nils-herrmann/clean_scirepeval huggingface dataset:
Depression and postoperative complications: an overview

The interaction of depression and anesthesia and surgery may result in significant increases in morbidity and mortality of patients. Major depressive disorder is a frequent complication of surgery, which may lead to further morbidity and mortality.


LITERATURE SEARCH
Several electronic data bases, including PubMed, were searched pairing "depression" with surgery, postoperative complications, postoperative cognitive impairment, cognition disorder, intensive care unit, mild cognitive impairment and Alzheimer's disease.


REVIEW OF THE LITERATURE
The suppression of the immune system in depressive disorders may expose the patients to increased rates of postoperative infections and increased mortality from cancer. Depression is commonly associated with cognitive impairment, which may be exacerbated postoperatively. There is evidence that acute postoperative pain causes depression and depression lowers the threshold for pain. Depression is also a strong predictor and correlate of chronic post-surgical pain. Many studies have identified depression as an independent risk factor for development of postoperative delirium, which may be a cause for a long and incomplete recovery after surgery. Depression is also frequent in intensive care unit patients and is associated with a lower health-related quality of life and increased mortality. Depression and anxiety have been widely reported soon after coronary artery bypass surgery and remain evident one year after surgery. They may increase the likelihood for new coronary artery events, further hospitalizations and increased mortality. Morbidly obese patients who undergo bariatric surgery have an increased risk of depression. Postoperative depression may also be associated with less weight loss at one year and longer. The extent of preoperative depression in patients scheduled for lumbar discectomy is a predictor of functional outcome and patient's dissatisfaction, especially after revision surgery. General postoperative mortality is increased.



Depression is a frequent cause of morbidity in surgery patients suffering from a wide range of conditions. Depression may be identified through the use of Patient Health Questionnaire-9 or similar instruments. Counseling interventions may be useful in ameliorating depression, but should be subject to clinical trials.
Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation

Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyperparameters for the spectral front-end. Therefore, we investigate end-to-end source separation in the time-domain, which allows modelling phase information and avoids fixed spectral transformations. Due to high sampling rates for audio, employing a long temporal input context on the sample level is difficult, but required for high quality separation results because of long-range temporal correlations. In this context, we propose the Wave-U-Net, an adaptation of the U-Net to the one-dimensional time domain, which repeatedly resamples feature maps to compute and combine features at different time scales. We introduce further architectural improvements, including an output layer that enforces source additivity, an upsampling technique and a context-aware prediction framework to reduce output artifacts. Experiments for singing voice separation indicate that our architecture yields a performance comparable to a stateof-the-art spectrogram-based U-Net architecture, given the same data. Finally, we reveal a problem with outliers in the currently used SDR evaluation metrics and suggest reporting rank-based statistics to alleviate this problem.
Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.
Multiagent Systems: A Survey from a Machine Learning Perspective

Distributed Artificial Intelligence (DAI) has existed as a s ubfield of AI for less than two decades. DAI is concerned with systems that consist of multiple indep endent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplin es: Distributed Problem Solving (DPS) focuses on the information management aspects of systems with sever al branches working together towards a common goal; Multiagent Systems (MAS) deals with behavior m anagement in collections of several independent entities, or agents. This survey of MAS is inten ded to serve as an introduction to the field and as an organizational framework. A series of general mult iagent scenarios are presented. For each scenario, the issues that arise are described along with a sa mpling of the techniques that exist to deal with them. The presented techniques are not exhaustive, but they highlight how multiagent systems can be and have been used to build complex systems. When options exi st, the techniques presented are biased towards machine learning approaches. Additional opportun ities for applying machine learning to MAS are highlighted and robotic soccer is presented as an approp riate test-bed for MAS. This survey does not focus exclusively on robotic systems since much of the prior research in non-robotic MAS applies to robotic systems as well. However, several robotic MAS, incl uding all of those presented in this issue, are discussed.
madmom: A New Python Audio and Music Signal Processing Library

In this paper, we present madmom, an open-source audio processing and music information retrieval (MIR) library written in Python. madmom features a concise, NumPy-compatible, object oriented design with simple calling conventions and sensible default values for all parameters, which facilitates fast prototyping of MIR applications. Prototypes can be seamlessly converted into callable processing pipelines through madmom's concept of Processors, callable objects that run transparently on multiple cores. Processors can also be serialised, saved, and re-run to allow results to be easily reproduced anywhere. Apart from low-level audio processing, madmom puts emphasis on musically meaningful high-level features. Many of these incorporate machine learning techniques and madmom provides a module that implements some methods commonly used in MIR such as hidden Markov models and neural networks. Additionally, madmom comes with several state-of-the-art MIR algorithms for onset detection, beat, downbeat and meter tracking, tempo estimation, and chord recognition. These can easily be incorporated into bigger MIR systems or run as stand-alone programs.
Multimedia Proxy Caching Mechanism for Quality Adaptive Streaming Applications in the Internet

The Internet has witnessed a rapid growth in deployment of Web-based streaming applications during recent years. I n these applications, server should be able to perform end-to-end con gestion control and quality adaptation to match the delivered stream quality to the average available bandwidth. Thus the delivered quality islimited by the bottleneck bandwidth on the path to the client. This paper proposes a proxy caching mechanism for layered-e ncoded multimedia streams in the Internet to maximize the delivered quality of popular streams to interested clients. The main challeng e is to replay a quality-variable cached stream while performing quality adaptation effectively in response to the variations in available bandwidth. We present a prefetching mechanism to support higher qualit y cached streams during subsequent playbacks and improve the qualit y of the cached stream with its popularity. We exploit inherent properties of multimedia streams to extend the semantics of popularity an d capture both level of interest among clients and usefulness of a laye r in the cache. We devise a fine-grain replacement algorithm suited for laye red-encoded streams. Our simulation results show that the interaction between the replacement algorithm and prefetching mechanism causes th e state of the cache to converge to an efficient state such that the quali ty of a cached stream is proportional to its popularity, and the variations in quality of a cached stream are inversely proportional to itspopularity. This implies that after serving several requests for a strea m, the proxy can effectively hide low bandwidth paths to the original ser ver from interested clients.  Multimedia Proxy Caching, Streaming Applications, Congestion Control, Quality Adaptation, Internet.
On value and value co-creation : A service systems and service logic perspective

r 200 3 lmaden.  The creation of value is the core purpose and central process of economic exchange. Traditional models of value creation focus on the firm’s output and price. We present an alternative perspective, one representing the intersection of two growing streams of thought, service science and service-dominant (S-D) logic. We take the view that (1) service, the application of competences (such as knowledge and skills) by one party for the benefit of another, is the underlying basis of exchange; (2) the proper unit of analysis for service-for-service exchange is the service system, which is a configuration of resources (including people, information, and technology) connected to other systems by value propositions; and (3) service science is the study of service systems and of the cocreation of value within complex configurations of resources. We argue that value is fundamentally derived and determined in use – the integration and application of resources in a specific context – rather than in exchange – embedded in firm output and captured by price. Service systems interact through mutual service exchange relationships, improving the adaptability and survivability of all service systems engaged in exchange, by allowing integration of resources that are mutually beneficial. This argument has implications for advancing service science by identifying research questions regarding configurations and processes of value co-creation and measurements of value-in-use, and by developing its ties with economics and other service-oriented disciplines.
Digital Screen Media and Cognitive Development.

In this article, we examine the impact of digital screen devices, including television, on cognitive development. Although we know that young infants and toddlers are using touch screen devices, we know little about their comprehension of the content that they encounter on them. In contrast, research suggests that children begin to comprehend child-directed television starting at ∼2 years of age. The cognitive impact of these media depends on the age of the child, the kind of programming (educational programming versus programming produced for adults), the social context of viewing, as well the particular kind of interactive media (eg, computer games). For children <2 years old, television viewing has mostly negative associations, especially for language and executive function. For preschool-aged children, television viewing has been found to have both positive and negative outcomes, and a large body of research suggests that educational television has a positive impact on cognitive development. Beyond the preschool years, children mostly consume entertainment programming, and cognitive outcomes are not well explored in research. The use of computer games as well as educational computer programs can lead to gains in academically relevant content and other cognitive skills. This article concludes by identifying topics and goals for future research and provides recommendations based on current research-based knowledge.
Power Laws in Economics and Finance ∗

A power law is the form taken by a large number of surprising empirical regularities in economics and finance. This article surveys well-documented empirical power laws concerning income and wealth, the size of cities and firms, stock market returns, trading volume, international trade, and executive pay. It reviews detail-independent theoretical motivations that make sharp predictions concerning the existence and coefficients of power laws, without requiring delicate tuning of model parameters. These theoretical mechanisms include random growth, optimization, and the economics of superstars coupled with extreme value theory. Some of the empirical regularities currently lack an appropriate explanation. This article highlights these open areas for
Event Structure Influences Language Production: Evidence from Structural Priming in Motion Event Description.

This priming study investigates the role of conceptual structure during language production, probing whether English speakers are sensitive to the structure of the event encoded by a prime sentence. In two experiments, participants read prime sentences aloud before describing motion events. Primes differed in 1) syntactic frame, 2) degree of lexical and conceptual overlap with target events, and 3) distribution of event components within frames. Results demonstrate that conceptual overlap between primes and targets led to priming of (a) the information that speakers chose to include in their descriptions of target events, (b) the way that information was mapped to linguistic elements, and (c) the syntactic structures that were built to communicate that information. When there was no conceptual overlap between primes and targets, priming was not successful. We conclude that conceptual structure is a level of representation activated during priming, and that it has implications for both Message Planning and Linguistic Formulation.
Learning Recurrent Neural Networks with Hessian-Free Optimization

In this work we resolve the long-outstanding problem of how to effectively train recurrent neural networks (RNNs) on complex and difficult sequence modeling problems which may contain long-term data dependencies. Utilizing recent advances in the Hessian-free optimization approach (Martens, 2010), together with a novel damping scheme, we successfully train RNNs on two sets of challenging problems. First, a collection of pathological synthetic datasets which are known to be impossible for standard optimization approaches (due to their extremely long-term dependencies), and second, on three natural and highly complex real-world sequence datasets where we find that our method significantly outperforms the previous state-of-theart method for training neural sequence models: the Long Short-term Memory approach of Hochreiter and Schmidhuber (1997). Additionally, we offer a new interpretation of the generalized Gauss-Newton matrix of sch (2002) which is used within the HF approach of Martens.
Data Mining for the Internet of Things: Literature Review and Challenges

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view and application view, including classification, clustering, association analysis, time series analysis, outlier analysis, etc. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data also reviewed, challenges and open research issues are discussed. At last a suggested big data mining system is proposed.
Economic-based Distributed Resource Management and Scheduling for Grid Computing

Computational Grids, emerging as an infrastructure for next generation computing, enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce. As the resources in the Grid are heterogeneous and geographically distributed with varying availability and a variety of usage and cost policies for diverse users at different times and, priorities as well as goals that vary with time. The management of resources and application scheduling in such a large and distributed environment is a complex task. This thesis proposes a distributed computational economy as an effective metaphor for the management of resources and application scheduling. It proposes an architectural framework that supports resource trading and quality of services based scheduling. It enables the regulation of supply and demand for resources and provides an incentive for resource owners for participating in the Grid and motives the users to trade-off between the deadline, budget, and the required level of quality of service. The thesis demonstrates the capability of economicbased systems for peer-to-peer distributed computing by developing users’ quality-of-service requirements driven scheduling strategies and algorithms. It demonstrates their effectiveness by performing scheduling experiments on the World-Wide Grid for solving parameter sweep applications.
Generating Topic-Oriented Summaries Using Neural Attention

Summarizing a document requires identifying the important parts of the document with an objective of providing a quick overview to a reader. However, a long article can span several topics and a single summary cannot do justice to all the topics. Further, the interests of readers can vary and the notion of importance can change across them. Existing summarization algorithms generate a single summary and are not capable of generating multiple summaries tuned to the interests of the readers. In this paper, we propose an attention based RNN framework to generate multiple summaries of a single document tuned to different topics of interest. Our method outperforms existing baselines and our results suggest that the attention of generative networks can be successfully biased to look at sentences relevant to a topic and effectively used to generate topic-tuned summaries.
Building Successful Knowledge Management Projects

It is widely acknowledged that developed economies have gradually been transformed over the past fifty years. Scholars and observers from disciplines as disparate as sociology, economics, and management science generally agree that knowledge has been at the center of this change. 1 Knowledge could be defined as information that has been combined with experience, context, interpretation, and reflection. Given the value of this asset to organizations, it is not surprising that greater attention is being paid to the subject of knowledge: what it is, how it differs from the related concepts of information and data, and how to begin to create, transfer, and use it more effectively. The subject of knowledge management, in particular, has had a recent flowering.
Building Detection from Satellite Imagery Using a Composite Loss Function

In this paper, we present a LinkNet-based architecture with SE-ResNeXt-50 encoder and a novel training strategy that strongly relies on image preprocessing and incorporating distorted network outputs. The architecture combines a pre-trained convolutional encoder and a symmetric expanding path that enables precise localization. We show that such a network can be trained on plain RGB images with a composite loss function and achieves competitive results on the DeepGlobe challenge on building extraction from satellite images
Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search

We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model that generates a sequence ŷ = {y0 . . . yT }, by maximizing p(y|x) = ∏ t p(yt|x; ). Lexical constraints take the form of phrases or words that must be present in the output sequence. This is a very general way to incorporate additional knowledge into a model’s output without requiring any modification of the model parameters or training data. We demonstrate the feasibility and flexibility of Lexically Constrained Decoding by conducting experiments on Neural Interactive-Predictive Translation, as well as Domain Adaptation for Neural Machine Translation. Experiments show that GBS can provide large improvements in translation quality in interactive scenarios, and that, even without any user input, GBS can be used to achieve significant gains in performance in domain adaptation scenarios.
State-of-art approaches for review spammer detection: a survey

E-commerce websites are now favourite for shopping comfortably at home without any burden of going to market. Their success depends upon the reviews written by the consumers who used particular products and subsequently shared their experiences with that product. The reviews also affects the buying decision of customer. Because of this reason the activity of fake reviews posting is increasing. The brand competitors of the product or the company itself may involve in posting fraud reviews to gain more profit. Such fraudulent reviews are spam review that badly affects the decision choice of the prospective consumer of the products. Many customers are misguided due to fake reviews. The person, who writes the fake reviews, is called the spammer. Identification of spammers is indirectly helpful in identifying whether the reviews are spam or not. The detection of review spammers is serious concern for the E-commerce business. To help researchers in this vibrant area, we present the state of art approaches for review spammer detection. This paper presents a comprehensive survey of the existing spammer detection approaches describing the features used for individual and group spammer detection, dataset summary with details of reviews, products and reviewers. The main aim of this paper is to provide a basic, comprehensive and comparative study of current research on detecting review spammer using machine learning techniques and give future directions. This paper also provides a concise summary of published research to help potential researchers in this area to innovate new techniques.
Dude, Where's That IP? Circumventing Measurement-based IP Geolocation

Many applications of IP geolocation can benefit from geolocation that is robust to adversarial clients. These include applications that limit access to online content to a specific geographic region and cloud computing, where some organizations must ensure their virtual machines stay in an appropriate geographic region. This paper studies the applicability of current IP geolocation techniques against an adversary who tries to subvert the techniques into returning a forged result. We propose and evaluate attacks on both delay-based IP geolocation techniques and more advanced topology-aware techniques. Against delay-based techniques, we find that the adversary has a clear trade-off between the accuracy and the detectability of an attack. In contrast, we observe that more sophisticated topology-aware techniques actually fare worse against an adversary because they give the adversary more inputs to manipulate through their use of topology and delay information.