Showing papers by "Florida Atlantic University published in 2019"
TL;DR: This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing DataAugmentation, a data-space solution to the problem of limited data.
Abstract: Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on GANs are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data.
5,782 citations
TL;DR: Examination of existing deep learning techniques for addressing class imbalanced data finds that research in this area is very limited, that most existing work focuses on computer vision tasks with convolutional neural networks, and that the effects of big data are rarely considered.
Abstract: The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g., fraud detection and cancer detection. Moreover, highly imbalanced data poses added difficulty, as most learners will exhibit bias towards the majority class, and in extreme cases, may ignore the minority class altogether. Class imbalance has been studied thoroughly over the last two decades using traditional machine learning models, i.e. non-deep learning. Despite recent advances in deep learning, along with its increasing popularity, very little empirical work in the area of deep learning with class imbalance exists. Having achieved record-breaking performance results in several complex domains, investigating the use of deep neural networks for problems containing high levels of class imbalance is of great interest. Available studies regarding class imbalance and deep learning are surveyed in order to better understand the efficacy of deep learning when applied to class imbalanced data. This survey discusses the implementation details and experimental results for each study, and offers additional insight into their strengths and weaknesses. Several areas of focus include: data complexity, architectures tested, performance interpretation, ease of use, big data application, and generalization to other domains. We have found that research in this area is very limited, that most existing work focuses on computer vision tasks with convolutional neural networks, and that the effects of big data are rarely considered. Several traditional methods for class imbalance, e.g. data sampling and cost-sensitive learning, prove to be applicable in deep learning, while more advanced methods that exploit neural network feature learning abilities show promising results. The survey concludes with a discussion that highlights various gaps in deep learning from class imbalanced data for the purpose of guiding future research.
1,377 citations
TL;DR: In this article, a new and independent determination of the local value of the Hubble constant based on a calibration of the Tip of the Red Giant Branch (TRGB) applied to Type Ia supernovae (SNeIa) is presented.
Abstract: We present a new and independent determination of the local value of the Hubble constant based on a calibration of the Tip of the Red Giant Branch (TRGB) applied to Type Ia supernovae (SNeIa). We find a value of Ho = 69.8 +/- 0.8 (+/-1.1\% stat) +/- 1.7 (+/-2.4\% sys) km/sec/Mpc. The TRGB method is both precise and accurate, and is parallel to, but independent of the Cepheid distance scale. Our value sits midway in the range defined by the current Hubble tension. It agrees at the 1.2-sigma level with that of the Planck 2018 estimate, and at the 1.7-sigma level with the SHoES measurement of Ho based on the Cepheid distance scale. The TRGB distances have been measured using deep Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) imaging of galaxy halos. The zero point of the TRGB calibration is set with a distance modulus to the Large Magellanic Cloud of 18.477 +/- 0.004 (stat) +/-0.020 (sys) mag, based on measurement of 20 late-type detached eclipsing binary (DEB) stars, combined with an HST parallax calibration of a 3.6 micron Cepheid Leavitt law based on Spitzer observations. We anchor the TRGB distances to galaxies that extend our measurement into the Hubble flow using the recently completed Carnegie Supernova Project I sample containing about 100 well-observed SNeIa. There are several advantages of halo TRGB distance measurements relative to Cepheid variables: these include low halo reddening, minimal effects of crowding or blending of the photometry, only a shallow (calibrated) sensitivity to metallicity in the I-band, and no need for multiple epochs of observations or concerns of different slopes with period. In addition, the host masses of our TRGB host-galaxy sample are higher on average than the Cepheid sample, better matching the range of host-galaxy masses in the CSP distant sample, and reducing potential systematic effects in the SNeIa measurements.
519 citations
TL;DR: A unique taxonomy is provided, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses.
Abstract: The security issue impacting the Internet-of-Things (IoT) paradigm has recently attracted significant attention from the research community. To this end, several surveys were put forward addressing various IoT-centric topics, including intrusion detection systems, threat modeling, and emerging technologies. In contrast, in this paper, we exclusively focus on the ever-evolving IoT vulnerabilities. In this context, we initially provide a comprehensive classification of state-of-the-art surveys, which address various dimensions of the IoT paradigm. This aims at facilitating IoT research endeavors by amalgamating, comparing, and contrasting dispersed research contributions. Subsequently, we provide a unique taxonomy, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses. This aims at providing the reader with a multidimensional research perspective related to IoT vulnerabilities, including their technical details and consequences, which is postulated to be leveraged for remediation objectives. Additionally, motivated by the lack of empirical (and malicious) data related to the IoT paradigm, this paper also presents a first look on Internet-scale IoT exploitations by drawing upon more than 1.2 GB of macroscopic, passive measurements’ data. This aims at practically highlighting the severity of the IoT problem, while providing operational situational awareness capabilities, which undoubtedly would aid in the mitigation task, at large. Insightful findings, inferences and outcomes in addition to open challenges and research problems are also disclosed in this paper, which we hope would pave the way for future research endeavors addressing theoretical and empirical aspects related to the imperative topic of IoT security.
451 citations
TL;DR: Resmetirom treatment resulted in significant reduction in hepatic fat after 12 weeks and 36 weeks of treatment in patients with NASH, with the possibility of documenting associations between histological effects and changes in non-invasive markers and imaging.
331 citations
TL;DR: Diversity can help organizations improve both patient care quality and financial results, and return on investments in diversity can be maximized when guided deliberately by existing evidence.
Abstract: Background Research on the effects of increasing workplace diversity has grown substantially. Unfortunately, little is focused on the healthcare industry, leaving organizations to make decisions based on conflicting findings regarding the association of diversity with quality and financial outcomes. To help improve the evidence-based research, this umbrella review summarizes diversity research specific to healthcare. We also look at studies focused on professional skills relevant to healthcare. The goal is to assess the association between diversity, innovation, patient health outcomes, and financial performance. Methods Medical and business research indices were searched for diversity studies published since 1999. Only meta-analyses and large-scale studies relating diversity to a financial or quality outcome were included. The research also had to include the healthcare industry or involve a related skill, such as innovation, communication and risk assessment. Results Most of the sixteen reviews matching inclusion criteria demonstrated positive associations between diversity, quality and financial performance. Healthcare studies showed patients generally fare better when care was provided by more diverse teams. Professional skills-focused studies generally find improvements to innovation, team communications and improved risk assessment. Financial performance also improved with increased diversity. A diversity-friendly environment was often identified as a key to avoiding frictions that come with change. Conclusions Diversity can help organizations improve both patient care quality and financial results. Return on investments in diversity can be maximized when guided deliberately by existing evidence. Future studies set in the healthcare industry, will help leaders better estimate diversity-related benefits in the context of improved health outcomes, productivity and revenue streams, as well as the most efficient paths to achieve these goals.
320 citations
TL;DR: Why the 30-million-word gap should not be abandoned, and the importance of retaining focus on the vital ingredient to language learning-quality speech directed to children rather than overheard speech, are addressed.
Abstract: Sperry, Sperry, and Miller (2018) aim to debunk what is called the 30-million-word gap by claiming that children from lower income households hear more speech than Hart and Risley () reported. We address why the 30-million-word gap should not be abandoned, and the importance of retaining focus on the vital ingredient to language learning-quality speech directed to children rather than overheard speech, the focus of Sperry et al.'s argument. Three issues are addressed: Whether there is a language gap; the characteristics of speech that promote language development; and the importance of language in school achievement. There are serious risks to claims that low-income children, on average, hear sufficient, high-quality language relative to peers from higher income homes. [ABSTRACT FROM AUTHOR]
238 citations
TL;DR: In this paper, a new and independent determination of the local value of the Hubble constant based on a calibration of the Tip of the Red Giant Branch (TRGB) applied to Type Ia supernovae (SNeIa) is presented.
Abstract: We present a new and independent determination of the local value of the Hubble constant based on a calibration of the Tip of the Red Giant Branch (TRGB) applied to Type Ia supernovae (SNeIa). We find a value of Ho = 69.8 +/- 0.8 (+/-1.1\% stat) +/- 1.7 (+/-2.4\% sys) km/sec/Mpc. The TRGB method is both precise and accurate, and is parallel to, but independent of the Cepheid distance scale. Our value sits midway in the range defined by the current Hubble tension. It agrees at the 1.2-sigma level with that of the Planck 2018 estimate, and at the 1.7-sigma level with the SHoES measurement of Ho based on the Cepheid distance scale. The TRGB distances have been measured using deep Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) imaging of galaxy halos. The zero point of the TRGB calibration is set with a distance modulus to the Large Magellanic Cloud of 18.477 +/- 0.004 (stat) +/-0.020 (sys) mag, based on measurement of 20 late-type detached eclipsing binary (DEB) stars, combined with an HST parallax calibration of a 3.6 micron Cepheid Leavitt law based on Spitzer observations. We anchor the TRGB distances to galaxies that extend our measurement into the Hubble flow using the recently completed Carnegie Supernova Project I sample containing about 100 well-observed SNeIa. There are several advantages of halo TRGB distance measurements relative to Cepheid variables: these include low halo reddening, minimal effects of crowding or blending of the photometry, only a shallow (calibrated) sensitivity to metallicity in the I-band, and no need for multiple epochs of observations or concerns of different slopes with period. In addition, the host masses of our TRGB host-galaxy sample are higher on average than the Cepheid sample, better matching the range of host-galaxy masses in the CSP distant sample, and reducing potential systematic effects in the SNeIa measurements.
233 citations
TL;DR: The role of AI-based systems in performing medical work in specializations including radiology, pathology, ophthalmology, and cardiology is researched and it is concluded that AI- based systems will augment physicians and are unlikely to replace the traditional physician–patient relationship.
Abstract: The practice of medicine is changing with the development of new Artificial Intelligence (AI) methods of machine learning. Coupled with rapid improvements in computer processing, these AI-based systems are already improving the accuracy and efficiency of diagnosis and treatment across various specializations. The increasing focus of AI in radiology has led to some experts suggesting that someday AI may even replace radiologists. These suggestions raise the question of whether AI-based systems will eventually replace physicians in some specializations or will augment the role of physicians without actually replacing them. To assess the impact on physicians this research seeks to better understand this technology and how it is transforming medicine. To that end this paper researches the role of AI-based systems in performing medical work in specializations including radiology, pathology, ophthalmology, and cardiology. It concludes that AI-based systems will augment physicians and are unlikely to replace the traditional physician-patient relationship.
224 citations
TL;DR: The term Latinx emerged recently as a gender-neutral label for Latino/a and Latin@ as discussed by the authors, and the purpose of this paper is to examine ways in which Latinx is used within the higher education context.
Abstract: The term Latinx emerged recently as a gender-neutral label for Latino/a and Latin@. The purpose of this paper is to examine ways in which Latinx is used within the higher education context, and to ...
222 citations
TL;DR: In this paper, the authors investigated the effect of online review contents on potential guests' trust perceptions in AirBnB.com and found salient themes determine trust perception: host attributes positively lead to benevolence, location and room aesthetics positively affect ability, room description positively affects integrity, and location and host attributes determine overall trust perception.
TL;DR: The authors examined how country-level institutional context moderates the relationship between three socio-cognitive traits (entrepreneurial self-efficacy, alertness to new business opportunities, and fear of failure) and opportunity entrepreneurship.
TL;DR: The proposed ADPED algorithm can be adaptive to both day-ahead and intra-day operation under uncertainty and can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation.
Abstract: This paper proposes an approximate dynamic programming (ADP)-based approach for the economic dispatch (ED) of microgrid with distributed generations. The time-variant renewable generation, electricity price, and the power demand are considered as stochastic variables in this paper. An ADP-based ED (ADPED) algorithm is proposed to optimally operate the microgrid under these uncertainties. To deal with the uncertainties, Monte Carlo method is adopted to sample the training scenarios to give empirical knowledge to ADPED. The piecewise linear function (PLF) approximation with improved slope updating strategy is employed for the proposed method. With sufficient information extracted from these scenarios and embedded in the PLF function, the proposed ADPED algorithm can not only be used in day-ahead scheduling but also the intra-day optimization process. The algorithm can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation. Numerical simulations demonstrate the effectiveness of the proposed approach. The near-optimal decision obtained by ADPED is very close to the global optimality. And it can be adaptive to both day-ahead and intra-day operation under uncertainty.
TL;DR: It is clearly time to move on from the dogma of viewing MMP inhibition as intractable, and multiple studies indicate that modulating MMP activity can improve immunotherapy.
Abstract: The pursuit of matrix metalloproteinase (MMP) inhibitors began in earnest over three decades ago. Initial clinical trials were disappointing, resulting in a negative view of MMPs as therapeutic targets. As a better understanding of MMP biology and inhibitor pharmacokinetic properties emerged, it became clear that initial MMP inhibitor clinical trials were held prematurely. Further complicating matters were problematic conclusions drawn from animal model studies. The most recent generation of MMP inhibitors have desirable selectivities and improved pharmacokinetics, resulting in improved toxicity profiles. Application of selective MMP inhibitors led to the conclusion that MMP-2, MMP-9, MMP-13, and MT1-MMP are not involved in musculoskeletal syndrome, a common side effect observed with broad spectrum MMP inhibitors. Specific activities within a single MMP can now be inhibited. Better definition of the roles of MMPs in immunological responses and inflammation will help inform clinic trials, and multiple studies indicate that modulating MMP activity can improve immunotherapy. There is a U.S. Food and Drug Administration (FDA)-approved MMP inhibitor for periodontal disease, and several MMP inhibitors are in clinic trials, targeting a variety of maladies including gastric cancer, diabetic foot ulcers, and multiple sclerosis. It is clearly time to move on from the dogma of viewing MMP inhibition as intractable.
TL;DR: This paper provides a survey of prediction, and forecasting methods used in cyber security, and discusses machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security.
Abstract: This paper provides a survey of prediction, and forecasting methods used in cyber security. Four main tasks are discussed first, attack projection and intention recognition, in which there is a need to predict the next move or the intentions of the attacker, intrusion prediction, in which there is a need to predict upcoming cyber attacks, and network security situation forecasting, in which we project cybersecurity situation in the whole network. Methods and approaches for addressing these tasks often share the theoretical background and are often complementary. In this survey, both methods based on discrete models, such as attack graphs, Bayesian networks, and Markov models, and continuous models, such as time series and grey models, are surveyed, compared, and contrasted. We further discuss machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security. The survey also focuses on the practical usability of the methods and problems related to their evaluation.
TL;DR: In this paper, the authors analyzed the spatiotemporal variations of land use and land cover and quantified the change in three important ecosystem services (food production, carbon storage, and habitat quality) in the Koshi River Basin, Nepal during 1996-2016 by using freely available data and tools such as, Landsat satellite images and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model.
Abstract: The provision of ecosystem services is directly related to the type of land use and land cover and management practices in a given area. Changes in land use and land cover can alter the supply of ecosystem services and affect the well-being of both humanity and nature. This study analyses the spatiotemporal variations of land use and land cover and quantifies the change in three important ecosystem services (food production, carbon storage, and habitat quality) in the Koshi River Basin, Nepal during 1996–2016 by using freely available data and tools such as, Landsat satellite images and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. During the observed time period, there was an overall gain in urban areas (190 sq.km), forests (773 sq.km) and grassland (431 sq.km); loss of cultivated land (220 sq.km) and shrub lands (847 sq.km), mostly occurring in the lowlands (≤1000 m). As a result of the land cover changes, while food production and carbon storage showed a declining trend, overall habitat quality in the basin increased. There is a need to design novel and effective landscape approaches that address local realities and that will aid the maintenance of ecosystem services. We recommend landscape level planning to improve urban and agricultural sectors and focus on halting the loss of ecosystem services.
TL;DR: How school communities can provide substantive instructional and emotional support to all teens, particularly with the increased prominence of these issues over the last decade is discussed.
Abstract: While previous studies have identified that school bullying and cyberbullying victimization among adolescents is associated with suicidal thoughts and attempts, no work has measured the severity of...
TL;DR: A systematic literature review was conducted on the past 12 years of research that examined natural disaster-related experiences and psychological and physiological health outcomes on populations who are more vulnerable to adverse weather impacts and found that fostering social capital is a way to combat stressors in disadvantaged communities.
Abstract: Climate change is acknowledged as being a crucial determinant of public health. The United States is experiencing an increase in the frequency and intensity of natural disasters as a result of clim...
TL;DR: In this article, the authors examined dual-class equity crowdfunding as a digital ownership model and found that a higher separation between ownership and control rights lowers the probability of success of the offering, the likelihood of attracting professional investors, as well as the long-run prospects.
TL;DR: Re-examine results from human studies, and suggest the use of more sensitive tasks to evaluate PA effects on age-related changes in the hippocampus, such as relational memory and mnemonic discrimination.
TL;DR: It is found that endothelial IL‐1R1 was necessary and sufficient for mediating sickness behavior and drove leukocyte recruitment to the CNS and impaired neurogenesis, whereas ventricular IL-1R 1 was critical for monocyte recruitmentto the CNS.
Agency for Science, Technology and Research1, Huazhong University of Science and Technology2, University of Texas MD Anderson Cancer Center3, Queensland University of Technology4, Florida Atlantic University5, National University of Singapore6, Singapore General Hospital7, University Health System8, Khoo Teck Puat Hospital9, Nanyang Technological University10, Tan Tock Seng Hospital11, The George Institute for Global Health12, University of Otago13
TL;DR: Wang et al. as mentioned in this paper used whole-genome sequencing of 4,810 Singapore Chinese, Malays, and Indians to identify 98.3 million SNPs and small insertions or deletions over half of which are novel.
TL;DR: In this paper, the authors investigated the effect of CBBE on customer loyalty and further examined the mediatory roles of customer satisfaction and trust in the hotel industry, concluding that CBBE consists of brand awareness, physical quality, staff behavior, and brand image.
TL;DR: In this paper, the authors examine how economic institutions, measured by the Economic Freedom of the World (EFW) index, affect the relationship between capital and opportunity-motivated entrepreneurship.
Abstract: We examine how economic institutions, measured by the Economic Freedom of the World (EFW) index, affect the relationship between capital—human, social, and financial—and opportunity-motivated entrepreneurship (OME). To do this, we develop a multi-level model that connects theories of human and social capital at the micro-level to institutional theories at the macro-level. Using data from the Global Entrepreneurship Monitor (GEM), we then test the predictions of our model and find evidence that economic institutions play a crucial role in the relationship between these three distinct types of capital and OME. Our results are somewhat counter-intuitive—as the quality of the institutional environment improves, human and financial capitals become less important determinants of entrepreneurship while the relationship between social capital and entrepreneurship substantially strengthens.
TL;DR: This work used CRISPR gene editing to endogenously tag the sole Drosophila Ca2+ channel responsible for synchronized neurotransmitter release, and found that channel abundance is regulated during homeostatic potentiation, but notHomeostatic depression.
Abstract: Neurons communicate through Ca2+-dependent neurotransmitter release at presynaptic active zones (AZs). Neurotransmitter release properties play a key role in defining information flow in circuits and are tuned during multiple forms of plasticity. Despite their central role in determining neurotransmitter release properties, little is known about how Ca2+ channel levels are modulated to calibrate synaptic function. We used CRISPR to tag the Drosophila CaV2 Ca2+ channel Cacophony (Cac) and, in males in which all Cac channels are tagged, investigated the regulation of endogenous Ca2+ channels during homeostatic plasticity. We found that heterogeneously distributed Cac is highly predictive of neurotransmitter release probability at individual AZs and differentially regulated during opposing forms of presynaptic homeostatic plasticity. Specifically, AZ Cac levels are increased during chronic and acute presynaptic homeostatic potentiation (PHP), and live imaging during acute expression of PHP reveals proportional Ca2+ channel accumulation across heterogeneous AZs. In contrast, endogenous Cac levels do not change during presynaptic homeostatic depression (PHD), implying that the reported reduction in Ca2+ influx during PHD is achieved through functional adaptions to pre-existing Ca2+ channels. Thus, distinct mechanisms bidirectionally modulate presynaptic Ca2+ levels to maintain stable synaptic strength in response to diverse challenges, with Ca2+ channel abundance providing a rapidly tunable substrate for potentiating neurotransmitter release over both acute and chronic timescales.SIGNIFICANCE STATEMENT Presynaptic Ca2+ dynamics play an important role in establishing neurotransmitter release properties. Presynaptic Ca2+ influx is modulated during multiple forms of homeostatic plasticity at Drosophila neuromuscular junctions to stabilize synaptic communication. However, it remains unclear how this dynamic regulation is achieved. We used CRISPR gene editing to endogenously tag the sole Drosophila Ca2+ channel responsible for synchronized neurotransmitter release, and found that channel abundance is regulated during homeostatic potentiation, but not homeostatic depression. Through live imaging experiments during the adaptation to acute homeostatic challenge, we visualize the accumulation of endogenous Ca2+ channels at individual active zones within 10 min. We propose that differential regulation of Ca2+ channels confers broad capacity for tuning neurotransmitter release properties to maintain neural communication.
TL;DR: Nursing education and nursing research will change to encompass a differentiated demand for professional nursing practice with, and not for, robots in healthcare.
TL;DR: This is the first study to compare multiple data-level and algorithm-level deep learning methods across a range of class distributions and a unique analysis of the relationship between minority class size and optimal decision threshold and state-of-the-art performance on the given Medicare fraud detection task.
Abstract: Access to affordable healthcare is a nationwide concern that impacts a large majority of the United States population Medicare is a Federal Government healthcare program that provides affordable health insurance to the elderly population and individuals with select disabilities Unfortunately, there is a significant amount of fraud, waste, and abuse within the Medicare system that costs taxpayers billions of dollars and puts beneficiaries’ health and welfare at risk Previous work has shown that publicly available Medicare claims data can be leveraged to construct machine learning models capable of automating fraud detection, but challenges associated with class-imbalanced big data hinder performance With a minority class size of 003% and an opportunity to improve existing results, we use the Medicare fraud detection task to compare six deep learning methods designed to address the class imbalance problem Data-level techniques used in this study include random over-sampling (ROS), random under-sampling (RUS), and a hybrid ROS–RUS The algorithm-level techniques evaluated include a cost-sensitive loss function, the Focal Loss, and the Mean False Error Loss A range of class ratios are tested by varying sample rates and desirable class-wise performance is achieved by identifying optimal decision thresholds for each model Neural networks are evaluated on a 20% holdout test set, and results are reported using the area under the receiver operating characteristic curve (AUC) Results show that ROS and ROS–RUS perform significantly better than baseline and algorithm-level methods with average AUC scores of 08505 and 08509, while ROS–RUS maximizes efficiency with a 4× speedup in training time Plain RUS outperforms baseline methods with up to 30× improvements in training time, and all algorithm-level methods are found to produce more stable decision boundaries than baseline methods Thresholding results suggest that the decision threshold always be optimized using a validation set, as we observe a strong linear relationship between the minority class size and the optimal threshold To the best of our knowledge, this is the first study to compare multiple data-level and algorithm-level deep learning methods across a range of class distributions Additional contributions include a unique analysis of the relationship between minority class size and optimal decision threshold and state-of-the-art performance on the given Medicare fraud detection task
TL;DR: The mechanistically non-traditional MMP inhibitors offer treatment strategies for tumor angiogenesis that avoid the off-target toxicities and lack of specificity that plagued Zn2+-chelating inhibitors.
Abstract: Angiogenesis is facilitated by the proteolytic activities of members of the matrix metalloproteinase (MMP) family. More specifically, MMP-9 and MT1-MMP directly regulate angiogenesis, while several studies indicate a role for MMP-2 as well. The correlation of MMP activity to tumor angiogenesis has instigated numerous drug development programs. However, broad-based and Zn2+-chelating MMP inhibitors have fared poorly in the clinic. Selective MMP inhibition by antibodies, biologicals, and small molecules has utilized unique modes of action, such as (a) binding to protease secondary binding sites (exosites), (b) allosterically blocking the protease active site, or (c) preventing proMMP activation. Clinical trials have been undertaken with several of these inhibitors, while others are in advanced pre-clinical stages. The mechanistically non-traditional MMP inhibitors offer treatment strategies for tumor angiogenesis that avoid the off-target toxicities and lack of specificity that plagued Zn2+-chelating inhibitors.
TL;DR: In this paper, the authors evaluate the factors associated with platforms' compliance expenses, and their due diligence application, and find that due diligence is related to legislation requirement, platform size, and type or complexity of crowdfunding campaigns.
Abstract: Crowdfunding platform due diligence comprises background checks, site visits, credit checks, cross-checks, account monitoring, and third party proof on funding projects. We evaluate the factors associated with platforms’ compliance expenses, and their due diligence application. We find that due diligence is related to legislation requirement, platform size, and type or complexity of crowdfunding campaigns. In addition, we find that platforms applying due diligence provide more services to project issuers and funders. Furthermore, due diligence is associated with higher percentage of successful campaigns, more fund contributors, and larger amount of capital raised on platforms. Our analyses are supported by platform-level data, covering the period 2014–2017.