nils-herrmann/clean_scirepeval huggingface dataset:
Re evaluation of individual diameter height allometric models to improve biomass estimation of tropical trees.
Venue: Ecological applications a publication of the Ecological Society of America
Year: 2016

Accurate estimation of tree biomass is necessary to provide realistic values of the carbon stored in the terrestrial biosphere. A recognized source of errors in tree aboveground biomass (AGB) estimation is introduced when individual tree height values (H) are not directly measured but estimated from diameter at breast height (DBH) using allometric equations. In this paper, we evaluate the performance of 12 alternative DBH H equations and compare their effects on AGB estimation for three tropical forests that occur in contrasting climatic and altitudinal zones. We found that fitting a three parameter Weibull function using data collected locally generated the lowest errors and bias in H estimation, and that equations fitted to these data were more accurate than equations with parameters derived from the literature. For computing AGB, the introduced error values differed notably among DBH H allometric equations, and in most cases showed a clear bias that resulted in either over or under estimation of AGB. Fitting the three parameter Weibull function minimized errors in AGB estimates in our study and we recommend its widespread adoption for carbon stock estimation. We conclude that many previous studies are likely to present biased estimates of AGB due to the method of H estimation.
CSR reporting practices and the quality of disclosure: An empirical analysis
Year: 2015

Research on CSR disclosure points to an increasing lack of completeness and decreasing amount of credibility in the information reported, as well as concerns about overall reporting practices. The aim of this paper is to investigate the (ab)use of three CSR reporting practices: the use of stand alone reports, assurance, and reporting guidance. These practices may be the outcome of a substantive approach to CSR undertaken to carry out duties of accountability to stakeholders, or conversely they could represent symbolic actions intended to portray corporations as genuinely committed to CSR. Thus, we investigate the use of these three practices in relation to disclosure proxies that capture the quality of disclosure along three different yet complementary dimensions: the content of the information disclosed (what and how much is disclosed) the type of information used to describe and discuss CSR issues (how it is disclosed) and the managerial orientation (the corporate approach to CSR) We find that, on average, companies that use these practices do not provide a higher quality of information, which we interpret as evidence of a symbolic use of these practices. Nevertheless, for those companies providing performance related disclosures, we obtain limited evidence indicating that disclosures by GRI followers are more likely to be balanced, comparable and precise. Overall, our evidence supports increasing scepticism about the use of CSR reporting practices as tools used to enhance perceived accountability.
Making Pre trained Language Models Better Few shot Learners
Venue: ACL/IJCNLP
Year: 2021

The recent GPT 3 model (Brown et al. 2020) achieves remarkable few shot performance solely by leveraging a natural language prompt and a few task demonstrations as input context. Inspired by their findings, we study few shot learning in a more practical scenario, where we use smaller language models for which fine tuning is computationally efficient. We present LM BFF better few shot fine tuning of language models1 a suite of simple and complementary techniques for finetuning language models on a small number of annotated examples. Our approach includes (1) prompt based fine tuning together with a novel pipeline for automating prompt generation; and (2) a refined strategy for dynamically and selectively incorporating demonstrations into each context. Finally, we present a systematic evaluation for analyzing few shot performance on a range of NLP tasks, including classification and regression. Our experiments demonstrate that our methods combine to dramatically outperform standard fine tuning procedures in this low resource setting, achieving up to 30% absolute improvement, and 11% on average across all tasks. Our approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task agnostic method for few shot
Alterations of the Fecal Microbiota in Chinese Patients With Multiple Sclerosis
Venue: Frontiers in Immunology
Year: 2020

Mounting evidence indicates that alterations in the intestinal microbiota may be associated with neurological disorders such as multiple sclerosis (MS) MS is a putative autoimmune disease of the central nervous system. However, it has not been determined whether the intestinal microbiota and host immune status are altered in Chinese patients with stable MS. In our study, 22 Chinese patients with stable MS and 33 healthy controls were enrolled for fecal microbiota analysis and host immunity evaluation. The microbial diversity and composition, bacterial co occurrence correlations, predictive functional profiles, and microbiota cytokine correlations between the two groups were compared. We observed that while the overall structure of the fecal microbiota did not change significantly, the abundances of several key functional bacteria, primarily Faecalibacterium, decreased remarkably. Faecalibacterium and Granulicatella could be used to distinguish between patients with MS and healthy controls with an area under the curve of 0.832. PiCRUSt analysis revealed that genes associated with fructose, mannose, and fatty acid metabolism were significantly enriched in the MS microbiota. In addition, we also observed that the levels of several pro and anti inflammatory cytokines and chemokines, such as IL 1ra, IL 8, IL 17, and TNF a changed observably, and the abundances of key functional bacteria like butyrate producers correlated with the changes in the cytokine levels. Our present study indicated that altered composition of the fecal microbiota might play vital roles in the etiopathogenesis of MS by regulating host immunity, which suggests that microbiota targeting patient tailored early intervention techniques might serve as novel therapeutic approaches for MS.
The Role of Internal Oxides on the Liquid Metal Embrittlement Cracking During Resistance Spot Welding of the Dual Phase Steel
Venue: Metallurgical and Materials Transactions A
Year: 2020

One of the most effective ways to improve reactive wetting of dual phase (DP) steels during galvanizing process is annealing under relatively high oxygen partial pressure. This annealing process can lead to internal oxide formation rather than external ones. Since these oxides are mostly observed in grain boundaries (GBs) their presence could influence GB related phenomena such as liquid metal embrittlement (LME) cracking during further manufacturing processes such as resistance spot welding (RSW) The present work has shown that internal oxides located at GBs assisted LME crack formation during RSW of DP steel. Two types of LME cracks were observed in the shoulder and center of the weld. It has been shown that at the initial stages of welding time, LME cracks initiate in the shoulder of the nugget by the Zn diffusion into oxide bearing GBs. As welding time continues, higher temperatures and tensile stresses are applied to the steel sheets which allow center cracks to be formed more easily due to the presence of oxides which degrade the GBs.
RAC1 P29S regulates PD L1 expression in melanoma
Venue: Pigment cell melanoma research
Year: 2015

Whole exome sequencing of cutaneous melanoma has led to the detection of P29 mutations in RAC1 in 5 9% of samples, but the role of RAC1 P29 mutations in melanoma biology remains unclear. Using reverse phase protein array analysis to examine the changes in protein/phospho protein expression, we identified cyclin B1, PD L1, Ets 1, and Syk as being selectively upregulated with RAC1 P29S expression and downregulated with RAC1 P29S depletion. Using the melanoma patient samples in TCGA, we found PD L1 expression to be significantly increased in RAC1 P29S patients compared to RAC1 WT as well as other RAC1 mutants. The finding that PD L1 is upregulated suggests that oncogenic RAC1 P29S may promote suppression of the antitumor immune response. This is a new insight into the biological function of RAC1 P29S mutations with potential clinical implications as PD L1 is a candidate biomarker for increased benefit from treatment with anti PD1 or anti PD L1 antibodies.
Propaganda Jepang pada Bidang Industri di Pulau Jawa dalam Majalah Djawa Baroe Edisi 1 24 Tahun 1943 1944
Year: 2015

Bidang Industri, Djawa Baroe, Karya Sastra, Propaganda, Sejarah. Invasi Jepang dimulai pada tahun 1937 dengan tujuan awal ke wilayah Manchuria dan dilanjutkan penyerangan ke markas besar Amerika Serikat di Pearl Harbor pada akhir tahun 1941 dan berujung dengan terjadinya perang Asia Pasifik. Terjadinya perang Asia Pasifik membuat Jepang bergerak ke Asia Tenggara untuk mencari sumber daya alam guna mendukung kebutuhan perang hingga ke wilayah Indonesia. Tibanya Jepang di Indonesia, disambut dengan perlawanan dari Belanda yang saat itu masih menduduki Indonesia. Namun perlawanan itu tidak berlangsung lama, pada tanggal 8 Maret Belanda menyerah kepada Jepang tanpa syarat di Kalijati. Kekalahan Belanda oleh Jepang, membuat Jepang diterima baik oleh masyarakat Indonesia dan kesempatan itu digunakan Jepang untuk membuat beberapa kebijakan dalam mengeksploitasi sumber daya alam salah satunya melalui propaganda. Dalam mengeksploitasi sumber daya alam Indonesia, Jepang melakukannya melalui tenaga sumber daya manusia yang dikenal dengan Romusha Seperti yang terlihat pada Majalah Djawa Baroe Jepang melakukan propaganda dalam segala bidang baik industri, militer, budaya, pendidikan, politik, dan lain sebagainya melalui artikel bergambar dan karya sastra. Dalam penelitian kali ini penulis tertarik meneliti mengenai salah satu bentuk propaganda Jepang pada bidang industri di Pulau Jawa dalam Majalah Djawa Baroe edisi 1 24 tahun 1943 1944. Pada penelitian kali ini, penulis menggunakan metode deskriptif kualitatif, pengumpulan data dan analisis serta teori yang digunakan adalah teori New Historicism Penelitian ini berfokus pada bentuk bentuk propaganda pada bidang industri yang dilakukan Jepang pada artikel bergambar dan karya sastra syair lagu serta membahas mengenai muatan propaganda yang terkandung dalam majalah ini. Hasil penelitian menunjukan bahwa artikel bergambar dan syair lagu dalam Majalah Djawa Baroe ini dijadikan Jepang sebagai media propaganda untuk mengeksploitasi sumber daya alam guna mendukung dan memenuhi kebutuhan perang Jepang melalui tenaga manusia sebagai mesin pekerja.
MIPDH: A Novel Computational Model for Predicting microRNA mRNA Interactions by DeepWalk on a Heterogeneous Network
Venue: ACS omega
Year: 2020

Analysis of miRNA target mRNA interaction (MTI) is of crucial significance in discovering new target candidates for miRNAs. However, the biological experiments for identifying MTIs have a high false positive rate and are high priced, time consuming, and arduous. It is an urgent task to develop effective computational approaches to enhance the investigation of miRNA target mRNA relationships. In this study, a novel method called MIPDH is developed for miRNA mRNA interaction prediction by using DeepWalk on a heterogeneous network. More specifically, MIPDH extracts two kinds of features, in which a biological behavior feature is learned using a network embedding algorithm on a constructed heterogeneous network derived from 17 kinds of associations among drug, disease, and 6 kinds of biomolecules, and the attribute feature is learned using the k mer method on sequences of miRNAs and target mRNAs. Then, a random forest classifier is trained on the features combined with the biological behavior feature and attribute feature. When implementing a 5 fold cross validation experiment, MIPDH achieved an average accuracy, sensitivity, specificity and AUC of 75.85, 74.37, 77.33% and 0.8044, respectively. To further evaluate the performance of MIPDH, other classifiers and feature descriptors are conducted for comparisons. MIPDH can achieve a better performance. Additionally, case studies on hsa miR 106b 5p, hsa let 7d 5p, and hsa let 7e 5p are also implemented. As a result, 14, 9, and 9 out of the top 15 targets that interacted with these miRNAs were verified using the experimental literature or other databases. All these prediction results indicate that MIPDH is an effective method for predicting miRNA target mRNA interactions.
Improved performance of sulfonated polyarylene ethers for proton exchange membrane fuel cells
Year: 2006

The performances of polyarylene ethers and sulfonated polyarylene ethers having different backbone structures and sulfonated position were comparatively investigated focusing on the determination of the proton conductivity, thermal and chemical stability. the results indicated that the sulfone groups in polymer backbones are highly thermal stable. the sulfonic acid group at the metasulfone position had better thermal, chemical stability and higher proton conductivity than those at the ortho ether and ortho sulfone position. the series of stability is meta sulfone sulfonated polyphenyl sulfone (spsu) ortho sulfone spsu ortho ether spsu. the knowledge of these parameters will lead to the synthesis of sulfonated arylene main chain ionomers that are optimized for application in fuel cells. also different sulfonated polyphenyl sulfone 4,4' biphenol/polybenzimidazole (spsu bp/pbi) acid base crosslinked blend membranes have been studied. the spsu/pbi crosslinked blend membranes had better mechanical stability than their parent membrane while maintaining good thermal stability and having suitable proton conductivity.
EVALUATION INTERNATIONALIZATION PROGRAMS OF CREATIVE INDUSTRY MICRO SMALL AND MEDIUM ENTERPRISES (MSMEs) IN PURBALINGGA: AN ANALYTICAL HIERARCHY PROCESS (AHP) METHOD
Year: 2019

This study aims to evaluate the program of internationalization of UMKM Creative Industries in Purbalingga Respondents in this study are the SMEs Creative Industries business people who are considered to have knowledge about the Creative Industry in Purbalingga. Data collection using in depth interviews, questionnaires, and Focus Group Discussion (FGD) Data analysis used is descriptive qualitative and Analytical Hierarchy Process (AHP) Based on the analysis concluded that several factors lead to non optimal of Creative Industry MSEs internationalization programs sequences was lack of financial, managerial capabilities, lack of information, government support, and the international environment. In order to optimal, Creative Industry of MSMEs internationalization programs should be conducted to use counseling and sustainable facilitation, in which the government and successful entrepreneurs in the international market help entrepreneurs MSMEs creative industries in Purbalingga to succeed in the international market.
Highly Efficient Multiview Depth Coding Based on Histogram Projection and Allowable Depth Distortion
Venue: IEEE Transactions on Image Processing
Year: 2021

Mismatches between the precisions of representing the disparity, depth value and rendering position in 3D video systems cause redundancies in depth map representations. In this paper, we propose a highly efficient multiview depth coding scheme based on Depth Histogram Projection (DHP) and Allowable Depth Distortion (ADD) in view synthesis. Firstly, DHP exploits the sparse representation of depth maps generated from stereo matching to reduce the residual error from INTER and INTRA predictions in depth coding. We provide a mathematical foundation for DHP based lossless depth coding by theoretically analyzing its rate distortion cost. Then, due to the mismatch between depth value and rendering position, there is a many to one mapping relationship between them in view synthesis, which induces the ADD model. Based on this ADD model and DHP, depth coding with lossless view synthesis quality is proposed to further improve the compression performance of depth coding while maintaining the same synthesized video quality. Experimental results reveal that the proposed DHP based depth coding can achieve an average bit rate saving of 20.66% to 19.52% for lossless coding on Multiview High Efficiency Video Coding (MV HEVC) with different groups of pictures. In addition, our depth coding based on DHP and ADD achieves an average depth bit rate reduction of 46.69% 34.12% and 28.68% for lossless view synthesis quality when the rendering precision varies from integer, half to quarter pixels, respectively. We obtain similar gains for lossless depth coding on the 3D HEVC, HEVC Intra coding and JPEG2000 platforms.
Jinn: synthesizing dynamic bug detectors for foreign language interfaces
Venue: PLDI '10
Year: 2010

Programming language specifications mandate static and dynamic analyses to preclude syntactic and semantic errors. Although individual languages are usually well specified, composing languages is not, and this poor specification is a source of many errors in multilingual programs. For example, virtually all Java programs compose Java and C using the Java Native Interface (JNI) Since JNI is informally specified, developers have difficulty using it correctly, and current Java compilers and virtual machines (VMs) inconsistently check only a subset of JNI constraints. This paper's most significant contribution is to show how to synthesize dynamic analyses from state machines to detect foreign function interface (FFI) violations. We identify three classes of FFI constraints encoded by eleven state machines that capture thousands of JNI and Python/C FFI rules. We use a mapping function to specify which state machines, transitions, and program entities (threads, objects, references) to check at each FFI call and return. From this function, we synthesize a context specific dynamic analysis to find FFI bugs. We build bug detection tools for JNI and Python/C using this approach. For JNI, we dynamically and transparently interpose the analysis on Java and C language transitions through the JVM tools interface. The resulting tool, called Jinn, is compiler and virtual machine independent. It detects and diagnoses a wide variety of FFI bugs that other tools miss. This approach greatly reduces the annotation burden by exploiting common FFI constraints: whereas the generated Jinn code is 22,000+ lines, we wrote only 1,400 lines of state machine and mapping code. Overall, this paper lays the foundation for a more principled approach to developing correct multilingual software and a more concise and automated approach to FFI specification.
R squared for Bayesian Regression Models
Venue: The American Statistician
Year: 2019

The usual definition of R2 (variance of the predicted values divided by the variance of the data) has a problem for Bayesian fits, as the numerator can be larger than the denominator. We propose an alternative definition similar to one that has appeared in the survival analysis literature: the variance of the predicted values divided by the variance of predicted values plus the expected variance of the errors.
Impact of co infections with enteric pathogens on children suffering from acute diarrhea in southwest China
Venue: Infectious Diseases of Poverty
Year: 2016

BackgroundAcute diarrhea is a global health problem, resulting in high morbidity and mortality in children. It has been suggested that enteric pathogen co infections play an important role in gastroenteritis, but most research efforts have only focused on a small range of species belonging to a few pathogen groups. This study aimed to assess the impact of co infections with a broad range of enteric pathogens on children aged below five years who suffer from acute diarrhea in southwest China. total of 1020 subjects (850 diarrhea cases and 170 healthy controls) were selected from four sentinel hospitals in Kunming, Yunnan province, southwest China, from June 2014 to July 2015. Stool specimens were collected to detect five virus (rotavirus group A, RVA; norovirus, NoV; Sapovirus, SaV; astrovirus, As; and adenovirus, Ad) seven bacterial (diarrheagenic Escherichia coli, DEC; non typhoidal Salmonella, NTS; Shigella spp. Vibrio cholera; Vibrio parahaemolyticus; Aeromonas spp. and Plesiomonas spp. and three protozoan (Cryptosporidium spp. Giardia lamblia, and Blastocystis hominis, B. hominis) species using standard microbiologic and molecular methods. Data were analyzed using the partial least square regression technique and chi square test.ResultsAt least one enteric pathogen was detected in 46.7 (n 397) of acute gastroenteritis cases and 13.5 (n 23) of healthy controls (kh2 64.4, P 0.05) Single infection with RVA was associated with acute diarrhea (26.5 vs. 5.8 P 0.05) The prevalence of a single infection with B. hominis in diarrhea cases was higher than in healthy controls (3.1 vs. 0.5 OR 4.7, 95 CI: 1.01 112.0) Single infection with NoV GII was not associated with diarrhea (4.4 vs. 3.5 OR 1.2, 95 CI: 0.5 3.3) Single infections with bacterial species were not observed. The prevalence of co infections with two enteric pathogens in diarrhea cases was higher than in asymptomatic children (20.1 vs. 5.3 P 0.05) RVA NoV GII was the most common co infection in symptomatic children (4.4 with it aggravating the severity of diarrhea.ConclusionsAlthough it is clear that RVA has an overwhelming impact on diarrhea illnesses in children, co infection with other enteric pathogens appears to also aggravate diarrhea severity. These findings should serve as evidence for public health services when planning and developing intervention programs.
Analysis of Liver Cancer Using Data Mining SVM Algorithm in MATLAB
Venue: SocProS
Year: 2017

Liver cancer is one among the normal types of cancer. Detection and determination of liver tumor at early stage are vital. The vast majority of the cancer passings can be anticipated by early detection, determination, and compelling treatment. It is required to fragment the liver tumor from the medical images for tumor analysis. A robotized framework is proposed for segmentation and classification of liver tumor which is an effective and simple to utilize technique. The proposed framework comprises of PreAprocessing, segmentation, postAprocessing, and a last classification as benign and malignant. Amid the preAprocessing stage, the image is resized to 256 x 256. In the segmentation stage, level set strategy is connected for sectioning the suspicious area. In postAprocessing stage, the district of intrigue is acquired from the first image. At long last the Pseudo Zenerike minute and GLDM is utilized for highlight extraction from CT image. These components are given as contribution to the SVM for classification of tumor as benign or malignant. The SVM is prepared utilizing four images. The proposed framework can accomplish precision rate of 86.7%