nils-herrmann/clean_scirepeval huggingface dataset:
A Unified Bayesian Model of Scripts, Frames and Language

We present the first probabilistic model to capture all levels of the Minsky Frame structure, with the goal of corpusbased induction of scenario definitions. Our model unifies prior efforts in discourse-level modeling with that of Fillmore’s related notion of frame, as captured in sentence-level,  semantic parses; as part of this, we resurrect the coupling among Minsky’s frames, Schank’s scripts and Fillmore’s frames, as originally laid out by those authors. Empirically, our approach yields improved scenario representations, reflected quantitatively in lower surprisal and more coherent latent scenarios.
Expanded Hemodialysis: A New Therapy for a New Class of Membranes.

A wide spectrum of molecules is retained in end-stage kidney disease, normally defined as uremic toxins. These solutes have different molecular weights and radii. Current dialysis membranes and techniques only remove solutes in the range of 50-15,000 Da, with limited or no capability to remove solutes in the middle to high molecular weight range (up to 50,000 Da). Improved removal has been obtained with high cut-off (HCO) membranes, with albumin loss representing a limitation to their practical application. Hemodiafiltration (HDF) at high volumes (>23 L/session) has produced some results on middle molecules and clinical outcomes, although complex hardware and high blood flows are required. A new class of membrane has been recently developed with a cut off (MWCO) close to the molecular weight of albumin. While presenting negligible albumin loss, these membranes have a very high retention onset (MWRO), allowing high clearances of solutes in a wide spectrum of molecular weights. These membranes originally defined (medium cut off) are probably better classified as high retention onset. The introduction of such membranes in the clinical routine has allowed the development of a new concept therapy called "expanded hemodialysis" (HDx). The new therapy is based on a special hollow fiber and dialyzer design. Its simple set-up and application offer the possibility to use it even in patients with suboptimal vascular access or even with an indwelling catheter. The system does not require a particular hardware or unusual nursing skill. The quality of dialysis fluid is, however, mandatory to ensure a safe conduction of the dialysis session. This new therapy is likely to modify the outcome of end-stage kidney disease patients, thanks to the enhanced removal of molecules traditionally retained by current dialysis techniques.
Multifocal planes head-mounted displays.

Stereoscopic head-mounted displays (HMD's) provide an effective capability to create dynamic virtual environments. For a user of such environments, virtual objects would be displayed ideally at the appropriate distances, and natural concordant accommodation and convergence would be provided. Under such image display conditions, the user perceives these objects as if they were objects in a real environment. Current HMD technology requires convergent eye movements. However, it is currently limited by fixed visual accommodation, which is inconsistent with real-world vision. A prototype multiplanar volumetric projection display based on a stack of laminated planes was built for medical visualization as discussed in a paper presented at a 1999 Advanced Research Projects Agency workshop (Sullivan, Advanced Research Projects Agency, Arlington, Va., 1999). We show how such technology can be engineered to create a set of virtual planes appropriately configured in visual space to suppress conflicts of convergence and accommodation in HMD's. Although some scanning mechanism could be employed to create a set of desirable planes from a two-dimensional conventional display, multiplanar technology accomplishes such function with no moving parts. Based on optical principles and human vision, we present a comprehensive investigation of the engineering specification of multiplanar technology for integration in HMD's. Using selected human visual acuity and stereoacuity criteria, we show that the display requires at most 27 equally spaced planes, which is within the capability of current research and development display devices, located within a maximal 26-mm-wide stack. We further show that the necessary in-plane resolution is of the order of 5 microm.
Efficient ray tracing of volume data

Volume rendering is a technique for visualizing sampled scalar or vector fields of three spatial dimensions without fitting geometric primitives to the data. A subset of these techniques generates images by computing 2-D projections of a colored semitransparent volume, where the color and opacity at each point are derived from the data using local operators. Since all voxels participate in the generation of each image, rendering time grows linearly with the size of the dataset. This paper presents a front-to-back image-order volume-rendering algorithm and discusses two techniques for improving its performance. The first technique employs a pyramid of binary volumes to encode spatial coherence present in the data, and the second technique uses an opacity threshold to adaptively terminate ray tracing. Although the actual time saved depends on the data, speedups of an order of magnitude have been observed for datasets of useful size and complexity. Examples from two applications are given: medical imaging and molecular graphics.
LARS: A Location-Aware Recommender System

This paper proposes LARS, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items, LARS, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS can apply these techniques separately, or in concert, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the Movie Lens movie recommendation system reveals that LARS is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.
Autism diagnostic interview: a standardized investigator-based instrument.

The development of a new standardized investigator-based interview for use in the differential diagnosis of pervasive developmental disorders is described, together with a diagnostic algorithm (using ICD-10 criteria) based on its use. Good interrater reliability for algorithm items was shown between four raters, two in Canada and two in the UK, who rated 32 videotaped interviews. The items also significantly discriminated between 16 autistic and 16 nonautistic mentally handicapped subjects. The algorithm based on ICD-10 identified all 16 autistic individuals and none of the 16 nonautistic subjects.
Multi-View K-Means Clustering on Big Data

In past decade, more and more data are collected from multiple sources or represented by multiple views, where different views describe distinct perspectives of the data. Although each view could be individually used for finding patterns by clustering, the clustering performance could be more accurate by exploring the rich information among multiple views. Several multi-view clustering methods have been proposed to unsupervised integrate different views of data. However, they are graph based approaches, e.g. based on spectral clustering, such that they cannot handle the large-scale data. How to combine these heterogeneous features for unsupervised large-scale data clustering has become a challenging problem. In this paper, we propose a new robust large-scale multi-view clustering method to integrate heterogeneous representations of largescale data. We evaluate the proposed new methods by six benchmark data sets and compared the performance with several commonly used clustering approaches as well as the baseline multi-view clustering methods. In all experimental results, our proposed methods consistently achieve superiors clustering performances.
Why do Larks Perform Better at School than Owls ? The Mediating Effect of Conscientiousness

Received 26.06.2016 Received in revised form 25.10.2016 Accepted 26.10.2016 Available online 25.11.2016 Circadian preference refers to individuals’ preference for morning or evening activities. Its two dimensions (i.e., morningness and eveningness) are related to a number of academic outcomes. While morningness shows positive relations with academic achievement, eveningness shows negative relations. Further, morningness and eveningness show the same correlational pattern with conscientiousness (i.e., positive relations for morningness, negative relations for eveningness), which – in turn – predicts academic achievement. Therefore, the main aim of the present study was to investigate if the relation between circadian preference and academic achievement was mediated by conscientiousness. The sample comprised 422 students attending the 11th grade at a grammar school in Germany. Circadian preference (morningness and eveningness) and conscientiousness were assessed by self-report questionnaires; academic achievement was operationalized by school grades. Using confirmatory analyses and structural equation modelling, the results supported the assumption that conscientiousness mediates the relation between circadian preference and academic achievement. Implications for research into circadian preference and for education are discussed.
Pixel and Feature Level Based Domain Adaption for Object Detection in Autonomous Driving

Annotating large scale datasets to train modern convolutional neural networks is prohibitively expensive and time-consuming for many real tasks. One alternative is to train the model on labled synthetic datasets and apply it in the real scenes. However, this straightforward method often fails to generalize well mainly due to the domain bias between the synthetic and real datasets. Many unsupervised domain adaptation (UDA) methods are introduced to address this problem but most of them only focus on the simple classfication task. In this paper, we present a novel UDA model to solve the more complex object detection problem in the context of autonomous driving. Our model integrates both pixel level and feature level based transformtions to fulfill the cross domain detection task and can be further trained end-to-end to pursue better performance. We employ objectives of the generative adversarial network and the cycle consistency loss for image translation in the pixel space. To address the potential semantic inconsistency problem, we propose region proposal based feature adversarial training to preserve the semantics of our target objects as well as further minimize the domain shifts. Extensive experiments are conducted on several different datasets, and the results demonstrate the robustness and superiority of our method.
Efficient Deep Learning on Multi-Source Private Data

Machine learning models benefit from large and diverse datasets. Using such datasets, however, often requires trusting a centralized data aggregator. For sensitive applications like healthcare and finance this is undesirable as it could compromise patient privacy or divulge trade secrets. Recent advances in secure and privacy-preserving computation, including trusted hardware enclaves and differential privacy, offer a way for mutually distrusting parties to efficiently train a machine learning model without revealing the training data. In this work, we introduce Myelin, a deep learning framework which combines these privacy-preservation primitives, and use it to establish a baseline level of performance for fully private machine learning.
Hierarchical Model-Based Motion Estimation

This paper describes a hierarchical estimation framework for the computation of diverse representations of motion information. The key features of the resulting framework (or family of algorithms) a,re a global model that constrains the overall structure of the motion estimated, a local rnodel that is used in the estimation process, and a coa,rse-fine refinement strategy. Four specific motion models: affine flow, planar surface flow, rigid body motion, and general optical flow, are described along with their application to specific examPles.
Individual- and organizational-level consequences of organizational citizenship behaviors: A meta-analysis.

Although one of the main reasons for the interest in organizational citizenship behaviors (OCBs) is the potential consequences of these behaviors, no study has been reported that summarizes the research regarding the relationships between OCBs and their outcomes. Therefore, the purpose of this study is to provide a meta-analytic examination of the relationships between OCBs and a variety of individual- and organizational-level outcomes. Results, based on 168 independent samples (N = 51,235 individuals), indicated that OCBs are related to a number of individual-level outcomes, including managerial ratings of employee performance, reward allocation decisions, and a variety of withdrawal-related criteria (e.g., employee turnover intentions, actual turnover, and absenteeism). In addition, OCBs were found to be related (k = 38; N = 3,611 units) to a number of organizational-level outcomes (e.g., productivity, efficiency, reduced costs, customer satisfaction, and unit-level turnover). Of interest, somewhat stronger relationships were observed between OCBs and unit-level performance measures in longitudinal studies than in cross-sectional studies, providing some evidence that OCBs are causally related to these criteria. The implications of these findings for both researchers and practitioners are discussed.
A hybrid SoC interconnect with dynamic TDMA-based transaction-less buses and on-chip networks

The two dominant architectural choices for implementing efficient communication fabrics for SoC's have been transaction-based buses and packet-based networks-on-chip (NoC). Both implementations have some inherent disadvantages - the former resulting from poor scalability and the transactional character of their operation, and the latter from inconsistent access times and deterioration of performance at high injection rates. In this paper, we propose a transaction-less, time-division-based bus architecture, which dynamically allocates timeslots on-the-fly - the dTDMA bus. This architecture addresses the contention issues of current bus architectures, while avoiding the multi-hop overhead of NoC's. It is compared to traditional bus architectures and NoC's and shown to outperform both for configurations with fewer than 10 PE's. In order to exploit the advantages of the dTDMA bus for smaller configurations, and the scalability of NoC's, we propose a new hybrid SoC interconnect combining the two, showing significant improvement in both latency and power consumption.
Nature-nurture reconceptualized in developmental perspective: a bioecological model.

In response to Anastasi's (1958) long-standing challenge, the authors propose an empirically testable theoretical model that (a) goes beyond and qualifies the established behavioral genetics paradigm by allowing for nonadditive synergistic effects, direct measures of the environment, and mechanisms of organism-environment interaction, called proximal processes, through which genotypes are transformed into phenotypes; (b) hypothesizes that estimates of heritability (e.g., h2) increase markedly with the magnitude of proximal processes; (c) demonstrates that heritability measures the proportion of variation in individual differences attributable only to actualized genetic potential, with the degree of nonactualized potential remaining unknown; (d) proposes that, by enhancing proximal processes and environments, it is possible to increase the extent of actualized genetic potentials for developmental competence.
A Survey of Face Recognition Techniques

Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.
Beyond 'Caveman Communities': Hubs and Spokes for Graph Compression and Mining

Given a real world graph, how should we lay-out its edges? How can we compress it? These questions are closely related, and the typical approach so far is to find clique-like communities, like the `cavemen graph', and compress them. We show that the block-diagonal mental image of the `cavemen graph' is the wrong paradigm, in full agreement with earlier results that real world graphs have no good cuts. Instead, we propose to envision graphs as a collection of hubs connecting spokes, with super-hubs connecting the hubs, and so on, recursively. Based on the idea, we propose the Slash Burn method (burn the hubs, and slash the remaining graph into smaller connected components). Our view point has several advantages: (a) it avoids the `no good cuts' problem, (b) it gives better compression, and (c) it leads to faster execution times for matrix-vector operations, which are the back-bone of most graph processing tools. Experimental results show that our Slash Burn method consistently outperforms other methods on all datasets, giving good compression and faster running time.
High Resolution AMOLED Pixel Using Negative Feedback Structure for Improving Image Quality

An active matrix organic light emitting diode (AMOLED) pixel structure is proposed to improve image quality of high resolution small sized displays. The proposed pixel structure increases luminance uniformity by decreasing the distortion of the voltage in the storage capacitors using negative feedback structure. The simulation results show that the distortion of the voltage in the storage capacitors with negative feedback structure decreases to 55.3% compared to that without negative feedback structure and the emission current error of the proposed pixel structure ranges from –0.71 LSB to 0.56 LSB when the threshold voltage of the driving TFT varies from –0.2 V to 0.2 V.
Bitter to Better - How to Make Bitcoin a Better Currency

Bitcoin is a distributed digital currency which has attracted a substantial number of users. We perform an in-depth investigation to understand what made Bitcoin so successful, while decades of research on cryptographic e-cash has not lead to a large-scale deployment. We ask also how Bitcoin could become a good candidate for a long-lived stable currency. In doing so, we identify several issues and attacks of Bitcoin, and propose suitable techniques to address them.
Genetic risk, number of previous depressive episodes, and stressful life events in predicting onset of major depression.

The association between stressful life events and the onset of major depression decreases as the number of previous depressive episodes increases. How do genetic risk factors for major depression impact on this "kindling" phenomenon? In particular, do those at high genetic risk exhibit an increase in the speed of kindling, or are they "prekindled"?



Using discrete-time survival analysis, the authors examined the interaction between genetic risk, number of previous depressive episodes, and life event exposure in the prediction of episodes of major depression in female-female twin pairs from a population-based registry. The twins were interviewed four times over a 9-year period, producing 92,521 person-months of exposure.



The decline in the association between stressful life events and risk for major depression as the number of previous depressive episodes increased was strongest in those at low genetic risk and was weak to absent in those at high genetic risk. In the absence of previous depressive episodes, those at high genetic risk frequently experienced depressive episodes without major environmental stressors.



Genetic risk factors for depression produce a "prekindling" effect rather than increase the speed of kindling. The "kindled" state, wherein depressive episodes occur with little provocation, may be reached by two pathways: many previous depressive episodes, perhaps driven by multiple adversities, and high genetic risk.
Tamper Hiding: Defeating Image Forensics

This paper introduces novel hiding techniques to counter the detection of image manipulations through forensic analyses. The presented techniques allow to resize and rotate (parts of) bitmap images without leaving a periodic pattern in the local linear predictor coefficients, which has been exploited by prior art to detect traces of manipulation. A quantitative evaluation on a batch of test images proves the proposed method’s efficacy, while controlling for key parameters and for the retained image quality compared to conventional linear interpolation.
An efficient transaction processing method on the distributed database

Distributed system have shown its good robustness, extensibility and effectiveness in the processing, storage and transmission of large data. Now with the expansion of the amount of data and application, it has become a challenging task to ensure its transaction in a distributed and heterogeneous environment. The sub transactions of a distributed transaction not only need to be coordinated with the local other transactions, but also with other sub transactions that generated in the global manager. Based on the discussion of distributed transaction processing model and its transaction commit protocol, the failure reasons with the model analysis of general distributed transaction processing in the practical application in our information database system are given, based on the interface in a relational database management system and super text pre-treatment language of distributed transaction processing implementation method. The research results show that this distributed transaction processing method is reliable, and can simplify the implementation of global program.