nils-herrmann/clean_scirepeval huggingface dataset:
Opposing effects of viral mediated brain expression of apolipoprotein E2 (apoE2) and apoE4 on apoE lipidation and Aβ metabolism in apoE4-targeted replacement mice

Human apolipoprotein E (apoE) exists in three major isoforms: apoE2, apoE3 and apoE4. In the brain, apoE is produced mostly by astrocytes and transports cholesterol to neurons via apoE receptors. Among the gene alleles encoding the three isoforms, the APOE4 allele is the strongest genetic risk factor for late-onset Alzheimer’s disease (AD), whereas APOE2 is protective. ApoE4 confers a gain of toxic function, a loss of neuroprotective function or a combination of both in AD pathogenesis. Given that therapeutic impacts of modulating apoE expression may be isoform-dependent, we sought to investigate the relationship between overexpressing apoE isoform and apoE-related functions in apoE-targeted replacement (TR) mice. Specifically, apoE isoform expression driven by the astrocyte-specific glial fibrillary acidic protein (GFAP) promoter was built into an adeno-associated virus serotype 8 (AAV8) vector and injected into the ventricles of postnatal day 2 (P2) apoE3-TR or apoE4-TR mice. Upon confirmation of apoE isoform expression, effects on apoE lipidation and the levels of amyloid-β (Aβ) in the brain were assessed. AAV8-GFAP-apoE isoforms were specifically expressed in astrocytes throughout all brain regions, which led to overall increased apoE levels in the brain. Viral mediated overexpression of apoE4 in the apoE4-TR background increased poorly-lipidated apoE lipoprotein particles and decreased apoE-associated cholesterol in apoE4-TR mice. Conversely, apoE2 overexpression in apoE4-TR mice enhanced apoE lipidation and associated cholesterol. Furthermore, overexpression of apoE4 elevated the levels of endogenous Aβ, whereas apoE2 overexpression trended to lower endogenous Aβ. Overexpression of apoE isoforms induces differential effects in the apoE4-TR background: apoE4 decreases apoE lipidation and enhances Aβ accumulation, whereas apoE2 has the opposite effects. Our findings suggest that increasing apoE2 in APOE4 carriers is a beneficial strategy to treat AD, whereas increasing apoE4 in APOE4 carriers is likely harmful. We have also established novel methods to express apoE isoforms in mouse brain to study apoE-related pathways in AD and related dementia.
CloneCloud: Boosting Mobile Device Applications Through Cloud Clone Execution

Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices. At the same time, such devices often enjoy strong connectivity with more powerful machines ranging from laptops and desktops to commercial clouds. This paper presents the design and implementation of CloneCloud, a system that automatically transforms mobile applications to benefit from the cloud. The system is a flexible application partitioner and execution runtime that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud. CloneCloud uses a combination of static analysis and dynamic profiling to optimally and automatically partition an application so that it migrates , executes in the cloud, and re-integrates computation in a fine-grained manner that makes efficient use of resources. Our evaluation shows that CloneCloud can achieve up to 21.2x speedup of smartphone applications we tested and it allows different partitioning for different inputs and n etworks.
Direct Pose Estimation and Refinement

We study a fundamental question in pose estimation from vision-only video data: should the pose of a camera be determined from fixed and known correspondences? Or should correspondences be simultaneously estimated alongside the pose? Determining pose from fixed correspondences is known as feature-based, where wellestablished tools from projective geometry are utilized to formulate and solve a plethora of pose estimation problems. Nonetheless, in degraded imaging conditions such as low light and blur, reliably detecting and precisely localizing interest points becomes challenging. Conversely, estimating correspondences alongside motion is known as the direct approach, where image data are used directly to determine geometric quantities without relying on sparse interest points as an intermediate representation. The approach is in general more precise by virtue of redundancy as many measurements are used to estimate a few degreesof-freedom. However, direct methods are more sensitive to changes in illumination. In this work, we combine the best of the feature-based approaches with the precision of direct methods. Namely, we make use of densely and sparsely evaluated local feature descriptors in a direct image alignment framework to address pose estimation in challenging conditions. Applications include tracking planar targets under sudden and drastic changes in illumination as well as visual odometry in poorly-lit subterranean mines. Motivated by the success of the approach, we introduce a novel formulation for the joint refinement of pose and structure across multiple views akin to feature-based bundle adjustment (BA). In contrast to minimizing the reprojection error using BA, initial estimates are refined such that the photometric consistency of their image projections is maximized without the need for correspondences. The technique is evaluated on a range of datasets and is shown to improve upon the accuracy of the current state-of-the-art in vision-based simultaneous localization and mapping (VSLAM).
ERMIA: Fast Memory-Optimized Database System for Heterogeneous Workloads

Large main memories and massively parallel processors have triggered not only a resurgence of high-performance transaction processing systems optimized for large main-memory and massively parallel processors, but also an increasing demand for processing heterogeneous workloads that include read-mostly transactions. Many modern transaction processing systems adopt a lightweight optimistic concurrency control (OCC) scheme to leverage its low overhead in low contention workloads. However, we observe that the lightweight OCC is not suitable for heterogeneous workloads, causing significant starvation of read-mostly transactions and overall performance degradation.
 In this paper, we present ERMIA, a memory-optimized database system built from scratch to cater the need of handling heterogeneous workloads. ERMIA adopts snapshot isolation concurrency control to coordinate heterogeneous transactions and provides serializability when desired. Its physical layer supports the concurrency control schemes in a scalable way. Experimental results show that ERMIA delivers comparable or superior performance and near-linear scalability in a variety of workloads, compared to a recent lightweight OCC-based system. At the same time, ERMIA maintains high throughput on read-mostly transactions when the performance of the OCC-based system drops by orders of magnitude.
On the Existence of a Spectrum of Policies that Subsumes the Least Recently Used (LRU) and Least Frequently Used (LFU) Policies

Sam H. Nohs Sang Lyul Mint t Department of Computer Engineering Seoul National University Seoul 151-742, Korea http://ssrnet.snu.ac.kr http://archi.snu.ac.kr We show that there exists a spectrum of block replacement policies that subsumes both the Least Recently Used (LRU) and the Least Frequently Used (LFU) policies. The spectrum is formed according to how much more weight we give to the recent history than to the older history, and is referred to as the LRFU (Least Recently/Frequently Used) policy. Unlike many previous policies that use limited history to make block replacement decisions, the LRFU policy uses the complete reference history of blocks recorded during their cache residency. Nevertheless, the LRFU requires only a few words for each block to maintain such history. This paper also describes an implementation of the LRFU that again subsumes the LRU and LFU implementations. The LRFU policy is applied to buffer caching, and results from trace-driven simulations show that the LRFU performs better than previously known policies for the workloads we considered. This point is reinforced by results from our integration of the LRFU into the FreeBSD operating system.
An ontology-based model to determine the automation level of an automated vehicle for co-driving

Full autonomy of ground vehicles is a major goal of the ITS (Intelligent Transportation Systems) community. However, reaching such highest autonomy level in all situations (weather, traffic, ...) may seem difficult in practice, despite recent results regarding driverless cars (e.g., Google Cars). In addition, an automated vehicle should also self-assess its own perception abilities, and not only perceive its environment. In this paper, we propose an intermediate approach towards full automation, by defining a spectrum of automation layers, from fully manual (the car is driven by a driver) to fully automated (the car is driven by a computer), based on an ontological model for representing knowledge. We also propose a second ontology for situation assessment (what does the automated car perceive?), including the sensors/actuators state, environmental conditions and driver's state. Finally, we also define inference rules to link the situation assessment ontology to the automation level one. Both ontological models have been built and first results are presented.
Unity: A General Platform for Intelligent Agents

Recent advances in Deep Reinforcement Learning and Robotics have been driven by the presence of increasingly realistic and complex simulation environments. Many of the existing platforms, however, provide either unrealistic visuals, inaccurate physics, low task complexity, or a limited capacity for interaction among artificial agents. Furthermore, many platforms lack the ability to flexibly configure the simulation, hence turning the simulation environment into a black-box from the perspective of the learning system. Here we describe a new open source toolkit for creating and interacting with simulation environments using the Unity platform: Unity ML-Agents Toolkit1. By taking advantage of Unity as a simulation platform, the toolkit enables the development of learning environments which are rich in sensory and physical complexity, provide compelling cognitive challenges, and support dynamic multi-agent interaction. We detail the platform design, communication protocol, set of example environments, and variety of training scenarios made possible via the toolkit.
An optimizing compiler for a purely functional web-application language

High-level scripting languages have become tremendously popular for development of dynamic Web applications. Many programmers appreciate the productivity benefits of automatic storage management, freedom from verbose type annotations, and so on. While it is often possible to improve performance substantially by rewriting an application in C or a similar language, very few programmers bother to do so, because of the consequences for human development effort. This paper describes a compiler that makes it possible to have most of the best of both worlds, coding Web applications in a high-level language but compiling to native code with performance comparable to handwritten C code. The source language is Ur/Web, a domain-specific, purely functional, statically typed language for the Web. Through a coordinated suite of relatively straightforward program analyses and algebraic optimizations, we transform Ur/Web programs into almost-idiomatic C code, with no garbage collection, little unnecessary memory allocation for intermediate values, etc. Our compiler is in production use for commercial Web sites supporting thousands of users, and microbenchmarks demonstrate very competitive performance versus mainstream tools.
Modeling Topic Diffusion in Multi-Relational Bibliographic Information Networks

Information diffusion has been widely studied in networks, aiming to model the spread of information among objects when they are connected with each other. Most of the current research assumes the underlying network is homogeneous, i.e., objects are of the same type and they are connected by links with the same semantic meanings. However, in the real word, objects are connected via different types of relationships, forming multi-relational heterogeneous information networks.
 In this paper, we propose to model information diffusion in such multi-relational networks, by distinguishing the power in passing information around for different types of relationships. We propose two variations of the linear threshold model for multi-relational networks, by considering the aggregation of information at either the model level or the relation level. In addition, we use real diffusion action logs to learn the parameters in these models, which will benefit diffusion prediction in real networks. We apply our diffusion models in two real bibliographic information networks, DBLP network and APS network, and experimentally demonstrate the effectiveness of our models compared with single-relational diffusion models. Moreover, our models can determine the diffusion power of each relation type, which helps us understand the diffusion process better in the multi-relational bibliographic network scenario.
Virtual and Physical Molecular Modeling: Fostering Model Perception and Spatial Understanding

Interpretation of symbols, as well as understanding the particulate nature of matter and spatial structures, are essential skills students need for solving problems in chemistry. However, model perception and understanding the spatial structure of organic molecules has been a source of difficulty for many chemistry students. The research objective was to investigate the effect of using virtual and physical models while teaching organic chemistry on student understanding of new concepts, the spatial structure of new molecules, and preference of a particular model type. The research involved a new teaching method that combines two types of three-dimensional molecular models: physical (plastic) and virtual (computerized). The research population included 276 students from nine high schools in Haifa and the northern part of Israel. Experimental students gained a better understanding of the model concept and were more capable of defining and implementing new concepts, such as isomerism and functional group. They were better capable of mentally traversing across four understanding levels in chemistry: symbol, macroscopic, microscopic and process. Experimental group students were more capable of applying transformation from two-dimensional representations of molecules, provided by either a symbolic or a structural formula, to three-dimensional representations – a drawing of a model, and vice versa. Based on the research findings, we recommend incorporating both virtual and physical models in chemistry teaching/learning as a means to foster model perception and spatial understanding of molecular structure.
Toward a perceptual theory of transparency.

Theories of perceptual transparency have typically been developed within the context of a physical model that generates the percept of transparency (F. Metelli's episcotister model, 1974b). Here 2 fundamental questions are investigated: (a) When does the visual system initiate the percept of one surface seen through another? (b) How does it assign surface properties to a transparent layer? Results reveal systematic deviations from the predictions of Metelli's model, both for initiating image decomposition into multiple surfaces and for assigning surface attributes. Specifically, results demonstrate that the visual system uses Michelson contrast as a critical image variable to initiate percepts of transparency and to assign transmittance to transparent surfaces. Findings are discussed in relation to previous theories of transparency, lightness, brightness, and contrast-contrast.
Online Learning for Neural Machine Translation Post-editing

Neural machine translation has meant a revolution of the field. Nevertheless, postediting the outputs of the system is mandatory for tasks requiring high translation quality. Post-editing offers a unique opportunity for improving neural machine translation systems, using online learning techniques and treating the post-edited translations as new, fresh training data. We review classical learning methods and propose a new optimization algorithm. We thoroughly compare online learning algorithms in a post-editing scenario. Results show significant improvements in translation quality and effort reduction.
Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning

We present the first domain adaptation model for authorship attribution to leverage unlabeled data. The model includes extensions to structural correspondence learning needed to make it appropriate for the task. For example, we propose a median-based classification instead of the standard binary classification used in previous work. Our results show that punctuation-based character n-grams form excellent pivot features. We also show how singular value decomposition plays a critical role in achieving domain adaptation, and that replacing (instead of concatenating) non-pivot features with correspondence features yields better performance.
Developing a frame of reference for ex-ante IT/IS investment evaluation

Investment appraisal techniques are an integral part of many traditional capital budgeting processes. However, the adoption of Information Systems (IS) and the development of resulting infrastructures are being increasingly viewed on the basis of consumption. Consequently, decision-makers are now moving away from the confines of rigid capital budgeting processes, which have traditionally compared IS with non-ISrelated investments. With this in mind, the authors seek to dissect investment appraisal from the broader capital budgeting process to allow a deeper understanding of the mechanics involved with IS justification. This analysis presents conflicting perspectives surrounding the scope and sensitivity of traditional appraisal methods. In contributing to this debate, the authors present taxonomies of IS benefit types and associated natures, and discuss the resulting implications of using traditional appraisal techniques during the IS planning and decision-making process. A frame of reference that can be used to navigate through the variety of appraisal methods available to decision-makers is presented and discussed. Taxonomies of appraisal techniques that are classified by their respective characteristics are also presented. Perspectives surrounding the degree of involvement that financial appraisal should play during decision making and the limitations surrounding investment appraisal techniques are identified.  (2002) 11, 74–82. DOI: 10.1057/palgrave/
Convolutional Neural Networks for Direct Text

In this work we address the problem of blind deconvolution and denoising. We focus on restoration of text documents and we show that this type of highly structured data can be successfully restored by a convolutional neural network. The networks are trained to reconstruct high-quality images directly from blurry inputs without assuming any specific blur and noise models. We demonstrate the performance of the convolutional networks on a large set of text documents and on a combination of realistic de-focus and camera shake blur kernels. On this artificial data, the convolutional networks significantly outperform existing blind deconvolution methods, including those optimized for text, in terms of image quality and OCR accuracy. In fact, the networks outperform even state-of-the-art non-blind methods for anything but the lowest noise levels. The approach is validated on real photos taken by various devices.
Bayesian Experimental Design : A Review

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NAViGaTOR: Network Analysis, Visualization and Graphing Toronto

NAViGaTOR is a powerful graphing application for the 2D and 3D visualization of biological networks. NAViGaTOR includes a rich suite of visual mark-up tools for manual and automated annotation, fast and scalable layout algorithms and OpenGL hardware acceleration to facilitate the visualization of large graphs. Publication-quality images can be rendered through SVG graphics export. NAViGaTOR supports community-developed data formats (PSI-XML, BioPax and GML), is platform-independent and is extensible through a plug-in architecture.


AVAILABILITY
NAViGaTOR is freely available to the research community from http://ophid.utoronto.ca/navigator/. Installers and documentation are provided for 32- and 64-bit Windows, Mac, Linux and Unix.



juris@ai.utoronto.ca



Supplementary data are available at Bioinformatics online.
Key drivers of internet banking services use

The purpose of this paper is to analyse the determinants of internet banking use, paying special attention to the role of product involvement, perceived risk and trust.  The impact of trust, perceived risks, product involvement and TAM beliefs (ease of use and usefulness) on internet banking adoption is tested through structural equation modelling techniques. The sample consists of 511 Spanish internet banking services users and the data are collected through an internet survey. Risk is measured as a formative construct.  Data analysis shows that TAM beliefs and perceived risks (security, privacy, performance and social) have a direct influence on e-banking adoption. Trust appears as a key variable that reduces perceived risk. Involvement plays an important role in increasing perceived ease of use.  This research provides banks with knowledge of what aspects to highlight in their communications strategies to increase their internet banking services adoption rates. The research findings show managers that web contents and design are key tools to increase internet banking services adoption. Practical recommendations to increase web usefulness and trust, and guidelines to reduce perceived risk dimensions are also provided.  Despite the importance of trust issues and risk perceptions for internet banking adoption, only limited work has been done to identify trust and risk dimensions in an online banking context. We have evaluated the impact of each risk dimension instead of treating risk as a whole. Furthermore, risk has been measured as a formative construct because there is no reason to expect that risk dimensions in online financial services are correlated.
Natural Language Processing (almost) from Scratch

We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.