nils-herrmann/clean_scirepeval huggingface dataset:
Deep Expander Networks: Efficient Deep Networks from Graph Theory

Efficient CNN designs like ResNets and DenseNet were proposed to improve accuracy vs efficiency trade-offs. They essentially increased the connectivity, allowing efficient information flow across layers. Inspired by these techniques, we propose to model connections between filters of a CNN using graphs which are simultaneously sparse and well connected. Sparsity results in efficiency while well connectedness can preserve the expressive power of the CNNs. We use a well-studied class of graphs from theoretical computer science that satisfies these properties known as Expander graphs. Expander graphs are used to model connections between filters in CNNs to design networks called X-Nets. We present two guarantees on the connectivity of X-Nets: Each node influences every node in a layer in logarithmic steps, and the number of paths between two sets of nodes is proportional to the product of their sizes. We also propose efficient training and inference algorithms, making it possible to train deeper and wider X-Nets effectively. Expander based models give a 4% improvement in accuracy on MobileNet over grouped convolutions, a popular technique, which has the same sparsity but worse connectivity. X-Nets give better performance trade-offs than the original ResNet and DenseNet-BC architectures. We achieve model sizes comparable to state-of-the-art pruning techniques using our simple architecture design, without any pruning. We hope that this work motivates other approaches to utilize results from graph theory to develop efficient network architectures.
Learning Hierarchical Information Flow with Recurrent Neural Modules

We propose ThalNet, a deep learning model inspired by neocortical communication via the thalamus. Our model consists of recurrent neural modules that send features through a routing center, endowing the modules with the flexibility to share features over multiple time steps. We show that our model learns to route information hierarchically, processing input data by a chain of modules. We observe common architectures, such as feed forward neural networks and skip connections, emerging as special cases of our architecture, while novel connectivity patterns are learned for the text8 compression task. Our model outperforms standard recurrent neural networks on several sequential benchmarks.
How 2 Sketch : Generating Easy-To-Follow Tutorials for Sketching 3 D Objects

Accurately drawing 3D objects is difficult for untrained individuals, as it requires an understanding of perspective and its effects on geometry and proportions. Step-by-step tutorials break the complex task of sketching an entire object down into easy-to-follow steps that even a novice can follow. However, creating such tutorials requires expert knowledge and is time-consuming. As a result, the availability of tutorials for a given object or viewpoint is limited. How2Sketch (H2S) addresses this problem by automatically generating easy-to-follow tutorials for arbitrary 3D objects. Given a segmented 3D model and a camera viewpoint, H2S computes a sequence of steps for constructing a drawing scaffold comprised of geometric primitives, which helps the user draw the final contours in correct perspective and proportion. To make the drawing scaffold easy to construct, the algorithm solves for an ordering among the scaffolding primitives and explicitly makes small geometric modifications to the size and location of the object parts to simplify relative positioning. Technically, we formulate this scaffold construction as a single selection problem that simultaneously solves for the ordering and geometric changes of the primitives. We generate different tutorials on man-made objects using our method and evaluate how easily the tutorials can be followed with a user study.
Number 2 Fall 1998 67 DIVERSIFICATION AND FIRM PERFORMANCE : AN EMPIRICAL EVALUATION

Diversification is a strategic option that many managers use to improve their firms’ performance. This interdisciplinary research attempts to verify whether firm level diversification has any impact on performance. The study finds that on average, diversified firms show better performance compared to undiversified firms on both risk and return dimensions. It also tests the robustness of these results by classifying firms by performance class. The results show that among the best performing class of firms, undiversified firms have higher returns, but these returns are accompanied by high variance. Whereas, highly diversified firms show lower returns, and much lower variance. Results further show that diversified firms perform better than undiversified firms on risk and return dimensions, in the low and average performance classes. The paper concludes that a dominant undiversified firm may perform better than a highly diversified firm in terms of return but its riskiness will be much greater. If managers of such firms opt for diversification, their returns will decrease, but their riskiness will reduce proportionately more than the reduction in their returns. In such firms, there will be a tradeoff between risk and return.
A new approach for supervised power disaggregation by using a deep recurrent LSTM network

This paper presents a new approach for supervised power disaggregation by using a deep recurrent long short term memory network. It is useful to extract the power signal of one dominant appliance or any subcircuit from the aggregate power signal. To train the network, a measurement of the power signal of the target appliance in addition to the total power signal during the same time period is required. The method is supervised, but less restrictive in practice since submetering of an important appliance or a subcircuit for a short time is feasible. The main advantages of this approach are: a) It is also applicable to variable load and not restricted to on-off and multi-state appliances. b) It does not require hand-engineered event detection and feature extraction. c) By using multiple networks, it is possible to disaggregate multiple appliances or subcircuits at the same time. d) It also works with a low cost power meter as shown in the experiments with the Reference Energy Disaggregation (REDD) dataset (1/3Hz sampling frequency, only real power).
3DCapsule: Extending the Capsule Architecture to Classify 3D Point Clouds

This paper introduces the 3DCapsule, which is a 3D extension of the recently introduced Capsule concept that makes it applicable to unordered point sets. The original Capsule relies on the existence of a spatial relationship between the elements in the feature map it is presented with, whereas in point permutation invariant formulations of 3D point set classification methods, such relationships are typically lost. Here, a new layer called ComposeCaps is introduced that, in lieu of a spatially relevant feature mapping, learns a new mapping that can be exploited by the 3DCapsule. Previous works in the 3D point set classification domain have focused on other parts of the architecture, whereas instead, the 3DCapsule is a drop-in replacement of the commonly used fully connected classifier. It is demonstrated via an ablation study, that when the 3DCapsule is applied to recent 3D point set classification architectures, it consistently shows an improvement, in particular when subjected to noisy data. Similarly, the ComposeCaps layer is evaluated and demonstrates an improvement over the baseline. In an apples-to-apples comparison against state-of-the-art methods, again, better performance is demonstrated by the 3DCapsule.
Nimrod/G: An Architecture of a Resource Management and Scheduling System in a Global Computational Grid

The availability of powerful microprocessors and high-speed networks as commodity components has enabled high performance computing on distributed systems (wide-area cluster computing). In this environment, as the resources are usually distributed geographically at various levels (department, enterprise, or worldwide) there is a great challenge in integrating, coordinating and presenting them as a single resource to the user; thus forming a computational grid. Another challenge comes from the distributed ownership of resources with each resource having its own access policy, cost, and mechanism. The proposed Nimrod/G grid-enabled resource management and scheduling system builds on our earlier work on Nimrod and follows a modular and component-based architecture enabling extensibility, portability, ease of development, and interoperability of independently developed components. It uses the Globus toolkit services and can be easily extended to operate with any other emerging grid middleware services. It focuses on the management and scheduling of computations over dynamic resources scattered geographically across the Internet at department, enterprise, or global level with particular emphasis on developing scheduling schemes based on the concept of computational economy for a real test bed, namely, the Globus testbed (GUSTO).
Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter

Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. The task of automatically extracting them have received considerable attention in recent decades. Different from previous studies, which are usually focused on automatically extracting keyphrases from documents or articles, in this study, we considered the problem of automatically extracting keyphrases from tweets. Because of the length limitations of Twitter-like sites, the performances of existing methods usually drop sharply. We proposed a novel deep recurrent neural network (RNN) model to combine keywords and context information to perform this problem. To evaluate the proposed method, we also constructed a large-scale dataset collected from Twitter. The experimental results showed that the proposed method performs significantly better than previous methods.
Federated MultiTask Learning

Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task learning is naturally suited to handle the statistical challenges of this setting, and propose a novel systems-aware optimization method, MOCHA, that is robust to practical systems issues. Our method and theory for the first time consider issues of high communication cost, stragglers, and fault tolerance for distributed multi-task learning. The resulting method achieves significant speedups compared to alternatives in the federated setting, as we demonstrate through simulations on real-world federated datasets.
Identifing influential users in an online healthcare social network

As an important information portal, online healthcare forum are playing an increasingly crucial role in disseminating information and offering support to people. It connects people with the leading medical experts and others who have similar experiences. During an epidemic outbreak, such as H1N1, it is critical for the health department to understand how the public is responding to the ongoing pandemic, which has a great impact on the social stability. In this case, identifying influential users in the online healthcare forum and tracking the information spreading in such online community can be an effective way to understand the public reaction toward the disease. In this paper, we propose a framework to monitor and identify influential users from online healthcare forum. We first develop a mechanism to identify and construct social networks from the discussion board of an online healthcare forum. We propose the UserRank algorithm which combines link analysis and content analysis techniques to identify influential users. We have also conducted an experiment to evaluate our approach on the Swine Flu forum which is a sub-community of a popular online healthcare community, MedHelp (www.medhelp.org). Experimental results show that our technique outperforms PageRank, in-degree and out-degree centrality in identifying influential user from an online healthcare forum.
Event2Vec: Neural Embeddings for News Events

Representation of news events as latent feature vectors is essential for several tasks, such as news recommendation, news event linking, etc. However, representations proposed in the past fail to capture the complex network structure of news events. In this paper we propose Event2Vec, a novel way to learn latent feature vectors for news events using a network. We use recently proposed network embedding techniques, which are proven to be very effective for various prediction tasks in networks. As events involve different classes of nodes, such as named entities, temporal information, etc, general purpose network embeddings are agnostic to event semantics. To address this problem, we propose biased random walks that are tailored to capture the neighborhoods of news events in event networks. We then show that these learned embeddings are effective for news event recommendation and news event linking tasks using strong baselines, such as vanilla Node2Vec, and other state-of-the-art graph-based event ranking techniques.
Shuling Huang Korean wave in Taiwan Nation-branding and transnational consumption : Japan-mania and the

One recent development of cultural globalization emerges in the convergence of taste in media consumption within geo-cultural regions, such as Latin American telenovelas, South Asian Bollywood films and East Asian trendy dramas. Originating in Japan, the so-called trendy dramas (or idol dramas) have created a craze for Japanese commodities in its neighboring countries (Ko, 2004). Following this Japanese model, Korea has also developed as a stronghold of regional exports, ranging from TV programs, movies and pop music to food, fashion and tourism. The fondness for all things Japanese and Korean in East Asia has been vividly captured by such buzz phrases as Japan-mania (hari in Chinese) and the Korean wave (hallyu in Korean and hanliu in Chinese). These two phenomena underscore how popular culture helps polish the image of a nation and thus strengthens its economic competitiveness in the global market. Consequently, nationbranding has become incorporated into the project of nation-building in light of globalization. However, Japan’s cultural spread and Korea’s cultural expansion in East Asia are often analysed from angles that are polar opposites. Scholars suggest that Japan-mania is initiated by the ardent consumers of receiving countries (Nakano, 2002), while the Korea wave is facilitated by the Korean state in order to boost its culture industry (Ryoo, 2008). Such claims are legitimate but neglect the analogues of these two phenomena. This article examines the parallel paths through which Japan-mania and the Korean wave penetrate into people’s everyday practices in Taiwan – arguably one of the first countries to be swept by these two trends. My aim is to illuminate the processes in which nation-branding is not only promoted by a nation as an international marketing strategy, but also appropriated by a receiving country as a pattern of consumption. Three seemingly contradictory arguments explain why cultural products ‘sell’ across national borders: cultural transparency, cultural difference and hybridization. First, cultural exports targeting the global market are rarely culturally specific so that they allow worldwide audiences to ‘project [into them] indigenous values, beliefs, rites, and rituals’ Media, Culture & Society 33(1) 3 –18
Inferring Traffic Cascading Patterns

There is an underlying cascading behavior over road networks. Traffic cascading patterns are of great importance to easing traffic and improving urban planning. However, what we can observe is individual traffic conditions on different road segments at discrete time intervals, rather than explicit interactions or propagation (e.g., A→B) between road segments. Additionally, the traffic from multiple sources and the geospatial correlations between road segments make it more challenging to infer the patterns. In this paper, we first model the three-fold influences existing in traffic propagation and then propose a data-driven approach, which finds the cascading patterns through maximizing the likelihood of observed traffic data. As this is equivalent to a submodular function maximization problem, we solve it by using an approximate algorithm with provable near-optimal performance guarantees based on its submodularity. Extensive experiments on real-world datasets demonstrate the advantages of our approach in both effectiveness and efficiency.
Unconstrained Face Alignment via Cascaded Compositional Learning

We present a practical approach to address the problem of unconstrained face alignment for a single image. In our unconstrained problem, we need to deal with large shape and appearance variations under extreme head poses and rich shape deformation. To equip cascaded regressors with the capability to handle global shape variation and irregular appearance-shape relation in the unconstrained scenario, we partition the optimisation space into multiple domains of homogeneous descent, and predict a shape as a composition of estimations from multiple domain-specific regressors. With a specially formulated learning objective and a novel tree splitting function, our approach is capable of estimating a robust and meaningful composition. In addition to achieving state-of-the-art accuracy over existing approaches, our framework is also an efficient solution (350 FPS), thanks to the on-the-fly domain exclusion mechanism and the capability of leveraging the fast pixel feature.
On the emergent properties of artificial stock markets : the efficient market hypothesis and the rational expectations hypothesis

By studying two well known hypotheses in economics, this paper illustrates how emergent properties can be shown in an agent-based artificial stock market. The two hypotheses considered are the efficient market hypothesis and the rational expectations hypothesis. We inquire whether the macrobehavior depicted by these two hypotheses is consistent with our understanding of the microbehavior. In this agent-based model, genetic programming is applied to evolving a population of traders learning over time. We first apply a series of econometric tests to show that the EMH and the REH can be satisfied with some portions of the artificial time series. Then, by analyzing traders’ behavior, we show that these aggregate results cannot be interpreted as a simple scaling-up of individual behavior. A conjecture based on sunspot-like signals is proposed to explain why macrobehavior can be very different from microbehavior. We assert that the huge search space attributable to genetic programming can induce sunspot-like signals, and we use simulated evolved complexity of forecasting rules and Granger causality tests to examine this assertion.
3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network

Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN) architectures. This paper introduces a conveying path-based convolutional encoder-decoder (CPCE) network in 2-D and 3-D configurations within the GAN framework for LDCT denoising. A novel feature of this approach is that an initial 3-D CPCE denoising model can be directly obtained by extending a trained 2-D CNN, which is then fine-tuned to incorporate 3-D spatial information from adjacent slices. Based on the transfer learning from 2-D to 3-D, the 3-D network converges faster and achieves a better denoising performance when compared with a training from scratch. By comparing the CPCE network with recently published work based on the simulated Mayo data set and the real MGH data set, we demonstrate that the 3-D CPCE denoising model has a better performance in that it suppresses image noise and preserves subtle structures.
Template-based Synthetic Handwriting Generation for the Training of Recognition Systems

In this paper we present a method for synthesizing English handwritten textlines from ASCII transcriptions. The method is based on templates of characters and the Delta LogNormal model of handwriting generation. To generate a textline, first a static image of the textline is built by concatenating perturbed versions of the character templates. Then strokes and corresponding virtual targets are extracted and randomly perturbed, and finally the textline is drawn using overlapping strokes and delta-lognormal velocity profiles in accordance with the Delta LogNormal theory. The generated textlines are used as training data for a hidden Markov model based off-line handwritten textline recognizer. First results show that adding such generated textlines to the natural training set may be beneficial.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.
Invertible Residual Networks

We show that standard ResNet architectures can be made invertible, allowing the same model to be used for classification, density estimation, and generation. Typically, enforcing invertibility requires partitioning dimensions or restricting network architectures. In contrast, our approach only requires adding a simple normalization step during training, already available in standard frameworks. Invertible ResNets define a generative model which can be trained by maximum likelihood on unlabeled data. To compute likelihoods, we introduce a tractable approximation to the Jacobian log-determinant of a residual block. Our empirical evaluation shows that invertible ResNets perform competitively with both stateof-the-art image classifiers and flow-based generative models, something that has not been previously achieved with a single architecture.