nils-herrmann/clean_scirepeval huggingface dataset:
Molecular mechanisms underlying ErbB2/HER2 action in breast cancer

Overexpression of ErbB2, a receptor-like tyrosine kinase, is shared by several types of human carcinomas. In breast tumors the extent of overexpression has a prognostic value, thus identifying the oncoprotein as a target for therapeutic strategies. Already, antibodies to ErbB2 are used in combination with chemotherapy in the treatment of metastasizing breast cancer. The mechanisms underlying the oncogenic action of ErbB2 involve a complex network in which ErbB2 acts as a ligand-less signaling subunit of three other receptors that directly bind a large repertoire of stroma-derived growth factors. The major partners of ErbB2 in carcinomas are ErbB1 (also called EGFR) and ErbB3, a kinase-defective receptor whose potent mitogenic action is activated in the context of heterodimeric complexes. Why ErbB2-containing heterodimers are relatively oncopotent is a function of a number of processes. Apparently, these heterodimers evade normal inactivation processes, by decreasing the rate of ligand dissociation, internalizing relatively slowly and avoiding the degradative pathway by returning to the cell surface. On the other hand, the heterodimers strongly recruit survival and mitogenic pathways such as the mitogen-activated protein kinases and the phosphatidylinositol 3-kinase. Hyper-activated signaling through the ErbB-signaling network results in dysregulation of the cell cycle homeostatic machinery, with upregulation of active cyclin-D/CDK complexes. Recent data indicate that cell cycle regulators are also linked to chemoresistance in ErbB2-dependent breast carcinoma. Together with D-type cyclins, it seems that the CDK inhibitor p21Waf1 plays an important role in evasion from apoptosis. These recent findings herald a preliminary understanding of the output layer which connects elevated ErbB-signaling to oncogenesis and chemoresistance.
Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence

The rapid growth of Location-based Social Networks (LBSNs) provides a great opportunity to satisfy the strong demand for personalized Point-of-Interest (POI) recommendation services. However, with the tremendous increase of users and POIs, POI recommender systems still face several challenging problems: (1) the hardness of modeling complex user-POI interactions from sparse implicit feedback; (2) the difficulty of incorporating the geographical context information. To cope with these challenges, we propose a novel autoencoder-based model to learn the complex user-POI relations, namely SAE-NAD, which consists of a self-attentive encoder (SAE) and a neighbor-aware decoder (NAD). In particular, unlike previous works equally treat users' checked-in POIs, our self-attentive encoder adaptively differentiates the user preference degrees in multiple aspects, by adopting a multi-dimensional attention mechanism. To incorporate the geographical context information, we propose a neighbor-aware decoder to make users' reachability higher on the similar and nearby neighbors of checked-in POIs, which is achieved by the inner product of POI embeddings together with the radial basis function (RBF) kernel. To evaluate the proposed model, we conduct extensive experiments on three real-world datasets with many state-of-the-art methods and evaluation metrics. The experimental results demonstrate the effectiveness of our model.
Attentive Recurrent Comparators

Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations. Using the representations extracted by ARCs, we develop a way of approximating a dynamic representation space and use it for oneshot learning. In the task of one-shot classification on the Omniglot dataset, we achieve the state of the art performance with an error rate of 1.5%. This represents the first super-human result achieved for this task with a generic model that uses only pixel information.
Building Rome on a Cloudless Day ( ECCV 2010 )

This paper introduces an approach for dense 3D reconstruc7 7 tion from unregistered Internet-scale photo collections with about 3 mil8 8 lion of images within the span of a day on a single PC (“cloudless”). Our 9 9 method advances image clustering, stereo, stereo fusion and structure 10 10 from motion to achieve high computational performance. We leverage 11 11 geometric and appearance constraints to obtain a highly parallel imple12 12 mentation on modern graphics processors and multi-core architectures. 13 13 This leads to two orders of magnitude higher performance on an order 14 14 of magnitude larger dataset than competing state-of-the-art approaches.
The diagnosis and incidence of allergic fungal sinusitis.

To reevaluate the current criteria for diagnosing allergic fungal sinusitis (AFS) and determine the incidence of AFS in patients with chronic rhinosinusitis (CRS).



This prospective study evaluated the incidence of AFS in 210 consecutive patients with CRS with or without polyposis, of whom 101 were treated surgically. Collecting and culturing fungi from nasal mucus require special handling, and novel methods are described. Surgical specimen handling emphasizes histologic examination to visualize fungi and eosinophils in the mucin. The value of allergy testing in the diagnosis of AFS is examined.



Fungal cultures of nasal secretions were positive in 202 (96%) of 210 consecutive CRS patients. Allergic mucin was found in 97 (96%) of 101 consecutive surgical cases of CRS. Allergic fungal sinusitis was diagnosed in 94 (93%) of 101 consecutive surgical cases with CRS, based on histopathologic findings and culture results. Immunoglobulin E-mediated hypersensitivity to fungal allergens was not evident in the majority of AFS patients.



The data presented indicate that the diagnostic criteria for AFS are present in the majority of patients with CRS with or without polyposis. Since the presence of eosinophils in the allergic mucin, and not a type I hypersensitivity, is likely the common denominator in the pathophysiology of AFS, we propose a change in terminology from AFS to eosinophilic fungal rhinosinusitis.
Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations

We investigate techniques for visualizing time series data and evaluate their effect in value comparison tasks. We compare line charts with horizon graphs - a space-efficient time series visualization technique - across a range of chart sizes, measuring the speed and accuracy of subjects' estimates of value differences between charts. We identify transition points at which reducing the chart height results in significantly differing drops in estimation accuracy across the compared chart types, and we find optimal positions in the speed-accuracy tradeoff curve at which viewers performed quickly without attendant drops in accuracy. Based on these results, we propose approaches for increasing data density that optimize graphical perception.
Piecewise Linear Value Function Approximation for Factored MDPs

A number of proposals have been put forth in recent years for the solution of Markov decision processes (MDPs) whose state (and sometimes action) spaces are factored. One recent class of methods involves linear value function approximation, where the optimal value function is assumed to be a linear combination of some set of basis functions, with the aim of finding suitable weights. While sophisticated techniques have been developed for finding the best approximation within this constrained space, few methods have been proposed for choosing a suitable basis set, or modifying it if solution quality is found wanting. We propose a general framework, and specific proposals, that address both of these questions. In particular, we examine <i>weakly coupled MDPs</i> where a number of subtasks can be viewed independently modulo resource constraints. We then describe methods for constructing a piecewise linear combination of the subtask value functions, using greedy decision tree techniques. We argue that this architecture is suitable for many types of MDPs whose combinatorics are determined largely by the existence multiple conflicting objectives.
Ethical challenges in the management of chronic nonmalignant pain: negotiating through the cloud of doubt.

After successful cancer pain initiatives, efforts have been recently made to liberalize the use of opioids for the treatment of chronic nonmalignant pain. However, the goals for this treatment and its place among other available treatments are still unclear. Cancer pain treatment is aimed at patient comfort and is validated by objective disease severity. For chronic nonmalignant pain, however, comfort alone is not an adequate treatment goal, and pain is not usually proportional to objective disease severity. Therefore, confusion about treatment goals and doubts about the reality of nonmalignant pain entangle therapeutic efforts. We present a case history to demonstrate that this lack of proportionality fosters fears about malingering, exaggeration, and psychogenic pain among providers. Doubt concerning the reality of patients' unrelieved chronic nonmalignant pain has allowed concerns about addiction to dominate discussions of treatment. We propose alternate patient-centered principles to guide efforts to relieve chronic nonmalignant pain, including accept all patient pain reports as valid but negotiate treatment goals early in care, avoid harming patients, and incorporate chronic opioids as one part of the treatment plan if they improve the patient's overall health-related quality of life. Although an outright ban on opioid use in chronic nonmalignant pain is no longer ethically acceptable, ensuring that opioids provide overall benefit to patients requires significant time and skill. Patients with chronic nonmalignant pain should be assessed and treated for concurrent psychiatric disorders, but those with disorders are entitled to equivalent efforts at pain relief. The essential question is not whether chronic nonmalignant pain is real or proportional to objective disease severity, but how it should be managed so that the patient's overall quality of life is optimized.



The management of chronic nonmalignant pain is moving from specialty settings into primary care. Primary care providers need an ethical framework within which to adopt the principles of palliative care to this population.
Near-eye light field holographic rendering with spherical waves for wide field of view interactive 3D computer graphics

Holograms display a 3D image in high resolution and allow viewers to focus freely as if looking through a virtual window, yet computer generated holography (CGH) hasn't delivered the same visual quality under plane wave illumination and due to heavy computational cost. Light field displays have been popular due to their capability to provide continuous focus cues. However, light field displays must trade off between spatial and angular resolution, and do not model diffraction.
 We present a light field-based CGH rendering pipeline allowing for reproduction of high-definition 3D scenes with continuous depth and support of intra-pupil view-dependent occlusion. Our rendering accurately accounts for diffraction and supports various types of reference illuminations for hologram. We avoid under- and over-sampling and geometric clipping effects seen in previous work. We also demonstrate an implementation of light field rendering plus the Fresnel diffraction integral based CGH calculation which is orders of magnitude faster than the state of the art [Zhang et al. 2015], achieving interactive volumetric 3D graphics.
 To verify our computational results, we build a see-through, near-eye, color CGH display prototype which enables co-modulation of both amplitude and phase. We show that our rendering accurately models the spherical illumination introduced by the eye piece and produces the desired 3D imagery at the designated depth. We also analyze aliasing, theoretical resolution limits, depth of field, and other design trade-offs for near-eye CGH.
Exercise as cardiovascular therapy.

Drs Shephard and Balady 1 discuss the potential dangers of excessive exercise and the appropriate dose of physical activity. They cite 2 reports showing that in healthy persons, prolonged exercise can cause myocardial fatigue with a temporary depression of myocardial function.  In addition, recent research demonstrates that prolonged aerobic exercise may cause subclinical myocardial necrosis in individuals with no risk factors for cardiovascular disease.  Evidence also exists that apparently healthy individuals who are not active enough to meet a traditional exercise prescription (structured vigorous activity) are at a high risk for subclinical myocardial damage caused by prolonged strenuous exercise.
Corporate Social Responsibility : Strategic Implications *

In recent years, scholars and managers have devoted greater attention to the strategic implications of corporate social responsibility (CSR). Consistent with McWilliams and Siegel (2001), we define CSR as situations where the firm goes beyond compliance and engages in ‘actions that appear to further some social good, beyond the interests of the firm and that which is required by law’. However, this is just one interpretation of CSR. Numerous definitions of CSR have been proposed and often no clear definition is given, making theoretical development and measurement difficult. CSR activities have been posited to include incorporating social characteristics or features into products and manufacturing processes (e.g. aerosol products with no fluorocarbons or using environmentally-friendly technologies), adopting progressive human resource management practices (e.g. promoting employee empowerment), achieving higher levels of environmental performance through recycling and pollution abatement (e.g. adopting an aggresJournal of Management Studies 43:1 January 2006 0022-2380
Maximum inner-product search using cone trees

The problem of efficiently finding the best match for a query in a given set with respect to the Euclidean distance or the cosine similarity has been extensively studied. However, the closely related problem of efficiently finding the best match with respect to the inner-product has never been explored in the general setting to the best of our knowledge. In this paper we consider this problem and contrast it with the previous problems considered. First, we propose a general branch-and-bound algorithm based on a (single) tree data structure. Subsequently, we present a dual-tree algorithm for the case where there are multiple queries. Our proposed branch-and-bound algorithms are based on novel inner-product bounds. Finally we present a new data structure, the cone tree, for increasing the efficiency of the dual-tree algorithm. We evaluate our proposed algorithms on a variety of data sets from various applications, and exhibit up to five orders of magnitude improvement in query time over the naive search technique in some cases.
Remarks on computational emotion classification from physiological signal - Evaluation of how jazz music chord progression influences emotion

This paper evaluates human emotional change by sound stimuli focused on chord progression in jazz music and conducts computational emotion classification from physiological information. Psychological experiments using chord progression tunes as sound stimuli are conducted with 117 subjects and the result of subjective evaluation shows that positive emotional valance chord progression tunes that have ascending fourth aroused positive images, and negative emotional valence chord progression tunes that have chromatic descent aroused negative images. Psychophysical experiments using chord progression tunes to excite emotions in subjects are conducted to gather acceleration plethysmogram data. For computational emotion classification, multi-layer neural network using feature values extracted from heart rate and acceleration plethysmogram is used to discriminate emotional class. In experiments of computational emotion classification, an average of 38.3% classification rate is attained in three emotions - positive, negative, and neutral.
Wind Noise Reduction using Non-Negative Sparse Coding

We introduce a new speaker independent method for reducing wind noise in single-channel recordings of noisy speech. The method is based on non-negative sparse coding and relies on a wind noise dictionary which is estimated from an isolated noise recording. We estimate the parameters of the model and discuss their sensitivity. We then compare the algorithm with the classical spectral subtraction method and the Qualcomm-ICSI-OGI noise reduction method. We optimize the sound quality in terms of signal-to-noise ratio and provide results on a noisy speech recognition task.
A Calculus for Propagating Semantic Annotations Through Scientific Workflow Queries

Scientific workflows facilitate automation, reuse, and reproducibility of scientific data management and analysis tasks. Scientific workflows are often modeled as dataflow networks, chaining together processing components (called actors) that query, transform, analyse, and visualize scientific datasets. Semantic annotations relate data and actor schemas with conceptual information from a shared ontology, to support scientific workflow design, discovery, reuse, and validation in the presence of thousands of potentially useful actors and datasets. However, the creation of semantic annotations is complex and time-consuming. We present a calculus and two inference algorithms to automatically propagate semantic annotations through workflow actors described by relational queries. Given an input annotation α and a query q, forward propagation computes an output annotation α′; conversely, backward propagation infers α from q and α′.
A functional representation for aiding biomimetic and artificial inspiration of new ideas

Inspiration is useful for exploration and discovery of new solution spaces. Systems in natural and artificial worlds and their functionality are seen as rich sources of inspiration for idea generation. However, unlike in the artificial domain where existing systems are often used for inspiration, those from the natural domain are rarely used in a systematic way for this purpose. Analogy is long regarded as a powerful means for inspiring novel idea generation. One aim of the work reported here is to initiate similar work in the area of systematic biomimetics for product development, so that inspiration from both natural and artificial worlds can be used systematically to help develop novel, analogical ideas for solving design problems. A generic model for representing causality of natural and artificial systems has been developed, and used to structure information in a database of systems from both the domains. These are implemented in a piece of software for automated analogical search of relevant ideas from the databases to solve a given problem. Preliminary experiments at validating the software indicate substantial potential for the approach.
Network forensics: Notions and challenges

Network forensics is an extension of the network security model which traditionally emphasizes prevention and detection of network attacks. It addresses the need for dedicated investigative capabilities in the current model to allow investigating malicious behavior in networks. It helps organizations in investigating outside and inside network attacks. It is also important for law enforcement investigations. In this paper, various aspects of network forensics are reviewed as well as related technologies and their limitations. Also, challenges in deploying a network forensics infrastructure are highlighted.
Inducing Value Sparsity for Parallel Inference in Tree-shaped Models

Single-core architectures are rapidly on the decline, and even the most common computational devices now contain multiple cores. With this easy access to parallelism, the machine learning community needs to go beyond treating the running time as the only computational resource and needs to study approaches that take this additional form of flexibility into account. In this work, we study inference in tree-shaped models. Specifically, we focus on balancing accuracy and efficient use of multiple cores when faced with running time constraints.
Current views on fetal surgical treatment of myelomeningocele - the Management of Myelomeningocele Study (MOMS) trial and Polish clinical experience.

Myelomeningocele (MMC) is the most frequent congenital defect of the central nervous system for which there is no satisfactory alternative to postnatal treatment. On the contrary prenatal MMC surgery is conducting before birth and is aimed at protecting from Chiari II malformation. The main goal of fetal MMC repair is to improve development and life quality of children with Chiari II malformation. Management of Myelomeningocele Study (MOMS) which was published in 2011 clearly confirmed effectiveness of prenatal surgery. In this paper we compare MOMS results with our own clinical experience. Thanks to high effectiveness and significant improvement in safety of maternal-fetal surgery prenatal MMC surgery become a new standard of treatment.
Exploring the Application of Deep Learning Techniques on Medical Text Corpora

With the rapidly growing amount of biomedical literature it becomes increasingly difficult to find relevant information quickly and reliably. In this study we applied the word2vec deep learning toolkit to medical corpora to test its potential for improving the accessibility of medical knowledge. We evaluated the efficiency of word2vec in identifying properties of pharmaceuticals based on mid-sized, unstructured medical text corpora without any additional background knowledge. Properties included relationships to diseases ('may treat') or physiological processes ('has physiological effect'). We evaluated the relationships identified by word2vec through comparison with the National Drug File - Reference Terminology (NDF-RT) ontology. The results of our first evaluation were mixed, but helped us identify further avenues for employing deep learning technologies in medical information retrieval, as well as using them to complement curated knowledge captured in ontologies and taxonomies.
Use of Augmented Reality in Terms of Creativity in School Learning

Education provides a plethora of tools that can be used (alone or combined) for achieving better results. One of the most recent technological advances that can be used as an educational tool is Augmented Reality, a technology that can combine virtual and physical objects in order to enhance the real world. However, little is known about this technology and its possible applications in primary and secondary education. This paper consists a literature review focused on AR and its current and future incorporation in modern education via various context aware technologies (e.g. tablets, smartphones) which can provide opportunities for more interactive and joyful educational experiences. Also, it is described the possibility of implementing AR in Open Course Project situations, such as the one which is available at the Eastern Macedonia and Thrace Institute of Technology. Its purpose is to inform “creators” and stimulate “users” so that the benefits of this promising technology may be diffused throughout the educational process.