nils-herrmann/clean_scirepeval huggingface dataset:
Real-time super-resolution Sound Source Localization for robots

Sound Source Localization (SSL) is an essential function for robot audition and yields the location and number of sound sources, which are utilized for post-processes such as sound source separation. SSL for a robot in a real environment mainly requires noise-robustness, high resolution and real-time processing. A technique using microphone array processing, that is, Multiple Signal Classification based on Standard EigenValue Decomposition (SEVD-MUSIC) is commonly used for localization. We improved its robustness against noise with high power by incorporating Generalized EigenValue Decomposition (GEVD). However, GEVD-based MUSIC (GEVD-MUSIC) has mainly two issues: 1) the resolution of pre-measured Transfer Functions (TFs) determines the resolution of SSL, 2) its computational cost is expensive for real-time processing. For the first issue, we propose a TF interpolation method integrating time-domain-based and frequency-domain-based interpolation. The interpolation achieves super-resolution SSL, whose resolution is higher than that of the pre-measured TFs. For the second issue, we propose two methods, MUSIC based on Generalized Singular Value Decomposition (GSVD-MUSIC), and Hierarchical SSL (H-SSL). GSVD-MUSIC drastically reduces the computational cost while maintaining noise-robustness in localization. H-SSL also reduces the computational cost by introducing a hierarchical search algorithm instead of using greedy search in localization. These techniques are integrated into an SSL system using a robot embedded microphone array. The experimental result showed: the proposed interpolation achieved approximately 1 degree resolution although we have only TFs at 30 degree intervals, GSVD-MUSIC attained 46.4% and 40.6% of the computational cost compared to SEVD-MUSIC and GEVD-MUSIC, respectively, H-SSL reduces 59.2% computational cost in localization of a single sound source.
Future Perspectives on Next Generation e-Sports Infrastructure and Exploring Their Benefits

e-Sports and cyber games are becoming popular among the young population and the effectiveness of gaming industry depends on the effective infrastructure. This paper contemplates some of the future developments required in the information and communications industry to suit the demands of online gamers and e-Sports industry. Some of the modern developments which can add greater facilities to the next generation e-Sports industry for enhancing the future players’ demands are also discussed. The article explores some of the potential future benefits of e-Sports in the field of education, training, learning and physical activities.
Laboratory Experiments for Tsunamis Generated by Underwater Landslides: Comparison with Numerical Modeling

Three-dimensional experiments and fully nonlinear computations are performed at the University of Rhode Island, to investigate tsunami generation by underwater landslides. Each experiment consists of a solid landslide of idealized smooth shape sliding over a plane slope. Surface elevations are measured using very accurate gages placed at strategic locations. Gage calibration is performed using a newly developed automated system. Landslide acceleration is measured with a micro-accelerometer. The repeatability of experiments is first investigated, and then by varying the initial depth of the landslide, different conditions of wave non-linearity and dispersion are generated and compared. The principle of numerical modeling, using an earlier developed model, is briefly explained. One application is presented and results compared to experiments. The agreement of computations with the latter is quite good. In the model, horizontal velocities are found quite non-uniform over depth above the moving landslide. This would preclude using a long wave model for such landslide tsunami wave generation.
A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery

In this paper, we present a model which incorporates biomedical knowledge graph, graph embedding and deep learning methods for literature-based discovery. Firstly, the relations between entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Secondly, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Thirdly, a bidirectional Long Short-Term Memory network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. We derive a lower bound of the mutual information objective that can be optimized efficiently. Specifically, InfoGAN successfully disentangles writing styles from digit shapes on the MNIST dataset, pose from lighting of 3D rendered images, and background digits from the central digit on the SVHN dataset. It also discovers visual concepts that include hair styles, presence/absence of eyeglasses, and emotions on the CelebA face dataset. Experiments show that InfoGAN learns interpretable representations that are competitive with representations learned by existing supervised methods. For an up-to-date version of this paper,
Using thematic analysis in psychology

Thematic analysis is a poorly demarcated, rarely acknowledged, yet widely used qualitative analytic method within psychology. In this paper, we argue that it offers an accessible and theoretically flexible approach to analysing qualitative data. We outline what thematic analysis is, locating it in relation to other qualitative analytic methods that search for themes or patterns, and in relation to different epistemological and ontological positions. We then provide clear guidelines to those wanting to start thematic analysis, or conduct it in a more deliberate and rigorous way, and consider potential pitfalls in conducting thematic analysis. Finally, we outline the disadvantages and advantages of thematic analysis. We conclude by advocating thematic analysis as a useful and flexible method for qualitative research in and beyond psychology.  Research in Psychology 2006; 3: 77
L G ] 5 J ul 2 01 7 Label Organized Memory Augmented Neural Networks

We propose an alternative design of memory augmented neural networks (MANNs) called Labeled Memory Networks (LMNs) suited for tasks requiring fast adaptation of batch-trained classification models. LMNs have two key differences with existing MANNs. First, LMNs organize the memory with the discrete class label as the primary key unlike the existing practice of key being a real vector derived from the input. Second, LMNs treat memory as a second boosted stage following a regular neural network thereby allowing the memory and the primary network to play complementary roles. Unlike existing MANNs that write to memory for every instance and use LRU based memory replacement, LMNs write only for instances with non-zero loss and use label-based memory replacement. These properties make them particularly suited for classification tasks requiring fast adaptation and memorizing rare events. We demonstrate significant accuracy gains on three such tasks: online sequence prediction, life-long learning of rare events, and few-shot learning.
Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images

The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as "fatty liver," "cirrhosis," and "hepatomegaly" produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that "mixed feature set" is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.
A Robust and Fast Non-local Means Algorithm for Image Denoising

In the paper, we propose a robust and fast image denoising method. The approach integrates both 'Non-Local means algorithm and Laplacian Pyramid. Given an image to be denoised, we first decompose it into Laplacian pyramid. Exploiting the redundancy property of Laplacian pyramid, we then perform non-local means on every level image of Laplacian pyramid. Essentially, we use the similarity of image features in Laplacian pyramid to act as weight to denoise image. Since the features extracted in Laplacian pyramid are localized in spatial position and scale, they are much more able to describe image, and computing the similarity between them is more reasonable and more robust. Also, based on the efficient Summed Square Image (SSI) scheme and Fast Fourier Transform (FFT), we present an accelerating algorithm to break the bottleneck of non-local means algorithm -similarity computation of compare windows. After speedup, our algorithm is fifty times faster than original non-local means algorithm. Experiments demonstrated the effectiveness of our algorithm.
Amorphous Silicon Solar Vivaldi Antenna

An ultrawideband solar Vivaldi antenna is proposed. Cut from amorphous silicon cells, it maintains a peak power at 4.25 V, which overcomes a need for lossy power management components. The wireless communications device can yield solar energy or function as a rectenna for dual-source energy harvesting. The solar Vivaldi performs with 0.5-2.8 dBi gain from 0.95-2.45 GHz , and in rectenna mode, it covers three bands for wireless energy scavenging.
Text Classification with Topic-based Word Embedding and Convolutional Neural Networks

Recently, distributed word embeddings trained by neural language models are commonly used for text classification with Convolutional Neural Networks (CNNs). In this paper, we propose a novel neural language model, Topic-based Skip-gram, to learn topic-based word embeddings for biomedical literature indexing with CNNs. Topic-based Skip-gram leverages textual content with topic models, e.g., Latent Dirichlet Allocation (LDA), to capture precise topic-based word relationship and then integrate it into distributed word embedding learning. We then describe two multimodal CNN architectures, which are able to employ different kinds of word embeddings at the same time for text classification. Through extensive experiments conducted on several real-world datasets, we demonstrate that combination of our Topic-based Skip-gram and multimodal CNN architectures outperforms state-of-the-art methods in biomedical literature indexing, clinical note annotation and general textual benchmark dataset classification.
CORRELATION AND DEPENDENCE IN RISK MANAGEMENT : PROPERTIES AND PITFALLS

Modern risk management calls for an understanding of stochastic dependence going beyond simple linear correlation. This paper deals with the static (non-time-dependent) case and emphasizes the copula representation of dependence for a random vector. Linear correlation is a natural dependence measure for multivariate normally and, more generally, elliptically distributed risks but other dependence concepts like comonotonicity and rank correlation should also be understood by the risk management practitioner. Using counterexamples the falsity of some commonly held views on correlation is demonstrated; in general, these fallacies arise from the naive assumption that dependence properties of the elliptical world also hold in the non-elliptical world. In particular, the problem of finding multivariate models which are consistent with prespecified marginal distributions and correlations is addressed. Pitfalls are highlighted and simulation algorithms avoiding these problems are constructed.
ShapeWorld - A new test methodology for multimodal language understanding

We introduce a novel framework for evaluating multimodal deep learning models with respect to their language understanding and generalization abilities. In this approach, artificial data is automatically generated according to the experimenter’s specifications. The content of the data, both during training and evaluation, can be controlled in detail, which enables tasks to be created that require true generalization abilities, in particular the combination of previously introduced concepts in novel ways. We demonstrate the potential of our methodology by evaluating various visual question answering models on four different tasks, and show how our framework gives us detailed insights into their capabilities and limitations. By opensourcing our framework, we hope to stimulate progress in the field of multimodal language understanding.
Reading Digits in Natural Images with Unsupervised Feature Learning

Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks.
Fast Pose Estimation with Parameter-Sensitive Hashing

Example-basedmethodsareeffectivefor parameterestimationproblemswhentheunderlyingsystemis simpleor thedimensionalityof the input is low. For complex andhigh-dimensional problemssuch asposeestimation,thenumberof required examplesand the computationalcomplexity rapidly becmeprohibitivelyhigh. We introducea new algorithm that learnsa setof hashingfunctionsthat efficiently index examplesrelevant to a particular estimationtask. Our algorithm extendsa recentlydevelopedmethodfor locality-sensitivehashing, which findsapproximateneighborsin timesublinearin thenumber of examples.Thismethoddependscritically on thechoiceof hashfunctions;weshowhowto find thesetof hashfunctions thatare optimallyrelevantto a particular estimationproblem.Experimentsdemonstr atethat theresultingalgorithm,which wecall Parameter -SensitiveHashing, canrapidlyandaccuratelyestimatethearticulatedposeof humanfiguresfroma large databaseof exampleimages. 0Part of thiswork wasdonewhenG.S.andP.V. werewith MitsubishiElectricResearchLabs,Cambridge,MA.
Optimizing Seed Selection for Fuzzing

Randomly mutating well-formed program inputs or simply fuzzing, is a highly effective and widely used strategy to find bugs in software. Other than showing fuzzers find bugs, there has been little systematic effort in understanding the science of how to fuzz properly. In this paper, we focus on how to mathematically formulate and reason about one critical aspect in fuzzing: how best to pick seed files to maximize the total number of bugs found during a fuzz campaign. We design and evaluate six different algorithms using over 650 CPU days on Amazon Elastic Compute Cloud (EC2) to provide ground truth data. Overall, we find 240 bugs in 8 applications and show that the choice of algorithm can greatly increase the number of bugs found. We also show that current seed selection strategies as found in Peach may fare no better than picking seeds at random. We make our data set and code publicly available.
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain

The Conjugate Gradient Method is the most prominent iterative method for solving sparse systems of linear equations. Unfortunately, many textbook treatments of the topic are written with neither illustrations nor intuition, and their victims can be found to this day babbling senselessly in the corners of dusty libraries. For this reason, a deep, geometric understanding of the method has been reserved for the elite brilliant few who have painstakingly decoded the mumblings of their forebears. Nevertheless, the Conjugate Gradient Method is a composite of simple, elegant ideas that almost anyone can understand. Of course, a reader as intelligent as yourself will learn them almost effortlessly. The idea of quadratic forms is introduced and used to derive the methods of Steepest Descent, Conjugate Directions, and Conjugate Gradients. Eigenvectors are explained and used to examine the convergence of the Jacobi Method, Steepest Descent, and Conjugate Gradients. Other topics include preconditioningand the nonlinear Conjugate Gradient Method. I have taken pains to make this article easy to read. Sixty-six illustrations are provided. Dense prose is avoided. Concepts are explained in several different ways. Most equations are coupled with an intuitive interpretation. Supported in part by the Natural Sciences and Engineering Research Council of Canada under a 1967 Science and Engineering Scholarship and by the National Science Foundation under Grant ASC-9318163. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either express or implied, of NSERC, NSF, or the U.S. Government.
Pooled motion features for first-person videos

In this paper, we present a new feature representation for first-person videos. In first-person video understanding (e.g., activity recognition), it is very important to capture both entire scene dynamics (i.e., egomotion) and salient local motion observed in videos. We describe a representation framework based on time series pooling, which is designed to ab] short-term/long-term changes in feature descriptor elements. The idea is to keep track of how descriptor values are changing over time and summarize them to represent motion in the activity video. The framework is general, handling any types of per-frame feature descriptors including conventional motion descriptors like histogram of optical flows (HOF) as well as appearance descriptors from more recent convolutional neural networks (CNN). We experimentally confirm that our approach clearly outperforms previous feature representations including bag-of-visual-words and improved Fisher vector (IFV) when using identical underlying feature descriptors. We also confirm that our feature representation has superior performance to existing state-of-the-art features like local spatio-temporal features and Improved Trajectory Features (originally developed for 3rd-person videos) when handling first-person videos. Multiple first-person activity datasets were tested under various settings to confirm these findings.
Text classification and named entities for new event detection

New Event Detection is a challenging task that still offers scope for great improvement after years of effort. In this paper we show how performance on New Event Detection (NED) can be improved by the use of text classification techniques as well as by using named entities in a new way. We explore modifications to the document representation in a vector space-based NED system. We also show that addressing named entities preferentially is useful only in certain situations. A combination of all the above results in a multi-stage NED system that performs much better than baseline single-stage NED systems.
The optimum received power levels of uplink non-orthogonal multiple access (NOMA) signals

Non-orthogonal multiple access (NOMA) has been recently considered as a promising multiple access technique for fifth generation (5G) mobile networks as an enabling technology to meet the demands of low latency, high reliability, massive connectivity, and high throughput. The two dominants types of NOMA are: power-domain and code-domain. The key feature of power-domain NOMA is to allow different users to share the same time, frequency, and code, but with different power levels. In code-domain NOMA, different spread-spectrum codes are assigned to different users and are then multiplexed over the same time-frequency resources. This paper concentrates on power-domain NOMA. In power-domain NOMA, Successive Interference Cancellation (SIC) is employed at the receiver. In this paper, the optimum received uplink power levels using a SIC detector is determined analytically for any number of transmitters. The optimum uplink received power levels using the SIC decoder in NOMA strongly resembles the μ-law encoding used in pulse code modulation (PCM) speech companders.
An inductorless wideband balun-LNA in 65nm CMOS with balanced output

An inductorless LNA with active balun is designed for multi-standard radio applications between 100 MHz and 6 GHz. It exploits a combination of a common gate stage and a common source stage with replica biasing to maximize balanced operation. The NF is designed to be around 3 dB by using the noise canceling technique. Its best performance is achieved between 300 MHz to 3.5 GHz with gain and phase errors below 0.3 dB and plusmn2 degrees, 15 dB gain, S11<-14 dB, IIP3=0 dBm and IIP2 higher than +20 dBm at a total power consumption of 21 mW. The circuit is fabricated in a baseline 65 nm CMOS process, with an active area of only 0.01 mm2. The circuit simultaneously achieves impedance matching, noise canceling and a well balanced output.