nils-herrmann/clean_scirepeval huggingface dataset:
Survey of Texture Mapping

Texture mapping is one of the most successful new techniques in high-quality image synthesis. It can enchance the visual richness of raster-scan images immensely while entailing only a relatively smann increase in computation. The technique has been applied to a number of surface attributes: surface color, surface normal, specularity, transparency, illumination, and surface displacement¿to name a few. Although the list is potentially endless, the techniques of texture mapping are essentially the same in all cases. This article surveys the fundamentals of texture mapping, which can be spilt into two topics: the geometric mapping that warps a texture onto a surface, and the filtering necessary to avoid aliasing. An extensive bibliography is included.
Organic light-emitting diodes on solution-processed graphene transparent electrodes.

Theoretical estimates indicate that graphene thin films can be used as transparent electrodes for thin-film devices such as solar cells and organic light-emitting diodes, with an unmatched combination of sheet resistance and transparency. We demonstrate organic light-emitting diodes with solution-processed graphene thin film transparent conductive anodes. The graphene electrodes were deposited on quartz substrates by spin-coating of an aqueous dispersion of functionalized graphene, followed by a vacuum anneal step to reduce the sheet resistance. Small molecular weight organic materials and a metal cathode were directly deposited on the graphene anodes, resulting in devices with a performance comparable to control devices on indium-tin-oxide transparent anodes. The outcoupling efficiency of devices on graphene and indium-tin-oxide is nearly identical, in agreement with model predictions.
High frequency active-clamp buck converter for low power automotive applications

Low power (5 W-25 W) automotive DC-DC converters have a very wide input voltage range from 4.5V to 42V, and it is usually operated at high switching frequency in order to comply with the strict CISPR-25 Standard for EMI performance. The conventional buck converter is currently employed for such applications owing to its simplicity and low cost, but it has low efficiency at high switching frequencies and high EMI emission due to hard switching. To solve these issues, an active-clamp buck converter is proposed in this paper, which features zero-voltage-switching (ZVS) and hence high efficiency and low EMI emission over a wide input voltage range, making it suitable for low power automotive applications. The operating principles including ZVS mechanism, detailed design considerations and experimental results from a 1 MHz prototype are presented.
New measures for assessing alcohol and drug abuse with the MMPI-2: The APS and AAS.

We introduce two new scales for assessing substance abuse problems with the Minnesota Multiphasic Personality Inventory-2 (MMPI-2): the Addiction Potential Scale (APS), a 39-item empirically derived scale, developed by contrasting the responses of a large residential substance abuse sample with responses from both normative and psychiatric control groups; and the Addiction Acknowledgement Scale (AAS), a 13-item face-valid scale, constructed rationally and with attention to internal consistency. Both new scales are shown to discriminate well between groups and substantially better than other selected substance abuse scales. Covariation between the scales and joint effectiveness are examined. Finally, limitations for their practical utility are expressed, and considerations for future research are identified.
Soft 3D reconstruction for view synthesis

We present a novel algorithm for view synthesis that utilizes a soft 3D reconstruction to improve quality, continuity and robustness. Our main contribution is the formulation of a soft 3D representation that preserves depth uncertainty through each stage of 3D reconstruction and rendering. We show that this representation is beneficial throughout the view synthesis pipeline. During view synthesis, it provides a soft model of scene geometry that provides continuity across synthesized views and robustness to depth uncertainty. During 3D reconstruction, the same robust estimates of scene visibility can be applied iteratively to improve depth estimation around object edges. Our algorithm is based entirely on O(1) filters, making it conducive to acceleration and it works with structured or unstructured sets of input views. We compare with recent classical and learning-based algorithms on plenoptic lightfields, wide baseline captures, and lightfield videos produced from camera arrays.
Event threading within news topics

With the overwhelming volume of online news available today, there is an increasing need for automatic techniques to analyze and present news to the user in a meaningful and efficient manner. Previous research focused only on organizing news stories by their topics into a flat hierarchy. We believe viewing a news topic as a flat collection of stories is too restrictive and inefficient for a user to understand the topic quickly. 
 In this work, we attempt to capture the rich structure of events and their dependencies in a news topic through our event models. We call the process of recognizing events and their dependencies <i>event threading</i>. We believe our perspective of modeling the structure of a topic is more effective in capturing its semantics than a flat list of on-topic stories.
 We formally define the novel problem, suggest evaluation metrics and present a few techniques for solving the problem. Besides the standard word based features, our approaches take into account novel features such as temporal locality of stories for event recognition and time-ordering for capturing dependencies. Our experiments on a manually labeled data sets show that our models effectively identify the events and capture dependencies among them.
Experiences in teaching quality attribute scenarios

The concept of the quality attribute scenario was introduced in 2003 to support the development of software architectures. This concept is useful because it provides an operational means to represent the quality requirements of a system. It also provides a more concrete basis with which to teach software architecture. Teaching this concept however has some unexpected issues. In this paper, I present my experiences of teaching quality attribute scenarios and outline Bus Tracker, a case study I have developed to support my teaching.
Efficient key-dependent message authentication in reconfigurable hardware

Cryptographic message authentication is a growing need for FPGA-based embedded systems. In this paper a customized FPGA implementation of a GHASH function that is used in AES-GCM, a widely-used message authentication protocol, is described. The implementation limits GHASH logic utilization by specializing the hardware implementation on a per-key basis. The implemented module can generate a 128bit message authentication code in both pipelined and unpipelined versions. The pipelined GHASH version achieves an authentication throughput of more than 14 Gbit/s on a Spartan-3 FPGA and 292 Gbit/s on a Virtex-6 device. To promote adoption in the field, the complete source code for this work has been made publically-available.
Vehicle Type Classification Using a Semisupervised Convolutional Neural Network

In this paper, we propose a vehicle type classification method using a semisupervised convolutional neural network from vehicle frontal-view images. In order to capture rich and discriminative information of vehicles, we introduce sparse Laplacian filter learning to obtain the filters of the network with large amounts of unlabeled data. Serving as the output layer of the network, the softmax classifier is trained by multitask learning with small amounts of labeled data. For a given vehicle image, the network can provide the probability of each type to which the vehicle belongs. Unlike traditional methods by using handcrafted visual features, our method is able to automatically learn good features for the classification task. The learned features are discriminative enough to work well in complex scenes. We build the challenging BIT-Vehicle dataset, including 9850 high-resolution vehicle frontal-view images. Experimental results on our own dataset and a public dataset demonstrate the effectiveness of the proposed method.
A practical time-delay estimator for localizing speech sources with a microphone array

A frequency-domain-based delay estimator is described, designed speci cally for speech signals in a microphone-array environment. It is shown to be capable of obtaining precision delay estimates over a wide range of SNR conditions and is simple enough computationally to make it practical for real-time systems. A location algorithm based upon the delay estimator is then developed. With this algorithm it is possible to localize talker positions to a region only a few centimeters in diameter (not very di erent from the size of the source), and to track a moving source. Experimental results using data from a real 16-element array are presented to indicate the true performance of the algorithms.
Comparison of inverse kinematics solutions using neural network for 6R robot manipulator with offset

An artificial neural network (ANN) using backpropagation algorithm is applied to solve inverse kinematics problems of industrial robot manipulator. 6R robot manipulator with offset wrist was chosen as industrial robot manipulator because geometric feature of this robot does not allow solving inverse kinematics problems analytically. In other words, there is no closed form solution for this problem. In order to define orientation of robot end-effector, three different representations are used here: homogeneous transformation matrix, Euler angles and equivalent angle axis. These representations were compared to obtain inverse kinematics solutions for 6R robot manipulator with offset wrist. Simulation results show that prediction performance from the approximation accuracy point of view is satisfactory with low effective errors based on 10 degrees data resolution
Lattice code decoder for space-time codes

We explore the lattice sphere packing representation of a multi-antenna system and the algebraic space-time (ST) codes. We apply the sphere decoding (SD) algorithm to the resulted lattice code. For the uncoded system, SD yields, with small increase in complexity, a huge improvement over the well-known V-BLAST detection algorithm. SD of algebraic ST codes exploits the full diversity of the coded multi-antenna system, and makes the proposed scheme very appealing to take advantage of the richness of the multi-antenna environment. The fact that the SD does not depend on the constellation size, gives rise to systems with very high spectral efficiency, maximum-likelihood performance, and low decoding complexity.
Review of Integrated Magnetics for Power Supply on Chip (PwrSoC)

This paper reviews the current state of power supply technology platforms and highlights future trends and challenges toward realizing fully monolithic power converters. This paper presents a detailed survey of relevant power converter technologies, namely power supply in package and power supply on chip (PwrSoC). The performance of different power converter solutions reported in the literature is benchmarked against existing commercial products. This paper presents a detailed review of integrated magnetics technologies, primarily microinductors, a key component in realizing a monolithic power converter. A detailed review and comparison of different microinductor structures and the magnetic materials used as inductor cores is presented. The deposition techniques for integrating the magnetic materials in the microinductor structures are discussed. This paper proposes the use of two performance metrics or figures of merit in order to compare the dc and ac performance of individual microinductor structures. Finally, the authors discuss future trends, key challenges, and potential solutions in the realization of the “holy grail” of monolithically integrated power supplies (PwrSoC).
Frequency-domain Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems

Channel estimation is useful in millimeter wave (mmWave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics such as mutual information or signal-to-interference-noise (SINR) ratio. At mmWave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of prior work on channel estimation for hybrid architectures assumes a frequencyflat mmWave channel model. In this paper, we consider a frequency-selective mmWave channel and propose compressed-sensing-based strategies to estimate the channel in the frequency domain. We evaluate different algorithms and compute their complexity to expose trade-offs in complexity-overheadperformance as compared to those of previous approaches. This work was partially funded by the Agencia Estatal de Investigacin (Spain) and the European Regional Development Fund (ERDF) under project MYRADA (TEC2016-75103-C2-2-R), the U.S. Department of Transportation through the DataSupported Transportation Operations and Planning (D-STOP) Tier 1 University Transportation Center, by the Texas Department of Transportation under Project 0-6877 entitled Communications and Radar-Supported Transportation Operations and Planning (CAR-STOP) and by the National Science Foundation under Grant NSF-CCF-1319556 and NSF-CCF-1527079. ar X iv :1 70 4. 08 57 2v 1 [ cs .I T ] 2 7 A pr 2 01 7
Compact Modified Wilkinson Power Divider With Physical Output Port Isolation

In this letter, a compact modified n-way Wilkinson power divider that allows physical isolation between output ports is presented. In the proposed power divider, the isolation impedances are positioned within quarter-wave transmission lines so that part of the lines can be used for interconnection and impedance transformation. Theoretical analysis is performed to derive the design equations of the proposed n-way power divider. Further, measurements show that the proposed power divider can reduce the insertion loss and circuit size when compared with a conventional Wilkinson power divider.
Big Five Audits and Accounting Fraud *

Empirical studies of earnings management, audit pricing, and audit reporting provide extensive evidence that the Big Five public accounting firms are associated with higher quality financial statements (Francis 2004). Nevertheless, the recent high-profile financial reporting failures that roiled the U.S. capital markets cast doubt on whether this proposition remains valid. Indeed, many commentators interpret the steep upward trend during the late 1990s and early 2000s in accounting misstatements by companies with Big Five auditors as almost conclusive evidence that their assurance services have deteriorated over time (e.g., Coffee 2002; Imhoff 2003; Zeff 2003). Among other explanations, some observers blame these watershed events on the more lenient litigation landscape having a disproportionate impact on Big Five quality as well as market conditions that led them to increasingly pursue lucrative consulting contracts to the detriment of their independence on audit engagements (e.g., Earley, Odabashian, and Willenborg 2003). Measuring trends in absolute audit quality is typically infeasible, so we more narrowly focus on discriminating between the relative performance of the Big Five and non-Big Five audit firms in preventing companies from orchestrating accounting fraud. Several commentators motivate arguments on the underlying reasons for the apparent erosion in Big Five audit quality by highlighting the well-known cases of fraudulent financial reporting by their clients (e.g., Coffee 2002; Cox 2003). However, rigorous evidence on whether companies are less likely to engage in accounting fraud if they are
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques

Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantages of a combination of ANN and FIS are obvious. There are several approaches to integrate ANN and FIS and very often it depends on the application. We broadly classify the integration of ANN and FIS into three categories namely concurrent model, cooperative model and fully fused model. This paper starts with a discussion of the features of each model and generalize the advantages and deficiencies of each model. We further focus the review on the different types of fused neuro-fuzzy systems and citing the advantages and disadvantages of each model.
The relationships among adaptive behaviors of children with autism, family support, parenting stress, and coping.

As the number of children diagnosed with autism continues to rise, resources must be available to support parents of children with autism and their families. Parents need help as they assess their unique situations, reach out for help in their communities, and work to decrease their stress levels by using appropriate coping strategies that will benefit their entire family.



A descriptive, correlational, cross-sectional study was conducted with 75 parents/primary caregivers of children with autism. Using the McCubbin and Patterson model of family behavior, adaptive behaviors of children with autism, family support networks, parenting stress, and parent coping were measured.



An association between low adaptive functioning in children with autism and increased parenting stress creates a need for additional family support as parents search for different coping strategies to assist the family with ongoing and new challenges. Professionals should have up-to-date knowledge of the supports available to families and refer families to appropriate resources to avoid overwhelming them with unnecessary and inappropriate referrals.
Collective Intelligence and Braess' Paradox

We consider the use of multi-agent systems to control network routing. Conventional approaches to this task are based on Ideal Shortest Path routing Algorithm (ISPA), under which at each moment each agent in the network sends all of its traffic down the path that will incur the lowest cost to that traffic. We demonstrate in computer experiments that due to the side-effects of one agent’s actions on another agent’s traffic, use of ISPA’s can result in large global cost. In particular, in a simulation of Braess’ paradox we see that adding new capacity to a network with ISPA agents can decrease overall throughput. The theory of COllective INtelligence (COIN) design concerns precisely the issue of avoiding such side-effects. We use that theory to derive an idealized routing algorithm and show that a practical machine-learning-based version of this algorithm, in which costs are only imprecisely estimated substantially outperforms the ISPA, despite having access to less information than does the ISPA. In particular, this practical COIN algorithm avoids Braess’ paradox.
A brain-computer interface with vibrotactile biofeedback for haptic information

It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only vibrotactile feedback, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy. A Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance. Subjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.
DEVELOPING SPECIES SPECIFIC VEGETATION MAPS USING MULTI-SPECTRAL HYPERSPATIAL IMAGERY FROM UNMANNED AERIAL VEHICLES

In remote, rugged or sensitive environments ground based mapping for condition assessment of species is both time consuming and potentially destructive. The application of photogrammetric methods to generate multispectral imagery and surface models based on UAV imagery at appropriate temporal and spatial resolutions is described. This paper describes a novel method to combine processing of NIR and visible image sets to produce multiband orthoimages and DEM models from UAV imagery with traditional image location and orientation uncertainties. This work extends the capabilities of recently developed commercial software (Pix4UAV from Pix4D) to show that image sets of different modalities (visible and NIR) can be automatically combined to generate a 4 band orthoimage. Reconstruction initially uses all imagery sets (NIR and visible) to ensure all images are in the same reference frame such that a 4-band orthoimage can be created. We analyse the accuracy of this automatic process by using ground control points and an evaluation on the matching performance between images of different modalities is shown. By combining sub-decimetre multispectral imagery with high spatial resolution surface models and ground based observation it is possible to generate detailed maps of vegetation assemblages at the species level. Potential uses with other conservation monitoring are discussed.