nils-herrmann/clean_scirepeval huggingface dataset:
Protein Availability and Satellite Cell Dynamics in Skeletal Muscle.

Human skeletal muscle satellite cells are activated in response to both resistance and endurance exercise. It was initially proposed that satellite cell proliferation and differentiation were only required to support resistance exercise-induced hypertrophy. However, satellite cells may also play a role in muscle fibre remodelling after endurance-based exercise and extracellular matrix regulation. Given the importance of dietary protein, particularly branched chain amino acids, in supporting myofibrillar and mitochondrial adaptations to both resistance and endurance-based training, a greater understanding of how protein intake impacts satellite cell activity would provide further insight into the mechanisms governing skeletal muscle remodelling with exercise. While many studies have investigated the capacity for protein ingestion to increase post-exercise rates of muscle protein synthesis, few investigations have examined the role for protein ingestion to modulate satellite cell activity. Here we review the molecular mechanisms controlling the activation of satellite cells in response to mechanical stress and protein intake in both in vitro and in vivo models. We provide a mechanistic framework that describes how protein ingestion may enhance satellite activity and promote exercise adaptations in human skeletal muscle.
Tackling the cancer stem cells — what challenges do they pose?

Since their identification in 1994, cancer stem cells (CSCs) have been objects of intensive study. Their properties and mechanisms of formation have become a major focus of current cancer research, in part because of their enhanced ability to initiate and drive tumour growth and their intrinsic resistance to conventional therapeutics. The discovery that activation of the epithelial-to-mesenchymal transition (EMT) programme in carcinoma cells can give rise to cells with stem-like properties has provided one possible mechanism explaining how CSCs arise and presents a possible avenue for their therapeutic manipulation. Here we address recent developments in CSC research, focusing on carcinomas that are able to undergo EMT. We discuss the signalling pathways that create these cells, cell-intrinsic mechanisms that could be exploited for selective elimination or induction of their differentiation, and the role of the tumour microenvironment in sustaining them. Finally, we propose ways to use our current knowledge of the complex biology of CSCs to design novel therapies to eliminate them.
Shadow removal of single texture region using local histogram matching

Shadows have long been a problem to computer vision algorithms. Removing shadow can significantly improve the performance of several vision task such as object detection and image segmentation. Various methods of shadow removal in images had been developed. In this paper, we discussed the characteristic of shadowed region with single texture statistically, based on an illumination model, and then developed a simple and fast way of removal shadow using local histogram matching in different illumination reduction level.
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network

Deep neural networks have achieved remarkable success in single image super-resolution (SISR). The computing and memory requirements of these methods have hindered their application to broad classes of real devices with limited computing power, however. One approach to this problem has been lightweight network architectures that balance the super-resolution performance and the computation burden. In this study, we revisit this problem from an orthogonal view, and propose a novel learning strategy to maximize the pixel-wise fitting capacity of a given lightweight network architecture. Considering that the initial capacity of the lightweight network is very limited, we present an adaptive importance learning scheme for SISR that trains the network with an easy-to-complex paradigm by dynamically updating the importance of image pixels on the basis of the training loss. Specifically, we formulate the network training and the importance learning into a joint optimization problem. With a carefully designed importance penalty function, the importance of individual pixels can be gradually increased through solving a convex optimization problem. The training process thus begins with pixels that are easy to reconstruct, and gradually proceeds to more complex pixels as fitting improves. Furthermore, the proposed learning scheme is able to seamlessly assimilate knowledge from a more powerful teacher network in the form of importance initialization, thus obtaining better initial capacity in the network. Through learning the network parameters, and updating pixel importance, the proposed learning scheme enables smaller, lightweight, networks to achieve better performance than has previously been possible. Extensive experiments on four benchmark datasets demonstrate the potential benefits ∗ Corresponding author. 1 School of Computer Science, The University of Adelaide, Australia 2 School of Computer Science, Northwestern Polytechnical University, Xian, China of the proposed learning strategy in lightweight SISR network enhancement. In some cases, our learned network with only 25% of the parameters and computational complexity can produce comparable or even better results than the corresponding full-parameter network.
Tag Disentangled Generative Adversarial Network for Object Image Re-rendering

In this paper, we propose a principled Tag Disentangled Generative Adversarial Networks (TDGAN) for re-rendering new images for the object of interest from a single image of it by specifying multiple scene properties (such as viewpoint, illumination, expression, etc.). The whole framework consists of a disentangling network, a generative network, a tag mapping net, and a discriminative network, which are trained jointly based on a given set of images that are completely/partially tagged (i.e., supervised/semi-supervised setting). Given an input image, the disentangling network extracts disentangled and interpretable representations, which are then used to generate images by the generative network. In order to boost the quality of disentangled representations, the tag mapping net is integrated to explore the consistency between the image and its tags. Furthermore, the discriminative network is introduced to implement the adversarial training strategy for generating more realistic images. Experiments on two challenging datasets demonstrate the state-of-the-art performance of the proposed framework in the problem of interest.
Multi-Adversarial Domain Adaptation

Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains. Existing domain adversarial adaptation methods based on single domain discriminator only align the source and target data distributions without exploiting the complex multimode structures. In this paper, we present a multi-adversarial domain adaptation (MADA) approach, which captures multimode structures to enable fine-grained alignment of different data distributions based on multiple domain discriminators. The adaptation can be achieved by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Empirical evidence demonstrates that the proposed model outperforms state of the art methods on standard domain adaptation datasets.
Progressive Reasoning by Module Composition

Humans learn to solve tasks of increasing complexity by building on top of previously acquired knowledge. Typically, there exists a natural progression in the tasks that we learn – most do not require completely independent solutions, but can be broken down into simpler subtasks. We propose to represent a solver for each task as a neural module that calls existing modules (solvers for simpler tasks) in a program-like manner. Lower modules are a black box to the calling module, and communicate only via a query and an output. Thus, a module for a new task learns to query existing modules and composes their outputs in order to produce its own output. Each module also contains a residual component that learns to solve aspects of the new task that lower modules cannot solve. Our model effectively combines previous skill-sets, does not suffer from forgetting, and is fully differentiable. We test our model in learning a set of visual reasoning tasks, and demonstrate state-ofthe-art performance in Visual Question Answering, the highest-level task in our task set. By evaluating the reasoning process using non-expert human judges, we show that our model is more interpretable than an attention-based baseline.
Subject: Design Considerations for an Rtsp-based Preex-caching Proxy for Multimedia Streams Design Considerations for an Rtsp-based Preex-caching Proxy for Multimedia Streams 2

Multimedia streaming applications typically experience high start-up delay, due to large protocol overheads and the poor delay, throughput, and loss properties of the Internet. Internet service providers can improve performance by caching the initial segment (the preex) of popular streams at proxies near the requesting clients. This paper analyzes the protocol and architectural challenges of realizing a preex-caching service within the RTP, RTSP, and RTCP standards. We emphasize how to exploit existing protocol features (such as Range requests), and suggest extensions to RTSP that would ease the development of new proxy services. In addition, we describe how to provide reliable transport, cache coherency, and RTCP feedback control under preex caching. Then, we present a preliminary implementation of preex caching on a Linux-based PC. We describe how the proxy operates with a commercial RTSP server and client. The paper ends with a summary of proposed extensions to RTSP and directions for future research.
STCN: Stochastic Temporal Convolutional Networks

Convolutional architectures have recently been shown to be competitive on many sequence modelling tasks when compared to the de-facto standard of recurrent neural networks (RNNs), while providing computational and modeling advantages due to inherent parallelism. However, currently there remains a performance gap to more expressive stochastic RNN variants, especially those with several layers of dependent random variables. In this work, we propose stochastic temporal convolutional networks (STCNs), a novel architecture that combines the computational advantages of temporal convolutional networks (TCN) with the representational power and robustness of stochastic latent spaces. In particular, we propose a hierarchy of stochastic latent variables that captures temporal dependencies at different time-scales. The architecture is modular and flexible due to decoupling of deterministic and stochastic layers. We show that the proposed architecture achieves state of the art log-likelihoods across several tasks. Finally, the model is capable of predicting high-quality synthetic samples over a long-range temporal horizon in modeling of handwritten text.
Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization

To be successful in financial market trading it is necessary to correctly predict future market trends. Most professional traders use technical analysis to forecast future market prices. In this paper, we present a new hybrid intelligent method to forecast financial time series, especially for the Foreign Exchange Market (FX). To emulate the way real traders make predictions, this method uses both historical market data and chart patterns to forecast market trends. First, wavelet full decomposition of time series analysis was used as an Adaptive Network-based Fuzzy Inference System (ANFIS) input data for forecasting future market prices. Also, Quantum-behaved Particle Swarm Optimization (QPSO) for tuning the ANFIS membership functions has been used. The second part of this paper proposes a novel hybrid Dynamic Time Warping (DTW)-Wavelet Transform (WT) method for automatic pattern extraction. The results indicate that the presented hybrid method is a very useful and effective one for financial price forecasting and financial pattern extraction.
RUM: network Representation learning throUgh Multi-level structural information preservation

We have witnessed the discovery of many techniques for network representation learning in recent years, ranging from encoding the context in random walks to embedding the lower order connections, to finding latent space representations with auto-encoders. However, existing techniques are looking mostly into the local structures in a network, while higherlevel properties such as global community structures are often neglected. We propose a novel network representations learning model framework called RUM (network Representation learning throUgh Multi-level structural information preservation). In RUM, we incorporate three essential aspects of a node that capture a network’s characteristics in multiple levels: a node’s affiliated local triads, its neighborhood relationships, and its global community affiliations. Therefore the framework explicitly and comprehensively preserves the structural information of a network, extending the encoding process both to the local end of the structural information spectrum and to the global end. The framework is also flexible enough to take various community discovery algorithms as its preprocessor. Empirical results show that the representations learned by RUM have demonstrated substantial performance advantages in real-life tasks.
Control and Performance of a Transformerless Cascade PWM STATCOM With Star Configuration

This paper presents a three-phase transformerless cascade pulsewidth-modulation (PWM) static synchronous compensator (STATCOM) intended for installation on industrial and utility power distribution systems. It proposes a control algorithm that devotes itself not only to meeting the demand of reactive power but also to voltage balancing of multiple galvanically isolated and floating dc capacitors. The control algorithm based on a phase-shifted carrier modulation strategy is prominent in having no restriction on the cascade number. Experimental waveforms verify that a 200-V 10-kVA cascade PWM STATCOM with star configuration has the capability of inductive to capacitive (or capacitive to inductive) operation at the rated reactive power of 10 kVA within 20 ms while keeping the nine dc mean voltages controlled and balanced even during the transient state.
Optimized Trade Execution with Reinforcement Learning Optimal orderexekvering med reinforcement learning

In this thesis, we study the problem of buying or selling a given volume of a financial asset within a given time horizon to the best possible price, a problem formally known as optimized trade execution. Our approach is an empirical one. We use historical data to simulate the process of placing artificial orders in a market. This simulation enables us to model the problem as a Markov decision process (MDP). Given this MDP, we train and evaluate a set of reinforcement learning (RL) algorithms all with the objective to minimize the transaction cost on unseen test data. We train and evaluate these for various instruments and problem settings, such as different trading horizons. Our first model was developed with the goal to validate results achieved by Nevmyvaka, Feng and Kearns , and it is thus called NFK. We extended this model into what we call Dual NFK, in an attempt to regularize the model against external price movement. Furthermore, we implemented and evaluated a classical RL algorithm, namely Sarsa(λ) with a modified reward function. Lastly, we evaluated proximal policy optimization (PPO), an actor-critic RL algorithm incorporating neural networks in order to find the optimal policy. Along with these models, we implemented five simple baseline strategies with various characteristics. These baseline strategies have partly been found in the literature and partly been developed by us, and are used to the evaluate the performance of our models. We achieve results on par with those found by Nevmyvaka, Feng and Kearns , but only for a few cases. Furthermore, dual NFK performed very similar to NFK, indicating that one can train one model (for both the buy and sell case) instead of two for the optimized trade execution problem. We also found that Sarsa(λ) with a modified reward function performed better than both these models, but is still outperformed by baseline strategies for many problem settings. Finally, we evaluated PPO for one problem setting and found that it outperformed even the best of the baseline strategies and models, showing promise for deep reinforcement learning methods for the problem of optimized trade execution.
Wicked Problems in Design Thinking

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The role of relative entropy in quantum information theory

Quantum mechanics and information theory are among the most important scientific discoveries of the last century. Although these two areas initially developed separately, it has emerged that they are in fact intimately related. In this review the author shows how quantum information theory extends traditional information theory by exploring the limits imposed by quantum, rather than classical, mechanics on information storage and transmission. The derivation of many key results differentiates this review from the usual presentation in that they are shown to follow logically from one crucial property of relative entropy. Within the review, optimal bounds on the enhanced speed that quantum computers can achieve over their classical counterparts are outlined using information-theoretic arguments. In addition, important implications of quantum information theory for thermodynamics and quantum measurement are intermittently discussed. A number of simple examples and derivations, including quantum superdense coding, quantum teleportation, and Deutsch’s and Grover’s algorithms, are also included.
Conceptions of how a learning or teaching curriculum, workplace culture and agency of individuals shape medical student learning and supervisory practices in the clinical workplace.

The role of workplace supervisors in the clinical education of medical students is currently under debate. However, few studies have addressed how supervisors conceptualize workplace learning and how conceptions relate to current sociocultural workplace learning theory. We explored physician conceptions of: (a) medical student learning in the clinical workplace and (b) how they contribute to student learning. The methodology included a combination of a qualitative, inductive (conventional) and deductive (directed) content analysis approach. The study triangulated two types of interview data from 4 focus group interviews and 34 individual interviews. A total of 55 physicians participated. Three overarching themes emerged from the data: learning as membership, learning as partnership and learning as ownership. The themes described how physician conceptions of learning and supervision were guided by the notions of learning-as-participation and learning-as-acquisition. The clinical workplace was either conceptualized as a context in which student learning is based on a learning curriculum, continuity of participation and partnerships with supervisors, or as a temporary source of knowledge within a teaching curriculum. The process of learning was shaped through the reciprocity between different factors in the workplace context and the agency of students and supervising physicians. A systems-thinking approach merged with the "co-participation" conceptual framework advocated by Billet proved to be useful for analyzing variations in conceptions. The findings suggest that mapping workplace supervisor conceptions of learning can be a valuable starting point for medical schools and educational developers working with changes in clinical educational and faculty development practices.
Entity Retrieval and Text Mining for Online Reputation Monitoring

Online Reputation Monitoring (ORM) is concerned with the use of computational tools to measure the reputation of entities online, such as politicians or companies. In practice, current ORM methods are constrained to the generation of data analytics reports, which aggregate statistics of popularity and sentiment on social media. We argue that this format is too restrictive as end users often like to have the flexibility to search for entity-centric information that is not available in predefined charts. As such, we propose the inclusion of entity retrieval capabilities as a first step towards the extension of current ORM capabilities. However, an entity’s reputation is also influenced by the entity’s relationships with other entities. Therefore, we address the problem of Entity-Relationship (E-R) retrieval in which the goal is to search for multiple connected entities. This is a challenging problem which traditional entity search systems cannot cope with. Besides E-R retrieval we also believe ORM would benefit of text-based entity-centric prediction capabilities, such as predicting entity popularity on social media based on news events or the outcome of political surveys. However, none of these tasks can provide useful results if there is no effective entity disambiguation and sentiment analysis tailored to the context of ORM. Consequently, this thesis address two computational problems in Online Reputation Monitoring: Entity Retrieval and Text Mining. We researched and developed methods to extract, retrieve and predict entity-centric information spread across the Web. We proposed a new probabilistic modeling of the problem of E-R retrieval together with two fusion-based design patterns for creating representations of both entities and relationships. Furthermore, we propose the Entity-Relationship Dependence Model, a novel early-fusion supervised model based on the Markov Random Field framework for Retrieval. Together with a new semi-automatic method to create test collections for E-R retrieval, we released a new test collection for that purpose that will foster research in this area. We performed experiments at scale with results showing that it is possible to perform E-R retrieval without using fix and pre-defined entity and relationship types, enabling a wide range of queries to be addressed.
Using deep neural networks to improve proficiency assessment for children English language learners

We investigated the use of context-dependent deep neural network hidden Markov models, or CD-DNN-HMMs, to improve speech recognition performance for a better assessment of children English language learners (ELLs). The ELL data used in the present study was obtained from a large language assessment project administered in schools in a U.S. state. Our DNN-based speech recognition system, built using rectified linear units (ReLU), greatly outperformed recognition accuracy of Gaussian mixture models (GMM)-HMMs, even when the latter models were trained with eight times more data. Large improvement was observed for cases of noisy and/or unclear responses, which are common in ELL children speech. We further explored the use of content and manner-of-speaking features, derived from the speech recognizer output, for estimating spoken English proficiency levels. Experimental results show that the DNN-based recognition approach achieved 31% relative WER reduction when compared to GMM-HMMs. This further improved the quality of the extracted features and final spoken English proficiency scores, and increased overall automatic assessment performance to the human performance level, for various open-ended spoken language tasks.