nils-herrmann/clean_scirepeval huggingface dataset:
Bringing "T" to the table: understanding individual support of transgender candidates for public office*
Year: 2017

Of central importance to groups is the representation of their interests in government. A direct strategy for representation is to elect officials that identify with the group. The lesbian, gay, bisexual, and transgender (LGBT) movement has increasingly been successful in fielding LGB candidates for local, state, and national offices, even though these candidates face barriers. But while many lesbian and gay candidates have achieved electoral success, few transgender candidates have run for office and even fewer have won. Our project examines the hurdles faced by transgender candidates and provides a predictive analysis of a unique 2015 national survey that queried American adult respondents about hypothetical transgender candidates for different political offices. We hypothesize that although transgender candidates are likely to be opposed by potential voters that would also oppose female, African American, or gay or lesbian candidates, for transgender candidates, there is a stronger influence of respondent disgust sensitivity and gender nonconformity. The findings largely support our arguments. We conclude that transgender candidates are in a similar electoral position to gay and lesbian candidates, with likely supporters fitting a profile that is similar to the Democratic voter base. We discuss the implications of our findings for theories of minority group symbolic representation and democratic citizenship more broadly.
Detect Globally, Refine Locally: A Novel Approach to Saliency Detection
Venue: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018

Effective integration of contextual information is crucial for salient object detection. To achieve this, most existing methods based on 'skip' architecture mainly focus on how to integrate hierarchical features of Convolutional Neural Networks (CNNs) They simply apply concatenation or element wise operation to incorporate high level semantic cues and low level detailed information. However, this can degrade the quality of predictions because cluttered and noisy information can also be passed through. To address this problem, we proposes a global Recurrent Localization Network (RLN) which exploits contextual information by the weighted response map in order to localize salient objects more accurately. Particularly, a recurrent module is employed to progressively refine the inner structure of the CNN over multiple time steps. Moreover, to effectively recover object boundaries, we propose a local Boundary Refinement Network (BRN) to adaptively learn the local contextual information for each spatial position. The learned propagation coefficients can be used to optimally capture relations between each pixel and its neighbors. Experiments on five challenging datasets show that our approach performs favorably against all existing methods in terms of the popular evaluation metrics.
A simulation based multi objective optimization framework: A case study on inventory management
Year: 2017

We propose a simulation based solution framework for tackling the multi objective inventory optimization problem. The goal is to find appropriate settings of reorder point and order quantity to minimize three objective functions simultaneously, which are the expected values of the total inventory cost, the average inventory level, and the frequency of inventory shortage. We develop new algorithms that can exploit statistically valid ranking and selection (R&S) procedures and the desirable mechanics of conventional multi objective optimization techniques. Two simulation algorithms are proposed to be applied in different scenarios depending on the preference information that is revealed either during or after the actual optimization process. Experimental results are provided to evaluate the efficiency of the developed algorithms and other existing solution frameworks.
Updates of the HbVar database of human hemoglobin variants and thalassemia mutations
Venue: Nucleic Acids Res.
Year: 2014

HbVar (http:/globin.bx.psu.edu/hbvar) is one of the oldest and most appreciated locus specific databases launched in 2001 by a multi center academic effort to provide timely information on the genomic alterations leading to hemoglobin variants and all types of thalassemia and hemoglobinopathies. Database records include extensive phenotypic descriptions, biochemical and hematological effects, associated pathology and ethnic occurrence, accompanied by mutation frequencies and references. Here, we report updates to >600 HbVar entries, inclusion of population specific data for 28 populations and 27 ethnic groups for a and b thalassemias and additional querying options in the HbVar query page. HbVar content was also inter connected with two other established genetic databases, namely FINDbase (http:/www.findbase.org) and Leiden Open Access Variation database (http:/www.lovd.nl) which allows comparative data querying and analysis. HbVar data content has contributed to the realization of two collaborative projects to identify genomic variants that lie on different globin paralogs. Most importantly, HbVar data content has contributed to demonstrate the microattribution concept in practice. These updates significantly enriched the database content and querying potential, enhanced the database profile and data quality and broadened the inter relation of HbVar with other databases, which should increase the already high impact of this resource to the globin and genetic database community.
Dynamic Role Based Access Control for Decentralized Applications
Venue: ICBC
Year: 2020

Access control management is an integral part of maintaining the security of an application. Although there has been significant work in the field of cloud access control mechanisms, however, with the advent of Distributed Ledger Technology (DLT) on chain access control management frameworks hardly exist. Existing access control management mechanisms are tightly coupled with the business logic, resulting in governance issues, non coherent with existing Identity Management Solutions, low security, and compromised usability. We propose a novel framework to implement dynamic role based access control for decentralized applications (dApps) The framework allows for managing access control on a dApp, which is completely decoupled from the business application and integrates seamlessly with any dApps. The smart contract architecture allows for the independent management of business logic and execution of access control policies. It also facilitates secure, low cost, and a high degree of flexibility of access control management. The proposed framework promotes decentralized governance of access control policies and efficient smart contract upgrades. We also provide quantitative and qualitative metrics for the efficacy and efficiency of the framework. Any Turing complete smart contract programming language is an excellent fit to implement the framework. We expect this framework to benefit enterprise and non enterprise dApps and provide greater access control flexibility and effective integration with traditional and state of the art identity management solutions.
A dual systems account of visual perception: Predicting candy consumption from distance estimates.
Venue: Acta psychologica
Year: 2017

A substantial amount of evidence shows that visual perception is influenced by forces that control human actions, ranging from motivation to physiological potential. However, studies have not yet provided convincing evidence that perception itself is directly involved in everyday behaviors such as eating. We suggest that this issue can be resolved by employing the dual systems account of human behavior. We tested the link between perceived distance to candies and their consumption for participants who were tired or depleted (impulsive system) versus those who were not (reflective system) Perception predicted eating only when participants were tired (Experiment 1) or depleted (Experiments 2 and 3) In contrast, a rational determinant of behavior eating restraint towards candies predicted eating for non depleted individuals (Experiment 2) Finally, Experiment 3 established that perceived distance was correlated with participants' self reported motivation to consume candies. Overall, these findings suggest that the dynamics between perception and behavior depend on the interplay of the two behavioral systems.
Liquidity Constraints of the Middle Class
Venue: American Economic Journal: Economic Policy
Year: 2019

Existing evidence from US middle class households shows that their MPCs out of tax rebates greatly exceed the PIH's prediction and are weakly related to their liquid assets. The standard precautionary saving model predicts the first fact but counterfactu ally requires MPCs to decrease with liquid wealth. Evidence from the Survey of Consumer Finances indicates widespread saving in anticipation of major expenditures like home purchases and college education. Adding such savings to the standard precautionary saving model allows it to generate realistic MPCs for households with liquid wealth: the approaching expenditure simultaneously motivates asset accumulation and raises MPCs by shortening the effective planning horizon.
Ribosome mediated synthesis of natural product like peptides via cell free translation.
Venue: Current opinion in chemical biology
Year: 2016

Peptide natural products (PNPs) represent a unique class of compounds known for their fascinating structural motifs with important biological activities. Lately, PNPs have garnered a lot of interest for their application in drug discovery. Nevertheless, lack of diversity oriented synthetic/biosynthetic platforms to generate large natural product like libraries has limited their development as peptide therapeutics. The promiscuity of cell free translation has allowed for the synthesis of artificial PNPs having complex structural features. Modified cell free translation systems coupled with the display technologies have generated diverse natural product like peptide libraries and led to the discovery of several biologically active molecules. Such technologies have drastically decreased the time to obtain peptide drug leads and therefore, revolutionized the field of peptide drug discovery. In this account, we review recent developments in the synthesis of natural product like peptides via cell free translation.
ASHP national survey of pharmacy practice in hospital settings: Dispensing and administration 2017
Venue: American journal of health system pharmacy AJHP official journal of the American Society of Health System Pharmacists
Year: 2018

The results of the 2017 ASHP national survey of pharmacy practice in hospital settings are described.  Pharmacy directors at 4,828 general and children's medical surgical hospitals in the United States were surveyed using a mixed mode method of contact by mail and e mail. Survey completion was online, using Qualtrics (Qualtrics, Provo, UT) IMS Health supplied data on hospital characteristics; the survey sample was drawn from the IMS hospital database (IMS Health, Yardley, PA) Results. The response rate was 14.4% Drug distribution systems have evolved from centralized unit dose programs to decentralized programs based on the use of automated dispensing cabinets (ADCs) These systems have been made safer by the use of lidded pockets, by the use of machine readable coding during ADC stocking, and by linking access to medications to results of pharmacist order review. Health system pharmacists continue to improve quality practices for compounding sterile preparations, including the use of safeguards in handling hazardous drugs. While some hospitals are prepared for more stringent standards, including United States Pharmacopeia chapter 800 requirements, much still needs to be done to meet these standards. Pharmacists are taking an active role in improving the responsible use of medications through antimicrobial stewardship and controlled substance diversion prevention programs. The quality of the pharmacy workforce continues to be improved through the increased credentialing of both pharmacists and pharmacy technicians.  Health system pharmacists continue to have a positive impact on improving healthcare through programs that improve efficiency, safety, and clinical outcomes of medication use in health systems.
Mobile Robot Path Planning in Dynamic Environments Through Globally Guided Reinforcement Learning
Venue: IEEE Robotics and Automation Letters
Year: 2020

Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoiding potential conflicts with other robots or dynamic objects. In the presence of dynamic obstacles, traditional solutions usually employ re planning strategies, which re call a planning algorithm to search for an alternative path whenever the robot encounters a conflict. However, such re planning strategies often cause unnecessary detours. To address this issue, we propose a learning based technique that exploits environmental spatio temporal information. Different from existing learning based methods, we introduce a globally guided reinforcement learning approach (G2RL) which incorporates a novel reward structure that generalizes to arbitrary environments. We apply G2RL to solve the multi robot path planning problem in a fully distributed reactive manner. We evaluate our method across different map types, obstacle densities, and the number of robots. Experimental results show that G2RL generalizes well, outperforming existing distributed methods, and performing very similarly to fully centralized state of the art benchmarks.
Insulator recognition based on convolution neural network
Year: 2017

Insulator fault detection plays an important role in maintaining the safety of transmission lines. Insulator recognition is a prerequisite for its fault detection. An insulator recognition algorithm based on convolution neural network (CNN) is proposed. A dataset is established to train the constructed CNN. The correct rate is 98.52% for 1220 training samples and the accuracy rate of testing is 89.04% on 1305 samples. The classification result of the CNN is further used to segment the insulator image. The test results show that the proposed method can realize the effective segmentation of insulators.
Rates and determinants of exclusive breastfeeding in first 6 months among women in Nova Scotia: a population based cohort study.
Venue: CMAJ open
Year: 2013

Despite compelling evidence that exclusive breastfeeding for the first 6 months of life provides important health benefits to both mothers and their infants, most mothers do not follow this practice. We conducted a study to identify predictors of early cessation of exclusive breastfeeding (before 6 months after delivery) METHODS For this population based longitudinal cohort study, we linked data from a perinatal database and a public health database for infants born between 2006 and 2009 in 2 regions in the province of Nova Scotia, Canada. The cohort was followed from the mother's first prenatal visit until her infant was 6 months old. Hazard ratios (HRs) for early cessation of exclusive breastfeeding were determined through Cox proportional hazards regression modelling.  Overall, 64.1% (2907/4533) of the mothers in the cohort initiated breastfeeding. Only 10.4% (413/3957) exclusively breastfed for the recommended 6 months. The largest drop in exclusive breastfeeding occurred within the first 6 weeks after birth. Among the mothers who initiated breastfeeding, significant predictors of early cessation of exclusive breastfeeding identified by multivariable modelling included less than high school education (HR 1.66, 95% confidence interval [CI] 1.35 2.04) lowest neighbourhood income quintile (HR 1.35, 95% CI 1.13 1.60) single motherhood (HR 1.24, 95% CI 1.10 1.41) prepregnancy obesity (HR 1.43, 95% CI 1.23 1.65) smoking throughout pregnancy (HR 1.39, 95% CI 1.21 1.60) no early breast contact by the infant 1 hour after birth) (HR 1.44, 95% CI 1.29 1.62) and no intention to breastfeed (HR 1.78, 95% CI 1.44 2.16) INTERPRETATION We found that most predictors of early cessation of breastfeeding were intertwined with social determinants of health. However, we identified potentially modifiable risk factors. Providing opportunities for early breast contact by the infant and continued efforts in smoking cessation and obesity reduction may contribute to a longer duration of exclusive breastfeeding.
Prevalence of Clostridium difficile infections in Prato hospital in the years 2010 2011
Year: 2013

Clostridium difficile, a Gram positive, spore forming, anaerobic bacillus, is an important nosocomial enteric pathogen causing diarrhoea and pseudomembranous colitis. Aim of this study was to evaluate the prevalence of Clostridium difficile in Prato Hospital. Stool samples were collected from 1197 patients hospitalized from January 2010 to December 2011. In all the samples the common antigen GDH was investigated and only in samples positive for the antigen the presence of the A and B toxins was investigated. Our results showed that 170/1197 samples (14% were positive for the antigen, and of these 170 patients, 84 (49% were found positive also for the toxins. In addition the percentage of samples positive for toxins was higher in 2010 (8.6% than in 2011 (5.9%
AIBench: An Agile Domain specific Benchmarking Methodology and an AI Benchmark Suite
Venue: ArXiv
Year: 2020

Domain specific software and hardware co design is encouraging as it is much easier to achieve efficiency for fewer tasks. Agile domain specific benchmarking speeds up the process as it provides not only relevant design inputs but also relevant metrics, and tools. Unfortunately, modern workloads like Big data, AI, and Internet services dwarf the traditional one in terms of code size, deployment scale, and execution path, and hence raise serious benchmarking challenges. This paper proposes an agile domain specific benchmarking methodology. Together with seventeen industry partners, we identify ten important end to end application scenarios, among which sixteen representative AI tasks are distilled as the AI component benchmarks. We propose the permutations of essential AI and non AI component benchmarks as end to end benchmarks. An end to end benchmark is a distillation of the essential attributes of an industry scale application. We design and implement a highly extensible, configurable, and flexible benchmark framework, on the basis of which, we propose the guideline for building end to end benchmarks, and present the first end to end Internet service AI benchmark. The preliminary evaluation shows the value of our benchmark suite AIBench against MLPerf and TailBench for hardware and software designers, micro architectural researchers, and code developers. The specifications, source code, testbed, and results are publicly available from the web site \url{this http URL}
Invitation to Fixed Parameter Algorithms
Year: 2006

PART I: FOUNDATIONS 1. Introduction to Fixed Parameter Algorithms 2. Preliminaries and Agreements 3. Parameterized Complexity Theory A Primer 4. Vertex Cover An Illustrative Example 5. The Art of Problem Parameterization 6.  Concluding Remarks PART II: ALGORITHMIC METHODS 7.  Tree Decompositions of Graphs 11. Further Advanced Techniques 12.  Concluding Remarks PART III: SOME THEORY, . Parameterized Complexity Theory 14. Connections to Approximation Algorithms 15. Selected Case Studies 16. Zukunftsmusik References Index
Marine and terrestrial endophytic fungi: a mine of bioactive xanthone compounds, recent progress, limitations, and novel applications.
Venue: Critical reviews in biotechnology
Year: 2021

Endophytic fungi are a kind of fungi that colonizes living plant tissues presenting a myriad of microbial adaptations that have been developed in such a hidden environment. Owing to its large diversity and particular habituation, they present a golden mine for research in the field of drug discovery. Endophytic fungal communities possess unique biocatalytic machinery that furnishes a myriad of complex natural product scaffolds. Xanthone compounds are examples of endophytic secondary metabolic products with pronounced biological activity to include: antioxidant, antimicrobial, anti inflammatory, antithrombotic, antiulcer, choleretic, diuretic, and monoamine oxidase inhibiting activity.The current review compiles the recent progress made on the microbiological production of xanthones using fungal endophytes obtained from both marine and terrestrial origins, with comparisons being made among both natural resources. The biosynthesis of xanthones in endophytic fungi is outlined along with its decoding enzymes. Biotransformation reactions reported to be carried out using different endophytic microbial models are also outlined for xanthones structural modification purposes and the production of novel molecules.A promising application of novel computational tools is presented as a future direction for the goal of optimizing microbial xanthones production to include establishing metabolic pathway databases and the in silico analysis of microbial interactions. Metagenomics methods and related bioinformatics platforms are highlighted as unexplored tools for the biodiversity analysis of endophytic microbial communities that are difficult to be cultured.
The Influence of Common Method Variance in Marketing Research: Reanalysis of Past Studies Using a Marker Variable Technique
Year: 2015

Despite criticisms regarding the effects of common method variance (CMV) in marketing research, there have been only a limited number of studies that directly assess the pervasiveness of CMV biases. This paper is an attempt to quantify the ubiquity of such effects in the marketing literature by employing a relatively new method called the marker variable technique (Lindell Whitney 2001) A marker variable is defined as a theoretically unrelated variable included in a study that can be used to estimate the effect of CMV. The technique is used as an analysis tool for assessing and controlling for CMV effects in published studies based on the reasoned action framework (Fishbein Ajzen 1975) The findings of this study show that after controlling for the effects of CMV, the majority of relationships between variables remain significant, even for relatively extreme values of CMV. Specifically, more than 80% of the 663 CMV adjusted correlations that were computed remained significant. This leads to the conclusion that the influence of CMV may not be as problematic as once thought.
Effect of substrate temperature on aligned high density carbon nanotubes deposited by RF PECVD
Year: 2011

The high density carbon nanotubes (CNTs) are synthesized on Fe/Si substrate in the mixture of acetylene and hydrogen gas by radio frequency plasma enhanced chemical vapor deposition (RF PECVD) method. The effects of substrate temperature on the growth of CNTs are studied by scanning electron microscopy (SEM) transmission electron microscopy (TEM) and Raman spectroscopy. The quality of CNTs is improved considerably by increasing the substrate temperature while the beam density is increased and graphitization degree (ID/IG) is enhanced. The best aligned CNTs are prepared at 750 degC, the beam D density is about 1.6 x 103/mm2, and ID/IG is about 0.93. Temperature influence is also discussed.