nils-herrmann/clean_scirepeval huggingface dataset:
Less is More : Financial Constraints and Innovative Efficiency

We show that financial constraints may benefit innovation by improving the efficiency of innovative activities. We measure firm-level innovative efficiency by patents (or patent citations) scaled by R&D (research and development) investment or the number of employees, and find that financial constraints are positively associated with innovative efficiency. Tests using the 1989 junk bond crisis as an exogenous shock to financial constraints suggest a causal interpretation for the link. Consistent with agency problems, the positive effect of financial constraints on innovative efficiency is stronger among firms with high excess cash holdings and low investment opportunities, and among firms in less competitive industries. Financial constraints appear to mitigate free cash flow problems that induce firms to make unproductive R&D investment in fields out of their direct expertise. Our findings point to a bright side of the role of financial constraints for corporate investment in intangible assets.
Robust Subspace Clustering for Multi-View Data by Exploiting Correlation Consensus

More often than not, a multimedia data described by multiple features, such as color and shape features, can be naturally decomposed of multi-views. Since multi-views provide complementary information to each other, great endeavors have been dedicated by leveraging multiple views instead of a single view to achieve the better clustering performance. To effectively exploit data correlation consensus among multi-views, in this paper, we study subspace clustering for multi-view data while keeping individual views well encapsulated. For characterizing data correlations, we generate a similarity matrix in a way that high affinity values are assigned to data objects within the same subspace across views, while the correlations among data objects from distinct subspaces are minimized. Before generating this matrix, however, we should consider that multi-view data in practice might be corrupted by noise. The corrupted data will significantly downgrade clustering results. We first present a novel objective function coupled with an angular based regularizer. By minimizing this function, multiple sparse vectors are obtained for each data object as its multiple representations. In fact, these sparse vectors result from reaching data correlation consensus on all views. For tackling noise corruption, we present a sparsity-based approach that refines the angular-based data correlation. Using this approach, a more ideal data similarity matrix is generated for multi-view data. Spectral clustering is then applied to the similarity matrix to obtain the final subspace clustering. Extensive experiments have been conducted to validate the effectiveness of our proposed approach.
Stability Criteria for Switched and Hybrid Systems

The study of the stability properties of switched and hybrid systems gives rise to a number of interesting and challenging mathematical problems. The objective of this paper is to outline some of these problems, to review progress made in solving these problems in a number of diverse communities, and to review some problems that remain open. An important contribution of our work is to bring together material from several areas of research and to present results in a unified manner. We begin our review by relating the stability problem for switched linear systems and a class of linear differential inclusions. Closely related to the concept of stability are the notions of exponential growth rates and converse Lyapunov theorems, both of which are discussed in detail. In particular, results on common quadratic Lyapunov functions and piecewise linear Lyapunov functions are presented, as they represent constructive methods for proving stability, and also represent problems in which significant progress has been made. We also comment on the inherent difficulty of determining stability of switched systems in general which is exemplified by NP-hardness and undecidability results. We then proceed by considering the stability of switched systems in which there are constraints on the switching rules, through both dwell time requirements and state dependent switching laws. Also in this case the theory of Lyapunov functions and the existence of converse theorems is reviewed. We briefly comment on the classical Lur’e problem and on the theory of stability radii, both of which contain many of the features of switched systems and are rich sources of practical results on the topic. Finally we present a list of questions and open problems which provide motivation for continued research in this area.
PROTEIN IDENTIFICATION USING FOURIER TRANFORM INFRARED ( FTIR )

The research was conducted to identify the presence of protein by indicating amide groups and measuring its level in food through specific groups of protein using FTIR (Fourier Transformed Infrared) method. The scanning process was conducted on wavenumber 400—4000 cm -1 . The determination of functional group was being done by comparing wavenumber of amide functional groups of the protein samples to existing standard. Protein level was measured by comparing absorbance of protein specific functional groups to absorbance of fatty acid functional groups. Result showed the FTIR spectrums of all samples were on 557-3381 cm -1 wavenumber range. The amides detected were Amide III, IV, and VI with absorbance between trace until 0.032%. The presence of protein can be detected in samples animal and vegetable cheese, butter, and milk through functional groups of amide III, IV, and VI were on 1240-1265 cm -1 , 713-721 cm -1 , and 551-586 cm -1 wavenumber respectively . Urine was detected through functional groups of amide III and IV were on 1639 cm -1 and 719 cm -1 wavenumber. The protein level of animal cheese, vegetable cheese, butter, and milk were 1.01%, 1.0%, 0.86%, and 1.55% respectively.
Accountable Anonymous Group Messaging

Users often wish to participate in online groups anonymously, but misbehaving users may abuse this anonymity to spam or disrupt the group. Messaging protocols such as Mix-nets and DC-nets leave online groups vulnerable to denial-of-service and Sybil attacks, while accountable voting protocols are unusable or inefficient for general anonymous messaging. We present the first general messaging protocol that offers provable anonymity with accountability for moderate-size groups, and efficiently handles unbalanced loads where few members have much data to transmit in a given round. The N group members first cooperatively shuffle an N ×N matrix of pseudorandom seeds, then use these seeds in N “preplanned” DC-nets protocol runs. Each DC-nets run transmits the variable-length bulk data comprising one member’s message, using the minimum number of bits required for anonymity under our attack model. The protocol preserves message integrity and one-to-one correspondence between members and messages, makes denial-of-service attacks by members traceable to the culprit, and efficiently handles large and unbalanced message loads. A working prototype demonstrates the protocol’s practicality for anonymous messaging in groups of 40+ member nodes.
Fetal Ventriculomegaly: Investigating Additional Brain Abnormalities by using MR Imaging

Fetal ventriculomegaly (VM), is known to have a prevalence of association with other brain abnormalities. By using in utero MR imaging (iuMR), we aimed to determine the prevalence of brain abnormalities other than VM and investigate the hypothesis that the prevalence of having additional brain abnormalities (ABA) increases with the degree of VM.  We reviewed iuMR images of 75 fetuses. The enlarged ventricles were graded as mild (≤ 12mm), moderate (> 12 to ≤15mm), and severe (> 15mm). Prevalence of having ABA were calculated and compared among groups with different grades of VM.  ABA was shown in 67% of fetuses. The most frequent ABA was agenesis of the corpus callosum. The prevalence of having ABA increased with the degree of fetal VM in all groups. Severe VM was associated with a higher prevalence of ABA when compared with mild VM in the prevalence of having ABA being present when compared with mild VM.  The prevalence of having ABA increases with the degree of fetal VM. Caution should be exercised that this trend of ABA does not apply to those fetuses with isolated VM by sonography due to the different subjects
Radio interferometric calibration using a riemannian manifold

In order to cope with the increased data volumes generated by modern radio interferometers such as LOFAR (Low Frequency Array) or SKA (Square Kilometre Array), fast and efficient calibration algorithms are essential. Traditional radio interferometric calibration is performed using nonlinear optimization techniques such as the Levenberg-Marquardt algorithm in Euclidean space. In this paper, we reformulate radio interferometric calibration as a nonlinear optimization problem on a Riemannian manifold. The reformulated calibration problem is solved using the Riemannian trust-region method. We show that calibration on a Riemannian manifold has faster convergence with reduced computational cost compared to conventional calibration in Euclidean space.
Parameter estimation of integrating and time delay processes using single relay feedback test.

Autotuning using relay feedback is widely used to identify low order integrating plus dead time (IPDT) systems as the method is simple and is operated in closed-loop without interrupting the production process. Oscillatory responses from the process due to ideal relay input are collected to calculate ultimate properties of the system that in turn are used to model the responses as functions of system model parameters. These theoretical models of relay response are validated. After adjusting the phase shift, input and output responses are used to find land mark points that are used to formulate algorithms for parameter estimation of the process model. The method is even applicable to distorted relay responses due to load disturbance or measurement noise. Closed-loop simulations are carried out using model based control strategy and performances are calculated.
Neural Autoregressive Flows

Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF) (Papamakarios et al., 2017), and to accelerate stateof-the-art WaveNet-based speech synthesis to 20x faster than real-time (Oord et al., 2017), via Inverse Autoregressive Flows (IAF) (Kingma et al., 2016). We unify and generalize these approaches, replacing the (conditionally) affine univariate transformations of MAF/IAF with a more general class of invertible univariate transformations expressed as monotonic neural networks. We demonstrate that the proposed neural autoregressive flows (NAF) are universal approximators for continuous probability distributions, and their greater expressivity allows them to better capture multimodal target distributions. Experimentally, NAF yields state-of-the-art performance on a suite of density estimation tasks and outperforms IAF in variational autoencoders trained on binarized MNIST.
On the complexity of numerical analysis

We study two quite different approaches to understanding the complexity of fundamental problems in numerical analysis. We show that both hinge on the question of understanding the complexity of the following problem, which we call PosSLP; given a division-free straight-line program producing an integer N, decide whether N > 0. We show that PosSLP lies in the counting hierarchy, and combining our results with work of Tiwari, we show that the Euclidean traveling salesman problem lies in the counting hierarchy - the previous best upper bound for this important problem (in terms of classical complexity classes) being PSPACE
Describing Semantic Representations of Brain Activity Evoked by Visual Stimuli

Quantitative modeling of human brain activity based on language representations has been actively studied in systems neuroscience. However, previous studies examined word-level representation, and little is known about whether we could recover structured sentences from brain activity. This study attempts to generate natural language descriptions of semantic contents from human brain activity evoked by visual stimuli. To effectively use a small amount of available brain activity data, our proposed method employs a pre-trained image-captioning network model using a deep learning framework. To apply brain activity to the image-captioning network, we train regression models that learn the relationship between brain activity and deep-layer image features. The results demonstrate that the proposed model can decode brain activity and generate descriptions using natural language sentences. We also conducted several experiments with data from different subsets of brain regions known to process visual stimuli. The results suggest that semantic information for sentence generations is widespread across the entire cortex.
Anesthetic Efficacy of Tricaine Methanesulfonate, Metomidate and Eugenol: Effects on Plasma Cortisol Concentration and Neutrophil Function in Fathead Minnows (Pimephales promelas Rafinesque, 1820)

Anesthetic efficacy, plasma cortisol concentration, and two parameters of neutrophil function (oxidative burst and degranulation of primary granules) were compared among three anesthetics in the fathead minnow: tricaine methanesulfonate (MS 222), metomidate hydrochloride (MTMD), and eugenol (EUG). The optimum anesthetic concentration was determined as: MS 222 75 mg L− 1, EUG 30 mg L− 1 and MTMD 4 mg L− 1. Handling and crowding stress was induced in fish with (SA) and without (S) anesthetic. Plasma cortisol concentration was measured at 0, 30, 90, and 240 min after stress and found to increase at 30 min post-stress in S and SA MS 222 groups, but not in SA MTMD and SA EUG groups. To test the effects of different anesthetics on neutrophil function, fish were divided into a baseline control group, a group exposed to handling and crowding stress (S) and a stressed anesthetized group (SA). Fish were assayed for neutrophil function before and after stress (24 h, 72 h and 7 days). The degranulation of neutrophil primary granules was measured as exocytosis of myeloperoxidase (MPO) using 3, 3′, 5, 5′-tetramethylbenzidine as a substrate. Degranulation of primary granules was decreased to 60–75% of non-stressed control in stressed and fish treated with MS 222, and was not affected when MTMD and EUG were used. The degranulation of primary granules proved to be a useful assay for measuring the effects of stress on neutrophil function in fish. Eugenol and metomidate prevented stress-induced decrease of neutrophil function while MS 222 did not.
Why Video Game Genres Fail: A Classificatory Analysis

This paper explores the current affordances and limitations of video game genre from a library and information science perspective with an emphasis on classification theory. We identify and discuss various purposes of genre relating to video games, including identity, collocation and retrieval, commercial marketing, and educational instruction. Through the use of examples, we discuss the ways in which these purposes are supported by genre classification and conceptualization, and the implications for video games. Suggestions for improved conceptualizations such as family resemblances, prototype theory, faceted classification, and appeal factors for video game genres are considered, with discussions of strengths and weaknesses. This analysis helps inform potential future practical applications for describing video games at cultural heritage institutions such as libraries, museums, and archives, as well as furthering the understanding of video game genre and genre classification for game studies at large. 3 Running head: WHY VIDEO GAME GENRES FAIL
Flood damage assessment for Dhaka City , Bangladesh

Flood damage assessment is a key component in the development of city flood risk management strategies. A flood damage assessment model is being developed by combining flood hazard information (depth, extent, velocity, duration, etc.) with geographic information (land use/cover, buildings, infrastructure, etc.), social-economic data and population demographics to estimate urban flood impacts. In this paper, Dhaka city is adopted to demonstrate the approach for damage modeling. Analysis show that coarse resolution modeling in such a densely developed urban area can lead to either overor under-estimation of the flood damage, depending on the thresholds to distinguish building and non-building cells in modeling. Increased accuracy can be achieved by fine resolution modeling but this would lead to large data sets that require long computational times. The paper demonstrates some key issues for damage assessment using advanced technology for a City like Dhaka.
Multi-Stage Math Formula Search: Using Appearance-Based Similarity Metrics at Scale

When using a mathematical formula for search (query-by-expression), the suitability of retrieved formulae often depends more upon symbol identities and layout than deep mathematical semantics. Using a Symbol Layout Tree representation for formula appearance, we propose the Maximum Subtree Similarity (MSS) for ranking formulae based upon the subexpression whose symbols and layout best match a query formula. Because MSS is too expensive to apply against a complete collection, the Tangent-3 system first retrieves expressions using an inverted index over symbol pair relationships, ranking hits using the Dice coefficient; the top-k formulae are then re-ranked by MSS. Tangent-3 obtains state-of-the-art performance on the NTCIR-11 Wikipedia formula retrieval benchmark, and is efficient in terms of both space and time. Retrieval systems for other graphical forms, including chemical diagrams, flowcharts, figures, and tables, may benefit from adopting this approach.
Improving Question Answering with External Knowledge

Prior background knowledge is essential for human reading and understanding. In this work, we investigate how to leverage external knowledge to improve question answering. We primarily focus on multiple-choice question answering tasks that require external knowledge to answer questions. We investigate the effects of utilizing external in-domain multiple-choice question answering datasets and enriching the reference corpus by external out-domain corpora (i.e., Wikipedia articles). Experimental results demonstrate the effectiveness of external knowledge on two challenging multiple-choice question answering tasks: ARC and OpenBookQA.
The Creation of Adam by Michelangelo Buonarroti (1475-1564).

Author Information Baltimore, MD Our propensity to believe that an outline of the human brain is inscribed in one of the most famous paintings of the godhead is both a reflection of the revolution initiated by the Renaissance and a function of how the organ of our humanity psychophysically interprets images. In 1306, Giotto completed a cycle of frescoes in the Arena Chapel in Padua and signed them, thus forsaking the anonymity of the old art of the Middle Ages and Byzantium and initiating the Italian Renaissance, a period in which the personality of the Western artist became a conspicuous component of his artwork. The figures in Giotto's paintings bore strong resemblances to living models, and the emotive content of their faces was true to life. The rediscovery of Greek and Roman artifacts, with their idealized conception of the human form, and the importation into Europe via Arab scholars of the humanistic works of Greek philosophers prepared the Italian soil for the straightforward assertion of individual identity and the inexorable evolution of the cultural craftsman from artisan to artist. Whereas the primitive simplifications and flatness of Byzantium remained a strong influence on artists in Spain and in the Germanic north, the innovations of Giotto and his successors radically changed the depiction of the human form throughout the south; as a consequence of the discovery of single point perspective before 1413 by the architect Filippo Brunellschi (4), this revolution reached its apogee towards the end of the fifteenth century in the city-states of Florence, Venice, and Rome. Almost two centuries after the completion of the Arena Chapel, artists regularly received commissions for both secular and religious subjects; in each realm they attempted to meld the religious prescriptions of the Catholic
Centered Weight Normalization in Accelerating Training of Deep Neural Networks

Training deep neural networks is difficult for the pathological curvature problem. Re-parameterization is an effective way to relieve the problem by learning the curvature approximately or constraining the solutions of weights with good properties for optimization. This paper proposes to reparameterize the input weight of each neuron in deep neural networks by normalizing it with zero-mean and unit-norm, followed by a learnable scalar parameter to adjust the norm of the weight. This technique effectively stabilizes the distribution implicitly. Besides, it improves the conditioning of the optimization problem and thus accelerates the training of deep neural networks. It can be wrapped as a linear module in practice and plugged in any architecture to replace the standard linear module. We highlight the benefits of our method on both multi-layer perceptrons and convolutional neural networks, and demonstrate its scalability and efficiency on SVHN, CIFAR-10, CIFAR-100 and ImageNet datasets.
SilSketch: Automated Sketch-Based Editing of Surface Meshes

We introduce an over-sketching interface for feature-preserving surface mesh editing. The user sketches a stroke that is the suggested position of part of a silhouette of the displayed surface. The system then segments all image-space silhouettes of the projected surface, identifies among all silhouette segments the best matching part, derives vertices in the surface mesh corresponding to the silhouette part, selects a sub-region of the mesh to be modified, and feeds appropriately modified vertex positions together with the sub-mesh into a mesh deformation tool. The overall algorithm has been designed to enable interactive modification of the surface --- yielding a surface editing system that comes close to the experience of sketching 3D models on paper.
Machine Learning and Lexicon Based Methods for Sentiment Classification: A Survey

Sentiment classification is an important subject in text mining research, which concerns the application of automatic methods for predicting the orientation of sentiment present on text documents, with many applications on a number of areas including recommender and advertising systems, customer intelligence and information retrieval. In this paper, we provide a survey and comparative study of existing techniques for opinion mining including machine learning and lexicon-based approaches, together with evaluation metrics. Also cross-domain and cross-lingual approaches are explored. Experimental results show that supervised machine learning methods, such as SVM and naive Bayes, have higher precision, while lexicon-based methods are also very competitive because they require few effort in human-labeled document and isn't sensitive to the quantity and quality of the training dataset.
Modeling, simulation and PC based implementation of a closed loop speed control of VSI fed induction motor drive

Modeling, simulation and implementation of Voltage Source Inverter (VSI) fed closed loop control of 3-phase induction motor drive is presented in this paper. A mathematical model of the drive system is developed and is used for the simulation study. Simulation is carried out using Scilab/Scicos, which is free and open source software. The above said drive system is implemented in laboratory using a PC and an add-on card. In this study the air gap flux of the machine is kept constant by maintaining Volt/Hertz (v/f) ratio constant. The experimental transient responses of the drive system obtained for change in speed under no load as well as under load conditions are presented.