nils-herrmann/clean_scirepeval huggingface dataset:
Cognitive model and cognitive behavior therapy for schizophrenia: an overview.

Schizophrenia causes great suffering for patients and families. Today, patients are treated with medications, but unfortunately many still have persistent symptoms and an impaired quality of life. During the last 20 years of research in cognitive behavioral therapy (CBT) for schizophrenia, evidence has been found that the treatment is good for patients but it is not satisfactory enough, and more studies are being carried out hopefully to achieve further improvement.



Clinical trials and meta-analyses are being used to try to prove the efficacy of CBT. In this article, we summarize recent research using the cognitive model for people with schizophrenia.



A systematic search was carried out in PubMed (Medline). Relevant articles were selected if they contained a description of cognitive models for schizophrenia or psychotic disorders.



There is now evidence that positive and negative symptoms exist in a continuum, from normality (mild form and few symptoms) to fully developed disease (intensive form with many symptoms). Delusional patients have reasoning bias such as jumping to conclusions, and those with hallucination have impaired self-monitoring and experience their own thoughts as voices. Patients with negative symptoms have negative beliefs such as low expectations regarding pleasure and success. In the entire patient group, it is common to have low self-esteem.



The cognitive model integrates very well with the aberrant salience model. It takes into account neurobiology, cognitive, emotional and social processes. The therapist uses this knowledge when he or she chooses techniques for treatment of patients.
The push-pull T technique: an easy and safe procedure in children with the buried bumper syndrome.

Percutaneous endoscopic gastrostomy (PEG) tube placement is a well-established procedure in adults as well as in pediatric patients who cannot be orally fed. However, potential serious complications may occur. The buried bumper syndrome is a well-recognized long-term complication of PEG. Overgrowth of gastric mucosa over the inner bumper of the tube will cause mechanical failure of formula delivery, rendering the tube useless. However, published experience in children with buried bumper syndrome is very scarce. In the authors' clinic, 76 PEG tubes were placed from 2001 to 2008, and buried bumper syndrome occurred in 1 patient. The authors report on their experience with buried bumper syndrome, an adapted safe endoscopic removal technique, as well as recommendations for prevention of buried bumper syndrome.
Anonymized data: generation, models, usage

Data anonymization techniques have been the subject of intense investigation in recent years, for many kinds of structured data, including tabular, graph and item set data. They enable publication of detailed information, which permits ad hoc queries and analyses, while guaranteeing the privacy of sensitive information in the data against a variety of attacks. In this tutorial, we aim to present a unified framework of data anonymization techniques, viewed through the lens of uncertainty. Essentially, anonymized data describes a set of possible worlds, one of which corresponds to the original data. We show that anonymization approaches such as suppression, generalization, perturbation and permutation generate different working models of uncertain data, some of which have been well studied, while others open new directions for research. We demonstrate that the privacy guarantees offered by methods such as k-anonymization and l-diversity can be naturally understood in terms of similarities and differences in the sets of possible worlds that correspond to the anonymized data. We describe how the body of work in query evaluation over uncertain databases can be used for answering ad hoc queries over anonymized data in a principled manner. A key benefit of the unified approach is the identification of a rich set of new problems for both the Data Anonymization and the Uncertain Data communities.
Voice Pathology Detection Using Deep Learning on Mobile Healthcare Framework

The feasibility and popularity of mobile healthcare are currently increasing. The advancement of modern technologies, such as wireless communication, data processing, the Internet of Things, cloud, and edge computing, makes mobile healthcare simpler than before. In addition, the deep learning approach brings a revolution in the machine learning domain. In this paper, we investigate a voice pathology detection system using deep learning on the mobile healthcare framework. A mobile multimedia healthcare framework is also designed. In the voice pathology detection system, voices are captured using smart mobile devices. Voice signals are processed before being fed to a convolutional neural network (CNN). We use a transfer learning technique to use the existing robust CNN models. In particular, the VGG-16 and CaffeNet models are investigated in the paper. The Saarbrucken voice disorder database is used in the experiments. Experimental results show that the voice pathology detection accuracy reaches up to 97.5% using the transfer learning of CNN models.
What Determines Corporate Transparency?

We investigate corporate transparency, defined as the availability of firmspecific information to those outside publicly traded firms. We conceptualize corporate transparency within a country as output from a multifaceted system whose components collectively produce, gather, validate, and disseminate information. We factor analyze a range of measures capturing countries’ firmspecific information environments, isolating two distinct factors. The first factor, interpreted as financial transparency, captures the intensity and timeliness of financial disclosures, and their interpretation and dissemination by analysts and the media. The second factor, interpreted as governance transparency, captures the intensity of governance disclosures used by outside investors to hold officers and directors accountable. We investigate whether these factors vary with countries’ legal/judicial regimes and political economies. Our main multivariate result is that the governance transparency factor is primarily related to a country’s legal/judicial regime, whereas the financial transparency factor is primarily related to political economy. ∗University of North Carolina at Chapel Hill; †University of Chicago. We appreciate the insightful suggestions of the referee and the editors. We thank workshop participants at Baruch College, Harvard Business School, HKUST, University of Iowa, Massachusetts Institute of Technology, University of Rochester, University of Wisconsin, and conference participants at Berkeley’s 2003 BART conference, the 2001 Duke/UNC Fall Camp, the 2001 University of Minnesota Accounting Theory conference, and the 2003 Journal of Accounting Research conference for helpful comments. We appreciate the valuable research assistance of Xia Chen, Sarah Eriksen, and Sarah Lindholm, and the financial support of the Kenan-Flagler Business School, University of North Carolina at Chapel Hill, the Institute of Professional Accounting of the GSB, University of Chicago, the William Ladany Faculty Research Fund, and Harvard Business School.
Variational Gram Functions: Convex Analysis and Optimization

We propose a new class of convex penalty functions, called variational Gram functions (VGFs), that can promote pairwise relations, such as orthogonality, among a set of vectors in a vector space. These functions can serve as regularizers in convex optimization problems arising from hierarchical classification, multitask learning, and estimating vectors with disjoint supports, among other applications. We study necessary and sufficient conditions under which a VGF is convex, and give a characterization of its subdifferential. We show how to compute its proximal operator, and discuss efficient optimization algorithms for regularized loss minimization problems where the loss admits a simple variational representation and the regularizer is a VGF. We also establish a general representer theorem for such learning problems. Lastly, numerical experiments on a hierarchical classification problem are presented to demonstrate the effectiveness of VGFs and the associated optimization algorithms.
Large Scale Terrain Generation from Tectonic Uplift and Fluvial Erosion

At large scale, landscapes result from the combination of two major processes: tectonics which generate the main relief through crust uplift, and weather which accounts for erosion. This paper presents the first method in computer graphics that combines uplift and hydraulic erosion to generate visually plausible terrains. Given a user-painted uplift map, we generate a stream graph over the entire domain embedding elevation information and stream flow. Our approach relies on the stream power equation introduced in geology for hydraulic erosion. By combining crust uplift and stream power erosion we generate large realistic terrains at a low computational cost. Finally, we convert this graph into a digital elevation model by blending landform feature kernels whose parameters are derived from the information in the graph. Our method gives high-level control over the large scale dendritic structures of the resulting river networks, watersheds, and mountains ridges.
Real-Time Face Reconstruction from a Single Depth Image

This paper contributes a real time method for recovering facial shape and expression from a single depth image. The method also estimates an accurate and dense correspondence field between the input depth image and a generic face model. Both outputs are a result of minimizing the error in reconstructing the depth image, achieved by applying a set of identity and expression blend shapes to the model. Traditionally, such a generative approach has shown to be computationally expensive and non-robust because of the non-linear nature of the reconstruction error. To overcome this problem, we use a discriminatively trained prediction pipeline that employs random forests to generate an initial dense but noisy correspondence field. Our method then exploits a fast ICP-like approximation to update these correspondences, allowing us to quickly obtain a robust initial fit of our model. The model parameters are then fine tuned to minimize the true reconstruction error using a stochastic optimization technique. The correspondence field resulting from our hybrid generative-discriminative pipeline is accurate and useful for a variety of applications such as mesh deformation and retexturing. Our method works in real-time on a single depth image i.e. Without temporal tracking, is free from per-user calibration, and works in low-light conditions.
The toxicology of mercury--current exposures and clinical manifestations.

s since January 1975, a full-text search capacity, and a personal archive for saving articles and search results of interest. All articles can be printed in a format that is virtually identical to that of the typeset pages. Beginning six months after publication, the full text of all Original Articles and Special Articles is available free to nonsubscribers who have completed a brief registration.  Downloaded from www.nejm.org at UNIV OF CINCINNATI SERIALS DEPT on August 8, 2007 .
Image segmentation: A survey of graph-cut methods

As a preprocessing step, image segmentation, which can do partition of an image into different regions, plays an important role in computer vision, objects recognition, tracking and image analysis. Till today, there are a large number of methods present that can extract the required foreground from the background. However, most of these methods are solely based on boundary or regional information which has limited the segmentation result to a large extent. Since the graph cut based segmentation method was proposed, it has obtained a lot of attention because this method utilizes both boundary and regional information. Furthermore, graph cut based method is efficient and accepted world-wide since it can achieve globally optimal result for the energy function. It is not only promising to specific image with known information but also effective to the natural image without any pre-known information. For the segmentation of N-dimensional image, graph cut based methods are also applicable. Due to the advantages of graph cut, various methods have been proposed. In this paper, the main aim is to help researcher to easily understand the graph cut based segmentation approach. We also classify this method into three categories. They are speed up-based graph cut, interactive-based graph cut and shape prior-based graph cut. This paper will be helpful to those who want to apply graph cut method into their research.
Risk factors for functional status decline in community-living elderly people: a systematic literature review.

To lay the groundwork for devising, improving and implementing strategies to prevent or delay the onset of disability in the elderly, we conducted a systematic literature review of longitudinal studies published between 1985 and 1997 that reported statistical associations between individual base-line risk factors and subsequent functional status in community-living older persons. Functional status decline was defined as disability or physical function limitation. We used MEDLINE, PSYCINFO, SOCA, EMBASE, bibliographies and expert consultation to select the articles, 78 of which met the selection criteria. Risk factors were categorized into 14 domains and coded by two independent abstractors. Based on the methodological quality of the statistical analyses between risk factors and functional outcomes (e.g. control for base-line functional status, control for confounding, attrition rate), the strength of evidence was derived for each risk factor. The association of functional decline with medical findings was also analyzed. The highest strength of evidence for an increased risk in functional status decline was found for (alphabetical order) cognitive impairment, depression, disease burden (comorbidity), increased and decreased body mass index, lower extremity functional limitation, low frequency of social contacts, low level of physical activity, no alcohol use compared to moderate use, poor self-perceived health, smoking and vision impairment. The review revealed that some risk factors (e.g. nutrition, physical environment) have been neglected in past research. This review will help investigators set priorities for future research of the Disablement Process, plan health and social services for elderly persons and develop more cost-effective programs for preventing disability among them.
Sentence Compression Beyond Word Deletion

In this paper we generalise the sentence compression task. Rather than simply shorten a sentence by deleting words or constituents, as in previous work, we rewrite it using additional operations such as substitution, reordering, and insertion. We present a new corpus that is suited to our task and a discriminative tree-totree transduction model that can naturally account for structural and lexical mismatches. The model incorporates a novel grammar extraction method, uses a language model for coherent output, and can be easily tuned to a wide range of compression specific loss functions.
Distribution-Matching Embedding for Visual Domain Adaptation

Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be directly suitable for such a comparison, since some of the features may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Distribution-Matching Embedding approach: An unsupervised domain adaptation method that overcomes this issue by mapping the data to a latent space where the distance between the empirical distributions of the source and target examples is minimized. In other words, we seek to extract the information that is invariant across the source and target data. In particular, we study two different distances to compare the source and target distributions: the Maximum Mean Discrepancy and the Hellinger distance. Furthermore, we show that our approach allows us to learn either a linear embedding, or a nonlinear one. We demonstrate the benefits of our approach on the tasks of visual object recognition, text categorization, and WiFi localization.
Feature-Rich Unsupervised Word Alignment Models

Brown et al. (1993) introduced five unsupervised, word-based, generative and statistical models, popularized as IBM models, for translating a sentence into another. These models introduce alignments which maps each word in the source language to a word in the target language. In these models there is a crucial independence assumption that all lexical entries are seen independently of one another. We hypothesize that this independence assumption might be too strong, especially for languages with a large vocabulary, for example because of rich morphology. We investigate this independence assumption by implementing IBM models 1 and 2, the least complex IBM models, and also implementing a feature-rich version of these models. Through features, similarities between lexical entries in syntax and possibly even meaning can be captured. This feature-richness, however, requires a change in parameterization of the IBM model. We follow the approach of Berg-Kirkpatrick et al. (2010) and parameterize our IBM model with a log-linear parametric form. Finally, we compare the IBM models with their log-linear variants on word alignment. We evaluate our models on the quality of word alignments with two languages with a richer vocabulary than English. Our results do not fully support our hypothesis yet, but they are promising. We believe the hypothesis can be confirmed, however, there are still many technical challenges left before the log-linear variants can become competitive with the IBM models in terms of quality and speed.
A systematic software-based radar system simulation method

With the increasing complexity of modern radar system and the increasing number of devices used in the radar system, it would be highly desirable to model the complete radar system including hardware and software by a single tool. This paper presents a novel software-based simulation method for modern radar system which here is automotive radar application. Various functions of automotive radar, like target speed, distance and azimuth and elevation angle detection, are simulated in test case and the simulation results are compared with the measurement results.
Distant supervision for emotion detection using Facebook reactions

We exploit the Facebook reaction feature in a distant supervised fashion to train a support vector machine classifier for emotion detection, using several feature combinations and combining different Facebook pages. We test our models on existing benchmarks for emotion detection and show that employing only information that is derived completely automatically, thus without relying on any handcrafted lexicon as it’s usually done, we can achieve competitive results. The results also show that there is large room for improvement, especially by gearing the collection of Facebook pages, with a view to the target domain.
Supporting Drivers in Keeping Safe Speed and Safe Distance: The SASPENCE Subproject Within the European Framework Programme 6 Integrating Project PReVENT

This paper describes a novel driver-support system that helps to maintain the correct speed and headway (distance) with respect to lane curvature and other vehicles ahead. The system has been developed as part of the Integrating Project PReVENT under the European Framework Programme 6, which is named SAfe SPEed and safe distaNCE (SASPENCE). The application uses a detailed description of the situation ahead of the vehicle. Many sensors [radar, video camera, Global Positioning System (GPS) and accelerometers, digital maps, and vehicle-to-vehicle wireless local area network (WLAN) connections] are used, and state-of-the-art data fusion provides a model of the environment. The system then computes a feasible maneuver and compares it with the driver's behavior to detect possible mistakes. The warning strategies are based on this comparison. The system “talks” to the driver mainly via a haptic pedal or seat belt and “listens” to the driver mainly via the vehicle acceleration. This kind of operation, i.e., the comparison between what the system thinks is possible and what the driver appears to be doing, and the consequent dialog can be regarded as simple implementations of the rider-horse metaphor (H-metaphor). The system has been tested in several situations (driving simulator, hardware in the loop, and real road tests). Objective and subjective data have been collected, revealing good acceptance and effectiveness, particularly in awakening distracted drivers. The system intervenes only when a problem is actually detected in the headway and/or speed (approaching curves or objects) and has been shown to cause prompt reactions and significant speed correction before getting into really dangerous situations.
Watermark-Driven Acoustic Echo Cancellation

The performance of adaptive acoustic echo cancelers AEC is sensitive to the nonstationarity and correlation of speech signals. In this paper, we explore a new approach based on an adaptive AEC driven by data hidden in speech, to enhance the AEC robustness. We propose a two-stage AEC, where the first stage is a classical NLMS-based AEC driven by the far-end speech. In the signal, we embed—in an extended conception of data hiding—an imperceptible white and stationary signal, i.e., a watermark. The goal of the second stage AEC is to identify the misalignment of the first stage. It is driven by the watermark solely and takes advantage of its appropriate properties stationary and white to improve the robustness of the two-stage AEC to the nonstationarity and correlation of speech, and thus reduce the overall system misadjustment. We test two kinds of implementations: in the first implementation, referred to as adaptive watermark driven AEC A-WdAEC, the watermark is a white stationary Gaussian noise. Driven by this signal, the second stage converges faster than the classical AEC and provides better performance in steady state. In the second implementation, referred to as maximum length sequences WdAEC MLS-WdAEC, the watermark is built from MLS. Thus, the second stage performs a block identification of the first stage misalignment, given by the circular correlation watermark/preprocessed version of the first stage residual echo. The advantage of this implementation lies in its robustness against noise and undermodeling. Simulation results show the relevance of the “WdAEC” approach, compared to the classical “error-driven AEC.”
Petri net modelling of biological networks

Mathematical modelling is increasingly used to get insights into the functioning of complex biological networks. In this context, Petri nets (PNs) have recently emerged as a promising tool among the various methods employed for the modelling and analysis of molecular networks. PNs come with a series of extensions, which allow different abstraction levels, from purely qualitative to more complex quantitative models. Noteworthily, each of these models preserves the underlying graph, which depicts the interactions between the biological components. This article intends to present the basics of the approach and to foster the potential role PNs could play in the development of the computational systems biology.