nils-herrmann/clean_scirepeval huggingface dataset:
One-Way Quantum Computer Simulation

In one-way quantum computation (1WQC) model, universal quantum computations are performed using measurements to designated qubits in a highly entangled state. The choices of bases for these measurements as well as the structure of the entanglements specify a quantum algorithm. As scalable and reliable quantum computers have not been implemented yet, quantum computation simulators are the only widely available tools to design and test quantum algorithms. However, simulating the quantum computations on a standard classical computer in most cases requires exponential memory and time. In this paper, a general direct simulator for 1WQC, called OWQS, is presented. Some techniques such as qubit elimination, pattern reordering and implicit simulation of actions are used to considerably reduce the time and memory needed for the simulations. Moreover, our simulator is adjusted to simulate the measurement patterns with a generalized flow without calculating the measurement probabilities which is called extended one-way quantum computation simulator (EOWQS). Experimental results validate the feasibility of the proposed simulators and that OWQS and EOWQS are faster as compared with the well-known quantum circuit simulators, i.e., QuIDDPro and libquantum for simulating 1WQC model. Quantum Computing, 1WQC, Simulation
PGQ: Combining policy gradient and Q-learning

Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting. However, vanilla online variants are on-policy only and not able to take advantage of off-policy data. In this paper we describe a new technique that combines policy gradient with off-policy Q-learning, drawing experience from a replay buffer. This is motivated by making a connection between the fixed points of the regularized policy gradient algorithm and the Q-values. This connection allows us to estimate the Q-values from the action preferences of the policy, to which we apply Q-learning updates. We refer to the new technique as ‘PGQ’, for policy gradient and Q-learning. We also establish an equivalency between action-value fitting techniques and actor-critic algorithms, showing that regularized policy gradient techniques can be interpreted as advantage function learning algorithms. We conclude with some numerical examples that demonstrate improved data efficiency and stability of PGQ. In particular, we tested PGQ on the full suite of Atari games and achieved performance exceeding that of both asynchronous advantage actor-critic (A3C) and Q-learning.
App store mining is not enough for app improvement

The rise in popularity of mobile devices has led to a parallel growth in the size of the app store market, intriguing several research studies and commercial platforms on mining app stores. App store reviews are used to analyze different aspects of app development and evolution. However, app users’ feedback does not only exist on the app store. In fact, despite the large quantity of posts that are made daily on social media, the importance and value that these discussions provide remain mostly unused in the context of mobile app development. In this paper, we study how Twitter can provide complementary information to support mobile app development. By analyzing a total of 30,793 apps over a period of six weeks, we found strong correlations between the number of reviews and tweets for most apps. Moreover, through applying machine learning classifiers, topic modeling and subsequent crowd-sourcing, we successfully mined 22.4% additional feature requests and 12.89% additional bug reports from Twitter. We also found that 52.1% of all feature requests and bug reports were discussed on both tweets and reviews. In addition to finding common and unique information from Twitter and the app store, sentiment and content analysis were also performed for 70 randomly selected apps. From this, we found that tweets provided more critical and objective views on apps than reviews from the app store. These results show that app store review mining is indeed not enough; other information sources ultimately provide added value and information for app developers.
Speaker dependent speech emotion recognition using MFCC and Support Vector Machine

In human computer interaction, speech emotion recognition is playing a pivotal part in the field of research. Human emotions consist of being angry, happy, sad, disgust, neutral. In this paper the features are extracted with hybrid of pitch, formants, zero crossing, MFCC and its statistical parameters. The pitch detection is done by cepstral algorithm after comparing it with autocorrelation and AMDF. The training and testing part of the SVM classifier is compared with different kernel function like linear, polynomial, quadratic and RBF. The polish database is used for the classification. The comparison between the different kernels is obtained for the corresponding feature vector.
A comprehensive review of 3D point cloud descriptors

The introduction of inexpensive 3D data acquisition devices has promisingly facilitated the wide availability and popularity of 3D point cloud, which attracts more attention on the effective extraction of novel 3D point cloud descriptors for accurate and efficient of 3D computer vision tasks. However, how to develop discriminative and robust feature descriptors from various point clouds remains a challenging task. This paper comprehensively investigates the existing approaches for extracting 3D point cloud descriptors which are categorized into three major classes: local-based descriptor, global-based descriptor and hybrid-based descriptor. Furthermore, experiments are carried out to present a thorough evaluation of performance of several state-of-the-art 3D point cloud descriptors used widely in practice in terms of descriptiveness, robustness and efficiency.
Theory IIIb: Generalization in Deep Networks

A main puzzle of deep neural networks (DNNs) revolves around the apparent absence of “overfitting”, defined in this paper as follows: the expected error does not get worse when increasing the number of neurons or of iterations of gradient descent. This is surprising because of the large capacity demonstrated by DNNs to fit randomly labeled data and the absence of explicit regularization. Recent results by Srebro et al. provide a satisfying solution of the puzzle for linear networks used in binary classification. They prove that minimization of loss functions such as the logistic, the cross-entropy and the exp-loss yields asymptotic, “slow” convergence to the maximum margin solution for linearly separable datasets, independently of the initial conditions. Here we prove a similar result for nonlinear multilayer DNNs near zero minima of the empirical loss. The result holds for exponential-type losses but not for the square loss. In particular, we prove that the normalized weight matrix at each layer of a deep network converges to a minimum norm solution (in the separable case). Our analysis of the dynamical system corresponding to gradient descent of a multilayer network suggests a simple criterion for predicting the generalization performance of different zero minimizers of the empirical loss. This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. ar X iv :1 80 6. 11 37 9v 1 [ cs .L G ] 2 9 Ju n 20 18 Theory IIIb: Generalization in Deep Networks Tomaso Poggio ∗1, Qianli Liao1, Brando Miranda1, Andrzej Banburski1, Xavier Boix1, and Jack Hidary2 1Center for Brains, Minds and Machines, MIT 2Alphabet (Google) X
Generalized Sampling Expansions with Multiple Sampling Rates for Lowpass and Bandpass Signals in the Fractional Fourier Transform Domain

The objective of generalized sampling expansion (GSE) is the reconstruction of an unknown, continuously defined function f (t) from samples of the responses from M linear time-invariant (LTI) systems that are each sampled using the 1/Mth Nyquist rate. In this paper, we investigate the GSE for lowpass and bandpass signals with multiple sampling rates in the fractional Fourier transform (FRFT) domain. First, we propose an improvement of Papoulis' GSE, which has multiple sampling rates in the FRFT domain. Based on the proposed GSE, we derive the periodic nonuniform sampling scheme and the derivative interpolation method by designing different fractional filters and selecting specific sampling rates. In addition, the Papoulis GSE and the previous GSE associated with FRFT are shown to be special instances of our results. Second, we address the problem of the GSE of fractional bandpass signals. A new GSE for fractional bandpass signals with equal sampling rates is derived. We show that the restriction of an even number of channels in the GSE for fractional bandpass signals is unnecessary, and perfect signal reconstruction is possible for any arbitrary number of channels. Further, we develop the GSE for a fractional bandpass signal with multiple sampling rates. Lastly, we discuss the application of the proposed method in the context of single-image super-resolution reconstruction based on GSE. Illustrations and simulations are presented to verify the validity and effectiveness of the proposed results.
Large Margin Deep Neural Networks: Theory and Algorithms

Deep neural networks (DNN) have achieved huge practical suc cess in recent years. However, its theoretical properties (in particular genera lization ability) are not yet very clear, since existing error bounds for neural networks cannot be directly used to explain the statistical behaviors of practically adopte d DNN models (which are multi-class in their nature and may contain convolutional l ayers). To tackle the challenge, we derive a new margin bound for DNN in this paper, in which the expected0-1 error of a DNN model is upper bounded by its empirical margin e rror plus a Rademacher Average based capacity term. This new boun d is very general and is consistent with the empirical behaviors of DNN models ob erved in our experiments. According to the new bound, minimizing the emp irical margin error can effectively improve the test performance of DNN. We ther efore propose large margin DNN algorithms, which impose margin penalty terms to the cross entropy loss of DNN, so as to reduce the margin error during the traini ng process. Experimental results show that the proposed algorithms can achiev e s gnificantly smaller empirical margin errors, as well as better test performance s than the standard DNN algorithm.
News Blogs and Citizen Journalism: New Directions for e-Journalism

Even for a casual observer of the journalistic industry it is becoming difficult to escape the conclusion that journalism is entering a time of crisis. At the same time that revenues and readerships for traditional publications from newspapers to broadcast news are declining, journalistic content is being overtaken by a flotilla of alternative options ranging from the news satire of The Daily Show in the United States to the citizen journalism of South Korea's OhmyNews and a myriad of other news blogs and citizen journalism Websites. Worse still, such new competitors with the products of the journalism industry frequently take professional journalists themselves to task where their standards have appeared to have slipped, and are beginning to match the news industry's incumbents in terms of insight and informational value: recent studies have shown, for example, that avid Daily Show viewers are as if not better informed about the U.S. political process as those who continue to follow mainstream print or television news (see e.g. Fox et al., 2007). The show's host Jon Stewart – who has consistently maintained his self-description as a comedian, not a journalist – even took the fight directly to the mainstream with his appearance on CNN's belligerent talk show Crossfire, repeatedly making the point that the show's polarised and polarising 'left vs. right' format was " hurting " politics in America (the show disappeared from CNN's line-up a few months after Stewart's appearance; Stewart, 2004). Similarly, news bloggers and citizen journalists have shown persistence and determination both in uncovering political and other scandals, and in highlighting the shortcomings of professional journalism as it investigates and reports on such scandals. This gradual decline of industrial journalism as the dominant force in the public sphere can be linked directly with a broader shift from industrial to post-industrial paradigms. As the industrial age makes way for the information age, and as its hierarchical and centralised structures for the organisation of production, distribution, and market economies transform towards a networked, heterarchical
Piriformospora indica: Potential and Significance in Plant Stress Tolerance

Owing to its exceptional ability to efficiently promote plant growth, protection and stress tolerance, a mycorrhiza like endophytic Agaricomycetes fungus Piriformospora indica has received a great attention over the last few decades. P. indica is an axenically cultiviable fungus which exhibits its versatility for colonizing/hosting a broad range of plant species through directly manipulating plant hormone-signaling pathway during the course of mutualism. P. indica-root colonization leads to a better plant performance in all respect, including enhanced root proliferation by indole-3-acetic acid production which in turn results into better nutrient-acquisition and subsequently to improved crop growth and productivity. Additionally, P. indica can induce both local and systemic resistance to fungal and viral plant diseases through signal transduction. P. indica-mediated stimulation in antioxidant defense system components and expressing stress-related genes can confer crop/plant stress tolerance. Therefore, P. indica can biotize micropropagated plantlets and also help these plants to overcome transplantation shock. Nevertheless, it can also be involved in a more complex symbiotic relationship, such as tripartite symbiosis and can enhance population dynamic of plant growth promoting rhizobacteria. In brief, P. indica can be utilized as a plant promoter, bio-fertilizer, bioprotector, bioregulator, and biotization agent. The outcome of the recent literature appraised herein will help us to understand the physiological and molecular bases of mechanisms underlying P. indica-crop plant mutual relationship. Together, the discussion will be functional to comprehend the usefulness of crop plant-P. indica association in both achieving new insights into crop protection/improvement as well as in sustainable agriculture production.
Melanin-associated pigmented lesions of the oral mucosa: presentation, differential diagnosis, and treatment.

Intraoral pigmentation is quite common and has numerous etiologies, ranging from exogenous to physiological to neoplastic. Many pigmented lesions of the oral cavity are associated with melanin pigment. The differential diagnosis of mucosal pigmented lesions includes hematomas, varices, and petechiae which may appear to be pigmented. Unlike cutaneous melanomas, oral melanomas are diagnosed late and have a poor prognosis regardless of depth of invasion. As such, the clinical presentation and treatment of intraoral melanoma will be discussed. Developing a differential diagnosis is imperative for a clinician faced with these lesions in order to appropriately treat the patient. This article will focus on the most common oral melanocytic lesions, along with mimics.
Deep Learning Based MIMO Communications

We introduce a novel physical layer scheme for single user Multiple-Input Multiple-Output (MIMO) communications based on unsupervised deep learning using an autoencoder. This method extends prior work on the joint optimization of physical layer representation and encoding and decoding processes as a single end-to-end task by expanding transmitter and receivers to the multi-antenna case. We introduce a widely used domain appropriate wireless channel impairment model (Rayleigh fading channel), into the autoencoder optimization problem in order to directly learn a system which optimizes for it. We considered both spatial diversity and spatial multiplexing techniques in our implementation. Our deep learning-based approach demonstrates significant potential for learning schemes which approach and exceed the performance of the methods which are widely used in existing wireless MIMO systems. We discuss how the proposed scheme can be easily adapted for open-loop and closed-loop operation in spatial diversity and multiplexing modes and extended use with only compact binary channel state information (CSI) as feedback.
Indexing Fingerprint Databases Based on Multiple Features

In a fingerprint identification system, a person is identified only by his fingerprint. To accomplish this, a database is searched by matching all entries to the given fingerprint. However, the maximum size of the database is limited, since each match takes some amount of time and has a small probability of error. A solution to this problem is to reduce the number of fingerprints that have to be matched. This is achieved by extracting features from the fingerprints and first matching the fingerprints that have the smallest feature distance to the query fingerprint. Using this indexing method, modern systems are able to search databases up to a few hundred fingerprints. In this paper, three possible fingerprint indexing features are discussed: the registered directional field estimate, FingerCode and minutiae triplets. It is shown that indexing schemes that are based on these features, are able to search a database more effectively than a simple linear scan. Next, a new indexing scheme is constructed that is based on advanced methods of combining these features. It is shown that this scheme results in a considerably better performance than the schemes that are based on the individual features or on more naive methods of combining the features, thus allowing much larger fingerprint databases to be searched. image processing, fingerprint image classification, database search, multiple classifiers.
The Evaluation of Sentence Similarity Measures

The ability to accurately judge the similarity between natural language sentences is critical to the performance of several applications such as text mining, question answering, and text summarization. Given two sentences, an effective similarity measure should be able to determine whether the sentences are semantically equivalent or not, taking into account the variability of natural language expression. That is, the correct similarity judgment should be made even if the sentences do not share similar surface form. In this work, we evaluate fourteen existing text similarity measures which have been used to calculate similarity score between sentences in many text applications. The evaluation is conducted on three different data sets, TREC9 question variants, Microsoft Research paraphrase corpus, and the third recognizing textual entailment data set.
Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels

Training deep networks for semantic segmentation requires annotation of large amounts of data, which can be time-consuming and expensive. Unfortunately, these trained networks still generalize poorly when tested in domains not consistent with the training data. In this paper, we show that by carefully presenting a mixture of labeled source domain and proxy-labeled target domain data to a network, we can achieve state-of-the-art unsupervised domain adaptation results. With our design, the network progressively learns features specific to the target domain using annotation from only the source domain. We generate proxy labels for the target domain using the network’s own predictions. Our architecture then allows selective mining of easy samples from this set of proxy labels, and hard samples from the annotated source domain. We conduct a series of experiments with the GTA5, Cityscapes and BDD100k datasets on synthetic-to-real domain adaptation and geographic domain adaptation, showing the advantages of our method over baselines and existing approaches.
A Comparison of FAST , SURF , Eigen , Harris , and MSER Features

Precise, successful in desire target, strong healthy and self loading image registration is critical task in the field of computer vision. The most require key steps of image alignment/ registration are: Feature matching, Feature detection, , derivation of transformation function based on corresponding features in images and reconstruction of images based on derived transformation function. This is also the aim of computer vision in many applications to achieve an optimal and accurate image, which depends on optimal features matching and detection. The investigation of this paper summarize the coincidence among five different methods for robust features/interest points (or landmarks) detector and indentify images which are (FAST), Speed Up Robust Features (SURF), (Eigen),( Harris) & Maximally Stable Extremal Regions ( MSER). This paper also focuses on the unique extraction from the images which can be used to perform good matching on different views of the images/objects/scenes.
Simple, Accurate, and Robust Projector-Camera Calibration

Structured-light systems are simple and effective tools to acquire 3D models. Built with off-the-shelf components, a data projector and a camera, they are easy to deploy and compare in precision with expensive laser scanners. But such a high precision is only possible if camera and projector are both accurately calibrated. Robust calibration methods are well established for cameras but, while cameras and projectors can both be described with the same mathematical model, it is not clear how to adapt these methods to projectors. In consequence, many of the proposed projector calibration techniques make use of a simplified model, neglecting lens distortion, resulting in loss of precision. In this paper, we present a novel method to estimate the image coordinates of 3D points in the projector image plane. The method relies on an uncalibrated camera and makes use of local homographies to reach sub-pixel precision. As a result, any camera model can be used to describe the projector, including the extended pinhole model with radial and tangential distortion coefficients, or even those with more complex lens distortion models.
Providing open access to PhD theses: visibility and citation benefits

The main purpose of this paper is to report the benefits of scientific communication model of open access repositories to e-theses. Specifically, the study focuses on benefits as accessibility, dissemination, visibility and impact of PhD etheses deposited in repositories.  The study analysed a random sample of 125 PhD e-theses deposited in an open access repository, specifically in the institutional repository of the University of Salamanca (Gredos). It tracked the sample for analysing on one side the visits and downloads of the e-theses, whose data are obtained from the statistics module of the repository, and otherwise for analysing the data citation provided by Google Scholar Citation on these e-theses. To analyse the differences between the indicators (visits, downloads and citations) in different knowledge areas, the Kurskall-Wallis test has been used.  The results indicate that Open Access institutional repositories become an advantageous channel of scientific communication to grey literature like dissertations and PhD theses, because it increases visibility and use and also it produces a rate of citation.  In this paper it uses metrics are used in institutional repository to measure the visibility and impact of a type of grey literature that is very difficult to track because it is unpublished. The dissemination of the grey literature through open access repositories makes it possible. The value of the work lies in the empirical evidence obtained from analysed PhD e-theses of Gredos repository.