nils-herrmann/clean_scirepeval huggingface dataset:
Top Management Human Capital , Inventor Mobility , and Corporate Innovation

We analyze the effect of the human capital or “quality” of the top management of a firm on its innovation activities. We extract a “management quality factor” using common factor analysis on various individual proxies for the quality of a firm’s management team, such as management team size, fraction of managers with MBAs, the average employmentand education-based connections of each manager in the management team, fraction of members with prior work experience in a top management position, the average number of prior board positions that each manager serves on, and the fraction of managers with doctoral degrees. We find that firms with higher quality management teams not only invest more in innovation (as measured by R&D expenditures), but also have a greater quantity and quality of innovation output, as measured by the number of patents and citations per patent, respectively. We control for the endogenous matching of higher quality managers and higher quality firms using an instrumental variable analysis where we use a function of the number of top managers who faced Vietnam War era drafts (and therefore had an incentive to go to graduate school to get a draft deferment) as an instrument for top management human capital. We also show that an important channel through which higher management quality firms achieve greater innovation success is by hiring a larger number of inventors (controlling for R&D expenditures), and also by hiring higher quality inventors (as measured by their prior citations per patent record). Finally, we show that firms with higher quality top management teams are able to develop a larger number of both exploratory and exploitative innovations.
Pathogen recognition by the innate immune system.

Microbial infection initiates complex interactions between the pathogen and the host. Pathogens express several signature molecules, known as pathogen-associated molecular patterns (PAMPs), which are essential for survival and pathogenicity. PAMPs are sensed by evolutionarily conserved, germline-encoded host sensors known as pathogen recognition receptors (PRRs). Recognition of PAMPs by PRRs rapidly triggers an array of anti-microbial immune responses through the induction of various inflammatory cytokines, chemokines and type I interferons. These responses also initiate the development of pathogen-specific, long-lasting adaptive immunity through B and T lymphocytes. Several families of PRRs, including Toll-like receptors (TLRs), RIG-I-like receptors (RLRs), NOD-like receptors (NLRs), and DNA receptors (cytosolic sensors for DNA), are known to play a crucial role in host defense. In this review, we comprehensively review the recent progress in the field of PAMP recognition by PRRs and the signaling pathways activated by PRRs.
ACTIVATION MAXIMIZATION GENERATIVE ADVER-

Class label information has been empirically proven to be very useful in improving the sample quality of generative adversarial nets (GANs). In this paper, we mathematically study current variants of GANs that make use of class label information to reveal how class labels and associated losses influence GAN’s training. Based on the analysis, we propose Activation Maximization Generative Adversarial Networks (AM-GAN) as an alternative solution. We conduct a set of controlled experiments to validate our analysis and study the effectiveness of our solution, where AM-GAN achieves the state-of-the-art Inception Score (8.91) on CIFAR-10. Through the experiments, we realize the common used metric for generative models needs further investigation and refinement. Thus we also delve into the widely-used evaluation metrics and accordingly propose a new metric as compensation to make the entire metrics complete and impartial. The proposed model also outperforms the baseline methods in the new metric.
Academic Self-Efficacy , Coping , and Academic Performance in College

This study serves as a pilot study for a possible future study including the same variables. The purpose of the pilot study was to find a relationship in the college academic setting between academic self-efficacy, stress coping skills, and academic performance. Sixty-six undergraduate students, 17 male and 49 female, from a university in northwestern United States participated in the study. Stress was measured using the COPE Inventory (Carver, Scheier, & Weintraub, 1989). Self-efficacy was measured using the Academic Self-Efficacy Scale (Chemers, Hu, & Garcia, 2001). Academic performance was measured using the participants’ college GPA. Academic Self-Efficacy and the Planning subscale of the COPE Inventory were positively correlated with GPA (r = .49, p < .01 and r = .32, p < .05). Academic self-efficacy was positively correlated with the COPE Inventory subscales Positive Reinterpretation and Growth (r = .36, p r = .35, p < .01), Acceptance (r = .46, p < .01), and Planning (r = .25, p < .05). Academic self-efficacy was negatively correlated with the COPE Inventory subscale Substance Use (r = -.32 at p < 0.1).
ICD-10 coding algorithms for defining comorbidities of acute myocardial infarction

With the introduction of ICD-10 throughout Canada, it is important to ensure that Acute Myocardial Infarction (AMI) comorbidities employed in risk adjustment methods remain valid and robust. Therefore, we developed ICD-10 coding algorithms for nine AMI comorbidities, examined the validity of the ICD-10 and ICD-9 coding algorithms in detection of these comorbidities, and assessed their performance in predicting mortality. The nine comorbidities that we examined were shock, diabetes with complications, congestive heart failure, cancer, cerebrovascular disease, pulmonary edema, acute renal failure, chronic renal failure, and cardiac dysrhythmias.



Coders generated a comprehensive list of ICD-10 codes corresponding to each AMI comorbidity. Physicians independently reviewed and determined the clinical relevance of each item on the list. To ensure that the newly developed ICD-10 coding algorithms were valid in recording comorbidities, medical charts were reviewed. After assessing ICD-10 algorithms' validity, both ICD-10 and ICD-9 algorithms were applied to a Canadian provincial hospital discharge database to predict in-hospital, 30-day, and 1-year mortality.



Compared to chart review data as a 'criterion standard', ICD-9 and ICD-10 data had similar sensitivities (ranging from 7.1-100%), and specificities (above 93.6%) for each of the nine AMI comorbidities studied. The frequencies for the comorbidities were similar between ICD-9 and ICD-10 coding algorithms for 49,861 AMI patients in a Canadian province during 1994-2004. The C-statistics for predicting 30-day and 1 year mortality were the same for ICD-9 (0.82) and for ICD-10 data (0.81).



The ICD-10 coding algorithms developed in this study to define AMI comorbidities performed similarly as past ICD-9 coding algorithms in detecting conditions and risk-adjustment in our sample. However, the ICD-10 coding algorithms should be further validated in external databases.
Substrate Integrated Waveguide Diplexer Based on Circular Triplet Combline Filters

The design of substrate integrated waveguide (SIW) diplexers, based on combline triplet sections with transmission zeros (TZs) placed below and above the passband, is presented. In order to control the location of the TZs, positive and negative couplings are conveniently provided. A highly compact implementation based on circular substrate integrated coaxial resonators is proposed. An X-band diplexer with channel center frequencies at 9.5 and 10.5 GHz and absolute bandwidths of 400 MHz is designed. The structure shows important advantages in terms of size reduction, while keeping good insertion losses as well as high rejection and isolation levels.
Differentially Private Dropout

Large data collections required for the training of neural networks often contain sensitive information such as the medical histories of patients, and the privacy of the training data must be preserved. In this paper, we introduce a dropout technique that provides an elegant Bayesian interpretation to dropout, and show that the intrinsic noise added, with the primary goal of regularization, can be exploited to obtain a degree of differential privacy. The iterative nature of training neural networks presents a challenge for privacy-preserving estimation since multiple iterations increase the amount of noise added. We overcome this by using a relaxed notion of differential privacy, called concentrated differential privacy, which provides tighter estimates on the overall privacy loss. We demonstrate the accuracy of our privacy-preserving dropout algorithm on benchmark datasets.
Drone Path Planning for Secure Positioning and Secure Position Verification

Many dependable systems rely on the integrity of the position of their components. In such systems, two key problems are secure localization and secure location verification of the components. Researchers proposed several solutions, which generally require expensive infrastructures of several fixed stations (anchors) with trusted positions. In this paper, we explore the approach of replacing all the fixed anchors with a single drone that flies through a sequence of waypoints. At each waypoint, the drone acts as an anchor and securely determines the positions. This approach completely eliminates the need for many expensive anchors. The main challenge becomes how to find a convenient path for the drone to do this for all the devices. The problem presents novel aspects, which make existing path planning algorithms unsuitable. We propose LocalizerBee, VerifierBee, and PreciseVerifierBee: three path planning algorithms that allow a drone to respectively measure, verify, and verify with a guaranteed precision a set of positions in a secure manner. They are able to securely localize all the positions in a generic deployment area, even in the presence of drone control errors. Moreover, they produce short path lengths and they run in a reasonable processing time.
Grammar induction from (lots of) words alone

Grammar induction is the task of learning syntactic structure in a setting where that structure is hidden. Grammar induction from words alone is interesting because it is similiar to the problem that a child learning a language faces. Previous work has typically assumed richer but cognitively implausible input, such as POS tag annotated data, which makes that work less relevant to human language acquisition. We show that grammar induction from words alone is in fact feasible when the model is provided with sufficient training data, and present two new streaming or mini-batch algorithms for PCFG inference that can learn from millions of words of training data. We compare the performance of these algorithms to a batch algorithm that learns from less data. The minibatch algorithms outperform the batch algorithm, showing that cheap inference with more data is better than intensive inference with less data. Additionally, we show that the harmonic initialiser, which previous work identified as essential when learning from small POStag annotated corpora (Klein and Manning, 2004), is not superior to a uniform initialisation.
Deep vessel tracking: A generalized probabilistic approach via deep learning

Analysis of vascular geometry is important in many medical imaging applications, such as retinal, pulmonary, and cardiac investigations. In order to make reliable judgments for clinical usage, accurate and robust segmentation methods are needed. Due to the high complexity of biological vasculature trees, manual identification is often too time-consuming and tedious to be used in practice. To design an automated and computerized method, a major challenge is that the appearance of vasculatures in medical images has great variance across modalities and subjects. Therefore, most existing approaches are specially designed for a particular task, lacking the flexibility to be adapted to other circumstances. In this paper, we present a generic approach for vascular structure identification from medical images, which can be used for multiple purposes robustly. The proposed method uses the state-of-the-art deep convolutional neural network (CNN) to learn the appearance features of the target. A Principal Component Analysis (PCA)-based nearest neighbor search is then utilized to estimate the local structure distribution, which is further incorporated within the generalized probabilistic tracking framework to extract the entire connected tree. Qualitative and quantitative results over retinal fundus data demonstrate that the proposed framework achieves comparable accuracy as compared with state-of-the-art methods, while efficiently producing more information regarding the candidate tree structure.
Psychological autopsy studies of suicide: a systematic review.

The psychological autopsy method offers the most direct technique currently available for examining the relationship between particular antecedents and suicide. This systematic review aimed to examine the results of studies of suicide that used a psychological autopsy method.



A computer aided search of MEDLINE, BIDS ISI and PSYCHLIT, supplemented by reports known to the reviewers and reports identified from the reference lists of other retrieved reports. Two investigators systematically and independently examined all reports. Median proportions were determined and population attributable fractions were calculated, where possible, in cases of suicide and controls.



One hundred and fifty-four reports were identified, of which 76 met the criteria for inclusion; 54 were case series and 22 were case-control studies. The median proportion of cases with mental disorder was 91% (95 % CI 81-98%) in the case series. In the case-control studies the figure was 90% (88-95%) in the cases and 27% (14-48%) in the controls. Co-morbid mental disorder and substance abuse also preceded suicide in more cases (38%, 19-57%) than controls (6%, 0-13%). The population attributable fraction for mental disorder ranged from 47-74% in the seven studies in which it could be calculated. The effects of particular disorders and sociological variables have been insufficiently studied to draw clear conclusions.



The results indicated that mental disorder was the most strongly associated variable of those that have been studied. Further studies should focus on specific disorders and psychosocial factors. Suicide prevention strategies may be most effective if focused on the treatment of mental disorders.
Formulation and Numerical Solution of Robot Manipulators in Point-to-Point Motion with Maximum Load Carrying Capacity

In this paper, a formulation is developed for obtaining the optimal trajectory of robot manipulators to maximize the load carrying capacity for a given point-to-point task. The presented method is based on open loop optimal control. The indirect approach is employed to derive optimality conditions based on Pontryagin's Minimum Principle. The obtained necessary conditions for optimality lead to a two-point boundary-value problem solved via a multiple shooting method with the BVP4C command in MATLAB r . Since the carrying payload is one of the system parameters, a computational algorithm is developed, which provides the capability of calculating the maximum payload for a point-to-point task. The main advantage of this method is obtaining various optimal trajectories with di erent maximum payloads and path characteristics by changing the penalty matrices values. To demonstrate the e ciency of the proposed method and algorithm in obtaining the maximum payload trajectory, simulation is performed on a two-link manipulator.
Alleviation of Heavy Metal Stress in Plants and Remediation of Soil by Rhizosphere Microorganisms

Increasing concentration of heavy metals (HM) due to various anthropogenic activities is a serious problem. Plants are very much affected by HM pollution particularly in contaminated soils. Survival of plants becomes tough and its overall health under HM stress is impaired. Remediation of HM in contaminated soil is done by physical and chemical processes which are costly, time-consuming, and non-sustainable. Metal-microbe interaction is an emerging but under-utilized technology that can be exploited to reduce HM stress in plants. Several rhizosphere microorganisms are known to play essential role in the management of HM stresses in plants. They can accumulate, transform, or detoxify HM. In general, the benefit from these microbes can have a vast impact on plant's health. Plant-microbe associations targeting HM stress may provide another dimension to existing phytoremediation and rhizoremediation uses. In this review, applied aspects and mechanisms of action of heavy metal tolerant-plant growth promoting (HMT-PGP) microbes in ensuring plant survival and growth in contaminated soils are discussed. The use of HMT-PGP microbes and their interaction with plants in remediation of contaminated soil can be the approach for the future. This low input and sustainable biotechnology can be of immense use/importance in reclaiming the HM contaminated soils, thus increasing the quality and yield of such soils.
General and specific utility measures for synthetic data

Data holders can produce synthetic versions of datasets when concerns about potential disclosure restrict the availability of the original records. This paper is concerned with methods to judge whether such synthetic data have a distribution that is comparable to that of the original data, what we will term general utility. We consider how general utility compares with specific utility, the similarity of results of analyses from the synthetic data and the original data. We adapt a previous general measure of data utility, the propensity score mean-squared-error (pMSE), to the specific case of synthetic data and derive its distribution for the case when the correct synthesis model is used to create the synthetic data. Our asymptotic results are confirmed by a simulation study. We also consider two specific utility measures, confidence interval overlap and standardized difference in summary statistics, which we compare with the general utility results. We present two examples examining this comparison of general and specific utility to real data syntheses and make recommendations for their use for evaluating synthetic data.
An Evaluation of Intel Software Guard Extensions Through Emulation

The Intel Software Guard Extensions (SGX) technology, recently introduced in the new generations of x86 processors, allows the execution of applications in a fully protected environment (i.e., within enclaves). Because it is a recent technology, machines that rely on this technology are still a minority. In order to evaluate the SGX, an emulator of this technology (called OpenSGX) implements and replicates the main functionalities and structures used in SGX. The focus is to evaluate the resulting overhead from running an application within an environment with emulated SGX. For the evaluation, benchmark applications from the MiBench platform were employed. As performance metrics, we gathered the total number of instructions and the total number of CPU cycles for the execution of each application with and without OpenSGX.
McDRAM: Low Latency and Energy-Efficient Matrix Computations in DRAM

We propose a novel memory architecture for in-memory computation called McDRAM, where DRAM dies are equipped with a large number of multiply accumulate (MAC) units to perform matrix computation for neural networks. By exploiting high internal memory bandwidth and reducing off-chip memory accesses, McDRAM realizes both low latency and energy efficient computation. In our experiments, we obtained the chip layout based on the state-of-the-art memory, LPDDR4 where McDRAM is equipped with 2048 MACs in a single chip package with a small area overhead (4.7%). Compared with the state-of-the-art accelerator, TPU and the power-efficient GPU, Nvidia P4, McDRAM offers <inline-formula> <tex-math notation="LaTeX">$9.5{\times }$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$14.4{\times }$ </tex-math></inline-formula> speedup, respectively, in the case that the large-scale MLPs and RNNs adopt the batch size of 1. McDRAM also gives <inline-formula> <tex-math notation="LaTeX">$2.1{\times }$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$3.7{\times }$ </tex-math></inline-formula> better computational efficiency in TOPS/W than TPU and P4, respectively, for the large batches.
Anonymity of Bitcoin Transactions An Analysis of Mixing Services

Bitcoin, a distributed, cryptographic, digital currency, gained a lot of media attention for being an anonymous e-cash system. But as all transactions in the network are stored publicly in the blockchain, allowing anyone to inspect and analyze them, the system does not provide real anonymity but pseudonymity. There have already been studies showing the possibility to deanonymize bitcoin users based on the transaction graph and publicly available data. Furthermore, users could be tracked by bitcoin exchanges or shops, where they have to provide personal information that can then be linked to their bitcoin addresses. Special bitcoin mixing services claim to obfuscate the origin of transactions and thereby increase the anonymity of its users. In this paper we evaluate three of these services – Bitcoin Fog, BitLaundry, and the Send Shared functionality of Blockchain.info – by analyzing the transaction graph. While Bitcoin Fog and Blockchain.info successfully mix our transaction, we are able to find a direct relation between the input and output transactions in the graph of BitLaundry.
Supine body position as a risk factor for nosocomial pneumonia in mechanically ventilated patients: a randomised trial

Risk factors for nosocomial pneumonia, such as gastro-oesophageal reflux and subsequent aspiration, can be reduced by semirecumbent body position in intensive-care patients. The objective of this study was to assess whether the incidence of nosocomial pneumonia can also be reduced by this measure.



This trial was stopped after the planned interim analysis. 86 intubated and mechanically ventilated patients of one medical and one respiratory intensive-care unit at a tertiary-care university hospital were randomly assigned to semirecumbent (n=39) or supine (n=47) body position. The frequency of clinically suspected and microbiologically confirmed nosocomial pneumonia (clinical plus quantitative bacteriological criteria) was assessed in both groups. Body position was analysed together with known risk factors for nosocomial pneumonia.



The frequency of clinically suspected nosocomial pneumonia was lower in the semirecumbent group than in the supine group (three of 39 [8%] vs 16 of 47 [34%]; 95% CI for difference 10.0-42.0, p=0.003). This was also true for microbiologically confirmed pneumonia (semirecumbent 2/39 [5%] vs supine 11/47 [23%]; 4.2-31.8, p=0.018). Supine body position (odds ratio 6.8 [1.7-26.7], p=0.006) and enteral nutrition (5.7 [1.5-22.8], p=0.013) were independent risk factors for nosocomial pneumonia and the frequency was highest for patients receiving enteral nutrition in the supine body position (14/28, 50%). Mechanical ventilation for 7 days or more (10.9 [3.0-40.4], p=0.001) and a Glasgow coma scale score of less than 9 were additional risk factors.



The semirecumbent body position reduces frequency and risk of nosocomial pneumonia, especially in patients who receive enteral nutrition. The risk of nosocomial pneumonia is increased by long-duration mechanical ventilation and decreased consciousness.