nils-herrmann/clean_scirepeval huggingface dataset:
Luminescence in Sulfides: A Rich History and a Bright Future

Sulfide-based luminescent materials have attracted a lot of attention for a wide range of photo-, cathodoand electroluminescent applications. Upon doping with Ce 3+ and Eu 2+ , the luminescence can be varied over the entire visible region by appropriately choosing the composition of the sulfide host. Main application areas are flat panel displays based on thin film electroluminescence, field emission displays and ZnS-based powder electroluminescence for backlights. For these applications, special attention is given to BaAl2S4:Eu, ZnS:Mn and ZnS:Cu. Recently, sulfide materials have regained interest due to their ability (in contrast to oxide materials) to provide a broad band, Eu 2+ -based red emission for use as a color conversion material in white-light emitting diodes (LEDs). The potential application of rare-earth doped binary alkaline-earth sulfides, like CaS and SrS, thiogallates, thioaluminates and thiosilicates as conversion phosphors is discussed. Finally, this review concludes with the size-dependent luminescence in intrinsic colloidal quantum dots like PbS and CdS, and with the luminescence in doped nanoparticles.
Load Balancing in Cloud Computing

Cloud computing helps to share data and provide many resources to users. Users pay only for those resources as much they used. Cloud computing stores the data and distributed resources in the open environment. The amount of data storage increases quickly in open environment. So, load balancing is a main challenge in cloud environment. Load balancing is helped to distribute the dynamic workload across multiple nodes to ensure that no single node is overloaded. It helps in proper utilization of resources .It also improve the performance of the system. Many existing algorithms provide load balancing and better resource utilization. There are various types load are possible in cloud computing like memory, CPU and network load. Load balancing is the process of finding overloaded nodes and then transferring the extra load to other nodes.
A Self-Adaptive Sliding Window Based Topic Model for Non-uniform Texts

The contents generated from different data sources are usually non-uniform, such as long texts produced by news websites and short texts produced by social media. Uncovering topics over large-scale non-uniform texts becomes an important task for analyzing network data. However, the existing methods may fail to recognize the difference between long texts and short texts. To address this problem, we propose a novel topic modeling method for non-uniform text topic modeling referred to as self-adaptive sliding window based topic model (SSWTM). Specifically, in all kinds of texts, relevant words have a closer distance to each other than irrelevant words. Based on this assumption, SSWTM extracts relevant words by using a selfadaptive sliding window and models on the whole corpus. The self-adaptive sliding window can filter noisy information and change the size of a window according to different text contents. Experimental results on short texts from Twitter and long texts from Chinese news articles demonstrate that our method can discover more coherent topics for non-uniform texts compared with state-of-the-art methods.
Towards Sparse Hierarchical Graph Classifiers

Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graphbased benchmark tasks. While novel approaches to learning node embeddings are highly suitable for node classification and link prediction, their application to graph classification (predicting a single label for the entire graph) remains mostly rudimentary, typically using a single global pooling step to aggregate node features or a hand-designed, fixed heuristic for hierarchical coarsening of the graph structure. An important step towards ameliorating this is differentiable graph coarsening—the ability to reduce the size of the graph in an adaptive, datadependent manner within a graph neural network pipeline, analogous to image downsampling within CNNs. However, the previous prominent approach to pooling has quadratic memory requirements during training and is therefore not scalable to large graphs. Here we combine several recent advances in graph neural network design to demonstrate that competitive hierarchical graph classification results are possible without sacrificing sparsity. Our results are verified on several established graph classification benchmarks, and highlight an important direction for future research in graph-based neural networks.
A unified deep artificial neural network approach to partial differential equations in complex geometries

In this paper we use deep feedforward artificial neural networks to approximate solutions to partial differential equations in complex geometries. We show how to modify the backpropagation algorithm to compute the partial derivatives of the network output with respect to the space variables which is needed to approximate the differential operator. The method is based on an ansatz for the solution which requires nothing but feedforward neural networks and an unconstrained gradient based optimization method such as gradient descent or a quasi-Newton method. We show an example where classical mesh based methods cannot be used and neural networks can be seen as an attractive alternative. Finally, we highlight the benefits of deep compared to shallow neural networks and device some other convergence enhancing techniques.
Predictive Current Control of a Voltage Source Inverter

This paper presents a predictive current control method and its application to a voltage source inverter. The method uses a discrete-time model of the system to predict the future value of the load current for all possible voltage vectors generated by the inverter. The voltage vector which minimizes a quality function is selected. The quality function used in this work evaluates the current error at the next sampling time. The performance of the proposed predictive control method is compared with hysteresis and pulsewidth modulation control. The results show that the predictive method controls very effectively the load current and performs very well compared with the classical solutions
Bayesian Deep Generative Models for Semi-Supervised and Active Learning

Despite the recent successes in machine learning, there remain many open challenges. Arguably one of the most important and interesting open research problems is that of data efficiency. Supervised machine learning models, and especially deep neural networks, are notoriously data hungry, often requiring millions of labeled examples to achieve desired performance. However, labeled data is often expensive or difficult to obtain, hindering advances in interesting and important domains. What avenues might we pursue to increase the data efficiency of machine learning models? One approach is semi-supervised learning. In contrast to labeled data, unlabeled data is often easy and inexpensive to obtain. Semi-supervised learning is concerned with leveraging unlabeled data to improve performance in supervised tasks. Another approach is active learning: in the presence of a labeling mechanism (oracle), how can we choose examples to be labeled in a way that maximizes the gain in performance? In this thesis we are concerned with developing models that enable us to improve data efficiency of powerful models by jointly pursuing both of these approaches. Deep generative models parameterized by neural networks have emerged recently as powerful and flexible tools for unsupervised learning. They are especially useful for modeling high-dimensional and complex data. We propose a deep generative model with a discriminative component. By including the discriminative component in the model, after training is complete the model is used for classification rather than variational approximations. The model further includes stochastic inputs of arbitrary dimension for increased flexibility and expressiveness. We leverage the stochastic layer to learn representations of the data which naturally accommodate semi-supervised learning. We develop an efficient Gibbs sampling procedure to marginalize the stochastic inputs while inferring labels. We extend the model to include uncertainty in the weights, allowing us to explicitly capture model uncertainty, and demonstrate how this allows us to use the model for active learning as well as semi-supervised learning. I would like to dedicate this thesis to my loving wife, parents, and sister . . .
Farewell Freebase: Migrating the SimpleQuestions Dataset to DBpedia

Question answering over knowledge graphs is an important problem of interest both commercially and academically. There is substantial interest in the class of natural language questions that can be answered via the lookup of a single fact, driven by the availability of the popular SIMPLEQUESTIONS dataset. The problem with this dataset, however, is that answer triples are provided from Freebase, which has been defunct for several years. As a result, it is difficult to build “real-world” question answering systems that are operationally deployable. Furthermore, a defunct knowledge graph means that much of the infrastructure for querying, browsing, and manipulating triples no longer exists. To address this problem, we present SIMPLEDBPEDIAQA, a new benchmark dataset for simple question answering over knowledge graphs that was created by mapping SIMPLEQUESTIONS entities and predicates from Freebase to DBpedia. Although this mapping is conceptually straightforward, there are a number of nuances that make the task non-trivial, owing to the different conceptual organizations of the two knowledge graphs. To lay the foundation for future research using this dataset, we leverage recent work to provide simple yet strong baselines with and without neural networks.
Geometric and force errors compensation in a 3-axis CNC milling machine

This paper proposes a new off line error compensation model by taking into accounting of geometric and cutting force induced errors in a 3-axis CNC milling machine. Geometric error of a 3-axis milling machine composes of 21 components, which can be measured by laser interferometer within the working volume. Geometric error estimation determined by back-propagation neural network is proposed and used separately in the geometric error compensation model. Likewise, cutting force induced error estimation by back-propagation neural network determined based on a flat end mill behavior observation is proposed and used separately in the cutting force induced error compensation model. Various experiments over a wide range of cutting conditions are carried out to investigate cutting force and machine error relation. Finally, the combination of geometric and cutting force induced errors is modeled by the combined back-propagation neural network. This unique model is used to compensate both geometric and cutting force induced errors simultaneously by a single model. Experimental tests have been carried out in order to validate the performance of geometric and cutting force induced errors compensation model.
Spectral character of sunlight modulates photosynthesis of previtamin D3 and its photoisomers in human skin.

The photosynthesis of previtamin D3 from 7-dehydrocholesterol in human skin was determined after exposure to narrow-band radiation or simulated solar radiation. The optimum wavelengths for the production of previtamin D3 were determined to be between 295 and 300 nanometers. When human skin was exposed to 295-nanometer radiation, up to 65 percent of the original 7-dehydrocholesterol content was converted to previtamin D3. In comparison, when adjacent skin was exposed to simulated solar radiation, the maximum formation of previtamin D3 was about 20 percent. Major differences in the formation of lumisterol3, and tachysterol3 from previtamin D3 were also observed. It is concluded that the spectral character of natural sunlight has a profound effect on the photochemistry of 7-dehydrocholesterol in human skin.
The restraint bias: how the illusion of self-restraint promotes impulsive behavior.

Four studies examined how impulse-control beliefs--beliefs regarding one's ability to regulate visceral impulses, such as hunger, drug craving, and sexual arousal-influence the self-control process. The findings provide evidence for a restraint bias: a tendency for people to overestimate their capacity for impulse control. This biased perception of restraint had important consequences for people's self-control strategies. Inflated impulse-control beliefs led people to overexpose themselves to temptation, thereby promoting impulsive behavior. In Study 4, for example, the impulse-control beliefs of recovering smokers predicted their exposure to situations in which they would be tempted to smoke. Recovering smokers with more inflated impulse-control beliefs exposed themselves to more temptation, which led to higher rates of relapse 4 months later. The restraint bias offers unique insight into how erroneous beliefs about self-restraint promote impulsive behavior.
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?

Recently, several techniques have been explored to detect unusual behaviour in surveillance videos. Nevertheless, few studies leverage features from pre-trained CNNs and none of then present a comparison of features generate by different models. Motivated by this gap, we compare features extracted by four state-of-the-art image classification networks as a way of describing patches from security video frames. We carry out experiments on the Ped1 and Ped2 datasets and analyze the usage of different feature normalization techniques. Our results indicate that choosing the appropriate normalization is crucial to improve the anomaly detection performance when working with CNN features. Also, in the Ped2 dataset our approach was able to obtain results comparable to the ones of several state-of-the-art methods. Lastly, as our method only considers the appearance of each frame, we believe that it can be combined with approaches that focus on motion patterns to further improve performance.
Deep Investment in Financial Markets using Deep Learning Models

The aim of this paper is to layout deep investment techniques in financial markets using deep learning models. Financial prediction problems usually involve huge variety of data-sets with complex data interactions which makes it difficult to design an economic model. Applying deep learning models to such problems can exploit potentially non-linear patterns in data. In this paper author introduces deep learning hierarchical decision models for prediction analysis and better decision making for financial domain problem set such as pricing securities, risk factor analysis and portfolio selection. The Section 3 includes architecture as well as detail on training a financial domain deep learning neural network. It further lays out different models such asLSTM, auto-encoding, smart indexing, credit risk analysis model for solving the complex data interactions. The experiments along with their results show how these models can be useful in deep investments for financial domain problems.
The cause-effect diagram : 20 performance indicators in the supply chain of SCC *

Supply chain Management has assumed a significant role in firm's performance and has attracted serious research attention over the last few years. In this paper attempt has been made to review the literature on Supply Chain Management. A literature review reveals a considerable spurt in research in theory and practice of SCM. We have presented a literature review for 29 research papers for the period between 2005 and 2011. The aim of this study was to provide an up-to-date and brief review of the SCM literature that was focused on broad areas of the SCM concept.
Roget's Thesaurus as a Lexical Resource for Natural Language Processing

WordNet proved that it is possible to construct a large-sc ale electronic lexical database on the principles of lexical semantics. It has been accept ed and used extensively by computational linguists ever since it was released. Some of its a pplications include information retrieval, language generation, question answering, text categ orization, text classification and word sense disambiguation. Inspired by WordNet's success, we propose as an alternative a similar re sou ce, based on the 1987 Penguin edition of R get’s Thesaurus of English Words and Phrases . Peter Mark Roget published his first Thesaurus over 150 years ago. Countless writers, orators and students of the English language have used it. Computational linguists have employed Roget’s for almost 50 years in Natural Language Processing . Some of the tasks they have used it for include machine translation, computing lexical ohesion in texts and constructing databases that can infer common sense knowledge. This dissert ation presents Roget’s merits by explaining what it really is and how it has been used, while c omparing its applications to those of WordNet . The NLP community has hesitated in accepting Roget’s Thesaurus because a proper machinetractable version was not available. This dissertation presents an implementation of a m achine-tractable version of the 1987 Penguin edition of Roget’s Thesaurus – the first implementation of its kind to use an e tir current edition. It explains the steps necessary for taking a machine-readable file and transforming it into a tractable system. This involves converting the le xical material into a format that can be more easily exploited, identifying data structures and d esigning classes to computerize the Thesaurus . Roget’s organization is studied in detail and contrasted w ith WordNet’s. We show two applications of the computerized Thesaurus : computing semantic similarity between words and phrases, and building lexical cha ins in a text. The experiments are performed using well-known benchmarks and the results are com pared to those of other systems that use Roget’s, WordNet and statistical techniques. Roget’s has turned out to be an excellent resource for measuring semantic similarity; lexical chains a re easily built but more difficult to evaluate. We also explain ways in which Roget’s Thesaurus and WordNet can be combined. To my parents, who are my most valued treasure.
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation. PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera. The 3D rotation of the object is estimated by regressing to a quaternion representation. We also introduce a novel loss function that enables PoseCNN to handle symmetric objects. In addition, we contribute a large scale video dataset for 6D object pose estimation named the YCB-Video dataset. Our dataset provides accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames. We conduct extensive experiments on our YCBVideo dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input. When using depth data to further refine the poses, our approach achieves state-of-the-art results on the challenging OccludedLINEMOD dataset. Our code and dataset are available at
Scalable Text Mining with Sparse Generative Models

The information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks to utilize the information contained in vast document collections. General data mining methods based on machine learning face challenges with the scale of text data, posing a need for scalable text mining methods. This thesis proposes a solution to scalable text mining: generative models combined with sparse computation. A unifying formalization for generative text models is defined, bringing together research traditions that have used formally equivalent models, but ignored parallel developments. This framework allows the use of methods developed in different processing tasks such as retrieval and classification, yielding effective solutions across different text mining tasks. Sparse computation using inverted indices is proposed for inference on probabilistic models. This reduces the computational complexity of the common text mining operations according to sparsity, yielding probabilistic models with the scalability of modern search engines. The proposed combination provides sparse generative models: a solution for text mining that is general, effective, and scalable. Extensive experimentation on text classification and ranked retrieval datasets are conducted, showing that the proposed solution matches or outperforms the leading task-specific methods in effectiveness, with a order of magnitude decrease in classification times for Wikipedia article categorization with a million classes. The developed methods were further applied in two 2014 Kaggle data mining prize competitions with over a hundred competing teams, earning first and second places.
CAPITAL MARKET IMPERFECTIONS AND THE SENSITIVITY OF INVESTMENT TO STOCK PRICES

Prior studies argue that investment by undervalued firms that require external equity is particularly sensitive to stock prices in irrational capital markets. We present a model in which investment can appear to be more sensitive to stock prices when capital markets are rational, but subject to imperfections such as debt overhang, information asymmetries, and financial distress costs. Our empirical tests support the rational (but imperfect) capital markets view. Specifically, investment-stock price sensitivity is related to firm leverage, financial slack, and probability of financial distress, but is not related to proxies for firm undervaluation or equity dependence. Because, in our model, stock prices reflect the NPVs of investment opportunities, our results are consistent with rational capital markets improving the allocation of capital by channeling more funds to firms with positive NPV projects. First draft: February 21, 2005 This draft: June 13, 2005 * We thank David Denis, Mara Faccio, and Raghavendra Rau for helpful comments and Jeffrey Wurgler for investor sentiment data.
Efficient evaluation of xml queries

Recently, the use of XML continues to grow in popularity, large repositories of XML documents are going to emerge, and users are likely to pose increasingly more complex queries on these data sets. In 2001 XQuery is decided by the World Wide Web Consortium (W3C) as the standard XML query language. In this article, we describe the design and implementation of an efficient and scalable purely relational XQuery processor which translates expressions of the XQuery language into their equivalent SQL evaluation scripts. The experiments of this article demonstrated the efficiency and scalability of our purely relational approach in comparison to the native XML/XQuery functionality supported by conventional RDBMSs and has shown that our purely relational approach for implementing XQuery processor deserves to be pursued further.