nils-herrmann/clean_scirepeval huggingface dataset:
Cascade R CNN: High Quality Object Detection and Instance Segmentation
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2021

In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives. The threshold used to train a detector defines its quality. While the commonly used threshold of 0.5 leads to noisy (low quality) detections, detection performance frequently degrades for larger thresholds. This paradox of high quality detection has two causes: 1) overfitting, due to vanishing positive samples for large thresholds, and 2) inference time quality mismatch between detector and test hypotheses. A multi stage object detection architecture, the Cascade R CNN, composed of a sequence of detectors trained with increasing IoU thresholds, is proposed to address these problems. The detectors are trained sequentially, using the output of a detector as training set for the next. This resampling progressively improves hypotheses quality, guaranteeing a positive training set of equivalent size for all detectors and minimizing overfitting. The same cascade is applied at inference, to eliminate quality mismatches between hypotheses and detectors. An implementation of the Cascade R CNN without bells or whistles achieves state of the art performance on the COCO dataset, and significantly improves high quality detection on generic and specific object datasets, including VOC, KITTI, CityPerson, and WiderFace. Finally, the Cascade R CNN is generalized to instance segmentation, with nontrivial improvements over the Mask R CNN.
Case based teaching using the Laboratory Animal System for learning C/C+ programming
Venue: Comput. Educ.
Year: 2014

Teachers and students often consider learning programming a difficult pursuit. Face to face learning alone cannot provide effective teaching or efficient solutions for learning. A case teaching model can make students active in programming courses, even as it relies on solid learning theory and pedagogical strategies. This paper reports a study based on a case teaching model in C/C+ programming. The Laboratory Animal System (LAS) is a standalone case for management of laboratory animals. This paper includes an overview of LAS architectural design and user interface by C/C+ and presents the design, implementation, and evaluation of the model, as well as its implications for learning computer programming. The case method provides an interactive learning environment for students. Based on a survey of student feedback, students can learn C/C+ programming and gain knowledge more quickly and effectively using the case teaching model than through some traditional methods of teaching.
Spatial variability of 2H and 18O composition of meteoric freshwater lakes in Scotland
Venue: Isotopes in environmental and health studies
Year: 2019

Coastal regions, and in particular islands where precipitation from clouds formed out at sea occurs for the first time, are prime candidates for regions where 2H and 18O composition of precipitation will deviate significantly from the global mean geographic and physiographic trends of vapour transport patterns. The results reported here are the outcome of a study that aimed to test this hypothesis by 'isotopographically' mapping the characteristic d2H and d18O signatures of Scottish freshwaters. The resulting isotope abundance landscapes or 'isoscapes' will underpin studies aiming to authenticate origin of Scottish produce but may also offer a baseline against which environmental changes could be assessed. Between April 2011 and May 2012 freshwater samples were collected from 127 different freshwater lochs and reservoirs across Scotland, and analysis results were compared to precipitation data provided by the British Geological Survey. Here we present the results of the 2H and 18O analyses of these water samples as well as the first detailed Scotland freshwater isoscapes with a grid resolution of about 5 x 5 km (0.05 degrees)
Edible flowers as sources of phenolic compounds with bioactive potential.
Venue: Food research international
Year: 2018

The edible flowers are widely used, but there is still a lot to be done in relation to its bioactive potential and its correlation with the presence of phenolic compounds. The aim of this study was determined the individual phenolic profile in the hydromethanolic extracts and infusion preparations of four different flower samples (Dahlia mignon, Rosa damascena 'Alexandria' and R. gallica 'Francesa' draft in R. canina, Calendula officinalis L. and Centaurea cyanus L. and their bioactive potential (antioxidant, antiproliferative, and antibacterial capacity) All the studied flowers presented different profiles regarding their phenolic composition and revealed biological potential. The bioactive potential of the studied flowers was moderate, the hydromethanolic extracts of rose petals showed the best results for antioxidant and antibacterial assays, while the antiproliferative properties were only present in some of the tested cell lines, for the hydromethanolic extracts, in which dahlia and rose showed the best results. These results demonstrate that edible flowers can be used as a source of phenolic compounds with bioactive potential, which can be applied in the food sector, as foods and as sources natural ingredients.
Deteksi Timbal (Pb) Kadmium (Cd) Dalam Ikan Kelabau (Osteochilus melanopleura Bleeker) Ikan Seluang (Rasbora argyrotaenia Bleeker) Dan Ikan Baung (Mystus nemurus C&V) Dari Pasar Induk Kota Palangka Raya, Indonesia
Year: 2013

Grass Carp (Osteochilus melanopleura Bleeker 1852) is herbivorous fish; Silver Rasbora (Rasbora argyrotaenia Bleeker 1849) is omnivorous fish; Catfish (Mystus nemurus C V) is carnivorous live in freshwater habitats. This study was aimed to evaluate the concentration of Pb and Cd in the gills, liver, kidney, muscle and muscle blood fish released. The samples of fish were taken from the central market of Palangka Raya city. The concentration of Pb and Cd were analyzed by using Atomic Absoption Spectrophotometer (AAS) The results showed that the concentrations of Pb and Cd in the liver is kidney> gill> muscle> muscle whose blood was removed. The concentration of Pb and Cd in Silver Rasbora is Catfish Grass Carp. The concentration of Pb and Cd in fish muscle blood issued less than muscle whose blood does not removed.
Learning Content Weighted Deep Image Compression
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2021

Learning based lossy image compression usually involves the joint optimization of rate distortion performance, and requires to cope with the spatial variation of image content and contextual dependence among learned codes. Traditional entropy models can spatially adapt the local bit rate based on the image content, but usually are limited in exploiting context in code space. On the other hand, most deep context models are computationally very expensive and cannot efficiently perform decoding over the symbols in parallel. In this paper, we present a content weighted encoder decoder model, where the channel wise multi valued quantization is deployed for the discretization of the encoder features, and an importance map subnet is introduced to generate the importance masks for spatially varying code pruning. Consequently, the summation of importance masks can serve as an upper bound of the length of bitstream. Furthermore, the quantized representations of the learned code and importance map are still spatially dependent, which can be losslessly compressed using arithmetic coding. To compress the codes effectively and efficiently, we propose an upper triangular masked convolutional network (triuMCN) for large context modeling. Experiments show that the proposed method can produce visually much better results, and performs favorably against deep and traditional lossy image compression approaches.
Abstract P3 11 23: GDC 0077 is a selective PI3K alpha inhibitor with robust efficacy in PIK3CA mutant hormone positive breast cancer models
Year: 2020

The phosphatidylinositol 3kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) signaling pathway is a major regulator of tumor cell growth, proliferation and survival. Hotspot mutations of PIK3CA are common in all subtypes of breast cancer with a prevalence of approximately 30% GDC 0077 is a potent ATP competitive inhibitor of PI3K alpha (IC50 0.038 0.003 nM) thereby inhibiting the phosphorylation of membranebound PIP2 to PIP3. Biochemically, GDC 0077 is more than 300fold selective over the other Class I PI3K isoforms, beta, delta, and gamma, and more than 2000 fold more selective over PI3K class II and III family members. Importantly, GDC 0077 is more selective for mutant versus wild type PI3K alpha in cell based assays. Mechanism of action studies indicate that GDC 0077 treatment leads to degradation of the mutant PI3K alpha protein in a proteasome dependent fashion, without changing WT PI3K alpha levels. Drug treatment results in sustained reduction of PI3K pathway biomarkers pAkt and pPRAS40, inhibition of cell proliferation, and increased apoptosis in human PIK3CA mutant breast cancer cell lines, to a greater extent than other non degrading inhibitors such as alpelisib. Combination studies of GDC 0077 with the CDK4/6 inhibitor palbociclib demonstrate a stronger effect in estrogen depleted HR+ PIK3CA mutant breast cell lines, compared to cells grown in the presence of estrogen. Daily dosing of GDC 0077 in PIK3CA mutant breast cancer patient derived xenograft (PDX) models (n=4) resulted in tumor regressions, induction of apoptosis and a reduction of pAkt, pPRAS40, and pS6RP in a dose dependent fashion. In vivo combination efficacy studies of GDC 0077 with fulvestrant and palbociclib also indicate the triple combination results in the greatest efficacy. Collectively, our preclinical data support evaluation of GDC 0077 in a triple combination with endocrine therapy and CDK4/6 inhibition in the clinic, in patients with locally advanced or metastatic hormone receptor positive breast cancer whose tumors harbor mutant PIK3CA.  Kyle Edgar, Rebecca Hong, Kyung Song, Steven Schmidt, Marc Hafner, Alfonso Arrazate, Erin Williams, Cecile De La Cruz, Jason Oeh, Deepak Sampath, Steve Staben, Lori Friedman. GDC 0077 is a selective PI3K alpha inhibitor with robust efficacy in PIK3CA mutant hormone positive breast cancer models [abstract]  San Antonio, TX. Philadelphia (PA) AACR;
A Review of Approaches to Detecting Malingering in Forensic Contexts and Promising Cognitive Load Inducing Lie Detection Techniques
Venue: Front. Psychiatry
Year: 2018

Malingering, the feigning of psychological or physical ailment for gain, imposes high costs on society, especially on the criminal justice system. In this article, we review some of the costs of malingering in forensic contexts. Then the most common methods of malingering detection are reviewed, including those for feigned psychiatric and cognitive impairments. The shortcomings of each are considered. The article continues with a discussion of commonly used means for detecting deception. Although not traditionally used to uncover malingering, new, innovative methods are emphasized that attempt to induce greater cognitive load on liars than truth tellers, some informed by theoretical accounts of deception. As a type of deception, we argue that such cognitive approaches and theoretical understanding can be adapted to the detection of malingering to supplement existing methods.
Multiple Cycle in Cycle Generative Adversarial Networks for Unsupervised Image Super Resolution
Venue: IEEE Transactions on Image Processing
Year: 2020

With the help of convolutional neural networks (CNN) the single image super resolution problem has been widely studied. Most of these CNN based methods focus on learning a model to map a low resolution (LR) image to a high resolution (HR) image, where the LR image is downsampled from the HR image with a known model. However, in a more general case when the process of the down sampling is unknown and the LR input is degraded by noises and blurring, it is difficult to acquire the LR and HR image pairs for traditional supervised learning. Inspired by the recent unsupervised image style translation applications using unpaired data, we propose a multiple Cycle in Cycle network structure to deal with the more general case using multiple generative adversarial networks (GAN) as the basis components. The first network cycle aims at mapping the noisy and blurry LR input to a noise free LR space, then a new cycle with a well trained <inline formula> <tex math notation="LaTeX"\times 2$ /tex math>/inline formula> network model is orderly introduced to super resolve the intermediate output of the former cycle. The number of total cycles depends on the different up sampling factors <inline formula> <tex math notation="LaTeX"\times 2$ /tex math>/inline formula> <inline formula> <tex math notation="LaTeX"\times 4$ /tex math>/inline formula> <inline formula> <tex math notation="LaTeX"\times 8$ /tex math>/inline formula> Finally, all modules are trained in an end to end manner to get the desired HR output. Quantitative indexes and qualitative results show that our proposed method achieves comparable performance with the state of the art supervised models.
THE APPLICATION OF GRAPHOLOGY AND ENNEAGRAM TECHNIQUES IN DETERMINING PERSONALITY TYPE BASED ON HANDWRITING FEATURES
Year: 2017

This research was conducted with the aim of developing previous studies that have successfully applied the science of graphology to analyze digital handwriting and characteristics of his personality through shape based feature extraction, which in the present study will be applied one method of psychological tests commonly used by psychologists to recognize human's personality that is Enneagram. The Enneagram method in principle will classify the personality traits of a person into nine types through a series of questions, which then calculated the amount of the overall weight of the answer. Thickness is what will provide direction personality type, which will then be matched with the personality type of the result of the graphology analysis of the handwriting. Personality type of handwritten analysis results is processed based on the personality traits that are the result of the identification of a combination of four dominant form of handwriting through the software output of previous studies, that Slant (tilt writing) Size (font size) Baseline, and Breaks (respite each word) From the results of this research can be found there is a correlation between personality analysis based on the psychology science to the graphology science, which results matching personality types by 81.6% of 49 respondents data who successfully tested.
Influence of mist intervals and aeration rate on growth and second metabolite production of Pseudostellaria heterophylla adventitious roots in a siphon mist bioreactor
Year: 2010

Plant adventitious root culture in bioreactors is a promising alternative for the efficient production of medicinal herbs. Adventitious roots of Pseudostellaria heterophylla were induced from callus and then cultivated in a siphon mist bioreactor. An orthogonal test established that the optimal medium for adventitious root induction was MS medium supplemented with 1.0 mg/L naphthaleneacetic acid and 2.0 mg/L 3 indolybutyric acid. Under these conditions, the average root number was more than 14 on each 1.0 cm diameter callus and the rooting rate reached 100% The bioreactor was equipped with an integral siphon spraying device designed to automatically supply the liquid medium. The operation parameters of the bioreactor were assessed by varying the mist interval and the aeration velocity. The mist interval was negatively related to average growth rate of the adventitious roots and positively related to saponin and polysaccharide content. A relatively high aeration rate was necessary to achieve the maximum biomass production, but the secondary metabolite production was not enhanced by increasing the aeration velocity.
All Roads Lead to UD: Converting Stanford and Penn Parses to English Universal Dependencies with Multilayer Annotations
Venue: LAW MWE CxG@COLING
Year: 2018

We describe and evaluate different approaches to the conversion of gold standard corpus data from Stanford Typed Dependencies (SD) and Penn style constituent trees to the latest English Universal Dependencies representation (UD 2.2) Our results indicate that pure SD to UD conversion is highly accurate across multiple genres, resulting in around 1.5% errors, but can be improved further to fewer than 0.5% errors given access to annotations beyond the pure syntax tree, such as entity types and coreference resolution, which are necessary for correct generation of several UD relations. We show that constituent based conversion using CoreNLP (with automatic NER) performs substantially worse in all genres, including when using gold constituent trees, primarily due to underspecification of phrasal grammatical functions.
Elicited imitation as a measure of second language proficiency: A narrative review and meta analysis
Year: 2016

Elicited imitation (EI) has been widely used to examine second language (L2) proficiency and development and was an especially popular method in the 1970s and early 1980s. However, as the field embraced more communicative approaches to both instruction and assessment, the use of EI diminished, and the construct related validity of EI scores as a representation of language proficiency was called into question. Current uses of EI, while not discounting the importance of communicative activities and assessments, tend to focus on the importance of processing and automaticity. This study presents a systematic review of EI in an effort to clarify the construct and usefulness of EI tasks in L2 research. The review underwent two phases: a narrative review and a meta analysis. We surveyed 76 theoretical and empirical studies from 1970 to 2014, to investigate the use of EI in particular with respect to the research/assessment context and task features. The results of the narrative review provided a theoretical basis for the meta analysis. The meta analysis utilized 24 independent effect sizes based on 1089 participants obtained from 21 studies. To investigate evidence of construct related validity for EI, we examined the following: (1) the ability of EI scores to distinguish speakers across proficiency levels; (2) correlations between scores on EI and other measures of language proficiency; and (3) key task features that moderate the sensitivity of EI. Results of the review demonstrate that EI tasks vary greatly in terms of task features; however, EI tasks in general have a strong ability to discriminate between speakers across proficiency levels (Hedges' g 1.34) Additionally, construct, sentence length, and scoring method were identified as moderators for the sensitivity of EI. Findings of this study provide supportive construct related validity evidence for EI as a measure of L2 proficiency and inform appropriate EI task development and administration in L2 research and assessment.
Lower tick sizes and futures pricing efficiency: evidence from the emerging Malaysian market
Year: 2018

We provide robust evidence of the impact on spot market liquidity and the pricing efficiency of FBM FKLI index futures following the introduction of lower tick sizes for the stocks listed in the Bursa Malaysia. Our findings show a significant increase in unexpected trading volume and the speed of mean reversion of the futures mispricing. We find that the increase in the unexpected trading volume of the underlying stocks helps in reducing inter market price discrepancies. The findings offer new evidence that lowering of tick sizes improves pricing efficiency in the Malaysian futures market.
Robust online model predictive control for image based visual servoing in polar coordinates
Venue: Trans. Inst. Meas. Control
Year: 2020

In this paper, a novel robust online model predictive control (RMPC) method for image based visual servoing (IBVS) in polar coordinates is proposed. First, the Jacobian matrix is transformed into a weighted combination of vertex matrices of convex polytopic by tensor product (TP) model transformation method. Then, a new IBVS control design condition for 6 DOF manipulator submitting to robot physical limitations and visibility constraints is obtained in polar coordinates by using RMPC technique. The optimal value of the control signal can be solved online when carrying out the convex optimization problem. The proposed control strategy can effectively improve the trajectory in the case that involves translation and rotation with fast response while averting the pseudo inverse of the image Jacobian matrix. Conclusively, the effectiveness of the proposed scheme is validated by simulations and experiments on a 6 DOF manipulator.
Coevolutionary Landscape Inference and the Context Dependence of Mutations in Beta Lactamase TEM 1
Venue: Molecular biology and evolution
Year: 2016

The quantitative characterization of mutational landscapes is a task of outstanding importance in evolutionary and medical biology: It is, for example, of central importance for our understanding of the phenotypic effect of mutations related to disease and antibiotic drug resistance. Here we develop a novel inference scheme for mutational landscapes, which is based on the statistical analysis of large alignments of homologs of the protein of interest. Our method is able to capture epistatic couplings between residues, and therefore to assess the dependence of mutational effects on the sequence context where they appear. Compared with recent large scale mutagenesis data of the beta lactamase TEM 1, a protein providing resistance against beta lactam antibiotics, our method leads to an increase of about 40% in explicative power as compared with approaches neglecting epistasis. We find that the informative sequence context extends to residues at native distances of about 20 A from the mutated site, reaching thus far beyond residues in direct physical contact.
Stock performance and the mispricing of accruals
Year: 2007

I investigate the relationship between contemporaneous stock price performance and the persistence of accrued earnings, and its impact on the accrual anomaly. I find that, in a fiscal year, accrued earnings for stocks that have performed poorly are less persistent in predicting future earnings than accrued earnings for stocks that have performed moderately. I further find that a hedge strategy based on accruals earns greater abnormal returns following bad news years. The results are consistent with conservative accounting causing accrued earnings to be even less persistent in bad news years and investors failing to efficiently price this differential in persistence.
Assessment of socio economic configuration of value chains a proposed analysis framework to facilitate integration of small rural producers with global agribusiness RESEARCH ARTICLE
Year: 2016

Value chain analysis is an important tool to assess and enhance the performance of agribusiness. This paper analyzes the empirical application of a conceptual framework known as the Rural Web to evaluate the socioeconomic complexity of a specific agribusiness value chain. This can be used as a complementary approach to traditional value chain analysis. The proposed framework goes beyond linear descriptions of product flows and examines how supply chains are built, shaped and reproduced over time and space, while considering social, cultural, environmental and political aspects. The results demonstrate that the proposed framework is a suitable method for value chain analysis, principally for those whose early stages are based on small and medium sized rural actors. The Rural Web analysis offers decision makers a platform to identify key actors not traditionally considered in value chain analysis, as well as the social interrelationships that occur at different dimensions. It also enables the identification of corrective and preventive measures to enhance agribusiness value chains.