nils-herrmann/clean_scirepeval huggingface dataset:
The acute management of intracerebral hemorrhage: a clinical review.

Intracerebral hemorrhage (ICH) is a devastating disease with high rates of mortality and morbidity. The major risk factors for ICH include chronic arterial hypertension and oral anticoagulation. After the initial hemorrhage, hematoma expansion and perihematoma edema result in secondary brain damage and worsened outcome. A rapid onset of focal neurological deficit with clinical signs of increased intracranial pressure is strongly suggestive of a diagnosis of ICH, although cranial imaging is required to differentiate it from ischemic stroke. ICH is a medical emergency and initial management should focus on urgent stabilization of cardiorespiratory variables and treatment of intracranial complications. More than 90% of patients present with acute hypertension, and there is some evidence that acute arterial blood pressure reduction is safe and associated with slowed hematoma growth and reduced risk of early neurological deterioration. However, early optimism that outcome might be improved by the early administration of recombinant factor VIIa (rFVIIa) has not been substantiated by a large phase III study. ICH is the most feared complication of warfarin anticoagulation, and the need to arrest intracranial bleeding outweighs all other considerations. Treatment options for warfarin reversal include vitamin K, fresh frozen plasma, prothrombin complex concentrates, and rFVIIa. There is no evidence to guide the specific management of antiplatelet therapy-related ICH. With the exceptions of placement of a ventricular drain in patients with hydrocephalus and evacuation of a large posterior fossa hematoma, the timing and nature of other neurosurgical interventions is also controversial. There is substantial evidence that management of patients with ICH in a specialist neurointensive care unit, where treatment is directed toward monitoring and managing cardiorespiratory variables and intracranial pressure, is associated with improved outcomes. Attention must be given to fluid and glycemic management, minimizing the risk of ventilator-acquired pneumonia, fever control, provision of enteral nutrition, and thromboembolic prophylaxis. There is an increasing awareness that aggressive management in the acute phase can translate into improved outcomes after ICH.
Fuzzy cognitive maps for artificial emotions forecasting

4 At the present, emotion is considered as a critical point of human behaviour, and thus it should be embedded within the reasoning module when an intelligent system or a autonomous robot aims to emulate or anticipate human reactions. Therefore, current research in Artificial Intelligence shows an increasing interest in artificial emotion research for developing human-like systems. Based on Thayer’s emotion model and Fuzzy Cognitive Maps, this paper presents a proposal for forecasting artificial emotions. It provides an innovative method for forecasting artificial emotions and designing an affective decision system. This work includes an experiment with three simulated artificial scenarios for testing the proposal. Each scenario generate different emotions according to the artificial experimental model.
A Hierarchical Generative Model for Eye Image Synthesis and Eye Gaze Estimation

In this work, we introduce a Hierarchical Generative Model (HGM) to enable realistic forward eye image synthesis, as well as effective backward eye gaze estimation. The proposed HGM consists of a hierarchical generative shape model (HGSM), and a conditional bidirectional generative adversarial network (c-BiGAN). The HGSM encodes eye geometry knowledge and relates eye gaze with eye shape, while c-BiGAN leverages on big data and captures the dependency between eye shape and eye appearance. As an intermediate component, eye shape connects knowledge-based model (HGSM) with data-driven model (c-BiGAN) and enables bidirectional inference. Through a top-down inference, the HGM can synthesize eye images consistent with the given eye gaze. Through a bottom-up inference, HGM can infer eye gaze effectively from a given eye image. Qualitative and quantitative evaluations on benchmark datasets demonstrate our model's effectiveness on both eye image synthesis and eye gaze estimation. In addition, the proposed model is not restricted to eye images only. It can be adapted to face images and any shape-appearance related fields.
RAMSES: RFID Augmented Module for Smart Environmental Sensing

This paper presents a radio frequency identification (RFID) augmented module for smart environmental sensing (RAMSES), which is a fully passive device with sensing and computation capabilities conceived to explore novel and unconventional RFID applications. RAMSES implements an RF energy-harvesting circuit enhanced by a dc-dc voltage booster in silicon-on-insulator technology, an ultralow-power microcontroller, temperature, light, and acceleration sensors, and a new-generation I2C-RFID chip to wirelessly deliver sensor data to standard RFID EPCglobal Class-1 Generation-2 readers. A preliminary RAMSES prototype, fabricated on a printed circuit board using low-cost off-the-shelf discrete components, has been extensively tested through experiments conducted both in lab and real-world application scenarios. The achieved results have demonstrated the ability of RAMSES to harvest the RF energy emitted by an interrogator placed up to 10 m of distance and autonomously perform sensing, computation, and data communication. To our knowledge, this is the longest range ever reported for fully passive RFID sensors. Furthermore, for applications requiring larger operating distances, RAMSES provides also a battery-assisted passive mode yielding up to 22-m communication range.
Vaccine hesitancy, vaccine refusal and the anti-vaccine movement: influence, impact and implications.

Despite being recognized as one of the most successful public health measures, vaccination is perceived as unsafe and unnecessary by a growing number of parents. Anti-vaccination movements have been implicated in lowered vaccine acceptance rates and in the increase in vaccine-preventable disease outbreaks and epidemics. In this review, we will look at determinants of parental decision-making about vaccination and provide an overview of the history of anti-vaccination movements and its clinical impact.
Layer-wise domain correction for unsupervised domain adaptation

Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities. However, conventional deep networks assume that the training and test data are sampled from the same distribution, and this assumption is often violated in real-world scenarios. To address the domain shift or data bias problems, we introduce layer-wise domain correction (LDC), a new unsupervised domain adaptation algorithm which adapts an existing deep network through additive correction layers spaced throughout the network. Through the additive layers, the representations of source and target domains can be perfectly aligned. The corrections that are trained via maximum mean discrepancy, adapt to the target domain while increasing the representational capacity of the network. LDC requires no target labels, achieves state-of-the-art performance across several adaptation benchmarks, and requires significantly less training time than existing adaptation methods.
Title Energy-Efficient Deep In-memory Architecture for NAND Flash Memories

This paper proposes an energy-efficient deep inmemory architecture for NAND flash (DIMA-F) to perform machine learning and inference algorithms on NAND flash memory. Algorithms for data analytics, inference, and decision-making require processing of large data volumes and are hence limited by data access costs. DIMA-F achieves energy savings and throughput improvement for such algorithms by reading and processing data in the analog domain at the periphery of NAND flash memory. This paper also provides behavioral models of DIMA-F that can be used for analysis and large scale system simulations in presence of circuit non-idealities and variations. DIMA-F is studied in the context of linear support vector machines and knearest neighbor for face detection and recognition, respectively. An estimated 8×-to-23× reduction in energy and 9×-to-15× improvement in throughput resulting in EDP gains up to 345× over the conventional NAND flash architecture incorporating an external digital ASIC for computation.
Scaling SGD Batch Size to 32K for ImageNet Training

The most natural way to speed-up the training of large networks is to use dataparallelism on multiple GPUs. To scale Stochastic Gradient (SG) based methods to more processors, one need to increase the batch size to make full use of the computational power of each GPU. However, keeping the accuracy of network with increase of batch size is not trivial. Currently, the state-of-the art method is to increase Learning Rate (LR) proportional to the batch size, and use special learning rate with "warm-up" policy to overcome initial optimization difficulty. By controlling the LR during the training process, one can efficiently use largebatch in ImageNet training. For example, Batch-1024 for AlexNet and Batch-8192 for ResNet-50 are successful applications. However, for ImageNet-1k training, state-of-the-art AlexNet only scales the batch size to 1024 and ResNet50 only scales it to 8192. The reason is that we can not scale the learning rate to a large value. To enable large-batch training to general networks or datasets, we propose Layer-wise Adaptive Rate Scaling (LARS). LARS LR uses different LRs for different layers based on the norm of the weights (||w||) and the norm of the gradients (||∇w||). By using LARS algoirithm, we can scale the batch size to 32768 for ResNet50 and 8192 for AlexNet. Large batch can make full use of the system’s computational power. For example, batch-4096 can achieve 3× speedup over batch-512 for ImageNet training by AlexNet model on a DGX-1 station (8 P100 GPUs).
Uncertainty in Managers ’ Reporting Objectives and Investors ’ Response to Earnings Reports : Evidence from the 2006 Executive Compensation Disclosures

We examine whether the information content of the earnings report, as captured by the earnings response coefficient (ERC), increases when investors’ uncertainty about the manager’s reporting objectives decreases, as predicted in Fischer and Verrecchia (2000). We use the 2006 mandatory compensation disclosures as an instrument to capture a decrease in investors’ uncertainty about managers’ incentives and reporting objectives. Employing a difference-in-differences design and exploiting the staggered adoption of the new rules, we find a statistically and economically significant increase in ERC for treated firms relative to control firms, largely driven by profit firms. Cross-sectional tests suggest that the effect is more pronounced in subsets of firms most affected by the new rules. Our findings represent the first empirical evidence of a role of compensation disclosures in enhancing the information content of financial reports.
Combination antibiotic therapy for multidrug-resistant Gram-negative bacteria

Combination antibiotic therapy for Gram-negative sepsis is controversial. The present review provides a brief summary of the existing knowledge on combination therapy for severe infections with multidrug-resistant Pseudomonas spp., Acinetobacter spp., and Enterobacteriaceae. Empirical combination antibiotic therapy is recommended for severe sepsis and septic shock to reduce mortality related to inappropriate antibiotic treatment. Because definitive combination therapy has not been proven superior to monotherapy in meta-analyses, it is generally advised to de-escalate antibiotic therapy when the antibiotic susceptibility profile is known, although it cannot be excluded that some subgroups of patients might still benefit from continued combination therapy. Definitive combination therapy is recommended for carbapenemase-producing Enterobacteriaceae and should also be considered for severe infections with Pseudomonas and Acinetobacter spp. when beta-lactams cannot be used. Because resistance to broad-spectrum beta-lactams is increasing in Gram-negative bacteria and because no new antibiotics are expected to become available in the near future, the antibacterial potential of combination therapy should be further explored. In vitro data suggest that combinations can be effective even if the bacteria are resistant to the individual antibiotics, although existing evidence is insufficient to support the choice of combinations and explain the synergistic effects observed. In vitro models can be used to screen for effective combinations that can later be validated in animal or clinical studies. Further, in the absence of clinical evidence, in vitro data might be useful in supporting therapeutic decisions for severe infections with multidrug-resistant Gram-negative bacteria.
Effective Disclosure in the Fast-Fashion Industry : from Sustainability Reporting to Action

This work, set in the context of the apparel industry, proposes an action-oriented disclosure tool to help solve the sustainability challenges of complex fast-fashion supply chains (SCs). In a search for effective disclosure, it focusses on actions towards sustainability instead of the measurements and indicators of its impacts. We applied qualitative and quantitative content analysis to the sustainability reporting of the world’s two largest fast-fashion companies in three phases. First, we searched for the challenges that the organisations report they are currently facing. Second, we introduced the United Nations’ Sustainable Development Goals (SDGs) framework to overcome the voluntary reporting drawback of ‘choosing what to disclose’, and revealed orphan issues. This broadened the scope from internal corporate challenges to issues impacting the ecosystems in which companies operate. Third, we analysed the reported sustainability actions and decomposed them into topics, instruments, and actors. The results showed that fast-fashion reporting has a broadly developed analysis base, but lacks action orientation. This has led us to propose the ‘Fast-Fashion Sustainability Scorecard’ as a universal disclosure framework that shifts the focus from (i) reporting towards action; (ii) financial performance towards sustainable value creation; and (iii) corporate boundaries towards value creation for the broader SC ecosystem.
Propagation Modeling for Accurate Indoor WLAN RSS-Based Localization

WLAN RSS-based localization has been a hot research topic for the last years. To obtain high accuracy in the noisy wireless channel, WLAN location determination systems usually use a calibration phase, where a radio map, capturing the signal strength signatures at different locations in the area of interest, is built. The radio map construction process takes a lot of time and effort, reducing the value of WLAN localization systems. In this paper, we propose 3D ray tracing as a way for automatically generating a highly accurate radiomap. We compare this method to previously used propagation modeling-based methods like the Wall Attenuation Factor and 2D ray tracing models. We evaluate the performance of each method and its computational cost in a typical residential environment. We also examine the sensitivity of the localization accuracy to inaccurate material parameters. Our results quantify the accuracy- complexity trade-off of the different proposed techniques with 3D ray tracing giving the best localization accuracy compared to measurements with acceptable computational requirements on a typical PC.
Extracting information from textual documents in the electronic health record: a review of recent research.

We examine recent published research on the extraction of information from textual documents in the Electronic Health Record (EHR).



Literature review of the research published after 1995, based on PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers already included.



174 publications were selected and are discussed in this review in terms of methods used, pre-processing of textual documents, contextual features detection and analysis, extraction of information in general, extraction of codes and of information for decision-support and enrichment of the EHR, information extraction for surveillance, research, automated terminology management, and data mining, and de-identification of clinical text.



Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Competitive challenges for information extraction from clinical text, along with the availability of annotated clinical text corpora, and further improvements in system performance are important factors to stimulate advances in this field and to increase the acceptance and usage of these systems in concrete clinical and biomedical research contexts.
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction

Facial beauty prediction (FBP) is a significant visual recognition problem to make assessment of facial attractiveness that is consistent to human perception. To tackle this problem, various data-driven models, especially state-of-the-art deep learning techniques, were introduced, and benchmark dataset become one of the essential elements to achieve FBP. Previous works have formulated the recognition of facial beauty as a specific supervised learning problem of classification, regression or ranking, which indicates that FBP is intrinsically a computation problem with multiple paradigms. However, most of FBP benchmark datasets were built under specific computation constrains, which limits the performance and flexibility of the computational model trained on the dataset. In this paper, we argue that FBP is a multi-paradigm computation problem, and propose a new diverse benchmark dataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty prediction. The SCUT-FBP5500 dataset has totally 5500 frontal faces with diverse properties (male/female, Asian/Caucasian, ages) and diverse labels (face landmarks, beauty scores within , , beauty score distribution), which allows different computational models with different FBP paradigms, such as appearance-based/shape-based facial beauty classification/regression model for male/female of Asian/Caucasian. We evaluated the SCUT-FBP5500 dataset for FBP using different combinations of feature and predictor, and various deep learning methods. The results indicates the improvement of FBP and the potential applications based on the SCUT-FBP5500.
Hough Transform from the Radon Transform

An appropriate special case of a transform developed by J. Radon in 1917 is shown to have the major properties of the Hough transform which is useful for finding line segments in digital pictures. Such an observation may be useful in further efforts to generalize the Hough transform. Techniques for applying the Radon transform to lines and pixels are developed through examples, and the appropriate generalization to arbitrary curves is discussed.
The life aquatic: advances in marine vertebrate genomics

The ocean is hypothesized to be where life on earth originated, and subsequent evolutionary transitions between marine and terrestrial environments have been key events in the origin of contemporary biodiversity. Here, we review how comparative genomic approaches are an increasingly important aspect of understanding evolutionary processes, such as physiological and morphological adaptation to the diverse habitats within the marine environment. In addition, we highlight how population genomics has provided unprecedented resolution for population structuring, speciation and adaptation in marine environments, which can have a low cost of dispersal and few physical barriers to gene flow, and can thus support large populations. Building upon this work, we outline the applications of genomics tools to conservation and their relevance to assessing the wide-ranging impact of fisheries and climate change on marine species.
SELF-EFFICACY , STRESS , AND ACADEMIC SUCCESS IN COLLEGE

This paper investigates the joint effects of academic self-efficacy and stress on the academic performance of 107 nontraditional, largely immigrant and minority, college freshmen at a large urban commuter institution. We developed a survey instrument to measure the level of academic self-efficacy and perceived stress associated with 27 college-related tasks. Both scales have high reliability, and they are moderately negatively correlated. We estimated structural equation models to assess the relative importance of stress and self-efficacy in predicting three academic performance outcomes: first-year college GPA, the number of accumulated credits, and college retention after the first year. The results suggest that academic self-efficacy is a more robust and consistent predictor than stress of academic success. ................................................................................................ ................................................................................................
Learning Collaborative Information Filters

Predicting items a user would like on the basis of other users’ ratings for these items has become a well-established strategy adopted by many recommendation services on the Internet. Although this can be seen as a classification problem, algorithms proposed thus far do not draw on results from the machine learning literature. We propose a representation for collaborative filtering tasks that allows the application of virtually any machine learning algorithm. We identify the shortcomings of current collaborative filtering techniques and propose the use of learning algorithms paired with feature extraction techniques that specifically address the limitations of previous approaches. Our best-performing algorithm is based on the singular value decomposition of an initial matrix of user ratings, exploiting latent structure that essentially eliminates the need for users to rate common items in order to become predictors for one another's preferences. We evaluate the proposed algorithm on a large database of user ratings for motion pictures and find that our approach significantly outperforms current collaborative filtering algorithms.
Automatic classification of bengali sentences based on sense definitions present in bengali wordnet

Based on the sense definition of words available in the Bengali WordNet, an attempt is made to classify the Bengali sentences automatically into different groups in accordance with their underlying senses. The input sentences are collected from 50 different categories of the Bengali text corpus developed in the TDIL project of the Govt. of India, while information about the different senses of particular ambiguous lexical item is collected from Bengali WordNet. In an experimental basis we have used Naive Bayes probabilistic model as a useful classifier of sentences. We have applied the algorithm over 1747 sentences that contain a particular Bengali lexical item which, because of its ambiguous nature, is able to trigger different senses that render sentences in different meanings. In our experiment we have achieved around 84% accurate result on the sense classification over the total input sentences. We have analyzed those residual sentences that did not comply with our experiment and did affect the results to note that in many cases, wrong syntactic structures and less semantic information are the main hurdles in semantic classification of sentences. The applicational relevance of this study is attested in automatic text classification, machine learning, information extraction, and word sense disambiguation.