nils-herrmann/clean_scirepeval huggingface dataset:
word2vec Parameter Learning Explained

The word2vec model and application by Mikolov et al. have attracted a great amount of attention in recent two years. The vector representations of words learned by word2vec models have been proven to be able to carry semantic meanings and are useful in various NLP tasks. As an increasing number of researchers would like to experiment with word2vec, I notice that there lacks a material that comprehensively explains the parameter learning process of word2vec in details, thus preventing many people with less neural network experience from understanding how exactly word2vec works. This note provides detailed derivations and explanations of the parameter update equations for the word2vec models, including the original continuous bag-of-word (CBOW) and skip-gram models, as well as advanced tricks, hierarchical soft-max and negative sampling. In the appendix a review is given on the basics of neuron network models and backpropagation. 1 Continuous Bag-of-Word Model 1.1 One-word context We start from the simplest version of the continuous bag-of-word model (CBOW) introduced in Mikolov et al. (2013a). We assume that there is only one word considered per context, which means the model will predict one target word given one context word, which is like a bigram model. Figure 1 shows the network model under the simplified context definition1. In our setting, the vocabulary size is V , and the hidden layer size is N . The nodes on adjacent layers are fully connected. The input vector is a one-hot encoded vector, which means for a given input context word, only one node of {x1, · · · , xV } will be 1, and all other nodes are 0. The weights between the input layer and the output layer can be represented by a V × N matrix W. Each row of W is the N -dimension vector representation vw of the In Figures 1, 2, 3, and the rest of this note, W′ is not the transpose of W, but a different matrix instead.
Network Representation Learning with Rich Text Information

Representation learning has shown its effectiveness in many tasks such as image classification and text mining. Network representation learning aims at learning distributed vector representation for each vertex in a network, which is also increasingly recognized as an important aspect for network analysis. Most network representation learning methods investigate network structures for learning. In reality, network vertices contain rich information (such as text), which cannot be well applied with algorithmic frameworks of typical representation learning methods. By proving that DeepWalk, a state-of-the-art network representation method, is actually equivalent to matrix factorization (MF), we propose text-associated DeepWalk (TADW). TADW incorporates text features of vertices into network representation learning under the framework of matrix factorization. We evaluate our method and various baseline methods by applying them to the task of multi-class classification of vertices. The experimental results show that, our method outperforms other baselines on all three datasets, especially when networks are noisy and training ratio is small. The source code of this paper can be obtained from https://github.com/ albertyang33/TADW.
Autonomous Driving in Reality with Reinforcement Learning and Image Translation

Supervised learning is widely used in training autonomous driving vehicle. However, it is trained with large amount of supervised labeled data. Reinforcement learning can be trained without abundant labeled data, but we cannot train it in reality because it would involve many unpredictable accidents. Nevertheless, training an agent with good performance in virtual environment is relatively much easier. Because of the huge difference between virtual and real, how to fill the gap between virtual and real is challenging. In this paper, we proposed a novel framework of reinforcement learning with image semantic segmentation network to make the whole model adaptable to reality. The agent is trained in TORCS, a car racing simulator.
SBVR Business Rules Generation from Natural Language Specification

In this paper, we present a novel approach of translating natural languages specification to SBVR business rules. The business rules constraint business structure or control behaviour of a business process. In modern business modelling, one of the important phases is writing business rules. Typically, a business rule analyst has to manually write hundreds of business rules in a natural language (NL) and then manually translate NL specification of all the rules in a particular rule language such as SBVR, or OCL, as required. However, the manual translation of NL rule specification to formal representation as SBVR rule is not only difficult, complex and time consuming but also can result in erroneous business rules. In this paper, we propose an automated approach that automatically translates the NL (such as English) specification of business rules to SBVR (Semantic Business Vocabulary and Rules) rules. The major challenge in NL to SBVR translation was complex semantic analysis of English language. We have used a rule based algorithm for robust semantic analysis of English and generate SBVR rules. Automated generation of SBVR based Business rules can help in improved and efficient constrained business aspects in a typical business modelling.
Maternal stress alters endocrine function of the feto-placental unit in rats.

Prenatal stress (PS) can cause early and long-term developmental effects resulting in part from altered maternal and/or fetal glucocorticoid exposure. The aim of the present study was to assess the impact of chronic restraint stress during late gestation on feto-placental unit physiology and function in embryonic (E) day 21 male rat fetuses. Chronic stress decreased body weight gain and food intake of the dams and increased their adrenal weight. In the placenta of PS rats, the expression of glucose transporter type 1 (GLUT1) was decreased, whereas GLUT3 and GLUT4 were slightly increased. Moreover, placental expression and activity of the glucocorticoid "barrier" enzyme 11beta-hydroxysteroid dehydrogenase type 2 was strongly reduced. At E21, PS fetuses exhibited decreased body, adrenal pancreas, and testis weights. These alterations were associated with reduced pancreatic beta-cell mass, plasma levels of glucose, growth hormone, and ACTH, whereas corticosterone, insulin, IGF-1, and CBG levels were unaffected. These data emphasize the impact of PS on both fetal growth and endocrine function as well as on placental physiology, suggesting that PS could program processes implied in adult biology and pathophysiology.
Modelling the Scene Dependent Imaging in Cameras with a Deep Neural Network

We present a novel deep learning framework that models the scene dependent image processing inside cameras. Often called as the radiometric calibration, the process of recovering RAWimages from processed images (JPEG format in the sRGB color space) is essential for many computer vision tasks that rely on physically accurate radiance values. All previous works rely on the deterministic imaging model where the color transformation stays the same regardless of the scene and thus they can only be applied for images taken under the manual mode. In this paper, we propose a datadriven approach to learn the scene dependent and locally varying image processing inside cameras under the automode. Our method incorporates both the global and the local scene context into pixel-wise features via multi-scale pyramid of learnable histogram layers. The results show that we can model the imaging pipeline of different cameras that operate under the automode accurately in both directions (from RAW to sRGB, from sRGB to RAW) and we show how we can apply our method to improve the performance of image deblurring.
Employee motivation and organizational performance

The majority of organizations are competing to survive in this volatile and fierce market environment. Motivation and performance of the employees are essential tools for the success of any organization in the long run. On the one hand, measuring performance is critical to organization’s management, as it highlights the evolution and achievement of the organization. On the other hand, there is a positive relationship between employee motivation and organizational effectiveness, reflected in numerous studies. This paper aims to analyze the drivers of employee motivation to high levels of organizational performance. The literature shows that factors such as empowerment and recognition increase employee motivation. If the empowerment and recognition of employees is increased, their motivation to work will also improve, as well as their accomplishments and the organizational performance. Nevertheless, employee dissatisfactions caused by monotonous jobs and pressure from clients, might weaken the organizational performance. Therefore, jobs absenteeism rates may increase and employees might leave the organization to joint competitors that offer better work conditions and higher incentives. Not all individuals are the same, so each one should be motivated using different strategies. For example, one employee may be motivated by higher commission, while another might be motivated by job satisfaction or a better work environment.
Antimicrobial Activity of Some Essential Oils—Present Status and Future Perspectives

Extensive documentation on the antimicrobial properties of essential oils and their constituents has been carried out by several workers. Although the mechanism of action of a few essential oil components has been elucidated in many pioneering works in the past, detailed knowledge of most of the compounds and their mechanism of action is still lacking. This knowledge is particularly important for the determination of the effect of essential oils on different microorganisms, how they work in combination with other antimicrobial compounds, and their interaction with food matrix components. Also, recent studies have demonstrated that nanoparticles (NPs) functionalized with essential oils have significant antimicrobial potential against multidrug- resistant pathogens due to an increase in chemical stability and solubility, decreased rapid evaporation and minimized degradation of active essential oil components. The application of encapsulated essential oils also supports their controlled and sustained release, which enhances their bioavailability and efficacy against multidrug-resistant pathogens. In the recent years, due to increasingly negative consumer perceptions of synthetic preservatives, interest in essential oils and their application in food preservation has been amplified. Moreover, the development of resistance to different antimicrobial agents by bacteria, fungi, viruses, parasites, etc. is a great challenge to the medical field for treating the infections caused by them, and hence, there is a pressing need to look for new and novel antimicrobials. To overcome these problems, nano-encapsulation of essential oils and exploiting the synergies between essential oils, constituents of essential oils, and antibiotics along with essential oils have been recommended as an answer to this problem. However, less is known about the interactions that lead to additive, synergistic, or antagonistic effects. A contributing role of this knowledge could be the design of new and more potent antimicrobial blends, and understanding of the interplay between the components of crude essential oils. This review is written with the purpose of giving an overview of current knowledge about the antimicrobial properties of essential oils and their mechanisms of action, components of essential oils, nano-encapsulated essential oils, and synergistic combinations of essential oils so as to find research areas that can facilitate applications of essential oils to overcome the problem of multidrug-resistant micro-organisms.
Ambiguity in Categorical Models of Meaning

Building on existing categorical accounts of natural language semantics, we propose a compositional distributional model of ambiguous meaning. Originally inspired by the high-level category theoretic language of quantum information protocols, the compositional, distributional categorical model provides a conceptually motivated procedure to compute the meaning of a sentence, given its grammatical structure and an empirical derivation of the meaning of its parts. Grammar is given a type-logical description in a compact closed category while the meaning of words is represented in a finite inner product space model. Since the category of finite-dimensional Hilbert spaces is also compact closed, the type-checking deduction process lifts to a concrete meaning-vector computation via a strong monoidal functor between the two categories. The advantage of reasoning with these structures is that grammatical composition admits an interpretation in terms of flow of meaning between words. Pushing the analogy with quantum mechanics further, we describe ambiguous words as statistical ensembles of unambiguous concepts and extend the semantics of the previous model to a category that supports probabilistic mixing. We introduce two different Frobenius algebras representing different ways of composing the meaning of words, and discuss their properties. We conclude with a range of applications to the case of definitions, including a meaning update rule that reconciles the meaning of an ambiguous word with that of its definition.
Densely Connected Attention Propagation for Reading Comprehension

We propose DECAPROP (Densely Connected Attention Propagation), a new densely connected neural architecture for reading comprehension (RC). There are two distinct characteristics of our model. Firstly, our model densely connects all pairwise layers of the network, modeling relationships between passage and query across all hierarchical levels. Secondly, the dense connectors in our network are learned via attention instead of standard residual skip-connectors. To this end, we propose novel Bidirectional Attention Connectors (BAC) for efficiently forging connections throughout the network. We conduct extensive experiments on four challenging RC benchmarks. Our proposed approach achieves state-of-the-art results on all four, outperforming existing baselines by up to 2.6% − 14.2% in absolute F1 score.
Self-Committee Approach for Image Restoration Problems using Convolutional Neural Network

There have been many discriminative learning methods using convolutional neural networks (CNN) for several image restoration problems, which learn the mapping function from a degraded input to the clean output. In this letter, we propose a self-committee method that can find enhanced restoration results from the multiple trial of a trained CNN with different but related inputs. Specifically, it is noted that the CNN sometimes finds different mapping functions when the input is transformed by a reversible transform and thus produces different but related outputs with the original. Hence averaging the outputs for several different transformed inputs can enhance the results as evidenced by the network committee methods. Unlike the conventional committee approaches that require several networks, the proposed method needs only a single network. Experimental results show that adding an additional transform as a committee always brings additional gain on image denoising and single image supreresolution problems.
Chocolate and the brain: Neurobiological impact of cocoa flavanols on cognition and behavior

Cocoa products and chocolate have recently been recognized as a rich source of flavonoids, mainly flavanols, potent antioxidant and anti-inflammatory agents with established benefits for cardiovascular health but largely unproven effects on neurocognition and behavior. In this review, we focus on neuromodulatory and neuroprotective actions of cocoa flavanols in humans. The absorbed flavonoids penetrate and accumulate in the brain regions involved in learning and memory, especially the hippocampus. The neurobiological actions of flavanols are believed to occur in two major ways: (i) via direct interactions with cellular cascades yielding expression of neuroprotective and neuromodulatory proteins that promote neurogenesis, neuronal function and brain connectivity, and (ii) via blood-flow improvement and angiogenesis in the brain and sensory systems. Protective effects of long-term flavanol consumption on neurocognition and behavior, including age- and disease-related cognitive decline, were shown in animal models of normal aging, dementia, and stroke. A few human observational and intervention studies appear to corroborate these findings. Evidence on more immediate action of cocoa flavanols remains limited and inconclusive, but warrants further research. As an outline for future research on cocoa flavanol impact on human cognition, mood, and behavior, we underscore combination of functional neuroimaging with cognitive and behavioral measures of performance.
An Analysis of Cloud Computing and its Security Issues

Cloud computing is a versatile technology for providing computing services by the internet on demand and pay per use access to a pool of shared resources like networks, servers, services, storage and applications, without acquiring physically . Cloud computing begins from the development of parallel computing, distributed computing, shared computing and grid computing. Although the technology and its application are not new, the rising awareness and implementations of cloud services and its underlying technologies cause the need for security requirements being up to date. Cloud Computing presents an added level of risk because essential services are often outsourced to a third party, which makes it harder to maintain data security and privacy, support data and service availability. In this paper, we explore the different concepts involved in cloud computing, vulnerabilities in this kind of systems and the most important threats found in the literature related to Cloud Computing and its environment as well as to identify and relate vulnerabilities and threats with possible solutions.
Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction

A key issue in software evolution analysis is the identification of particular changes that occur across several versions of a program. We present change distilling, a tree differencing algorithm for fine-grained source code change extraction. For that, we have improved the existing algorithm by Chawathe et al. for extracting changes in hierarchically structured data. Our algorithm extracts changes by finding both a match between the nodes of the compared two abstract syntax trees and a minimum edit script that can transform one tree into the other given the computed matching. As a result, we can identify fine-grained change types between program versions according to our taxonomy of source code changes. We evaluated our change distilling algorithm with a benchmark that we developed, which consists of 1,064 manually classified changes in 219 revisions of eight methods from three different open source projects. We achieved significant improvements in extracting types of source code changes: Our algorithm approximates the minimum edit script 45 percent better than the original change extraction approach by Chawathe et al. We are able to find all occurring changes and almost reach the minimum conforming edit script, that is, we reach a mean absolute percentage error of 34 percent, compared to the 79 percent reached by the original algorithm. The paper describes both our change distilling algorithm and the results of our evolution.
AutoExtend: Combining Word Embeddings with Semantic Resources

We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource. The method is based on encoding and decoding the word embeddings and is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The obtained embeddings live in the same vector space as the input word embeddings. A sparse tensor formalization guarantees efficiency and parallelizability. We use WordNet, GermaNet, and Freebase as semantic resources. AutoExtend achieves state-of-the-art performance on Word-in-Context Similarity and Word Sense Disambiguation tasks.
Advances in closed-loop deep brain stimulation devices

Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner. This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research. Although we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state. The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized.
Physiology of Bone Formation, Remodeling, and Metabolism

Bone is a highly specialized supporting framework of the body, characterized by its rigidity, hardness, and power of regeneration and repair. It protects the vital organs, provides an environment for marrow (both blood forming and fat storage), acts as a mineral reservoir for calcium homeostasis and a reservoir of growth factors and cytokines, and also takes part in acid–base balance (Taichman 2005 ) . Bone constantly undergoes modeling (reshaping) during life to help it adapt to changing biomechanical forces, as well as remodeling to remove old, microdamaged bone and replace it with new, mechanically stronger bone to help preserve bone strength. The bones have two components – the cortical bone which is dense, solid, and surrounds the marrow space and the trabecular bone which is composed of a honeycomb-like network of trabecular plates and rods interspersed in the bone marrow compartment. Cortical bone has an outer periosteal surface and inner endosteal surface. The periosteum is a fi brous connective tissue sheath that surrounds the outer cortical surface of bone, except at joints where bone is lined by articular cartilage. It contains blood vessels, nerve fi bers, osteoblasts, and osteoclasts. It protects, nourishes, and aides in bone formation. It plays an important role in appositional growth and fracture repair. The endosteum is a membranous structure covering the inner surface of cortical and cancellous bone and the blood vessel canals (Volkmann’s canals) present in bone. Physiology of Bone Formation, Remodeling, and Metabolism