Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning in New Venture Competitions

This paper uses administrative data from 87 new venture competitions in 17 U.S. states to show that winning has large, positive effects on measures of subsequent venture success, including employment and financing. While cash prizes are valuable, especially for founders who are likely financially constrained, winning is independently useful. Certification may be one mechanism, but it does not seem to be the primary one. An alternative is that competitions help entrepreneurs learn about their projects’ quality. Receiving negative feedback is shown to increase venture abandonment, suggesting that competitions are useful in part because they facilitate faster type revelation. ∗New York University Stern School of Business. 44 West 4th St KMC 9-93, NY NY 10012; Phone: +1-917-519-8052; Email: sabrina.howell@nyu.edu. This project was funded by the Kauffman Foundation. For especially useful comments, I thank Manuel Adelino, Tom Åstebro, Shai Bernstein, Edward Glaeser, Will Gornall, Boyan Jovanovic, Saul Lach, Augustin Landier, Josh Lerner, Song Ma, Ramana Nanda, David Robinson, Rick Townsend, Annette Vissing-Jørgensen, and participants at the NBER Entrepreneurship Working Group, Yale Junior Finance Conference, IDC Herzliya Eagle Labs Conference, Queens University Economics of Entrepreneurship and Innovation Conference, UC Berkeley-Stanford Innovation and Finance Conference, Georgia State CEAR Conference, and the Tech Scheller seminar. Finally, I thank Adam Rentschler of Valid Evaluation, and all the others who provided the data, including Lea Lueck, Allison Ernst, and Catherine Cronin. Lucy Gong, Sreyoshi Mukherjee, and Jack Reiss provided excellent research assistance.
Using Ensemble Learning for Diagnostics of Eye Diseases

- Deep learning is the most widely used image recognition technology today, and it provides more insight into how computers can understand and learn from data. In deep learning, artificial neural networks analyze a large set of data to automatically detect patterns. In this article, we'll introduce some of these techniques to see how we can use deep learning to create our own model for diagnosing eye disease. When we talk about determining a diagnosis by medical imaging, we mean that we are going to explain some of the main areas of computer vision and how we can classify images data using convolutional neural networks, one of most important idea is the evaluation of model performance using ensemble learning. In this study, CNN, Vgg16 and Inceptionv3 networks will be compared in detail using ensemble learning. In our work, a convolutional neural network based on Keras and Ten-sorFlow is implemented using python. 1692 images were used for classification, which contained four types of eye diseases (diabetic retinopathy, glaucoma, myopia and normal), including 1,200 for training, 246 for validation and 246 for testing. CNN, VGG16 and InceptionV3 networks are trained in Colab GPU processors with three different combinations of classifiers using bagging, boosting and stacking. All types of ensemble learning have been implemented and the result is obtained. We found that through ensemble learning we could achieve higher performance than we could achieve from any of learning algorithms alone. On the other hand, we used the confusion matrix in our experiments to show us where our classifier is confused when it makes predictions.
Qualities of a Good English Language Teacher from the Perspectives of Textbook Authors in the Field of Language Teaching, Teachers, and Learners

Researchers in the field of language teaching have long been fascinated by the qualities of good teachers. Such qualities have rarely been approached qualitatively from different angles in a single study. Thus, this research aimed to unearth the qualities in question by employing a qualitative multimethod approach. To this end, 23 randomly selected standard language teaching textbooks were analyzed by two coders using qualitative content analysis. The judiciously adopted codes from the analysis were employed to design and carry out semi-structured interviews with nine effective language teachers as well as ten good learners nominated via purposeful sampling. The data gleaned from the semi-interviews were once again analyzed through qualitative content analysis. The collective initial coding of the three datasets resulted in thirteen themes: knowledge of pedagogy, rapport, critical thinking, empathy, rhetoric, knowledge of subject matter, enthusiasm, respect, humor, consciousness, fairness, authenticity, and physical attributes. Conducting a second-level coding, we uncovered two broad themes, namely personality and professionalism with the former being significantly weightier collectively. The application of Chi-Square at this point showed that the associations between the three datasets were meaningful. The findings of the semi-structured interviews of participant teachers and students corroborated those of authors in the field of language teaching. Also, rapport and pedagogy were respectively viewed as the most salient attributes associated with good language teachers by the interviewed informants. The study holds clear implications for interested researchers, language teachers, and teacher educators.
Did our Course Design on Software Architecture meet our Student’s Learning Expectations?

This Innovative Practice Full Paper discusses our course design on software architecture to meet the learning expectations of two groups of software engineers. Software engineers with working experiences frequently find themselves the need to upskill in their lifelong learning journey. Their learning expectations are shaped not just by their need to know but also other learning characteristics such as their working experiences. In many cases, we design courses based on the required learning outcomes and assessment criteria. In this paper, we wish to find out whether our course design on software architecture has met the learning expectations of our students over eight years. Our study data involves two groups of software engineers to upskill in two courses (1) software engineering practitioners taking a public software architecture design course (2) postgraduate students taking a Master’s level software engineering programme. We explain how we evolve the course design based on their students’ feedback after each run of the courses. Although the feedback of each run show encouraging results, we discover gaps in our design when we cumulatively analyze the trends of their learning expectations with their qualitative comments over the years. Our design is able to consistently meet some but not all the learning expectations that recur over the years and we discuss the reasons for this outcome and potential further interventions. We hope this discussion and trend analysis process can help course designers to improve their course design.
EVALUATION OF THE RELEVANCE OF STAFF CAREER DEVELOPMENT ON CORPORATE CORE BUSINESS IN TAMALE POLYTECHNIC

This paper evaluates the relevance of staff career development on corporate core business of Tamale Polytechnic. The study adopted a case study approach because it probed specific tertiary institution in Ghana. A mixed research method was adopted with both Simple and purposive sampling techniques being employed. 140 closed-ended questionnaires was distributed to teachers’ of Tamale Polytechnic of which 118 responded which gave a response rate 83.3%. Data analysis and presentation was handled both qualitative and quantitatively. The finding as per the objectives revealed that majority of academic staff has acquired higher qualification through academic career development activities. Findings also showed there is no significant relationship on nature of staff development and relevance to school core business. It was concluded that the relevance of academic staff career development is to aligned with the organization’s vision and long-term goals in order to enhance the skills, knowledge, learning and innovative capabilities of the Polytechnic to meet the exigency of time. Therefore, efforts need be made for strict compliance the staff development policy.
Discrete Wavelet Transform Based Multimodal Medical Image Fusion

Image fusion is an extensively discussed topic for improving the information content of images. The main objective of image fusion algorithm is to combine information from multiple images of a scene. The result of image fusion is a new image which is more feasible for human and machine perception for further image processing operations such as segmentation, feature extraction and object recognition. This paper explores the possibility of using the specialized wavelet approach in image fusion and de-noising. These algorithms are compared on digital microscope images. The approach uses an affine transform-based image registration followed by wavelet fusion. The disease of the brain is identified using the Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). The many more scan variation of MRI and PET has been executed for the medical diagnosis. The medical expert needs a solid strain of the computational scan and its related for diagnosis. For the diagnosis and treatment of disorders requires precise information that is attained through various modalities of medical images such as Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI). In the image processing the image fusion is the method of merging two images into a single picture. The obtained single fused image using various multi-modality medical images is enhanced anatomical, highly desirable spectral information compared to the raw single scanned image. This multi – modal fused image is useful for clinical diagnosis of medical experts. In this research work, the system is prepared for the preprocessing of the MRI and PET scan images. The pre-processing techniques enhance the quality of the input images which are degraded and non-readable. For the pre-processing approach we have applied the Gaussian filters of spatial filtering techniques. The enhanced images passed to the fusion of different region of brain images using Discrete Wavelet Transform (DWT). The system achieved around 90-95% more accurate outcomes by diluting the color change. The outcome fused image is achieved without losing the spectral and anatomical data.
Computational Model for Understanding Emotions in Sarcasm: A Survey

Sarcasm is generally associated with a negative emotion. The question is which negative emotion-anger, sadness, disgust, any other? This paper presents a methodology of detecting the exact emotion(s) in a sarcastic sentence. Sarcasm arises from contextual incongruity in a sentence and bears a surface sentiment which is different from the intended sentiment. While the surface sentiment may be positive, the intended sentiment is negative. Thus the underlying emotion recognition task becomes one of the most difﬁcult parts of the conundrum. Previous works have extensively studied sentiment and emotion in language, while the relationship between sarcasm and emotion has been largely un-addressed. In this paper, we investigate various challenges and techniques for Sar-casm Detection, Emotion Analysis and relationship between them.
Data-Driven Characterization and Detection of COVID-19 Themed Malicious Websites

COVID-19 has hit hard on the global community, and organizations are working diligently to cope with the new norm of "work from home". However, the volume of remote work is unprecedented and creates opportunities for cyber attackers to penetrate home computers. Attackers have been leveraging websites with COVID-19 related names, dubbed COVID-19 themed malicious websites. These websites mostly contain false information, fake forms, fraudulent payments, scams, or malicious payloads to steal sensitive information or infect victims’ computers. In this paper, we present a data-driven study on characterizing and detecting COVID-19 themed malicious websites. Our characterization study shows that attackers are agile and are deceptively crafty in designing geolocation targeted websites, often leveraging popular domain registrars and top-level domains. Our detection study shows that the Random Forest classifier can detect COVID-19 themed malicious websites based on the lexical and WHOIS features defined in this paper, achieving a 98% accuracy and 2.7% false-positive rate.
Two‐Point Mixing, Buoyancy Sorting, and Updraft Dilution in the RACORO Campaign

Aircraft measurements of shallow cumulus cases from the Routine Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) campaign are studied in a transformed Paluch diagram. Previous studies have interpreted samples in Paluch diagrams in terms of two‐point mixing and buoyancy sorting. The two‐point mixing behavior is frequently seen in the RACORO observations and can be unambiguously distinguished from buoyancy sorting. The latter is not found in the RACORO observations and is also rare in a large‐eddy simulation (LES) of a RACORO case. As opposed to often assumed gradual dilution of updrafts, around half of the instances of two‐point mixing show no clear evidence of updraft dilution. We also find substantial spread in the properties of parcels that have adjusted to their level of neutral buoyancy and that a LES with horizontally homogeneous surface underestimates this spread. The inclusion of surface heterogeneity in the LES improves the agreement of its horizontal moisture variations with the aircraft measurements.
Apport de l'analyse protéomique à l'étude de la signalisation du fibroblast growth factor-2 dans les cellules de cancer du sein

Nous avons etudie la signalisation intracellulaire du fibroblast growth factor-2 dans les cellules de cancer du sein mcf-7 par analyse proteomique basee sur le couplage de l'electrophorese bidimensionnelle et de la spectrometrie de masse (maldi-tof et ms-ms). A l'aide d'inhibiteurs pharmacologiques et d'anticorps, nous avons montre que la stimulation des cellules mcf-7 par le fgf-2 declenche une augmentation de la tyrosine-phosphorylation du recepteur de haute affinite du fgf-2, du fgf receptor substrate-2 , de la kinase src, des p42/p44 mitogen-activated protein kinases (mapk), et d'une proteine de 30 kda. L'analyse en spectrometrie de masse maldi-tof nous a permis d'identifier cette proteine de 30 kda comme etant la cycline d2, element majeur de la regulation du cycle cellulaire. De plus, nos resultats suggerent que la phosphorylation de la cycline d2 est dependante de src mais pas des mapk. Nous avons ensuite etudie les modifications de synthese de proteines induites par le fgf-2 apres incorporation d'acides amines marques au 3 5s. Nous avons montre une augmentation de synthese de 4 proteines ( heat shock proteins hsp90 et hsp70, proliferating cell nuclear antigen (pcna) et transcriptionaly controlled tumor protein (tctp)) qui ont ete identifiees par spectrometrie de masse maldi-tof et ms-ms.
Early and mid-Holocene coastal settlement and demography in southeastern Norway : Comparing distribution of radiocarbon dates and shoreline

In this paper we explore temporal variation in demography and settlement intensity in southeastern Norway during the Early and mid-Holocene. In order to investigate the temporal variation in demography and settlement we have applied and compared two different proxies: Summed radiocarbon probability distributions and site count data of shoreline-dated sites. The proxies display similar patterns, and we suggest that they indicate stability in settlement in the coastal areas of southeastern Norway between 8500 cal. BCE and 2000 cal. BCE.
Production Function Estimation with Factor-Augmenting Technology: An Application to Markups

Traditional production function models rely on factor-neutral technology and functional form assumptions. These assumptions impose strong theoretical restrictions and are often rejected by the data. This paper develops a new method for estimating production functions with factor-augmenting technology. The method does not impose parametric restrictions and generalizes prior approaches that rely on the CES production function. I first extend the canonical Olley-Pakes framework to accommodate factor-augmenting technology. Then, I show how to identify output elasticities based on a novel control variable approach and the optimality of input expenditures. I use this method to estimate output elasticities and markups in manufacturing industries in the US and four developing countries. Neglecting labor-augmenting productivity and imposing parametric restrictions mismeasures output elasticities and heterogeneity in the production function. My estimates suggest that standard models (i) underestimate capital elasticity, and (ii) overestimate labor elasticity. These biases propagate into markup estimates inferred from output elasticities. My estimates point to a much more muted markup growth (about half) in the US manufacturing sector than recent estimates. Post-Doctoral Researcher, Microsoft Research. Email: mdemirer@mit.edu. I am indebted to Nikhil Agarwal, Daron Acemoglu, Anna Mikusheva, and Victor Chernozhukov for invaluable guidance and support. This paper benefited from feedback from Oguzhan Celebi, Allan Collard-Wexler, Max Cytrynbaum, Glenn Ellison, Kirill Evdekimov, Sara Fisher Ellison, Jerry Hausman, Stefan Hoderlein, Paul Joskow, Omer Karaduman, Ali Kakhbod, Christopher Knittel, Jing Li, Madeline Mckelway, Whitney Newey, Alex Olssen, Will Rafey, Nancy Rose, Tobias Salz, Karthik Sastry, Cansu Sen, Erol Taymaz, John Van Reenen, Michael Whinston, Samuel Yang, Kamil Yilmaz, Nathan Zorzi, as well as various seminar participants. I would like to thank State Institute of Statistics in Turkey for allowing us access to the data under the project “Growth and Technological Change in Turkey”. I would also like to thank Davesh Raval for helping me to obtain the Colombian and Chilean data.
Equivalent second order cone programs for distributionally robust zero-sum games

We consider a two-player zero-sum game with stochastic linear constraints. The probability distributions of the vectors associated with the constraints are partially known. The available information with respect to the distribution is based mainly on the first two moments. In this vein, we formulate the stochastic linear constraints as distributionally robust chance constraints. We consider three different types of moments based uncertainty sets. For each uncertainty set, we show that there exists a saddle point equilibrium of the game. The latter corresponds to the optimal solution of a primal-dual pair of second order cone programs. In order to illustrate our theoretical results, we consider an instance of a zero-sum game together with distributionally robust chance constraints. A comparison with expected value constraints zero-sum game is drawn to illustrate the effectiveness of our approach.
Bayesian Inverse Problems

Inverse problems arise from the need to gain information about an unknown object of interest from given indirect measurements. Inverse problems have several applications varying from medical imaging and industrial process monitoring to ozone layer tomography and modelling of financial markets. The common feature for inverse problems is the need to understand indirect measurements and to overcome extreme sensitivity to noise and modelling inaccuracies. In this course we employ probabilistic approach to inverse problems to find stable and meaningful solutions that allow us quantify how inaccuracies in the data or model affect the obtained estimate. 1 Bayesian approach to discrete inverse problems 1.
Targeting of glioma stem-like cells with a parthenolide derivative ACT001 through inhibition of AEBP1/PI3K/AKT signaling

Glioblastoma (GBM) is the most lethal primary brain tumor in adults with a median survival of around 15 months. A potential treatment strategy involves targeting glioma stem-like cells (GSCs) that are able to initiate, maintain, and repopulate the tumor mass. Here, we identify ACT001, a parthenolide derivative, targeting GSCs through regulation of adipocyte enhancer binding protein 1 (AEBP1) signaling. Methods: The effects of ACT001 on cell survival of normal human astrocytes (NHA) and patient-derived glioma stem-like cells (GSCs) were evaluated. RNA-Seq were performed to detect differentially expressed genes. ACT001 efficacy as a single agent or in combination with SHP-2 inhibitor SHP099 was assessed using a GSC orthotopic xenograft model. Results: GSCs exhibit high response to ACT001 in compared with normal human astrocytes. AEBP1 is a putative target of ACT001 by RNA-Seq analysis, which expression associates with prognosis of GBM patients. Knockdown of AEBP1 inhibits GSC proliferation and glioma sphere formation. Treatment with ACT001 or PI3K inhibitor or AEBP1 depletion would impair AKT phosphorylation and GSC proliferation, whereas constitutive AKT activation rescues ACT001 treatment or AEBP1 depletion-inhibited cell proliferation. Moreover, ACT001 blocks TGF-β-activated AEBP1/AKT signaling in GSCs. ACT001 exhibits antitumor activity either as a single agent or in combination with SHP099, which provides significant survival benefits for GSC tumor xenograft-bearing animals. Conclusions: Our data demonstrate AEBP1 as a new druggable target in GBM and ACT001 as a potential therapeutic option for improving the clinical treatment of GBM in combination with SHP099.
Survey on Text Summarization

Automatic text summarization is considered to be one of the hard problems because computationally there is no exact way of evaluating summary but the human can distinguish between good summary and bad summary. Also, summaries can be of various types like abstractive where new words and phrases are used, unlike extractive summarization where top scoring sentences from input text gets extracted as a summary sentence. Traditionally the focus of researcher was on building natural language generation which requires proper planning and realization of language. Various machine learning based approaches based on sequence labelling and SVR has been applied to extract summary sentences from the input text. Nowadays deep neural network models like sequence-to-sequence, LSTM, pointer-generator model are getting implemented to generate summaries. This report will give a brief idea about types of summary, summary evaluation measures and various ways to get summary.
Respect the Unstable: Delays and Saturation in Contact Tracing for Disease Control

Motivated by the novel coronavirus disease (COVID-19) pandemic, this paper aims to apply Gunter Stein's cautionary message of respecting the unstable to the problem of controlling the spread of an infectious disease. With this goal, we study the effect that delays and capacity constraints have in the test, trace, and isolate (TeTrIs) process, and how they impact its ability to prevent exponential disease spread. Our analysis highlights the critical importance of speed and scale in the TeTrIs process. Precisely, ensuring that the delay in the TeTrIs process is much smaller than the doubling time of the disease spread is necessary for achieving acceptable performance. Similarly, limited TeTrIs capacity introduces a threshold on the size of an outbreak beyond which the disease spreads almost like the uncontrolled case. Along the way, we provide numerical illustrations to highlight these points. © 2022 Society for Industrial and Applied Mathematics Publications. All rights reserved.