nils-herrmann/clean_scirepeval huggingface dataset:
DisasterMapper: A CyberGIS framework for disaster management using social media data

Traditional GIS tools and systems are powerful for analyzing geographic information for various applications but they are not designed for processing dynamic streams of data. This paper presents a CyberGIS framework that can automatically synthesize multi-sourced data, such as social media and socioeconomic data, to track disaster events, to produce maps, and to perform spatial and statistical analysis for disaster management. Within our framework, Apache Hive, Hadoop, and Mahout are used as scalable distributed storage, computing environment and machine learning library to store, process and mine massive social media data. The proposed framework is capable of supporting big data analytics of multiple sources. A prototype is implemented and tested using the 2011 Hurricane Sandy as a case study.
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)

Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost. In order to reduce this cost, several quantization schemes have gained attention recently with some focusing on weight quantization, and others focusing on quantizing activations. This paper proposes novel techniques that target weight and activation quantizations separately resulting in an overall quantized neural network (QNN). The activation quantization technique, PArameterized Clipping acTivation (PACT), uses an activation clipping parameter α that is optimized during training to find the right quantization scale. The weight quantization scheme, statistics-aware weight binning (SAWB), finds the optimal scaling factor that minimizes the quantization error based on the statistical characteristics of the distribution of weights without the need for an exhaustive search. The combination of PACT and SAWB results in a 2-bit QNN that achieves state-of-the-art classification accuracy (comparable to full precision networks) across a range of popular models and datasets.
Reducing Overfitting in Deep Networks by Decorrelating Representations

One major challenge in training Deep Neural Networks is preventing overfitting. Many techniques such as data augmentation and novel regularizers such as Dropout have been proposed to prevent overfitting without requiring a massive amount of training data. In this work, we propose a new regularizer called DeCov which leads to significantly reduced overfitting (as indicated by the difference between train and val performance), and better generalization. Our regularizer encourages diverse or non-redundant representations in Deep Neural Networks by minimizing the cross-covariance of hidden activations. This simple intuition has been explored in a number of past works but surprisingly has never been applied as a regularizer in supervised learning. Experiments across a range of datasets and network architectures show that this loss always reduces overfitting while almost always maintaining or increasing generalization performance and often improving performance over Dropout.
Content-Based Routing: Different Plans for Different Data

Query optimizers in current database systems are designed to pick a single efficient plan for a given query based on current statistical properties of the data. However, different subsets of the data can sometimes have very different statistical properties. In such scenarios it can be more efficient to process different subsets of the data for a query using different plans. We propose a new query processing technique called content-based routing (CBR) that eliminates the single-plan restriction in current systems. We present low-overhead adaptive algorithms that partition input data based on statistical properties relevant to query execution strategies, and efficiently route individual tuples through customized plans based on their partition. We have implemented CBR as an extension to the Eddies query processor in the TelegraphCQ system, and we present an extensive experimental evaluation showing the significant performance benefits of CBR.
Steganographic Generative Adversarial Networks

Steganography is collection of methods to hide secret information (“payload”) within non-secret information (“container”). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.
History of Optimal Power Flow and Formulations

The purpose of this paper is to present a literature review of the AC Optimal Power Flow (ACOPF) problem and propose areas where the ACOPF could be improved. The ACOPF is at the heart of Independent System Operator (ISO) power markets, and is solved in some form every year for system planning, every day for day‐ahead markets, every hour, and even every 5 minutes. It was first formulated in 1962, and formulations have changed little over the years. With advances in computing power and solution algorithms, we can model more of the constraints and remove unnecessary limits and approximations that were previously required to find a solution in reasonable time. One example is nonlinear voltage magnitude constraints that are modeled as linear thermal proxy constraints. In this paper, we refer to the full ACOPF as an ACOPF that simultaneously optimizes real and reactive power. Today, 50 years after the problem was formulated, we still do not have a fast, robust solution technique for the full ACOPF. Finding a good solution technique for the full ACOPF could potentially save tens of billions of dollars annually. Based on our literature review, we find that the ACOPF research community lacks a common understanding of the problem, its formulation, and objective functions. However, we do not claim that this literature review is a complete review—our intent was simply to capture the major formulations of the ACOPF. Instead, in this paper, we seek to clearly present the ACOPF problem through clear formulations of the problem and its parameters. This paper defines and discusses the polar power‐voltage, rectangular power‐voltage, and rectangular current‐voltage formulations of the ACOPF. Additionally, it discusses the different types of constraints and objective functions. This paper lays the groundwork for further research on the convex approximation of the ACOPF solution space, a survey of solution techniques, and computational performance of different formulations.  The views presented are the personal views of the authors and not the Federal Energy Regulatory Commission or any of its Commissioners. History of Optimal Power Flow and Formulations • December 2012
Peak-To-Average Power Ratio of Single Carrier FDMA Signals with Pulse Shaping

Single carrier frequency division multiple access (SC-FDMA), which utilizes single carrier modulation and frequency domain equalization is a technique that has similar performance and essentially the same overall complexity as those of OFDM, in which high peak-to-average power ratio (PAPR) is a major drawback. An outstanding advantage of SC-FDMA is its lower PAPR due to its single carrier structure. In this paper, we analyze the PAPR of SC-FDMA signals with pulse shaping. We analytically derive the time domain SC-FDMA signals and numerically compare PAPR characteristics using the complementary cumulative distribution function (CCDF) of PAPR. The results show that SC-FDMA signals indeed have lower PAPR compared to those of OFDMA. Comparing the two forms of SC-FDMA, we find that localized FDMA (LFDMA) has higher PAPR than interleaved FDMA (IFDMA) but somewhat lower PAPR than OFDMA. Also noticeable is the fact that pulse shaping increases PAPR
PAST: scalable ethernet for data centers

We present PAST, a novel network architecture for data center Ethernet networks that implements a Per-Address Spanning Tree routing algorithm. PAST preserves Ethernet's self-configuration and mobility support while increasing its scalability and usable bandwidth. PAST is explicitly designed to accommodate unmodified commodity hosts and Ethernet switch chips. Surprisingly, we find that PAST can achieve performance comparable to or greater than Equal-Cost Multipath (ECMP) forwarding, which is currently limited to layer-3 IP networks, without any multipath hardware support. In other words, the hardware and firmware changes proposed by emerging standards like TRILL are not required for high-performance, scalable Ethernet networks. We evaluate PAST on Fat Tree, , and Jellyfish topologies, and show that it is able to capitalize on the advantages each offers. We also describe an OpenFlow-based implementation of PAST in detail.
Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm

In this paper, we propose a real-time image superpixel segmentation method with 50 frames/s by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. In order to decrease the computational costs of superpixel algorithms, we adopt a fast two-step framework. In the first clustering stage, the DBSCAN algorithm with color-similarity and geometric restrictions is used to rapidly cluster the pixels, and then, small clusters are merged into superpixels by their neighborhood through a distance measurement defined by color and spatial features in the second merging stage. A robust and simple distance function is defined for obtaining better superpixels in these two steps. The experimental results demonstrate that our real-time superpixel algorithm (50 frames/s) by the DBSCAN clustering outperforms the state-of-the-art superpixel segmentation methods in terms of both accuracy and efficiency.
PSYCHOLOGY AND ECONOMICS

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Multimodal classification of Alzheimer's disease and mild cognitive impairment

Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers.
Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices

Acoustic models based on long short-term memory recurrent neural networks (LSTM-RNNs) were applied to statistical parametric speech synthesis (SPSS) and showed significant improvements in naturalness and latency over those based on hidden Markov models (HMMs). This paper describes further optimizations of LSTM-RNN-based SPSS for deployment on mobile devices; weight quantization, multi-frame inference, and robust inference using an -contaminated Gaussian loss function. Experimental results in subjective listening tests show that these optimizations can make LSTM-RNN-based SPSS comparable to HMM-based SPSS in runtime speed while maintaining naturalness. Evaluations between LSTM-RNNbased SPSS and HMM-driven unit selection speech synthesis are also presented.
Internal Permanent Magnet Motor Design for Electric Vehicle Drive

Power compaction and high efficiency are two key advantages of permanent magnet motors. This paper proposes an enhanced internal permanent magnet motor that delivers high torque, power compaction, and exceptionally high efficiency in the same operation area. The advantage of the proposed scheme is the magnetic flux accumulation in the air gap, which allows much higher values of magnetic flux density, compared to a surface permanent magnet motor of the same size. The original contribution of this paper resides on the adopted motor configuration, enabling to efficiently utilize the energy stored in the permanent magnet and to provide total loss minimization at the most frequently used speed range.
System identification and cable force control for a cable-driven parallel robot with industrial servo drives

In a cable-driven parallel robot, elastic cables are used to manipulate the end effector in the workspace. In this paper we present a dynamic analysis and system identification for the complete actuator unit of a cable robot including servo controller, winch, cable, cable force sensor and field bus communication. We establish a second-order system with dead time as an analagous model. Based on this investigation, we propose the design and stability analysis of a cable force controller. We present the implementation of feed-forward and integral controllers based on a stiffness model of the cables. As the platform position is not observable the challenge is to control the cable force while maintaining the positional accuracy. Experimental evaluation of the force controller shows, that the absolute positional accuracy is even improved.
Supervised Hashing Using Graph Cuts and Boosted Decision Trees

To build large-scale query-by-example image retrieval systems, embedding image features into a binary Hamming space provides great benefits. Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the binary Hamming space. Most existing approaches apply a single form of hash function, and an optimization process which is typically deeply coupled to this specific form. This tight coupling restricts the flexibility of those methods, and can result in complex optimization problems that are difficult to solve. In this work we proffer a flexible yet simple framework that is able to accommodate different types of loss functions and hash functions. The proposed framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problem-specific hashing methods. Our framework decomposes the hashing learning problem into two steps: binary code (hash bit) learning and hash function learning. The first step can typically be formulated as binary quadratic problems, and the second step can be accomplished by training a standard binary classifier. For solving large-scale binary code inference, we show how it is possible to ensure that the binary quadratic problems are submodular such that efficient graph cut methods may be used. To achieve efficiency as well as efficacy on large-scale high-dimensional data, we propose to use boosted decision trees as the hash functions, which are nonlinear, highly descriptive, and are very fast to train and evaluate. Experiments demonstrate that the proposed method significantly outperforms most state-of-the-art methods, especially on high-dimensional data.
VLAD3: Encoding Dynamics of Deep Features for Action Recognition

Previous approaches to action recognition with deep features tend to process video frames only within a small temporal region, and do not model long-range dynamic information explicitly. However, such information is important for the accurate recognition of actions, especially for the discrimination of complex activities that share sub-actions, and when dealing with untrimmed videos. Here, we propose a representation, VLAD for Deep Dynamics (VLAD3), that accounts for different levels of video dynamics. It captures short-term dynamics with deep convolutional neural network features, relying on linear dynamic systems (LDS) to model medium-range dynamics. To account for long-range inhomogeneous dynamics, a VLAD descriptor is derived for the LDS and pooled over the whole video, to arrive at the final VLAD3 representation. An extensive evaluation was performed on Olympic Sports, UCF101 and THUMOS15, where the use of the VLAD3 representation leads to state-of-the-art results.
Management of inter-dental/inter-implant papilla.

The aims of this paper are to review and compare existing techniques for creation of interdental/interimplant papillae, to address factors that may influence its appearance and to present an approach that authors developed that could help clinicians to manage and recreate the interproximal papillae.



Papers related to interdental and interimplant papillae published over the last 30 years were selected and analyzed.



Thorough treatment planning is essential for maintenance of the height of the interproximal papillae following tooth removal. The key for achieving an esthetically pleasing outcome is the clinicians' ability of properly managing/creating interdental/interimplant papillae. Bone support is the foundation for any soft tissue existence, techniques such as socket augmentation, orthodontic extrusion, guided bone regeneration, onlay graft and distraction osteogenesis are often used for this purpose. Soft tissue grafts as well as esthetic mimic restorations can also be used to enhance the esthetic outcomes.



An esthetic triangle is developed to address the foundations that are essential for maintaining/creating papilla. These include adequate bone volume, proper soft tissue thickness as well as esthetic appearing restorations.
AGIL: Learning Attention from Human for Visuomotor Tasks

When intelligent agents learn visuomotor behaviors from human demonstrations, they may benefit from knowing where the human is allocating visual attention, which can be inferred from their gaze. A wealth of information regarding intelligent decision making is conveyed by human gaze allocation; hence, exploiting such information has the potential to improve the agents’ performance. With this motivation, we propose the AGIL (Attention Guided Imitation Learning) framework. We collect high-quality human action and gaze data while playing Atari games in a carefully controlled experimental setting. Using these data, we first train a deep neural network that can predict human gaze positions and visual attention with high accuracy (the gaze network) and then train another network to predict human actions (the policy network). Incorporating the learned attention model from the gaze network into the policy network significantly improves the action prediction accuracy and task performance.
Parametric and Non-parametric User-aware Sentiment Topic Models

The popularity of Web 2.0 has resulted in a large number of publicly available online consumer reviews created by a demographically diverse user base. Information about the authors of these reviews, such as age, gender and location, provided by many on-line consumer review platforms may allow companies to better understand the preferences of different market segments and improve their product design, manufacturing processes and marketing campaigns accordingly. However, previous work in sentiment analysis has largely ignored these additional user meta-data. To address this deficiency, in this paper, we propose parametric and non-parametric User-aware Sentiment Topic Models (USTM) that incorporate demographic information of review authors into topic modeling process in order to discover associations between market segments, topical aspects and sentiments. Qualitative examination of the topics discovered using USTM framework in the two datasets collected from popular online consumer review platforms as well as quantitative evaluation of the methods utilizing those topics for the tasks of review sentiment classification and user attribute prediction both indicate the utility of accounting for demographic information of review authors in opinion mining.