nils-herrmann/clean_scirepeval huggingface dataset:
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images. However, automated nucleus detection is complicated by 1) the large number of nuclei and the size of high resolution digitized pathology images, and 2) the variability in size, shape, appearance, and texture of the individual nuclei. Recently there has been interest in the application of “Deep Learning” strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer. The SSAE learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. A sliding window operation is applied to each image in order to represent image patches via high-level features obtained via the auto-encoder, which are then subsequently fed to a classifier which categorizes each image patch as nuclear or non-nuclear. Across a cohort of 500 histopathological images (2200 × 2200) and approximately 3500 manually segmented individual nuclei serving as the groundtruth, SSAE was shown to have an improved F-measure 84.49% and an average area under Precision-Recall curve (AveP) 78.83%. The SSAE approach also out-performed nine other state of the art nuclear detection strategies.
Customer Loyalty to C2C Online Shopping Platforms: An Exploration of the Role of Customer Engagement

With the keen competition in C2C e-marketplaces, C2C online shopping platform providers are finding ways to keep and maintain customer relationship. In this study, a research model that examines the role of customer engagement in customer loyalty to C2C online shopping platforms is proposed and empirically tested with 385 customers who have used Taobao to perform online shopping. The results provide supports to our research hypotheses and illustrate the importance of customer engagement in building customer loyalty to C2C online shopping platforms. Findings of this study are expected to provide useful insights for emarketers to develop loyalty through customer engagement in online shopping platforms as well as for researchers to better understand the relationship between customer engagement and loyalty in consumer-based e-marketplace.
Teaching computer organization/architecture with limited resources using simulators

As the complexity and variety of computer system hardware increases, its suitability as a pedagogical tool in computer organization/architecture courses diminishes. As a consequence, many instructors are turning to simulators as teaching aids, often using valuable teaching/research time to construct them. Many of these simulators have been made freely available on the Internet, providing a useful and time-saving resource for other instructors. However, finding the right simulator for a particular course or topic can itself be a time-consuming process. The goal of this paper is to provide an easy-to-use survey of free and Internet-accessible computer system simulators as a resource for all instructors of computer organization and computer architecture courses.
Robust Deep Multi-modal Learning Based on Gated Information Fusion Network

The goal of multi-modal learning is to use complimentary information on the relevant task provided by the multiple modalities to achieve reliable and robust performance. Recently, deep learning has led significant improvement in multi-modal learning by allowing for fusing high level features obtained at intermediate layers of the deep neural network. This paper addresses a problem of designing robust deep multimodal learning architecture in the presence of the modalities degraded in quality. We introduce deep fusion architecture for object detection which processes each modality using the separate convolutional neural network (CNN) and constructs the joint feature maps by combining the intermediate features obtained by the CNNs. In order to facilitate the robustness to the degraded modalities, we employ the gated information fusion (GIF) network which weights the contribution from each modality according to the input feature maps to be fused. The combining weights are determined by applying the convolutional layers followed by the sigmoid function to the concatenated intermediate feature maps. The whole network including the CNN backbone and GIF is trained in an end-toend fashion. Our experiments show that the proposed GIF network offers the additional architectural flexibility to achieve the robust performance in handling some degraded modalities.
Non-interactive Zero-Knowledge from Homomorphic Encryption

We propose a method for compiling a class of Σ-protocols (3-move public-coin protocols) into non-interactive zero-knowledge arguments. The method is based on homomorphic encryption and does not use random oracles. It only requires that a private/public key pair is set up for the verifier. The method applies to all known discrete-log based Σ-protocols. As applications, we obtain non-interactive threshold RSA without random oracles, and non-interactive zero-knowledge for NP more efficiently than by previous methods.
On linear genetic programming

The thesis is about linear genetic programming (LGP), a machine learning approach that evolves computer programs as sequences of imperative instructions. Two fundamental differences to the more common tree-based variant (TGP) may be identified. These are the graph-based functional structure of linear genetic programs, on the one hand, and the existence of structurally noneffective code, on the other hand. The two major objectives of this work comprise (1) the development of more advanced methods and variation operators to produce better and more compact program solutions and (2) the analysis of general EA/GP phenomena in linear GP, including intron code, neutral variations, and code growth, among others. First, we introduce efficient algorithms for extracting features of the imperative and functional structure of linear genetic programs. In doing so, especially the detection and elimination of noneffective code during runtime will turn out as a powerful tool to accelerate the time-consuming step of fitness evaluation in GP. Variation operators are discussed systematically for the linear program representation. We will demonstrate that so called effective instruction mutations achieve the best performance in terms of solution quality. These mutations operate only on the (structurally) effective code and restrict the mutation step size to one instruction. One possibility to further improve their performance is to explicitly increase the probability of neutral variations. As a second, more time-efficient alternative we explicitly control the mutation step size on the effective code (effective step size). Minimum steps do not allow more than one effective instruction to change its effectiveness status. That is, only a single node may be connected to or disconnected from the effective graph component. It is an interesting phenomenon that, to some extent, the effective code becomes more robust against destructions over the generations already implicitly. A special concern of this thesis is to convince the reader that there are some serious arguments for using a linear representation. In a crossover-based comparison LGP has been found superior to TGP over a set of benchmark problems. Furthermore, linear solutions turned out to be more compact than tree solutions due to (1) multiple usage of subgraph results and (2) implicit parsimony pressure by structurally noneffective code. The phenomenon of code growth is analyzed for different linear genetic operators. When applying instruction mutations exclusively almost only neutral variations may be held responsible for the emergence and propagation of intron code. It is noteworthy that linear genetic programs may not grow if all neutral variation effects are rejected and if the variation step size is minimum. For the same reasons effective instruction mutations realize an implicit complexity control in linear GP which reduces a possible negative effect of code growth to a minimum. Another noteworthy result in this context is that program size is strongly increased by crossover while it is hardly influenced by mutation even if step sizes are not explicitly restricted.
Layout of Decoupling Capacitors in IP Blocks for 90-nm CMOS

On-chip decoupling capacitors (decaps) in the form of MOS transistors are widely used to reduce power supply noise. This paper provides guidelines for standard cell layouts of decaps for use within Intellectual Property (IP) blocks in application-specific integrated circuit (ASIC) designs. At 90-nm CMOS technology and below, a tradeoff exists between high-frequency effects and electrostatic discharge (ESD) reliability when designing the layout of such decaps. In this paper, the high-frequency effects are modeled using simple equations. A metric is developed to determine the optimal number of fingers based on the frequency response. Then, a cross-coupled design is described that has been recently introduced by cell library developers to handle ESD problems. Unfortunately, it suffers from poor response times due the large resistance inherent in its design. Improved cross-coupled designs are presented that properly balance issues of frequency response with ESD performance, while greatly reducing thin-oxide gate leakage.
Bayesian modeling of human sequential decision-making on the multi-armed bandit problem

Belief update based on m last rewards • Gaussian belief model instead of Beta • Limited lookahead to h steps and a myopic function in the horizon. •  Correct sequential decision-making is critical for life success, and optimal approaches require signi!cant computational look ahead. However, simple models seem to explain people’s behavior.  (1) Why we seem so simple compared to a rational agent? (2) What is the built-in model that we use to sequentially choose between courses of actions?
Skeletal muscle mass reference curves for children and adolescents.

Skeletal muscle is key to motor development and represents a major metabolic end organ that aids glycaemic regulation.



To create gender-specific reference curves for fat-free mass (FFM) and appendicular (limb) skeletal muscle mass (SMMa) in children and adolescents. To examine the muscle-to-fat ratio in relation to body mass index (BMI) for age and gender.



Body composition was measured by segmental bioelectrical impedance (BIA, Tanita BC418) in 1985 Caucasian children aged 5-18.8 years. Skeletal muscle mass data from the four limbs were used to derive smoothed centile curves and the muscle-to-fat ratio.



The centile curves illustrate the developmental patterns of %FFM and SMMa. While the %FFM curves differ markedly between boys and girls, the SMMa (kg), %SMMa and %SMMa/FFM show some similarities in shape and variance, together with some gender-specific characteristics. Existing BMI curves do not reveal these gender differences. Muscle-to-fat ratio showed a very wide range with means differing between boys and girls and across fifths of BMI z-score.



BIA assessment of %FFM and SMMa represents a significant advance in nutritional assessment since these body composition components are associated with metabolic health. Muscle-to-fat ratio has the potential to provide a better index of future metabolic health.
Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity

Dense kernel matrices Θ ∈ RN×N obtained from point evaluations of a covariance function G at locations {xi}1≤i≤N arise in statistics, machine learning, and numerical analysis. For covariance functions that are Green’s functions of elliptic boundary value problems and approximately equally spaced sampling points, we show how to identify a subset S ⊂ {1, . . . , N} × {1, . . . , N}, with #S = O(N log(N) log(N/ )), such that the zero fill-in incomplete Cholesky factorisation of Θi,j1(i,j)∈S is an -approximation of Θ. This blockfactorisation can provably be obtained in complexity O(N log(N) log(N/ )) in space and O(N log(N) log(N/ )) in time. The algorithm only needs to know the spatial configuration of the xi and does not require an analytic representation of G. Furthermore, an approximate PCA with optimal rate of convergence in the operator norm can be easily read off from this decomposition. Hence, by using only subsampling and the incomplete Cholesky decomposition, we obtain at nearly linear complexity the compression, inversion and approximate PCA of a large class of covariance matrices. By inverting the order of the Cholesky decomposition we also obtain a solver for elliptic PDE with complexity O(N log(N) log(N/ )) in space and O(N log(N) log(N/ )) in time.
FPGA Implementation of Convolutional Neural Networks with Fixed-Point Calculations

Neural network-based methods for image processing are becoming widely used in practical applications. Modern neural networks are computationally expensive and require specialized hardware, such as graphics processing units. Since such hardware is not always available in real life applications, there is a compelling need for the design of neural networks for mobile devices. Mobile neural networks typically have reduced number of parameters and require a relatively small number of arithmetic operations. However, they usually still are executed at the software level and use floating-point calculations. The use of mobile networks without further optimization may not provide sufficient performance when high processing speed is required, for example, in real-time video processing (30 frames per second). In this study, we suggest optimizations to speed up computations in order to efficiently use already trained neural networks on a mobile device. Specifically, we propose an approach for speeding up neural networks by moving computation from software to hardware and by using fixed-point calculations instead of floating-point. We propose a number of methods for neural network architecture design to improve the performance with fixed-point calculations. We also show an example of how existing datasets can be modified and adapted for the recognition task in hand. Finally, we present the design and the implementation of a floating-point gate array-based device to solve the practical problem of real-time handwritten digit classification from mobile camera video feed.
Micropropagation of Ilex dumosa (Aquifoliaceae) from nodal segments in a tissue culture system.

Micropropagation of Ilex dumosa var. dumosa R. ("yerba señorita") from nodal segments containing one axillary bud was investigated. Shoot regeneration from explants of six-year-old plants was readily achieved in 1/4 strength Murashige and Skoog medium (1/4 MS) plus 30 gr x L(-1) sucrose and supplemented with 4.4 microM BA. Further multiplication and elongation of the regenerated shoots were obtained by subculture in a fresh medium of similar composition with 1.5 gr x L(-1) sucrose. Rooting induction from shoots were achieved in two steps: 1) 7 days in 1/4 MS (30 gr x L(-1) sucrose, 0.25% Phytagel) with 7.3 microM IBA and 2) 21 days in the same medium without IBA and 20 microM of cadaverine added. Regenerated plants were successfully transferred to soil. This micropropagation schedule can be implemented in breeding programs of Ilex dumosa.
Learning Compact Hash Codes for Multimodal Representations Using Orthogonal Deep Structure

As large-scale multimodal data are ubiquitous in many real-world applications, learning multimodal representations for efficient retrieval is a fundamental problem. Most existing methods adopt shallow structures to perform multimodal representation learning. Due to a limitation of learning ability of shallow structures, they fail to capture the correlation of multiple modalities. Recently, multimodal deep learning was proposed and had proven its superiority in representing multimodal data due to its high nonlinearity. However, in order to learn compact and accurate representations, how to reduce the redundant information lying in the multimodal representations and incorporate different complexities of different modalities in the deep models is still an open problem. In order to address the aforementioned problem, in this paper we propose a hashing-based orthogonal deep model to learn accurate and compact multimodal representations. The method can better capture the intra-modality and inter-modality correlations to learn accurate representations. Meanwhile, in order to make the representations compact, the hashing-based model can generate compact hash codes and the proposed orthogonal structure can reduce the redundant information lying in the codes by imposing orthogonal regularizer on the weighting matrices. We also theoretically prove that, in this case, the learned codes are guaranteed to be approximately orthogonal. Moreover, considering the different characteristics of different modalities, effective representations can be attained with different number of layers for different modalities. Comprehensive experiments on three real-world datasets demonstrate a substantial gain of our method on retrieval tasks compared with existing algorithms.
Identifying Multi-word Expressions by Leveraging Morphological and Syntactic Idiosyncrasy

Multi-word expressions constitute a significant portion of the lexicon of every natural language, and handling them correctly is mandatory for various NLP applications. Yet such entities are notoriously hard to define, and are consequently missing from standard lexicons and dictionaries. Multi-word expressions exhibit idiosyncratic behavior on various levels: orthographic, morphological, syntactic and semantic. In this work we take advantage of the morphological and syntactic idiosyncrasy of Hebrew noun compounds and employ it to extract such expressions from text corpora. We show that relying on linguistic information dramatically improves the accuracy of compound extraction, reducing over one third of the errors compared with the best baseline.
Contribution of perfusion-weighted magnetic resonance imaging in the differentiation of meningiomas and other extra-axial tumors: case reports and literature review

We present six cases of extra-axial lesions: three meningiomas [including one intraventricular and one cerebellopontine angle (CPA) meningioma], one dural metastasis, one CPA schwannoma and one choroid plexus papilloma which were chosen from a larger cohort of extra-axial tumors evaluated in our institution. Apart from conventional MR examinations, all the patients also underwent perfusion-weighted imaging (PWI) using dynamic susceptibility contrast method on a 1.5 T MR unit (contrast: 0.3 mmol/kg, rate 5 ml/s). Though the presented tumors showed very similar appearance on conventional MR images, they differed significantly in perfusion examinations. The article draws special attention to the usefulness of PWI in the differentiation of various extra-axial tumors and its contribution in reaching final correct diagnoses. Finding a dural lesion with low perfusion parameters strongly argues against the diagnosis of meningioma and should raise a suspicion of a dural metastasis. In cases of CPA tumors, a lesion with low relative cerebral blood volume values should be suspected to be schwannoma, allowing exclusion of meningioma to be made. In intraventricular tumors arising from choroid plexus, low perfusion parameters can exclude a diagnosis of meningioma. In our opinion, PWI as an easy and quick to perform functional technique should be incorporated into the MR protocol of all intracranial tumors including extra-axial neoplasms.
I Know What You Want to Express: Sentence Element Inference by Incorporating External Knowledge Base

Sentence auto-completion is an important feature that saves users many keystrokes in typing the entire sentence by providing suggestions as they type. Despite its value, the existing sentence auto-completion methods, such as query completion models, can hardly be applied to solving the object completion problem in sentences with the form of (subject, verb, object), due to the complex natural language description and the data deficiency problem. Towards this goal, we treat an SVO sentence as a three-element triple (subject, sentence pattern, object), and cast the sentence object completion problem as an element inference problem. These elements in all triples are encoded into a unified low-dimensional embedding space by our proposed TRANSFER model, which leverages the external knowledge base to strengthen the representation learning performance. With such representations, we can provide reliable candidates for the desired missing element by a linear model. Extensive experiments on a real-world dataset have well-validated our model. Meanwhile, we have successfully applied our proposed model to factoid question answering systems for answer candidate selection, which further demonstrates the applicability of the TRANSFER model.
Software and Hardware FPGA-Based Digital Watermarking and Steganography Approaches: Toward New Methodology for Evaluation and Benchmarking Using Multi-Criteria Decision-Making Techniques

Evaluating and benchmarking software and hardware ̄eld programmable gate array (FPGA)based digital watermarking are considered challenging tasks because of multiple and con°icting evaluation criteria. A few evaluation and benchmarking techniques/frameworks have been implemented to digital watermarking or steganography; however, these approaches still present certain limitations. In particular, ̄xing some attributes on account of other attributes and wellknown benchmarking approaches are limited to robust watermarking techniques. Thus, this study aims toward a new methodology for evaluation and benchmarking using multi-criteria analysis for software and hardware \FPGA"-based digital watermarking or steganography. To achieve this objective, two iterations are conducted. The ̄rst iteration consists of two stages: discussing software and hardware \FPGA"-based digital watermarking or steganography to create a dataset with various samples for benchmarking and discussing the evaluation method and then discussing the test for software and hardware \FPGA"-based digital watermarking or steganography according to multi-criteria evaluation (i.e., complexity, payload and quality) to create a decision matrix. The second iteration applies di®erent decision-making techniques (i.e., SAW, MEW, HAW, TOPSIS, WSM and WPM)) to benchmark the results of the ̄rst iteration (i.e., software or hardware FPGA-based digital watermarking or steganography approaches). Then, the discussed mean, standard deviation and paired sample t-test results are used to measure the correlations among di®erent techniques based on the ranking results. The discussion ̄ndings are described as follows: (1) the integration of developer and evaluator preferences into the evaluation and benchmarking for software and hardware FPGA-based digital watermarking or steganography, (2) the process of assigning weights and (3) visualizing large-scale data sample in either software or hardware FPGA-based digital watermarking or steganography algorithms.