nils-herrmann/clean_scirepeval huggingface dataset:
Lateral Brow Lift: A Multi-Point Suture Fixation Technique

Descent of the lateral aspect of the brow is one of the earliest signs of aging. The purpose of this study was to describe an open surgical technique for lateral brow lifts, with the goal of achieving reliable, predictable, and long-lasting results.



An incision was made behind and parallel to the temporal hairline, and then extended deeper through the temporoparietal fascia to the level of the deep temporal fascia. Dissection was continued anteriorly on the surface of the deep temporal fascia and subperiosteally beyond the temporal crest, to the level of the superolateral orbital rim. Fixation of the lateral brow and tightening of the orbicularis oculi muscle was achieved with the placement of sutures that secured the tissue directly to the galea aponeurotica on the lateral aspect of the incision. An additional fixation was made between the temporoparietal fascia and the deep temporal fascia, as well as between the temporoparietal fascia and the galea aponeurotica. The excess skin in the temporal area was excised and the incision was closed.



A total of 519 patients were included in the study. Satisfactory lateral brow elevation was obtained in most of the patients (94.41%). The following complications were observed: total relapse (n=8), partial relapse (n=21), neurapraxia of the frontal branch of the facial nerve (n=5), and limited alopecia in the temporal incision (n=9).



We consider this approach to be a safe and effective procedure, with long-lasting results.
The nature and determinants of neuropsychological functioning in late-life depression.

Cognitive impairment in late-life depression (LLD) is highly prevalent, disabling, poorly understood, and likely related to long-term outcome.



To determine the characteristics and determinants of neuropsychological functioning LLD.



Cross-sectional study of groups of LLD patients and control subjects.



Outpatient, university-based depression research clinic.



One hundred patients without dementia 60 years and older who met DSM-IV criteria for current episode of unipolar major depression (nonpsychotic) and 40 nondepressed, age- and education-equated control subjects.



A comprehensive neuropsychological battery.



Relative to control subjects, LLD patients performed poorer in all cognitive domains. More than half exhibited significant impairment (performance below the 10th percentile of the control group). Information processing speed and visuospatial and executive abilities were the most broadly and frequently impaired. The neuropsychological impairments were mediated almost entirely by slowed information processing (beta =.45-.80). Education (beta =.32) and ventricular atrophy (beta =.28) made additional modest contributions to variance in measures of language ability. Medical and vascular disease burden, apolipoprotein E genotype, and serum anticholinergicity did not contribute to variance in any cognitive domain.



Late-life depression is characterized by slowed information processing, which affects all realms of cognition. This supports the concept that frontostriatal dysfunction plays a key role in LLD. The putative role of some risk factors was validated (eg, advanced age, low education, depression severity), whereas others were not (eg, medical burden, age at onset of first depressive episode). Further studies of neuropsychological functioning in remitted LLD patients are needed to parse episode-related and persistent factors and to relate them to underlying neural dysfunction.
Stripe Boundary Codes for Real-Time Structured-Light Range Scanning of Moving Objects

We present a novel approach to real-time structured light range scanning. After an analysis of the underlying assumptions of existing structured light techniques, we derive a new set of illumination patterns based on coding the boundaries between projected stripes. These stripe boundary codes allow range scanning of moving objects, with only modest assumptions about scene continuity and reflectance. We describe an implementation that integrates these new codes with real-time algorithms for tracking stripe boundaries and determining depths. Our system uses a standard video camera and DLP projector, and produces dense range images at 60 Hz with 100 m accuracy over a 10 cm working volume. As an application, we demonstrate the creation of complete models of rigid objects: the objects are rotated in front of the scanner by hand, and successive range images are automatically aligned.
Practical Compressed Sensing: Modern data acquisition and signal processing

Since 2004, the field of compressed sensing has grown quickly and seen tremendous interest because it provides a theoretically sound and computationally tractable method to stably recover signals by sampling at the information rate. This thesis presents in detail the design of one of the world’s first compressed sensing hardware devices, the random modulation pre-integrator (RMPI). The RMPI is an analog-to-digital converter (ADC) that bypasses a current limitation in ADC technology and achieves an unprecedented 8 effective number of bits over a bandwidth of 2.5 GHz. Subtle but important design considerations are discussed, and state-of-the-art reconstruction techniques are presented. Inspired by the need for a fast method to solve reconstruction problems for the RMPI, we develop two efficient large-scale optimization methods, NESTA and TFOCS, that are applicable to a wide range of other problems, such as image denoising and deblurring, MRI reconstruction, and matrix completion (including the famous Netflix problem). While many algorithms solve unconstrained `1 problems, NESTA and TFOCS can solve the constrained form of `1 minimization, and allow weighted norms. In addition to `1 minimization problems such as the LASSO, both NESTA and TFOCS solve total-variation minimization problem. TFOCS also solves the Dantzig selector and most variants of the nuclear norm minimization problem. A common theme in both NESTA and TFOCS is the use of smoothing techniques, which make the problem tractable, and the use of optimal first-order methods that have an accelerated convergence rate yet have the same cost per iteration as gradient descent. The conic dual methodology is introduced in TFOCS and proves to be extremely flexible, covering such generic problems as linear programming, quadratic programming, and semi-definite programming. A novel continuation scheme is presented, and it is shown that the Dantzig selector benefits from an exact-penalty property. Both NESTA and TFOCS are released as software packages available freely for academic use.
PROJECT MANAGEMENT INFORMATION SYSTEMS: AN EMPIRICAL STUDY OF THEIR IMPACT ON PROJECT MANAGERS AND PROJECT SUCCESS

Project management information systems (PMIS) usually acquired by organizations as software packages are meant to provide managers with the decision-making support needed in planning, organizing and controlling projects. However, the actual contribution of PMIS to project success or performance is still unknown. The purpose of this study is to empirically assess the quality of the PMIS presently used in organizations and to examine their impact on project managers and project performance, based on a model of PMIS success model. This model is composed of five constructs: the quality of the PMIS, the quality of the PMIS information output, the use of the PMIS, the individual impacts of the PMIS and the impacts of the PMIS on project success. Analysis of questionnaire data obtained from 39 project managers confirms the significant contribution of PMIS to successful project management. Improvements in effectiveness and efficiency in managerial tasks were observed here in terms of better project planning, scheduling, monitoring, and control. Improvements were also observed in terms of timelier decision-making. Advantages obtained from PMIS use are not limited to individual performance but also include project performance. These systems were found to have direct impacts on project success, as they contribute to improving budget control and meeting project deadlines as well as fulfilling technical specifications.
Cook. The Traveling Salesman Problem: A Computational Study.

The symmetric traveling salesman problem (TSP) is one of the best-known problems of combinatorial optimisation, very easy to explain and visualise, yet with a semblance of real-world applicability. Given a set of points, the cost of moving between each two in either direction, and a constant k, the task in the TSP is to decide, whether it is possible to visit all points, each one exactly once, and to return back to the point of departure, at a total cost of no more than k. The latest book by Applegate, Bixby, Chvátal, and Cook provides an excellent survey of methods that kick-started this " engine of discovery in applied mathematics " (invoked on pp. In more than 600 pages, the authors present a survey of methods used in their present-best TSP solver Concorde, almost to the exclusion of any other content. Chapters 1–4 describe the TSP and Chapters 5–6 provide a brief introduction to solving the TSP by using the branch and cut method. At the heart of the book are then Chapters 7–11, which survey various classes of cuts, in some cases first proposed by the authors themselves. Chapter 7 surveys cuts from blossoms and blocks, Chapter 8 presents cuts from combs and consecutive ones, and Chapter 9 introduces cuts from dominoes. Chapters 11 and 12 then describe in yet more detail separation and metamorphoses of strong valid inequalities. Other variants of the problem, such as the asymmetric TSP, and other solution approaches, including metaheuristics and approximation algorithms, are mentioned only in the passing. They are, however, well-covered elsewhere (Gutin & Punnen, 2002), and the seemingly narrow focus consequently enables the authors to provide an outstandingly in-depth treatment. The treatment especially benefits from authors' extensive experience with implementation of solvers for problems of combinatorial optimisation. In many textbooks on combinatorial optimisation, primal heuristics are mentioned only in passing and cuts are presented in the very mathematical style of definition – proof of validity – proof of dimensionality. Not here. Chapter 6-11 suggest separation routines, exact or heuristic, alongside the description of strong valid inequalities, Chapter 12 is devoted to management of cuts and instances of linear programming, Chapter 13 describes pricing routines for column generation, and last but not least, Chapter 15 is devoted to primal (tour-finding) heuristics. " Implementation details " , such as the choice of suitable data structures and trade-offs between heuristic and exact separation, are …
Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test

Computational thinking (CT) is being located at the focus of educational innovation, as a set of problemsolving skills that must be acquired by the new generations of students to thrive in a digital world full of objects driven by software. However, there is still no consensus on a CT definition or how to measure it. In response, we attempt to address both issues from a psychometric approach. On the one hand, a Computational Thinking Test (CTt) is administered on a sample of 1,251 Spanish students from 5th to 10th grade, so its descriptive statistics and reliability are reported in this paper. On the second hand, the criterion validity of the CTt is studied with respect to other standardized psychological tests: the Primary Mental Abilities (PMA) battery, and the RP30 problem-solving test. Thus, it is intended to provide a new instrument for CT measurement and additionally give evidence of the nature of CT through its associations with key related psychological constructs. Results show statistically significant correlations at least moderately intense between CT and: spatial ability (r 1⁄4 0.44), reasoning ability (r 1⁄4 0.44), and problemsolving ability (r 1⁄4 0.67). These results are consistent with recent theoretical proposals linking CT to some components of the Cattel-Horn-Carroll (CHC) model of intelligence, and corroborate the conceptualization of CT as a problem-solving ability.
A Survey of Software Quality for Machine Learning Applications

Machine learning (ML) is now widespread. Traditional software engineering can be applied to the development ML applications. However, we have to consider specific problems with ML applications in therms of their quality. In this paper, we present a survey of software quality for ML applications to consider the quality of ML applications as an emerging discussion. From this survey, we raised problems with ML applications and discovered software engineering approaches and software testing research areas to solve these problems. We classified survey targets into Academic Conferences, Magazines, and Communities. We targeted 16 academic conferences on artificial intelligence and software engineering, including 78 papers. We targeted 5 Magazines, including 22 papers. The results indicated key areas, such as deep learning, fault localization, and prediction, to be researched with software engineering and testing.
Procedures for Early-Stage Naval Ship Design Evaluation of Dynamic Stability : Influence of the Wave Crest

A method is presented to assess stability changes in waves in early-stage ship design. The method is practical: the calculations can be completed quickly and can be applied as soon as lines are available. The intended use of the described method is for preliminary analysis. If stability changes that result in large roll motion are indicated early in the design process, this permits planning and budgeting for direct assessments using numerical simulations and/or model experiments. The main use of the proposed method is for the justification for hull form shape modification or for necessary additional analysis to better quantify potentially increased stability risk. The method is based on the evaluation of changing stability in irregular seas and can be applied to any type of ship. To demonstrate the robustness of the method, results for ten naval ship types are presented and discussed. The proposed method is shown to identify ships with known risk for large stability changes in waves.
Want to make me emotional ? The influence of emotional advertisements on women ’ s consumption behavior

A wide difference of opinion exists about the content and composition of emotions. Advertising may influence an audience and their buying decisions about products and services. The objective of this study is to better conceptualize how women emotionally respond to emotional advertisements (EAs). The variant views are integrated into an ACE model, composed of subordinate levels of emotions (E), celebrity endorsements (C), and appeal drivers (A). This empirical study examines women’s emotional response using data from 240 Chinese women respondents. The study participants were invited to develop ACE mix based advertisements and fill out questionnaires. PLS-SEM analysis, a novel approach in ACE advertisement development and its applicability to consumer behavior, was used. The results show that showbiz celebrities expressing the emotion of happiness with music and color make the most effective ACE mix to influence the consumption behavior of women. The results are significantly mediated by attention levels and are widely applicable in the burgeoning advertising industry. The study also calls for further research with different ACE mixes in different contexts and on different audiences. It also opens doors for policy making and an appropriate understanding of women’s consumption behavior in the Chinese context.
Cost-analysis : How do monads and comonads differ ?

Monads are a de facto standard for the type-based analysis of impure aspects of programs, such as runtime cost [9, 5]. Recently, the logical dual of a monad, the comonad, has also been used for the cost analysis of programs, in conjunction with a linear type system [6, 8]. The logical duality of monads and comonads extends to cost analysis: In monadic type systems, costs are (side) effects, whereas in comonadic type systems, costs are coeffects. However, it is not clear whether these two methods of cost analysis are related and, if so, how. Are they equally expressive? Are they equally well-suited for cost analysis with all reduction strategies? Are there translations from type systems with effects to type systems with coeffects and viceversa? The goal of this work-in-progress paper is to explore some of these questions in a simple context — the simply typed lambda-calculus (STLC). As we show, even this simple context is already quite interesting technically and it suffices to bring out several key points.
Semantic Clustering of Pivot Paraphrases

Paraphrases extracted from parallel corpora by the pivot method (Bannard and Callison-Burch, 2005) constitute a valuable resource for multilingual NLP applications. In this study, we analyse the semantics of unigram pivot paraphrases and use a graph-based sense induction approach to unveil hidden sense distinctions in the paraphrase sets. The comparison of the acquired senses to gold data from the Lexical Substitution shared task (McCarthy and Navigli, 2007) demonstrates that sense distinctions exist in the paraphrase sets and highlights the need for a disambiguation step in applications using this resource.
SMS spam detection for Indian messages

The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. Though in most parts of the world, mobile messaging channel is currently regarded as “clean” and trusted, on the contrast recent reports clearly indicate that the volume of mobile phone spam is dramatically increasing year by year. It is an evolving setback especially in the Middle East and Asia. SMS spam filtering is a comparatively recent errand to deal such a problem. It inherits many concerns and quick fixes from Email spam filtering. However it fronts its own certain issues and problems. This paper inspires to work on the task of filtering mobile messages as Ham or Spam for the Indian Users by adding Indian messages to the worldwide available SMS dataset. The paper analyses different machine learning classifiers on large corpus of SMS messages for Indian people.
Learning to Attend via Word-Aspect Associative Fusion for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) tries to predict the polarity of a given document with respect to a given aspect entity. While neural network architectures have been successful in predicting the overall polarity of sentences, aspectspecific sentiment analysis still remains as an open problem. In this paper, we propose a novel method for integrating aspect information into the neural model. More specifically, we incorporate aspect information into the neural model by modeling word-aspect relationships. Our novel model, Aspect Fusion LSTM (AF-LSTM) learns to attend based on associative relationships between sentence words and aspect which allows our model to adaptively focus on the correct words given an aspect term. This ameliorates the flaws of other state-of-the-art models that utilize naive concatenations to model word-aspect similarity. Instead, our model adopts circular convolution and circular correlation to model the similarity between aspect and words and elegantly incorporates this within a differentiable neural attention framework. Finally, our model is end-to-end differentiable and highly related to convolution-correlation (holographic like) memories. Our proposed neural model achieves state-of-the-art performance on benchmark datasets, outperforming ATAE-LSTM by 4%− 5% on average across multiple datasets.
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations

We introduce the concept of numerical Gaussian processes, which we define as Gaussian processes with covariance functions resulting from temporal discretization of time-dependent partial differential equations. Numerical Gaussian processes, by construction, are designed to deal with cases where: (1) all we observe are noisy data on black-box initial conditions, and (2) we are interested in quantifying the uncertainty associated with such noisy data in our solutions to time-dependent partial differential equations. Our method circumvents the need for spatial discretization of the differential operators by proper placement of Gaussian process priors. This is an attempt to construct structured and data-efficient learning machines, which are explicitly informed by the underlying physics that possibly generated the observed data. The effectiveness of the proposed approach is demonstrated through several benchmark problems involving linear and nonlinear time-dependent operators. In all examples, we are able to recover accurate approximations of the latent solutions, and consistently propagate uncertainty, even in cases involving very long time integration.
Zero-Resource Neural Machine Translation with Multi-Agent Communication Game

While end-to-end neural machine translation (NMT) has achieved notable success in the past years in translating a handful of resource-rich language pairs, it still suffers from the data scarcity problem for low-resource language pairs and domains. To tackle this problem, we propose an interactive multimodal framework for zero-resource neural machine translation. Instead of being passively exposed to large amounts of parallel corpora, our learners (implemented as encoder-decoder architecture) engage in cooperative image description games, and thus develop their own image captioning or neural machine translation model from the need to communicate in order to succeed at the game. Experimental results on the IAPR-TC12 and Multi30K datasets show that the proposed learning mechanism significantly improves over the state-of-the-art methods.
'Finding the person you really are … on the inside': Compassion focused therapy for adults with intellectual disabilities.

This study utilized a mixed methods approach to examine the feasibility and acceptability of group compassion focused therapy for adults with intellectual disabilities (CFT-ID). Six participants with mild ID participated in six sessions of group CFT, specifically adapted for adults with ID. Session-by-session feasibility and acceptability measures suggested that participants understood the group content and process and experienced group sessions and experiential practices as helpful and enjoyable. Thematic analysis of focus groups identified three themes relating to (1) direct experiences of the group, (2) initial difficulties in being self-compassionate and (3) positive emotional changes. Pre- and post-group outcome measures indicated significant reductions in both self-criticism and unfavourable social comparisons. Results suggest that CFT can be adapted for individuals with ID and provide preliminary evidence that people with ID and psychological difficulties may experience a number of benefits from this group intervention.
Siamese Network with Soft Attention for Semantic Text Understanding

We propose a task independent neural networks model, based on a Siamese-twin architecture. Our model specifically benefits from two forms of attention scheme, which we use to extract high level feature representation of the underlying texts, both at word level (intra-attention) as well as sentence level (inter-attention). The inter attention scheme uses one of the text to create a contextual interlock with the other text, thus paying attention to mutually important parts. We evaluate our system on three tasks, i.e. Textual Entailment, Paraphrase Detection and Answer-Sentence selection. We set a near state-of-the-art result on the textual entailment task with the SNLI corpus while obtaining strong performance across the other tasks that we evaluate our model on.