission of information through time, per se. Rather, the goal of memory is to optimize decision-making. As such, transience is as important as persistence in mnemonic systems.