nils-herrmann/clean_scirepeval huggingface dataset:
Place navigation impaired in rats with hippocampal lesions

Electrophysiological studies have shown that single cells in the hippocampus respond during spatial learning and exploration1–4, some firing only when animals enter specific and restricted areas of a familiar environment. Deficits in spatial learning and memory are found after lesions of the hippocampus and its extrinsic fibre connections5,6 following damage to the medial septal nucleus which successfully disrupts the hippocampal theta rhythm7, and in senescent rats which also show a correlated reduction in synaptic enhancement on the perforant path input to the hippocampus8. We now report, using a novel behavioural procedure requiring search for a hidden goal, that, in addition to a spatial discrimination impairment, total hippocampal lesions also cause a profound and lasting placenavigational impairment that can be dissociated from correlated motor, motivational and reinforcement aspects of the procedure.
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative neural networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feedforward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic texture, or photos to artistic paintings. With adversarial training, we obtain quality comparable to recent neural texture synthesis methods. As no optimization is required any longer at generation time, our run-time performance (0.25M pixel images at 25Hz) surpasses previous neural texture synthesizers by a significant margin (at least 500 times faster). We apply this idea to texture synthesis, style transfer, and video stylization.
Audience Involvement in Advertising : Four Levels

The effectiveness of advertising messages is widely believed to be moderated by audience involvement. In this paper, psychological theories of attention and levels of processing are used to establish a framework that can accommodate the major consumer behavior theories of audience involvement. Four levels of involvement are identified (in order from low to high) as preattention, focal attention, comprehension, and elaboration. These levels allocate increasing attentional capacity to a message source, as needed for analysis of the message by increasingly abstract-and qualitatively distinct-representational systems. Lower levels use relatively little capacity and extract information needed to determine whether higher levels will be invoked. The higher levels require greater capacity and result in increasingly durable cognitive and attitudinal effects.
BakerSFIeld : Bringing software fault isolation to x 64

We present BakerSFIeld, a Software Fault Isolation (SFI) framework for the x64 instruction set architecture. BakerSFIeld is derived from PittSFIeld, and retains the assembly language rewriting style of sandboxing, but has been substantially re-architected for greater clarity, separating the concerns of rewriting the program for memory safety, and re-aligning the code for control-flow safety. BakerSFIeld also corrects several significant security flaws we found in PittSFIeld. We measure the performance of BakerSFIeld on a modern 64-bit desktop machine and determine that it has significantly lower overhead than its 32-bit cousin.
Iterative Synchronization: EM Algorithm Versus Newton-Raphson Method

This paper deals with iterative maximum-likelihood synchronization of a scalar parameter. An efficient implementation of the Newton-Raphson (NR) maximum-search method is proposed. Considering the latter implementation, the NR approach is shown to be an attractive alternative to synchronization methods based on the expectation-maximization (EM) algorithm. Simulation results for the case of phase-offset synchronization show that NR method usually increases the speed of convergence of the synchronization algorithm
Fabrication of multi-layer SU-8 microstructures

The fabrication of multi-level SU-8 microstructures using multiple coating and exposure steps and a single developing step has been achieved for up to six layers of SU-8. Alternating layers of SU-8 2010 (thin) and SU-8 2100 (thick) photoresist films were spin coated, followed by soft-bake, ultraviolet (UV) exposure and post-exposure bake steps. The multiple SU-8 layers were simultaneously developed to create patterned microstructures with overall thicknesses of up to 500 μm and minimum lateral feature size of 10 μm. The use of a single developing step facilitated fabrication of complex multi-level SU-8 microstructures that might be difficult, or even impossible, to achieve by sequential processing of multiple SU-8 layers that are individually coated, baked, exposed and developed.
Extremely randomized trees

This paper proposes a new tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while splitting a tree node. In the extreme case, it builds totally randomized trees whose structures are independent of the output values of the learning sample. The strength of the randomization can be tuned to problem specifics by the appropriate choice of a parameter. We evaluate the robustness of the default choice of this parameter, and we also provide insight on how to adjust it in particular situations. Besides accuracy, the main strength of the resulting algorithm is computational efficiency. A bias/variance analysis of the Extra-Trees algorithm is also provided as well as a geometrical and a kernel characterization of the models induced.
Usage of SCRUM Practices within a Global Company

Global companies that experimented extensive waterfall phased plans are trying to improve their existing processes to expedite team engagement. SCRUM has become an acceptable path to follow for those companies because it comprises project management as part of its practices. SCRUM has been used with the objective of simplifying project control through simple processes, easy to update documentation and higher team iteration over exhaustive documentation. Instead of investing team effort on producing static documentation, SCRUM proposes to focus on team continuous improvement aiming to add value to business processes. The purpose of this industry report is to describe two projects that experimented SCRUM practices within a globally distributed company. This company has development centers across North America, South America and Asia. This report covers challenges faced by the project teams, strengths and practical recommendations of using SCRUM in a globally distributed environment.
EARTh: An Environmental Application Reference Thesaurus in the Linked Open Data cloud

The paper aims at providing a description of EARTh, the Environmental Application Reference Thesaurus. It represents a general-purpose thesaurus for the environment, which has been published as a SKOS dataset in the Linked Open Data cloud. It promises to become a core tool for indexing and discovery environmental resources by refining and extending GEMET, which is considered the de facto standard when speaking of general-purpose thesaurus for the environment in Europe, besides it has been interlinked to popular LOD datasets as AGROVOC, EUROVOC, DBPEDIA and UMTHES. The paper illustrates the main characteristics of EARTh as a guide to its usage. It clarifies (i) the methodology adopted to define the EARTh content; (ii) the design and technological choices made when publishing EARTh as Linked Data; (iii) the information pertaining to its access and maintenance. Descriptions of EARTh applications and future relevance are highlighted. CNR-IIA-EKOLab Environmental Knowledge Organization Laboratory, Area della Ricerca di Roma 1, Via Salaria Km 29,300 C.P. 10,I-00016 Monterotondo stazione RM, {difranco, vds, plini} @iia.cnr.it
The Causal Foundations of Structural Equation Modeling

The role of causality in SEM research is widely perceived to be, on the one hand, of pivotal methodological importance and, on the other hand, confusing, enigmatic and controversial. The confusion is vividly portrayed, for example, in the influential report of Wilkinson and Task Force’s (1999) on “Statistical Methods  Guidelines and Explanations.” In discussing SEM, the report starts with the usual warning: “It is sometimes thought that correlation does not prove causation but ‘causal modeling’ does. [Wrong! There are] dangers in this practice.” But then ends with a startling conclusion: “The use of complicated causal-modeling software [read SEM] rarely yields any results that have any interpretation as causal effects.” The implication being that the entire enterprise of causal modeling, from Sewell Wright (1921) to Blalock (1964) and Duncan (1975), the entire literature in econometric research, including modern advances in graphical and nonparametric structural models have all been misguided, for they have been chasing parameters that have no causal interpretation. The motives for such overstatements notwithstanding, readers may rightly ask: “If SEM methods do not ‘prove’ causation, how can they yield results that have causal interpretation?” Put another way, if the structural coefficients that SEM researchers labor to estimate can legitimately be interpreted as causal effects then, unless these parameters are grossly misestimated, why deny SEM researchers the honor of “establishing causation” or at least of deriving some useful claims about causation. The answer is that a huge logical gap exists between “establishing causation,” which requires careful manipulative experiments, and “interpreting parameters as causal effects,” which may be based on firm scientific knowledge or on previously conducted experiments, perhaps by other researchers. One can legitimately be in a possession of a parameter that stands for a causal effect and still be unable, using statistical means alone, to determine the magnitude of that parameter given non-experimental data. As a matter of fact, we know that
Riemann's Hypothesis and Tests for Primality

The purpose of this paper is to present new upper bounds on the complexity of algorithms for testing the primality of a number. The first upper bound is 0(n&frac17;); it improves the previously best known bound of 0(n¼) due to Pollard [11].
 The second upper bound is dependent on the Extended Riemann Hypothesis (ERH): assuming ERH, we produce an algorithm which tests primality and runs in time 0((log n)4) steps. Thus we show that primality is testable in time a polynomial in the length of the binary representation of a number.
 Finally, we give a partial solution to the relationship between the complexity of computing the prime factorization of a number, computing the Euler phi function, and computing other related functions.
BiDirectional optical communication with AquaOptical II

This paper describes AquaOptical II, a bidirectional, high data-rate, long-range, underwater optical communication system. The system uses the software radio principle. Each AquaOptical II modem can be programmed to transmit user defined waveforms and record the received waveforms for detailed analysis. This allows for the use of many different modulation schemes. We describe the hardware and software architecture we developed for these goals. We demonstrate bidirectional communication between two AquaOptical II modems in a pool experiment. During the experiment AquaOptical II achieved a signal to noise ration of 5.1 over a transmission distance of 50 m at pulse widths of 1 µsec, 500 ns, and 250 ns. When using discrete pulse interval modulation (DPIM) this corresponds to a bit-rate of 0.57 Mbit/s, 1.14 Mbit/s, and 2.28 Mbit/s.
An energy-efficient dual sampling SAR ADC with reduced capacitive DAC

This paper presents an energy-efficient SAR ADC which adopts reduced MSB cycling step with dual sampling of the analog signal. By sampling and holding the analog signal asymmetrically at both input sides of comparator, the MSB cycling step can be hidden by hold mode. Benefits from this technique, not only the total capacitance of DAC is reduced by half, but also the average switching energy is reduced by 68% compared with conventional SAR ADC. Moreover, switching energy distribution is more uniform over entire output code compared with previous works.
IT capability and organizational performance: the roles of business process agility and environmental factors

School of Business Administration, Southwestern University of Finance and Economics, Sichuan, P. R. of China; Business School, Shantou University, Guangdong, P. R. of China; Information Technology Management, School of Business, University at Albany, Albany, NY, U.S.A; Department of Management, Hong Kong University of Science and Technology, Hong Kong, P.R. China; Department of Finance and Decision Sciences, School of Business, Hong Kong Baptist University, Hong Kong, P.R. China
A Vision Enriched Intelligent Agent with Image Description Generation: (Demonstration)

In this paper, we present an intelligent conversational agent enriched with automatic image understanding and facial expression recognition using state-of-the-art machine learning techniques for the advancement of autonomous interaction with the elderly or infirm. The agent is developed to conduct health and emotion well-being monitoring for the elderly. It is not only capable of conducting question-answering via speech-based interaction, but also able to provide analysis of the user’s surroundings, emotional states, hazards and fall actions via visual data. The agent is accessible from a web browser and can be communicated with via voice or text means, with a webcam required for the visual analysis functionality. The system has been evaluated with diverse real-life images to prove its efficiency.
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
Searching images with MPEG-7 (& MPEG-7-like) Powered Localized dEscriptors: The SIMPLE answer to effective Content Based Image Retrieval

In this paper we propose and evaluate a new technique that localizes the description ability of the well established MPEG-7 and MPEG-7-like global descriptors. We employ the SURF detector to define salient image patches of blob-like textures and use the MPEG-7 Scalable Color (SC), Color Layout (CL) and Edge Histogram (EH) descriptors and the global MPEG-7-like Color and Edge Directivity Descriptor (CEDD), to produce the final local features' vectors. In order to test the new descriptors in the most straightforward fashion, we use the Bag-Of-Visual-Words framework for indexing and retrieval. The experimental results conducted on two different benchmark databases with varying codebook sizes, revealed an astonishing boost in the retrieval performance of the proposed descriptors compared both to their own performance (in their original form) and to other state-of-the-art methods of local and global descriptors. Open-source implementation of the proposed descriptors is available in c#, Java and MATLAB.
Electric Motor Drive Selection Issues for HEV Propulsion Systems: A Comparative Study

This paper describes a comparative study allowing the selection of the most appropriate electric-propulsion system for a parallel hybrid electric vehicle (HEV). This paper is based on an exhaustive review of the state of the art and on an effective comparison of the performances of the four main electric-propulsion systems, namely the dc motor, the induction motor (IM), the permanent magnet synchronous motor, and the switched reluctance motor. The main conclusion drawn by the proposed comparative study is that it is the cage IM that better fulfills the major requirements of the HEV electric propulsion
Stochastic volatility models and the pricing of VIX options

In this paper we examine and compare the performance of a variety of continuoustime volatility models in their ability to capture the behaviour of the VIX. The `3/2model' with a di®usion structure which allows the volatility of volatility changes to be highly sensitive to the actual level of volatility is found to outperform all other popular models tested. Analytic solutions for option prices on the VIX under the 3/2model are developed and then used to calibrate at-the-money market option prices.
Estimating Android applications' CPU energy usage via bytecode profiling

Optimizing the energy efficiency of mobile applications can greatly increase user satisfaction. However, developers lack easily applied tools for estimating the energy consumption of their applications. This paper proposes a new approach, eCalc, that is lightweight in terms of its developer requirements and provides code-level estimates of energy consumption. The approach achieves this using estimation techniques based on program analysis of the mobile application. In evaluation, eCalc is able to estimate energy consumption within 9.5% of the ground truth for a set of mobile applications. Additionally, eCalc provides useful and meaningful feedback to the developer that helps to characterize energy consumption of the application.
Time Series Featurization via Topological Data Analysis: an Application to Cryptocurrency Trend Forecasting

We propose a novel methodology for feature extraction from time series data based on topological data analysis. The proposed procedure applies a dimensionality reduction technique via principal component analysis to the point cloud of the Takens’ embedding from the observed time series and then evaluates the persistence landscape and silhouettes based on the corresponding Rips complex. We define a new notion of Rips distance function that is especially suited for persistence homologies built on Rips complexes and prove stability theorems for it. We use these results to demonstrate in turn some stability properties of the topological features extracted using our procedure with respect to additive noise and sampling. We further apply our method to the problem of trend forecasting for cryptocurrency prices, where we manage to achieve significantly lower error rates than more standard, non TDA-based methodologies in complex pattern classification tasks. We expect our method to provide a new insight on feature engineering for granular, noisy time series data.
VR-STEP: Walking-in-Place using Inertial Sensing for Hands Free Navigation in Mobile VR Environments

Low-cost smartphone adapters can bring virtual reality to the masses, but input is typically limited to using head tracking, which makes it difficult to perform complex tasks like navigation. Walking-in-place (WIP) offers a natural and immersive form of virtual locomotion that can reduce simulation sickness. WIP, however, is difficult to implement in mobile contexts as it typically relies on bulky controllers or an external camera. We present VR-STEP; a WIP implementation that uses real-time pedometry to implement virtual locomotion. VR-STEP requires no additional instrumentation outside of a smartphone's inertial sensors. A user study with 18 users compares VR-STEP with a commonly used auto-walk navigation method and finds no significant difference in performance or reliability, though VR-STEP was found to be more immersive and intuitive.
Real-time RGB-D based template matching pedestrian detection

Pedestrian detection is one of the most popular topics in computer vision and robotics. Considering challenging issues in multiple pedestrian detection, we present a real-time depth-based template matching people detector. In this paper, we propose different approaches for training the depth-based template. We train multiple templates for handling issues due to various upper-body orientations of the pedestrians and different levels of detail in depth-map of the pedestrians with various distances from the camera. And, we take into account the degree of reliability for different regions of sliding window by proposing the weighted template approach. Furthermore, we combine the depth-detector with an appearance based detector as a verifier to take advantage of the appearance cues for dealing with the limitations of depth data. We evaluate our method on the challenging ETH dataset sequence. We show that our method outperforms the state-of-the-art approaches.
A chebyshev semi-iterative approach for accelerating projective and position-based dynamics

In this paper, we study the use of the Chebyshev semi-iterative approach in projective and position-based dynamics. Although projective dynamics is fundamentally nonlinear, its convergence behavior is similar to that of an iterative method solving a linear system. Because of that, we can estimate the "spectral radius" and use it in the Chebyshev approach to accelerate the convergence by at least one order of magnitude, when the global step is handled by the direct solver, the Jacobi solver, or even the Gauss-Seidel solver. Our experiment shows that the combination of the Chebyshev approach and the direct solver runs fastest on CPU, while the combination of the Chebyshev approach and the Jacobi solver outperforms any other combination on GPU, as it is highly compatible with parallel computing. Our experiment further shows position-based dynamics can be accelerated by the Chebyshev approach as well, although the effect is less obvious for tetrahedral meshes. The whole approach is simple, fast, effective, GPU-friendly, and has a small memory cost.
A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains

Voxel-based-morphometry (VBM) is a whole-brain, unbiased technique for characterizing regional cerebral volume and tissue concentration differences in structural magnetic resonance images. We describe an optimized method of VBM to examine the effects of age on grey and white matter and CSF in 465 normal adults. Global grey matter volume decreased linearly with age, with a significantly steeper decline in males. Local areas of accelerated loss were observed bilaterally in the insula, superior parietal gyri, central sulci, and cingulate sulci. Areas exhibiting little or no age effect (relative preservation) were noted in the amygdala, hippocampi, and entorhinal cortex. Global white matter did not decline with age, but local areas of relative accelerated loss and preservation were seen. There was no interaction of age with sex for regionally specific effects. These results corroborate previous reports and indicate that VBM is a useful technique for studying structural brain correlates of ageing through life in humans.