nils-herrmann/clean_scirepeval huggingface dataset:
Empirical study of PROXTONE and PROXTONE+ for fast learning of large scale sparse models

PROXTONE is a novel and fast method for the optimization of large scale non-smooth convex problems . In this work, we try to use the PROXTONE method in solving large scale non-smooth non-convex problems, for example training of sparse deep neural networks (sparse DNN) or sparse convolutional neural networks (sparse CNN) for embedded or mobile device. PROXTONE converges much faster than first order methods, while first order methods are easy to derive and control the sparseness of the solutions. Thus in some applications, in order to train sparse models fast, we propose to combine the merits of both methods, that is we use PROXTONE in the first several epochs to reach the neighborhood of an optimal solution, and then use the first order method to explore the possibility of sparsity in the following training. We call such method PROXTONE plus (PROXTONE+). Both PROXTONE and PROXTONE+ are tested in our experiments, and which demonstrate both methods improved convergence speed twice as fast at least on diverse sparse model learning problems, and at the same time reduce the size to 0.5% for DNN models. The source of all the algorithms is available upon request.
Protein therapeutics: a summary and pharmacological classification

Once a rarely used subset of medical treatments, protein therapeutics have increased dramatically in number and frequency of use since the introduction of the first recombinant protein therapeutic — human insulin — 25 years ago. Protein therapeutics already have a significant role in almost every field of medicine, but this role is still only in its infancy. This article overviews some of the key characteristics of protein therapeutics, summarizes the more than 130 protein therapeutics used currently and suggests a new classification of these proteins according to their pharmacological action.
Social networking site use by mothers of young children

In this paper, we present the first formal study of how mothers of young children (aged three and under) use social networking sites, particularly Facebook and Twitter, including mothers' perceptions of which SNSes are appropriate for sharing information about their children, changes in post style and frequency after birth, and the volume and nature of child-related content shared in these venues. Our findings have implications for improving the utility and usability of SNS tools for mothers of young children, as well as for creating and improving sociotechnical systems related to maternal and child health.
ONLINE, REVERSE AUCTIONS: ISSUES, THEMES, AND PROSPECTS FOR THE FUTURE* Invited manuscript forthcoming in the MSI/JAMS Special Issue on Marketing to and Serving Customers Through the Internet: Conceptual Frameworks, Practical Insights and Research Directions

Online, reverse auctions are increasingly being utilized in industrial sourcing activities. This phenomenon represents a novel, emerging area of inquiry with significant implications for sourcing strategies. However, there is little systematic thinking or empirical evidence on the topic. In this paper, the use of these auctions in sourcing activities is reviewed and four key aspects are highlighted: (i) the differences from physical auctions or those of the theoretical literature, (ii) the conditions for using online, reverse auctions, (iii) methods for structuring the auctions, and (iv) evaluations of auction performance. Some empirical evidence on these issues is also provided.  ISSUES, THEMES, AND PROSPECTS FOR THE FUTURE INTRODUCTION For nearly the past decade, managers, analysts, researchers, and the business press have been remarking that, “The Internet will change everything.” And since the advent of the Internet, we have seen it challenge nearly every aspect of marketing practice. This raises the obligation to consider the consequences of the Internet to management practices, the theme of this special issue. Yet, it may take decades to fully understand the impact of the Internet on marketing practice, in general. This paper is one step in that direction. Specifically, I consider the impact of the Internet in a business-to-business context, the sourcing of direct and indirect materials from a supply base. It has been predicted that the Internet will bring about $1 trillion in efficiencies to the annual $7 trillion that is spent on the procurement of goods and services worldwide (USA Today, 2/7/00, B1). How and when this will happen remains an open question. However, one trend that is showing increasing promise is the use of online, reverse auctions. Virtually every major industry has begun to use and adopt these auctions on a regular basis (Smith 2002). During the late 1990s, slow-growth, manufacturing firms such as Boeing, SPX/Eaton, United Technologies, and branches of the United States military, utilized these auctions. Since then, consumer product companies such as Emerson Electronics, Nestle, and Quaker have followed suit. Even high-tech firms such as Dell, Hewlett-Packard, Intel, and Sun Microsystems have increased their usage of auctions in sourcing activities. And the intention and potential for the use of these auctions to continue to grow in the future is clear. In their annual survey of purchasing managers, Purchasing magazine found that 25% of its respondents expected to use reverse auctions in their sourcing efforts. Currently, the annual throughput in these auctions is estimated to be $40 billion; however, the addressable spend of the Global 500 firms is potentially $6.3 trillion.
New challenges and innovation in forensic toxicology: focus on the "New Psychoactive Substances".

In the recent years, new molecules have appeared in the illicit market, claimed to contain "non-illegal" compounds, although exhibiting important psychoactive effects; this heterogeneous and rapidly evolving class of compounds are commonly known as "New Psychoactive Substances" or, less properly, "Smart Drugs" and are easily distributed through the e-commerce or in the so-called "Smart Shops". They include, among other, synthetic cannabinoids, cathinones and tryptamine analogs of psylocin. Whereas cases of intoxication and death have been reported, the phenomenon appears to be largely underestimated and is a matter of concern for Public Health. One of the major points of concern depends on the substantial ineffectiveness of the current methods of toxicological screening of biological samples to identify the new compounds entering the market. These limitations emphasize an urgent need to increase the screening capabilities of the toxicology laboratories, and to develop rapid, versatile yet specific assays able to identify new molecules. The most recent advances in mass spectrometry technology, introducing instruments capable of detecting hundreds of compounds at nanomolar concentrations, are expected to give a fundamental contribution to broaden the diagnostic spectrum of the toxicological screening to include not only all these continuously changing molecules but also their metabolites. In the present paper a critical overview of the opportunities, strengths and limitations of some of the newest analytical approaches is provided, with a particular attention to liquid phase separation techniques coupled to high accuracy, high resolution mass spectrometry.
Machine learning approach to color constancy

A number of machine learning (ML) techniques have recently been proposed to solve color constancy problem in computer vision. Neural networks (NNs) and support vector regression (SVR) in particular, have been shown to outperform many traditional color constancy algorithms. However, neither neural networks nor SVR were compared to simpler regression tools in those studies. In this article, we present results obtained with a linear technique known as ridge regression (RR) and show that it performs better than NNs, SVR, and gray world (GW) algorithm on the same dataset. We also perform uncertainty analysis for NNs, SVR, and RR using bootstrapping and show that ridge regression and SVR are more consistent than neural networks. The shorter training time and single parameter optimization of the proposed approach provides a potential scope for real time video tracking application.
An Empirical Investigation of Systematic Reviews in Software Engineering

Systematic Literature Reviews (SLRs) have gained significant popularity among software engineering (SE) researchers since 2004. Several researchers have also been working on improving the scientific and technological support for SLRs in SE. We argue that there is also an essential need for evidence-based body of knowledge about different aspects of the adoption of SLRs in SE.  The main objective of this research is to empirically investigate the adoption and use of SLRs in SE research from various perspectives.  We used multi-method approach as it is based on a combination of complementary research methods which are expected to compensate each others' limitations.  A large majority of the participants are convinced of the value of using a rigorous and systematic methodology for literature reviews. However, there are concerns about the required time and resources for SLRs. One of the most important motivators for performing SLRs is new findings and inception of innovative ideas for further research. The reported SLRs are more influential compared to the traditional literature reviews in terms of number of citations. One of the main challenges of conducting SLRs is drawing a balance between rigor and required effort.  SLR has become a popular research methodology for conducting literature review and evidence aggregation in SE. There is an overall positive perception about this methodology. The findings provide interesting insights into different aspects of SLRs. We expect that the findings can provide valuable information to readers on what can be expected from conducting SLRs and the potential impact of such reviews.
Extracting the frequencies of the pinna spectral notches in measured head related impulse responses.

The head related impulse response (HRIR) characterizes the auditory cues created by scattering of sound off a person's anatomy. The experimentally measured HRIR depends on several factors such as reflections from body parts (torso, shoulder, and knees), head diffraction, and reflection/ diffraction effects due to the pinna. Structural models (Algazi et al., 2002; Brown and Duda, 1998) seek to establish direct relationships between the features in the HRIR and the anatomy. While there is evidence that particular features in the HRIR can be explained by anthropometry, the creation of such models from experimental data is hampered by the fact that the extraction of the features in the HRIR is not automatic. One of the prominent features observed in the HRIR, and one that has been shown to be important for elevation perception, are the deep spectral notches attributed to the pinna. In this paper we propose a method to robustly extract the frequencies of the pinna spectral notches from the measured HRIR, distinguishing them from other confounding features. The method also extracts the resonances described by Shaw (1997). The techniques are applied to the publicly available CIPIC HRIR database (Algazi et al., 2001c). The extracted notch frequencies are related to the physical dimensions and shape of the pinna.
Detection and Recognition of Text Embedded in Online Images via Neural Context Models

We address the problem of detecting and recognizing the text embedded in online images that are circulated over the Web. Our idea is to leverage context information for both text detection and recognition. For detection, we use local image context around the text region, based on that the text often sequentially appear in online images. For recognition, we exploit the metadata associated with the input online image, including tags, comments, and title, which are used as a topic prior for the word candidates in the image. To infuse such two sets of context information, we propose a contextual text spotting network (CTSN). We perform comparative evaluation with five state-of-the-art text spotting methods on newly collected Instagram and Flickr datasets. We show that our approach that benefits from context information is more successful for text spotting in online images.
The Intelligent Crude Oil Anti-theft System Based on IoT Under Different Scenarios

Oil theft always results in huge economic loss, human casualties, and extremely environmental pollution especially when the leaks from crude oil pipeline are not detected and repaired timely. In this paper, we focus on how to detect and monitor abnormal noise and vibration beforehand or in real time by the Internet of Things (IoT). Firstly, the diversities of crude oil theft and the difficulties of oil anti-theft are analyzed in China, and the requirement analysis of the IoT application is stated. Secondly, the intelligent antitheft system based on the IoT is planned and designed for crude oil transportation by tank trucks and by oil pipelines according to the current situation in China. Thirdly, the problems of anti-theft system implementation are discussed, and the suggestions and advice are put forward to ensure that the system can be implemented successfully. The intelligent anti-theft system application can not only stop oil theft timely, but also prevent oil mice from stealing crude oil beforehand.
The ABCs of solute carriers: physiological, pathological and therapeutic implications of human membrane transport proteins

The Human Genome Organisation (HUGO) Nomenclature Committee Database provides a list of transporter families of the solute carrier (SLC) gene series . Currently, it includes 43 families and 298 transporter genes. This special issue features mini-reviews on each of these SLC families written by the experts in each field. A WEB site has been established ( http://www.pharmaconference.org/slctable.asp ) that gives the latest updates for the SLC families and their members as well as relevant links to gene databases and reviews in the literature. A list of all currently known SLC families, a discussion of additional SLC families and family members as well as a brief summary of non-SLC transporter genes is included in this introduction.
Automatic Trimap Generation with Structured Lighting and Chromakey

This project has as an objective of the extraction of humans in the foreground of image by creating a trimap which combines a depth map analysis and the Chromakey technique. The trimap is generated automatically, differing from the manual implementations which require user interaction. The extraction is based on extra information deriving from a structured lighting device (Kinect) integrated with a high resolution camera. With the junction of the monochromatic Kinect camera and the high definition camera, the results so far have been more expressive than only using the RGB and monochromatic cameras from the Kinect.
Recent advances in bioprinting techniques: approaches, applications and future prospects

Bioprinting technology shows potential in tissue engineering for the fabrication of scaffolds, cells, tissues and organs reproducibly and with high accuracy. Bioprinting technologies are mainly divided into three categories, inkjet-based bioprinting, pressure-assisted bioprinting and laser-assisted bioprinting, based on their underlying printing principles. These various printing technologies have their advantages and limitations. Bioprinting utilizes biomaterials, cells or cell factors as a “bioink” to fabricate prospective tissue structures. Biomaterial parameters such as biocompatibility, cell viability and the cellular microenvironment strongly influence the printed product. Various printing technologies have been investigated, and great progress has been made in printing various types of tissue, including vasculature, heart, bone, cartilage, skin and liver. This review introduces basic principles and key aspects of some frequently used printing technologies. We focus on recent advances in three-dimensional printing applications, current challenges and future directions.
Compressive Sampling and Lossy Compression

Recent results in compressive sampling have shown that sparse signals can be recovered from a small number of random measurements. This property raises the question of whether random measurements can provide an efficient representation of sparse signals in an information-theoretic sense. Through both theoretical and experimental results, we show that encoding a sparse signal through simple scalar quantization of random measurements incurs a significant penalty relative to direct or adaptive encoding of the sparse signal. Information theory provides alternative quantization strategies, but they come at the cost of much greater estimation complexity.
Falls and consequent injuries in hospitalized patients: effects of an interdisciplinary falls prevention program

Patient falls in hospitals are common and may lead to negative outcomes such as injuries, prolonged hospitalization and legal liability. Consequently, various hospital falls prevention programs have been implemented in the last decades. However, most of the programs had no sustained effects on falls reduction over extended periods of time.



This study used a serial survey design to examine in-patient fall rates and consequent injuries before and after the implementation of an interdisciplinary falls prevention program (IFP) in a 300-bed urban public hospital. The population under study included adult patients, hospitalized in the departments of internal medicine, geriatrics, and surgery. Administrative patient data and fall incident report data from 1999 to 2003 were examined and summarized using frequencies, proportions, means and standard deviations and were analyzed accordingly.



A total of 34,972 hospitalized patients (mean age: 67.3, SD +/- 19.3 years; female 53.6%, mean length of stay: 11.9 +/- 13.2 days, mean nursing care time per day: 3.5 +/- 1.4 hours) were observed during the study period. Overall, a total of 3,842 falls affected 2,512 (7.2%) of the hospitalized patients. From these falls, 2,552 (66.4%) were without injuries, while 1,142 (29.7%) falls resulted in minor injuries, and 148 (3.9%) falls resulted in major injuries. The overall fall rate in the hospitals' patient population was 8.9 falls per 1,000 patient days. The fall rates fluctuated slightly from 9.1 falls in 1999 to 8.6 falls in 2003. After the implementation of the IFP, in 2001 a slight decrease to 7.8 falls per 1,000 patient days was observed (p = 0.086). The annual proportion of minor and major injuries did not decrease after the implementation of the IFP. From 1999 to 2003, patient characteristics changed in terms of slight increases (female gender, age, consumed nursing care time) or decreases (length of hospital stay), as well as the prevalence of fall risk factors increased up to 46.8% in those patients who fell.



Following the implementation of an interdisciplinary falls prevention program, neither the frequencies of falls nor consequent injuries decreased substantially. Future studies need to incorporate strategies to maximize and evaluate ongoing adherence to interventions in hospital falls prevention programs.
Specific Diversity of Metarhizium Isolates Infecting Aeneolamia spp. (Hemiptera: Cercopidae) in Sugarcane Plantations

Spittlebugs from the genus Aeneolamia are important pests of sugarcane. Although the use of the entomopathogenic fungus Metarhizum anisopliae s.l. for control of this pest is becoming more common in Mexico, fundamental information regarding M. anisopliae in sugarcane plantations is practically non-existent. Using phylogenetic analysis, we determined the specific diversity of Metarhizium spp. infecting adult spittlebugs in sugarcane plantations from four Mexican states. We obtained 29 isolates of M. anisopliae s.str. Haplotype network analysis revealed the existence of eight haplotypes. Eight selected isolates, representing the four Mexican states, were grown at different temperatures in vitro; isolates from Oaxaca achieved the greatest growth followed by isolates from Veracruz, San Luis Potosi and Tabasco. No relationship was found between in vitro growth and haplotype diversity. Our results represent a significant contribution to the better understanding of the ecology of Metarhizum spp. in the sugarcane agroecosystem.
The Psychological Inventory of Criminal Thinking Styles (PICTS): a review and meta-analysis.

The Psychological Inventory of Criminal Thinking Styles (PICTS) is an 80-item self-report measure designed to assess crime-supporting cognitive patterns. Data from men (N = 450) and women (N = 227) offenders indicate that the PICTS thinking, validity, and content scales possess moderate to moderately high internal consistency and test-retest stability. Meta-analyses of studies in which the PICTS has been administered reveal that besides correlating with measures of past criminality, several of the PICTS thinking and content scales are capable of predicting future adjustment/release outcome at a low but statistically significant level, and two scales (En, CUR) are sensitive to program-assisted change beyond what control subjects achieve spontaneously. The factor structure of the PICTS is then examined with the aid of exploratory and confirmatory factor analysis, the results of which denote the presence of two major and two minor factors.
A precision 140MHz relaxation oscillator in 40nm CMOS with 28ppm/°C frequency stability for automotive SoC applications

The need for high-frequency, low-power, wide temperature range, precision on-chip reference clock generation makes relaxation oscillator topology an attractive solution for various automotive applications. This paper presents for the first time a 140MHz relaxation oscillator with robust-against-process-variation temperature compensation scheme. The high-frequency relaxation oscillator achieves 28 ppm/°C frequency stability over the automotive temperature range from −40 to 175°C. The circuit is fabricated in 40nm CMOS technology, occupies 0.009 mm2 and consumes 294µW from 1.2V supply.
Quality of life, depression, anxiety and loneliness in patients with
bullous pemphigoid. A case control study*

Bullous pemphigoid (BP) is a chronic, autoimmune blistering skin disease that affects patients' daily life and psychosocial well-being.



The aim of the study was to evaluate the quality of life, anxiety, depression and loneliness in BP patients.



Fifty-seven BP patients and fifty-seven healthy controls were recruited for the study. The quality of life of each patient was assessed using the Dermatology Life Quality Index (DLQI) scale. Moreover, they were evaluated for anxiety and depression according to the Hospital Anxiety Depression Scale (HADS-scale), while loneliness was measured through the Loneliness Scale-Version 3 (UCLA) scale.



The mean DLQI score was 9.45±3.34. Statistically significant differences on the HADS total scale and in HADS-depression subscale (p=0.015 and p=0.002, respectively) were documented. No statistically significant difference was found between the two groups on the HADS-anxiety subscale. Furthermore, significantly higher scores were recorded on the UCLA Scale compared with healthy volunteers (p=0.003).



BP had a significant impact on quality of life and the psychological status of patients, probably due to the appearance of unattractive lesions on the skin, functional problems and disease chronicity.