nils-herrmann/clean_scirepeval huggingface dataset:
TensorLog: A Differentiable Deductive Database

Large knowledge bases (KBs) are useful in many tasks, but it is unclear how to integrate this sort of knowledge into “deep” gradient-based learning systems. To address this problem, we describe a probabilistic deductive database, called TensorLog, in which reasoning uses a differentiable process. In TensorLog, each clause in a logical theory is first converted into certain type of factor graph. Then, for each type of query to the factor graph, the message-passing steps required to perform belief propagation (BP) are “unrolled” into a function, which is differentiable. We show that these functions can be composed recursively to perform inference in non-trivial logical theories containing multiple interrelated clauses and predicates. Both compilation and inference in TensorLog are efficient: compilation is linear in theory size and proof depth, and inference is linear in database size and the number of message-passing steps used in BP. We also present experimental results with TensorLog and discuss its relationship to other first-order probabilistic logics.
Facile one-pot solvothermal method to synthesize sheet-on-sheet reduced graphene oxide (RGO)/ZnIn2S4 nanocomposites with superior photocatalytic performance.

Highly reductive RGO (reduced graphene oxide)/ZnIn2S4 nanocomposites with a sheet-on-sheet morphology have been prepared via a facile one-pot solvothermal method in a mixture of N,N-dimethylformamide (DMF) and ethylene glycol (EG) as solvent. A reduction of GO (graphene oxide) to RGO and the formation of ZnIn2S4 nanosheets on highly reductive RGO has been simultaneously achieved. The effect of the solvents on the morphology of final products has been investigated and the formation mechanism was proposed. The as-prepared RGO/ZnIn2S4 nanoscomposites were characterized by powder X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), N2-adsorption BET surface area, UV-vis diffuse reflectance spectroscopy (DRS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM). The photocatalytic activity for hydrogen evolution under visible light irradiations over the as-prepared RGO/ZnIn2S4 nanocomposites has been investigated. The as-prepared RGO/ZnIn2S4 nanocomposites show enhanced photocatalytic activity for hydrogen evolution under visible light irradiations and an optimum photocatalytic activity is observed over 1.0 wt % RGO incorporated ZnIn2S4 nanocomposite. The superior photocatalytic performance observed over RGO/ZnIn2S4 nanocomposites can be ascribed to the existence of highly reductive RGO which has strong interactions with ZnIn2S4 nanosheets. The existence of the strong interaction between ZnIn2S4 nanosheets and RGO in the nancomposites facilitates the electron transfer from ZnIn2S4 to RGO, with the latter serving as a good electron acceptor, mediator as well as the co-catalyst for hydrogen evolution. This study can provide some guidance for us in the developing of RGO-incorporated nanocomposite photocatalysts.
Histological assessment of the palatal mucosa and greater palatine artery with reference to subepithelial connective tissue grafting

This study aimed to measure the thickness of the epithelium and lamina propria of the palatal mucosa and to elucidate the location of the greater palatine artery to provide the anatomical basis for subepithelial connective tissue grafting. Thirty-two maxillary specimens, taken from the canine distal area to the first molar distal area, were embedded in paraffin and stained with hematoxylin-eosin. The thickness of the epithelium and lamina propria of the palatal mucosa was measured at three positions on these specimens, starting from 3 mm below the alveolar crest and in 3-mm intervals. The location of the greater palatine artery was evaluated by using image-processing software. The mean epithelial thickness decreased significantly in the posterior teeth; it was 0.41, 0.36, 0.32, and 0.30 mm in the canine, first premolar, second premolar, and first molar distal areas, respectively. The lamina propria was significantly thicker in the canine distal; it was 1.36, 1.08, 1.09, and 1.05 mm, respectively. The mean length from the alveolar crest to the greater palatine artery increased toward the posterior molar; it was 7.76, 9.21, 10.93, and 11.28 mm, respectively. The mean depth from the surface of the palatal mucosa to the greater palatine artery decreased from the canine distal to the first premolar distal but increased again toward the posterior molar; it was 3.97, 3.09, 3.58, and 5.50 mm, respectively. Detailed histological assessments of the lamina propria of the palatal mucosa and the greater palatine artery are expected to provide useful anatomical guidelines for subepithelial connective tissue grafting.
Learning Probabilistic Models of Link Structure

Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with “flat” data representations, forcing us to convert our data into a form that loses much of the link structure. The recently introduced framework of probabilistic relational models(PRMs) embraces the object-relational nature of structured data by capturing probabilistic interactions between attributes of related entities. In this paper, we extend this framework by modeling interactions between the attributes and the link structure itself. An advantage of our approach is a unified generative model for both content and relational structure. We propose two mechanisms for representing a probabilistic distribution over link structures: reference uncertaintyandexistence uncertainty . We describe the appropriate conditions for using each model and present learning algorithms for each. We present experimental results showing that the learned models can be used to predict link structure and, moreover, the observed link structure can be used to provide better predictions for the attributes in the model.
Building citizen trust through e-government

The trust of citizens in their governments has gradually eroded. One response by several North American governments has been to introduce e-government, or Web-mediated citizen-to-government interaction. This paper tests the extent to which online initiatives have succeeded in increasing trust and external political efficacy in voters. An Internet-based survey of 182 Canadian voters shows that using the Internet to transact with government has a significantly positive impact on trust and external political efficacy. Interestingly, though the quality of the interaction is important, it is secondary to internal political efficacy in determining trust levels, and not significant in determining levels of external political efficacy (or perceived government responsiveness). For policy-makers, this suggests e-government efforts might be better-aimed at citizens with high pre-extant levels of trust, rather than in developing better Web sites. For researchers, this paper introduces political efficacy as an important determinant of trust as it pertains to e-government.
Automatic classification of irregularly sampled time series with unequal lengths: A case study on estimated glomerular filtration rate

A patient's estimated glomerular filtration rate (eGFR) can provide important information about disease progression and kidney function. Traditionally, an eGFR time series is interpreted by a human expert labelling it as stable or unstable. While this approach works for individual patients, the time consuming nature of it precludes the quick evaluation of risk in large numbers of patients. However, automating this process poses significant challenges as eGFR measurements are usually recorded at irregular intervals and the series of measurements differs in length between patients. Here we present a two-tier system to automatically classify an eGFR trend. First, we model the time series using Gaussian process regression (GPR) to fill in `gaps' by resampling a fixed size vector of fifty time-dependent observations. Second, we classify the resampled eGFR time series using a K-NN/SVM classifier, and evaluate its performance via 5-fold cross validation. Using this approach we achieved an F-score of 0.90, compared to 0.96 for 5 human experts when scored amongst themselves.
Accurate vertical road profile estimation using v-disparity map and dynamic programming

Detecting obstacles on the road is crucial for the advanced driver assistance systems. Obstacle detection on the road can be greatly facilitated if we have a vertical road profile. Therefore, in this paper, we present a novel method that can estimate an accurate vertical road profile of the scene from the stereo images. Unlike conventional stereo-based road profile estimation methods that heavily rely on a parametric model of the road surface, our method can obtain a road profile for an arbitrary complicated road. To this end, an energy function that includes the stereo matching fidelity and spatio-temporal smoothness of the road profile is presented, and thus the road profile is extracted by maximizing the energy function via dynamic programming. The experimental results demonstrate the effectiveness of the proposed method.
Combining space exploration and heuristic search in online motion planning for nonholonomic vehicles

This paper presents an efficient motion planning method for nonholonomic vehicles, which combines space exploration and heuristic search to achieve online performance. The space exploration employs simple geometric shapes to investigate the collision-free space for the dimension and topology information. Then, the heuristic search is guided by this knowledge to generate vehicle motions under kinodynamic constraints. The overall performance of this framework greatly benefits from the cooperation of these two simple generic algorithms in suitable domains, which sequentially handles the free-space information and kinodynamic constraints. Experimental results show that this method is able to generate motions for nonholonomic vehicles in a time frame of less than 100 milliseconds for the given problem settings. The contribution of this work is the development of a Space Exploration Guided Heuristic Search with a circle-path based heuristics and adaptable search step size. The approach is grid-free and able to plan nonholonomic vehicle motions under kinodynamic constraints.
'Strong is the new skinny': A content analysis of #fitspiration images on Instagram.

'Fitspiration' is an online trend designed to inspire viewers towards a healthier lifestyle by promoting exercise and healthy food. This study provides a content analysis of fitspiration imagery on the social networking site Instagram. A set of 600 images were coded for body type, activity, objectification and textual elements. Results showed that the majority of images of women contained only one body type: thin and toned. In addition, most images contained objectifying elements. Accordingly, while fitspiration images may be inspirational for viewers, they also contain a number of elements likely to have negative effects on the viewer's body image.
High-Efficiency Slim Adapter With Low-Profile Transformer Structure

This paper presents the implementation of a high-efficiency slim adapter using an resonant converter. A new structure of the slim-type transformer is proposed, which is composed of copper wire as the primary winding and printed-circuit-board winding on the outer layer as the secondary winding. The proposed structure is suitable for a slim and high-efficiency converter because it has advantages of easy utilization and wide conductive cross-sectional area. In addition, the voltage-doubler rectifier is applied to the secondary side due to its simple structure of secondary winding, and a filter is adopted to reduce the output filter size. The validity of this study is confirmed by the experimental results from an 85-W prototype.
Orestes: A scalable Database-as-a-Service architecture for low latency

Today, the applicability of database systems in cloud environments is considerably restricted because of three major problems: I) high network latencies for remote/mobile clients, II) lack of elastic horizontal scalability mechanisms, and III) missing abstraction of storage and data models. In this paper, we propose an architecture, a REST/HTTP protocol and a set of algorithms to solve these problems through a Database-as-a-Service middleware called Orestes (Objects RESTfully Encapsulated in Standard Formats). Orestes exposes cloud-hosted NoSQL database systems through a scalable tier of REST servers. These provide database-independent, object-oriented schema design, a client-independent REST-API for database operations, globally distributed caching, cache consistency mechanisms and optimistic ACID transactions. By comparative evaluations we offer empirical evidence that the proposed Database-as-a-Service architecture indeed solves common latency, scalability and abstraction problems encountered in modern cloud-based applications.
Extensions of recurrent neural network language model

We present several modifications of the original recurrent neural network language model (RNN LM).While this model has been shown to significantly outperform many competitive language modeling techniques in terms of accuracy, the remaining problem is the computational complexity. In this work, we show approaches that lead to more than 15 times speedup for both training and testing phases. Next, we show importance of using a backpropagation through time algorithm. An empirical comparison with feedforward networks is also provided. In the end, we discuss possibilities how to reduce the amount of parameters in the model. The resulting RNN model can thus be smaller, faster both during training and testing, and more accurate than the basic one.
Call graph construction for Java libraries

Today, every application uses software libraries. Yet, while a lot of research exists w.r.t. analyzing applications, research that targets the analysis of libraries independent of any application is scarce. This is unfortunate, because, for developers of libraries, such as the Java Development Kit (JDK), it is crucial to ensure that the library behaves as intended regardless of how it is used. To fill this gap, we discuss the construction of call graphs for libraries that abstract over all potential library usages. Call graphs are particularly relevant as they are a precursor of many advanced analyses, such as inter-procedural data-flow analyses. 
 We show that the current practice of using call graph algorithms designed for applications to analyze libraries leads to call graphs that, at the same time, lack relevant call edges and contain unnecessary edges. This motivates the need for call graph construction algorithms dedicated to libraries. Unlike algorithms for applications, call graph construction algorithms for libraries must take into consideration the goals of subsequent analyses. Specifically, we show that it is essential to distinguish between the scenario of an analysis for potential exploitable vulnerabilities from the scenario of an analysis for general software quality attributes, e.g., dead methods or unused fields. This distinction affects the decision about what constitutes the library-private implementation, which therefore, needs special treatment. Thus, building one call graph that satisfies all needs is not sensical. Overall, we observed that the proposed call graph algorithms reduce the number of call edges up to 30% when compared to existing approaches.
Face Recognition Using Kernel Ridge Regression

In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem efface recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisher faces and Laplacian faces) and also performs comparably with the recently developed Orthogonal Laplacian faces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.
Sampling Large Databases for Association Rules

Discovery of association rules is an important database mining problem. Current algorithms for nding association rules require several passes over the analyzed database, and obviously the role of I/O overhead is very signi cant for very large databases. We present new algorithms that reduce the database activity considerably. The idea is to pick a random sample, to nd using this sample all association rules that probably hold in the whole database, and then to verify the results with the rest of the database. The algorithms thus produce exact association rules, not approximations based on a sample. The approach is, however, probabilistic, and in those rare cases where our sampling method does not produce all association rules, the missing rules can be found in a second pass. Our experiments show that the proposed algorithms can nd association rules very e ciently in only one database pass.
Methods for Pretreatment of Lignocellulosic Biomass for Efficient Hydrolysis and Biofuel Production

Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Review Methods for Pretreatment of Lignocellulosic Biomass for Efficient Hydrolysis and Biofuel Production Parveen Kumar, Diane M. Barrett, Michael J. Delwiche, and Pieter Stroeve Ind.   Downloaded from http://pubs.acs.org on March 26, 2009
An Original Classification of Rehabilitation Hand Exoskeletons

The hand is an organ of grasping as well as sensation, communication, and fine dexterity. Since the 80’s, many researchers have been attempting to develop robotic devices aiming at replicating the functions of the human hand in the fields of industrial robotics, tele-manipulation, humanoid robotics, and upper limb prosthetics. A special kind of robotic hand is the hand exoskeleton, that is directly attached to the human hand with the aim of providing assistance in motion/power generation. Hand exoskeletons are increasingly widespread in robot-based rehabilitation of patients suffering from different pathologies (in particular neurological diseases). This paper reviews the state-of-the-art of hand exoskeletons developed for rehabilitation purposes and proposes a new systematic classification according to three key points related to the kinematic architecture: (i) mobility of a single finger exoskeleton, (ii) number of physical connections between the exoskeleton and the human finger phalanges, and (iii) way of integration of the exoskeleton mechanism with the human parts. The discussion based upon the classification can be helpful to understand the reasons of adopting certain solutions for specific applications and the advantages and drawbacks of different designs, based on the work already done by other researchers. The final purpose of the proposed classification is then to provide guidelines useful for the design of new hand exoskeletons on the basis of a systematic analysis. As an example, the solution designed, manufactured and clinically tested by the authors is reported.
Taming Subgraph Isomorphism for RDF Query Processing

RDF data are used to model knowledge in various areas such as l ife sciences, Semantic Web, bioinformatics, and social graphs . The size of real RDF data reaches billions of triples. This calls for a framework for efficiently processing RDF data. The core func tion of processing RDF data is subgraph pattern matching. There h av been two completely different directions for supporting ef ficient subgraph pattern matching. One direction is to develop spec ialized RDF query processing engines exploiting the properties of R DF data for the last decade, while the other direction is to deve lop fficient subgraph isomorphism algorithms for general, labele d graphs for over 30 years. Although both directions have a similar go al (i.e., finding subgraphs in data graphs for a given query grap h), they have been independently researched without clear reas on. We argue that a subgraph isomorphism algorithm can be easily mo dified to handle the graph homomorphism, which is the RDF patter n matching semantics, by just removing the injectivity const raint. In this paper, based on the state-of-the-art subgraph isomorp hism algorithm, we propose an in-memory solution, TurboHOM++, which is tamed for the RDF processing, and we compare it with the rep resentative RDF processing engines for several RDF benchmark s in a server machine where billions of triples can be loaded in mem ory. In order to speed upTurboHOM++, we also provide a simple yet effective transformation and a series of optimization tech niques. Extensive experiments using several RDF benchmarks show th at TurboHOM++ consistently and significantly outperforms the representative RDF engines. Specifically, TurboHOM++ outperforms its competitors by up tofiveorders of magnitude.
Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering

Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i.e., intrasubspace data points). Recent works achieve good performance by modeling errors into their objective functions to remove the errors from the inputs. However, these approaches face the limitations that the structure of errors should be known prior and a complex convex problem must be solved. In this paper, we present a novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space. We first prove that <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\ell }_{\boldsymbol {1}}$ </tex-math></inline-formula>-, <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\ell }_{\boldsymbol {2}}$ </tex-math></inline-formula>-, <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\ell }_{\boldsymbol {\infty }}$ </tex-math></inline-formula>-, and nuclear-norm-based linear projection spaces share the property of intrasubspace projection dominance, i.e., the coefficients over intrasubspace data points are larger than those over intersubspace data points. Based on this property, we introduce a method to construct a sparse similarity graph, called L2-graph. The subspace clustering and subspace learning algorithms are developed upon L2-graph. We conduct comprehensive experiment on subspace learning, image clustering, and motion segmentation and consider several quantitative benchmarks classification/clustering accuracy, normalized mutual information, and running time. Results show that L2-graph outperforms many state-of-the-art methods in our experiments, including L1-graph, low rank representation (LRR), and latent LRR, least square regression, sparse subspace clustering, and locally linear representation.