nils-herrmann/clean_scirepeval huggingface dataset:
Guaranteed Tensor PCA with Optimality in Statistics and Computation

Tensors, or high-order arrays, attract much attention in recent research. In this paper, we propose a general framework for tensor principal component analysis (tensor PCA), which focuses on the methodology and theory for extracting the hidden low-rank structure from the high-dimensional tensor data. A unified solution is provided for tensor PCA with considerations in both statistical limits and computational costs. The problem exhibits three different phases according to the signal-noise-ratio (SNR). In particular, with strong SNR, we propose a fast spectral power iteration method that achieves the minimax optimal rate of convergence in estimation; with weak SNR, the information-theoretical lower bound shows that it is impossible to have consistent estimation in general; with moderate SNR, we show that the non-convex maximum likelihood estimation provides optimal solution, but with NPhard computational cost; moreover, under the hardness hypothesis of hypergraphic planted clique detection, there are no polynomial-time algorithms performing consistently in general. Simulation studies show that the proposed spectral power iteration method have good performance under a variety of settings. Anru Zhang is Assistant Professor, Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, ; Dong Xia is Visiting Assistant Professor, Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, E-mail: dongxia@stat.wisc.edu.
"Smart Girls" versus "Sleeping Beauties" in the Sciences: The Identification of Instant and Delayed Recognition by Using the Citation Angle

In recent years, a number of studies have introduced methods for identifying papers with delayed recognition (so called " sleeping beauties " , SBs) or have presented single publications as cases of SBs. Most recently, Ke et al. (2015) proposed the so called " beauty coefficient " (denoted as B) to quantify how much a given paper can be considered as a paper with delayed recognition. In this study, the new term " smart girl " (SG) is suggested to differentiate instant credit or " flashes in the pan " from SBs. While SG and SB are qualitatively defined, the dynamic citation angle β is introduced in this study as a simple way for identifying SGs and SBs quantitatively – complementing the beauty coefficient B. The citation angles for all articles from 1980 (n=166870) in natural sciences are calculated for identifying SGs and SBs and their extent. We reveal that about 3% of the articles are typical SGs and about 0.1% typical SBs. The potential advantages of the citation angle approach are explained.
Skin Disease Recognition Method Based on Image Color and Texture Features

Skin diseases have a serious impact on people's life and health. Current research proposes an efficient approach to identify singular type of skin diseases. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases. In this paper, three type skin diseases such as herpes, dermatitis, and psoriasis skin disease could be identified by a new recognition method. Initially, skin images were preprocessed to remove noise and irrelevant background by filtering and transformation. Then the method of grey-level co-occurrence matrix (GLCM) was introduced to segment images of skin disease. The texture and color features of different skin disease images could be obtained accurately. Finally, by using the support vector machine (SVM) classification method, three types of skin diseases were identified. The experimental results demonstrate the effectiveness and feasibility of the proposed method.
Self-reported psychotic disorders among Individuals with substance use disorders: findings from the National Epidemiologic Survey on Alcohol and Related Conditions.

Comorbidity of substance use disorders (SUDs) and psychotic disorders (PDs) presents many challenges in diagnosis and treatment. Most reports to-date focus on the prevalence of SUDs among clinical populations of patients with PDs, and there is a lack of data pertaining to rates of PDs among individuals with substance use and SUDs.



We analyzed data on 43,093 respondents age 18 and above from the National Epidemiologic Survey on Alcohol and Related Conditions, a nationally representative US survey (Wave 1, 2001-2002). Cross-tabulations were used to derive prevalence estimates of PDs among individuals with 12-month substance use or SUDs across 10 categories of substances. Odds ratios (ORs) were derived from bivariate logistic regression analyses to examine the relationships between lifetime PDs and 12-month substance use or SUDs for the specific categories of substances.



Among individuals with 12-month substance use, prevalence of PDs was found to be elevated in 8 of 10 categories of substances, particularly among amphetamine (OR = 8.8) and cocaine (OR = 10.3) users compared to nonusers. Among individuals with SUDs, prevalence of PDs was elevated in 9 of 10 categories of substances compared to individuals without SUDs.



Our findings on the increased rates of PDs among substance users and individuals with SUDs across a wide range of substances emphasize the importance of screening for PDs while treating patients with substance use and SUDs. This may allow for early intervention and adequate referral to appropriate settings.
Angular Disinhibition Effect in a Modified Poggendorff Illusion

Visual illusion can be strengthened or weakened with the addition of extra visual elements. For example, in Poggendorff illusion, with an additional bar added, the illusory skew in the perceived angle can be enlarged or reduced. In this paper, we show that a nontrivial interaction between lateral inhibitory processes in the early visual system (i.e., disinhibition) can explain such enhancement or degradation of the illusory percept. The computational model we derived successfully predicted the perceived angle in a modified Poggendorff illusion task with an extra thick bar. The concept of disinhibition employed in the model is general enough that we expect it can be further extended to account for other classes of geometric illusions.
Beyond Dominant Resource Fairness: Extensions, Limitations, and Indivisibilities

We study the problem of allocating multiple resources to agents with heterogeneous demands. Technological advances such as cloud computing and data centers provide a new impetus for investigating this problem under the assumption that agents demand the resources in fixed proportions, known in economics as Leontief preferences. In a recent paper, Ghodsi et al. [2011] introduced the dominant resource fairness (DRF) mechanism, which was shown to possess highly desirable theoretical properties under Leontief preferences. We extend their results in three directions. First, we show that DRF generalizes to more expressive settings, and leverage a new technical framework to formally extend its guarantees. Second, we study the relation between social welfare and properties such as truthfulness; DRF performs poorly in terms of social welfare, but we show that this is an unavoidable shortcoming that is shared by every mechanism that satisfies one of three basic properties. Third, and most importantly, we study a realistic setting that involves indivisibilities. We chart the boundaries of the possible in this setting, contributing a new relaxed notion of fairness and providing both possibility and impossibility results.
Continuous Time Controller Based on SMC and Disturbance Observer for Piezoelectric Actuators

In this work, analog application for the Sliding Mode Control (SMC) to piezoelectric actuators (PEA) is presented. DSP application of the algorithm suffers from ADC and DAC conversions and mainly faces limitations in sampling time interval. Moreover piezoelectric actuators are known to have very large bandwidth close to the DSP operation frequency. Therefore, with the direct analog application, improvement of the performance and high frequency operation are expected. Design of an appropriate SMC together with a disturbance observer is suggested to have continuous control output and related experimental results for position tracking are presented with comparison of DSP and analog control application.
Cystic hygroma: anesthetic considerations and review

Cystic hygroma (CH) in the cervical region presents as a challenge to the anesthetist. The anaesthetic difficulties are usually associated with CH because of tumor extension into the mouth, airway management, thoracic extension, hemorrhage, involvement of pretracheal region, Post operative respiratory obstruction and coexisting anomalies (Down syndromes, Turner syndromes and congenital cardiac defects). Disorders of the CH relevant to anaesthesia and intensive care medicine are discussed in this review.
A Type-Theoretical system for the FraCaS test suite: Grammatical Framework meets Coq

We present a type-theoretical framework for formal semantics, which leverages two existing well established tools: Grammatical Framework (GF) and Coq. The framework is the semantic equivalent of GF’s resource grammars: every syntactic construction is mapped to a (compositional) semantics. Our tool thus extends the standard GF grammar with a formal semantic backbone. We evaluated our framework on 5 sections of the FraCaS test-suite (174 examples) and significantly improved on the state of the art by 14 percentage points, obtaining 83% accuracy. Our semantics is free software and available at this url:
Using Fast Weights to Attend to the Recent Past

Until recently, research on artificial neural networks was largely restricted to systems with only two types of variable: Neural activities that represent the current or recent input and weights that learn to capture regularities among inputs, outputs and payoffs. There is no good reason for this restriction. Synapses have dynamics at many different time-scales and this suggests that artificial neural networks might benefit from variables that change slower than activities but much faster than the standard weights. These “fast weights” can be used to store temporary memories of the recent past and they provide a neurally plausible way of implementing the type of attention to the past that has recently proved very helpful in sequence-to-sequence models. By using fast weights we can avoid the need to store copies of neural activity patterns.
Agricultural waste material as potential adsorbent for sequestering heavy metal ions from aqueous solutions - a review.

Heavy metal remediation of aqueous streams is of special concern due to recalcitrant and persistency of heavy metals in environment. Conventional treatment technologies for the removal of these toxic heavy metals are not economical and further generate huge quantity of toxic chemical sludge. Biosorption is emerging as a potential alternative to the existing conventional technologies for the removal and/or recovery of metal ions from aqueous solutions. The major advantages of biosorption over conventional treatment methods include: low cost, high efficiency, minimization of chemical or biological sludge, regeneration of biosorbents and possibility of metal recovery. Cellulosic agricultural waste materials are an abundant source for significant metal biosorption. The functional groups present in agricultural waste biomass viz. acetamido, alcoholic, carbonyl, phenolic, amido, amino, sulphydryl groups etc. have affinity for heavy metal ions to form metal complexes or chelates. The mechanism of biosorption process includes chemisorption, complexation, adsorption on surface, diffusion through pores and ion exchange etc. The purpose of this review article is to provide the scattered available information on various aspects of utilization of the agricultural waste materials for heavy metal removal. Agricultural waste material being highly efficient, low cost and renewable source of biomass can be exploited for heavy metal remediation. Further these biosorbents can be modified for better efficiency and multiple reuses to enhance their applicability at industrial scale.
Probiotic bacteria as biological control agents in aquaculture.

There is an urgent need in aquaculture to develop microbial control strategies, since disease outbreaks are recognized as important constraints to aquaculture production and trade and since the development of antibiotic resistance has become a matter of growing concern. One of the alternatives to antimicrobials in disease control could be the use of probiotic bacteria as microbial control agents. This review describes the state of the art of probiotic research in the culture of fish, crustaceans, mollusks, and live food, with an evaluation of the results obtained so far. A new definition of probiotics, also applicable to aquatic environments, is proposed, and a detailed description is given of their possible modes of action, i.e., production of compounds that are inhibitory toward pathogens, competition with harmful microorganisms for nutrients and energy, competition with deleterious species for adhesion sites, enhancement of the immune response of the animal, improvement of water quality, and interaction with phytoplankton. A rationale is proposed for the multistep and multidisciplinary process required for the development of effective and safe probiotics for commercial application in aquaculture. Finally, directions for further research are discussed.
SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions

Symmetric positive semidefinite (SPSD) matrix approximation is an important problem with applications in kernel methods. However, existing SPSD matrix approximation methods such as the Nyström method only have weak error bounds. In this paper we conduct in-depth studies of an SPSD matrix approximation model and establish strong relative-error bounds. We call it the prototype model for it has more efficient and effective extensions, and some of its extensions have high scalability. Though the prototype model itself is not suitable for large-scale data, it is still useful to study its properties, on which the analysis of its extensions relies. This paper offers novel theoretical analysis, efficient algorithms, and a highly accurate extension. First, we establish a lower error bound for the prototype model, and we improve the error bound of an existing column selection algorithm to match the lower bound. In this way, we obtain the first optimal column selection algorithm for the prototype model. We also prove that the prototype model is exact under certain conditions. Second, we develop a simple column selection algorithm with a provable error bound. Third, we propose a socalled spectral shifting model to make the approximation more accurate when the spectrum of the matrix decays slowly, and the improvement is theoretically quantified. The spectral shifting method can also be applied to improve other SPSD matrix approximation models.
DETECTING OUTLIERS BY USING TRIMMING CRITERION BASED ON ROBUST SCALE ESTIMATORS WITH SAS PROCEDURE 1

Non-normal data and heteroscedasticity are two common problems encountered when dealing with testing for location measures. Non-normality exists either from the shape of the distributions or by the presence of outliers. Outliers occur when there exist data values that are very different from the majority of cases in the data set. Outliers are important because they can influence the results of the data analysis. This paper demonstrated the detection of outliers by using robust scale estimators such as MADn, Tn and LMSn as trimming criteria. These criteria will trim extreme values without prior determination of trimming percentage. Sample data was used in this study to illustrate how extreme values are removed by these trimming criteria. We will present how these were done in a SAS program.
Shape-from-Mask: A Deep Learning Based Human Body Shape Reconstruction from Binary Mask Images

3D content creation is referred to as one of the most fundamental tasks of computer graphics. And many 3D modeling algorithms from 2D images or curves have been developed over the past several decades. Designers are allowed to align some conceptual images or sketch some suggestive curves, from front, side, and top views, and then use them as references in constructing a 3D model automatically or manually. However, to the best of our knowledge, no studies have investigated on 3D human body reconstruction in a similar manner. In this paper, we propose a deep learning based reconstruction of 3D human body shape from 2D orthographic views. A novel CNN-based regression network, with two branches corresponding to frontal and lateral views respectively, is designed for estimating 3D human body shape from 2D mask images. We train our networks separately to decouple the feature descriptors which encode the body parameters from different views, and fuse them to estimate an accurate human body shape. In addition, to overcome the shortage of training data required for this purpose, we propose some significantly data augmentation schemes for 3D human body shapes, which can be used to promote further research on this topic. Extensive experimental results demonstrate that visually realistic and accurate reconstructions can be achieved effectively using our algorithm. Requiring only binary mask images, our method can help users create their own digital avatars quickly, and also make it easy to create digital human body for 3D game, virtual reality, online fashion shopping.
Fast visibility restoration from a single color or gray level image

One source of difficulties when processing outdoor images is the presence of haze, fog or smoke which fades the colors and reduces the contrast of the observed objects. We introduce a novel algorithm and variants for visibility restoration from a single image. The main advantage of the proposed algorithm compared with other is its speed: its complexity is a linear function of the number of image pixels only. This speed allows visibility restoration to be applied for the first time within real-time processing applications such as sign, lane-marking and obstacle detection from an in-vehicle camera. Another advantage is the possibility to handle both color images or gray level images since the ambiguity between the presence of fog and the objects with low color saturation is solved by assuming only small objects can have colors with low saturation. The algorithm is controlled only by a few parameters and consists in: atmospheric veil inference, image restoration and smoothing, tone mapping. A comparative study and quantitative evaluation is proposed with a few other state of the art algorithms which demonstrates that similar or better quality results are obtained. Finally, an application is presented to lane-marking extraction in gray level images, illustrating the interest of the approach.
Mechanisms, pathophysiology, and therapy of arterial stiffness.

Arterial stiffness is a growing epidemic associated with increased risk of cardiovascular events, dementia, and death. Decreased compliance of the central vasculature alters arterial pressure and flow dynamics and impacts cardiac performance and coronary perfusion. This article reviews the structural, cellular, and genetic contributors to arterial stiffness, including the roles of the scaffolding proteins, extracellular matrix, inflammatory molecules, endothelial cell function, and reactive oxidant species. Additional influences of atherosclerosis, glucose regulation, chronic renal disease, salt, and changes in neurohormonal regulation are discussed. A review of the hemodynamic impact of arterial stiffness follows. A number of lifestyle changes and therapies that reduce arterial stiffness are presented, including weight loss, exercise, salt reduction, alcohol consumption, and neuroendocrine-directed therapies, such as those targeting the renin-angiotensin aldosterone system, natriuretic peptides, insulin modulators, as well as novel therapies that target advanced glycation end products.
Dense-Captioning Events in Videos

Most natural videos contain numerous events. For example, in a video of a “man playing a piano”, the video might also contain “another man dancing” or “a crowd clapping”. We introduce the task of dense-captioning events, which involves both detecting and describing events in a video. We propose a new model that is able to identify all events in a single pass of the video while simultaneously describing the detected events with natural language. Our model introduces a variant of an existing proposal module that is designed to capture both short as well as long events that span minutes. To capture the dependencies between the events in a video, our model introduces a new captioning module that uses contextual information from past and future events to jointly describe all events. We also introduce ActivityNet Captions, a large-scale benchmark for dense-captioning events. ActivityNet Captions contains 20k videos amounting to 849 video hours with 100k total descriptions, each with its unique start and end time. Finally, we report performances of our model for dense-captioning events, video retrieval and localization.
Shadow Detection and Compensation in High Resolution Satellite Image Based on Retinex

In this paper, shadow detection and compensation are treated as image enhancement tasks. The principal components analysis (PCA) and luminance based multi-scale Retinex (LMSR) algorithm are explored to detect and compensate shadow in high resolution satellite image. PCA provides orthogonally channels, thus allow the color to remain stable despite the modification of luminance. Firstly, the PCA transform is used to obtain the luminance channel, which enables us to detect shadow regions using histogram threshold technique. After detection, the LMSR technique is used to enhance the image only in luminance channel to compensate for shadows. Then the enhanced image is obtained by inverse transform of PCA. The final shadow compensation image is obtained by comparison of the original image, the enhanced image and the shadow detection image. Experiment results show the effectiveness of the proposed method.
Ground penetrating synthetic aperture radar imaging providing soil permittivity estimation

In this paper a combined Ground Penetrating Radar (GPR) and Synthetic Aperture Radar (SAR) technique is introduced, which considers the soil surface refraction and the wave propagation in the ground. By using Fermat's principle and the Sober operator, the SAR image of the GPR data is optimized, whereas the soil's permittivity is estimated. The theoretical approach is discussed thoroughly and measurements that were carried out on a test sand box verify the proposed technique.