nils-herrmann/clean_scirepeval huggingface dataset:
Venture Capital Patronage to Apprenticeship to Profession
Venue: The Journal of Private Equity
Year: 2019

Investing in creative or entrepreneurial ventures is part of the human condition and goes back as far as recorded history. The patronage of the Medici family of Florence supported the work of Leonardo de Vinci, Michelangelo, and Galileo. Queen Isabella was essentially a venture capitalist, supporting Christopher Columbus in his exploration of the New World. Many of the critical inflection points in modern history that had dramatic impacts on the human condition resulted from entrepreneurial initiatives supported by wealthy individuals and families. The history of modern venture capital was catalyzed by efforts at mobilizing capital for the many companies emerging after World War II, in part from both necessity and opportunity the need to support the GIs returning from the war and the opportunity to commercialize the new technologies that resulted from the war effort. Commercial access and use of the Internet represents the latest catalyst for the acceleration in entrepreneurial activity around the world, with a corresponding growth in individuals investing in new ventures, as well as new funds. Unfortunately, the process of underwriting an entrepreneurial venture remains much like it was in the time of Leonardo de Vinci and Columbus: Energetic entrepreneurs convince a supporter (investor) of their vision and seek to share the rewards with the supporter, should there be any rewards to share. Investing was, and largely continues to be, transacted on a case by case basis. Ironically, there has been little innovation in the very funding mechanism of entrepreneurial finance. This has led to a critical shortage of early stage risk capital in various regions around the United States where historically blue collar jobs have been disintermediated by technological advancements, but capital has not been available in these communities to support the new (digital) jobs that would replace those old jobs and provide hope for the next generation residing in those communities. This article will review the history of venture capital, explore the ineffectiveness of the historical model, and explore some of the innovative initiatives to improve the effectiveness of venture capital, thus funding more startups more efficiently and in more communities while delivering better risk adjusted returns for investors.  Private equity
Full regularization path for sparse principal component analysis
Venue: ICML '07
Year: 2007

Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a particular linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse principal component analysis and has a wide array of applications in machine learning and engineering. We formulate a new semidefinite relaxation to this problem and derive a greedy algorithm that computes a full set of good solutions for all numbers of non zero coefficients, with complexity O(n3) where n is the number of variables. We then use the same relaxation to derive sufficient conditions for global optimality of a solution, which can be tested in O(n3) We show on toy examples and biological data that our algorithm does provide globally optimal solutions in many cases.
Experimental Study of tool Wear Mechanisms in Conventional and High Pressure Coolant Assisted Machining of Titanium Alloy Ti17
Year: 2013

Titanium alloys are known for their excellent mechanical properties, especially at high temperature. But this specificity of titanium alloys can cause high cutting forces as well as a significant release of heat that may entail a rapid wear of the cutting tool. To cope with these problems, research has been taken in several directions. One of these is the development of assistances for machining. In this study, we investigate the high pressure coolant assisted machining of titanium alloy Ti17. High pressure coolant consists of projecting a jet of water between the rake face of the tool and the chip. The efficiency of the process depends on the choice of the operating parameters of machining and the parameters of the water jet such as its pressure and its diameter. The use of this type of assistance improves chip breaking and increases tool life. Indeed, the machining of titanium alloys is generally accompanied by rapid wear of cutting tools, especially in rough machining. The work done focuses on the wear of uncoated tungsten carbide tools during machining of Ti17. Rough and finish machining in conventional and in high pressure coolant assistance conditions were tested. Different techniques were used in order to explain the mechanisms of wear. These tests are accompanied by measurement of cutting forces, surface roughness and tool wear. The Energy dispersive X ray spectroscopy (EDS) analysis technique made it possible to draw the distribution maps of alloying elements on the tool rake face. An area of material deposition on the rake face, characterized by a high concentration of titanium, was noticed. The width of this area and the concentration of titanium decreases in proportion with the increasing pressure of the coolant. The study showed that the wear mechanisms with and without high pressure coolant assistance are different. In fact, in the condition of conventional machining, temperature in the cutting zone becomes very high and, with lack of lubrication, the cutting edge deforms plastically and eventually collapses quickly. By contrast, in high pressure coolant assisted machining, this problem disappears and flank wear (VB) is stabilized at high pressure. The sudden rupture of the cutting edge observed under these conditions is due to the propagation of a notch and to the crater wear that appears at high pressure. Moreover, in rough condition, high pressure assistance made it possible to increase tool life by up to 400%
Tourism on demand. New form of urban and social demand of use after the pandemic event
Year: 2020

The Covid 19 pandemic is expected to lead to a critical reconsideration of the global volume growth model for tourism, for reasons related to the risks posed by global travel and the contribution of the tourism industry to global pollution, climate change and, more in general, the socio cultural instability of certain geographical areas of the world Nevertheless, it is possible to propose a different point of view by trying to accept this historical moment as an occasion to ponder over the need for a change in the unsustainability of the present lifestyles Tourism in this can be a driver function to support this transition by giving to tourism flows a higher sense Tourist destinations will have to change but how and how many tourist cities will be able to re calibrate their supply system of services and facilities to host the "new tourists" Could the on demand model be a new way for tourists to enjoy their experience? The paper, trying to give answers to these questions, proposes some reflections about the inevitable change imposed by the pandemic, aiming at outlining the possible scenarios that will characterize the tourism city relationship in the next future and paying attention to the social aspects
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
Venue: NeurIPS
Year: 2019

Mixup~\cite{zhang2017mixup} is a recently proposed method for training deep neural networks where additional samples are generated during training by convexly combining random pairs of images and their associated labels. While simple to implement, it has been shown to be a surprisingly effective method of data augmentation for image classification: DNNs trained with mixup show noticeable gains in classification performance on a number of image classification benchmarks. In this work, we discuss a hitherto untouched aspect of mixup training the calibration and predictive uncertainty of models trained with mixup. We find that DNNs trained with mixup are significantly better calibrated i.e. the predicted softmax scores are much better indicators of the actual likelihood of a correct prediction than DNNs trained in the regular fashion. We conduct experiments on a number of image classification architectures and datasets including large scale datasets like ImageNet and find this to be the case. Additionally, we find that merely mixing features does not result in the same calibration benefit and that the label smoothing in mixup training plays a significant role in improving calibration. Finally, we also observe that mixup trained DNNs are less prone to over confident predictions on out of distribution and random noise data. We conclude that the typical overconfidence seen in neural networks, even on in distribution data is likely a consequence of training with hard labels, suggesting that mixup be employed for classification tasks where predictive uncertainty is a significant concern.
Poverty and a child's height development during early childhood: A double disadvantage? A study of the 2006 2009 birth cohorts in Flanders
Venue: PloS one
Year: 2019

Poverty is a well known risk factor for a child's health and development. This paper aimed to establish whether poverty negatively affected both intra uterine growth and early childhood growth, i.e. whether children facing poverty were at a double disadvantage.  For this study, we made use of routinely collected data on child development throughout early childhood from the 2006 2009 birth cohorts in Kind Gezin's Ikaros database collected during 2,605,975 consultations with 273,935 children from birth to 730 days old. Indicators for child development at birth were gestational age and height at birth. A standardized height for age indicator captured height development throughout early childhood. A multidimensional indicator measured the risk of poverty. For the analysis of development at birth, we used linear and logistic regression; for the analysis of height development during early childhood, we estimated linear and logistic growth curve models.  The risk of poverty negatively affected both gestational age and height at birth. Throughout early childhood, we observed a negative relation between the risk of poverty and height for age indicators. However, the effect varied throughout childhood. Children at risk of poverty (over)compensated for their smaller stature at birth, and between ages 6 and 18 months, approximately, the negative effects of risk of poverty decreased substantially or disappeared. However, towards the end of the period studied, children born in households at risk of poverty started to lag again in height development.  This study found that the risk of poverty indeed negatively affected a child's growth, both in utero and in early childhood. However, the results suggest that developmental lags later in childhood were not merely an extension of such lags at birth.
Introduction to Ligand Field Theory
Year: 2013

Ligand field theory is a conerstone of modern coordination chemistry. Ligand field theory creates a simple, yet effctive and pictorial language in which a multitude of properties of coordination complexes can be succesfully accounted for. In this chapter an elementary and non mathematical introduction to ligand field theory is provided which is aimed at a non expert audience with mainly experimental background. The chapter stresses important fundamental concepts and proceeds to the stage where comparison between theory and experiment is possible.
Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis
Venue: ArXiv
Year: 2020

The rapid advancement in the field of deep learning and high performance computing has highly augmented the scope of video based vehicle counting system. In this paper, the authors deploy several state of the art object detection and tracking algorithms to detect and track different classes of vehicles in their regions of interest (ROI) The goal of correctly detecting and tracking vehicles' in their ROI is to obtain an accurate vehicle count. Multiple combinations of object detection models coupled with different tracking systems are applied to access the best vehicle counting framework. The models' addresses challenges associated to different weather conditions, occlusion and low light settings and efficiently extracts vehicle information and trajectories through its computationally rich training and feedback cycles. The automatic vehicle counts resulting from all the model combinations are validated and compared against the manually counted ground truths of over 9 hours' traffic video data obtained from the Louisiana Department of Transportation and Development. Experimental results demonstrate that the combination of CenterNet and Deep SORT, Detectron2 and Deep SORT, and YOLOv4 and Deep SORT produced the best overall counting percentage for all vehicles.
Sentinel 2 cropland mapping using pixel based and object based time weighted dynamic time warping analysis
Year: 2018

Efficient methodologies for mapping croplands are an essential condition for the implementation of sustainable agricultural practices and for monitoring crops periodically. The increasing spatial and temporal resolution of globally available satellite images, such as those provided by Sentinel 2, creates new possibilities for generating accurate datasets on available crop types, in ready to use vector data format. Existing solutions dedicated to cropland mapping, based on high resolution remote sensing data, are mainly focused on pixel based analysis of time series data. This paper evaluates how a time weighted dynamic time warping (TWDTW) method that uses Sentinel 2 time series performs when applied to pixel based and object based classifications of various crop types in three different study areas (in Romania, Italy and the USA) The classification outputs were compared to those produced by Random Forest (RF) for both pixel and object based image analysis units. The sensitivity of these two methods to the training samples was also evaluated. Object based TWDTW outperformed pixel based TWDTW in all three study areas, with overall accuracies ranging between 78.05% and 96.19% it also proved to be more efficient in terms of computational time. TWDTW achieved comparable classification results to RF in Romania and Italy, but RF achieved better results in the USA, where the classified crops present high intra class spectral variability. Additionally, TWDTW proved to be less sensitive in relation to the training samples. This is an important asset in areas where inputs for training samples are limited.
Emotion Oriented Systems: The Humaine Handbook
Year: 2011

Emotion pervades human life in general, and human communication in particular, and this sets information technology a challenge. Traditionally, IT has focused on allowing people to accomplish practical tasks efficiently, setting emotion to one side. That was acceptable when technology was a small part of life, but as technology and life become increasingly interwoven we can no longer ask people to suspend their emotional nature and habits when they interact with technology. The European Commission funded a series of related research projects on emotion and computing, culminating in the HUMAINE project which brought together leading academic researchers from the many related disciplines. This book grew out of that project, and its chapters are arranged according to its working areas: theories and models; signals to signs; data and databases; emotion in interaction; emotion in cognition and action; persuasion and communication; usability; and ethics and good practice. The fundamental aim of the book is to offer researchers an overview of the related areas, sufficient for them to do credible work on affective or emotion oriented computing. The book serves as an academically sound introduction to the range of disciplines involved technical, empirical and conceptual and will be of value to researchers in the areas of artificial intelligence, psychology, cognition and usermachine interaction.
SWiBluX: Multi Sensor Deep Learning Fingerprint for Precise Real Time Indoor Tracking
Venue: IEEE Sensors Journal
Year: 2019

Indoor/outdoor localization topic has gained a significant research interest due to the wide range of potential applications. Commonly, the Fingerprinting methods for spatial characterization of the environments monitored are employed in deterministic/statistical estimation. However, there are Fingerprint parameters that are generally neglected and can seriously affect the performance yielding to low accurate location. Nowadays, machine and deep learning (DL) methods are employed in this topic due to its ability to approximate complex non linear models being capable of mitigating the undesirable effects of wireless propagation. In this paper, a complete overview of most influential aspects in Fingerprinting and indoor tracking methods is presented. Furthermore, a novel multi modal complete tracking system, called SWiBluX, based on statistic and DL techniques is presented. The system relies on relevant feature extraction from available data sources to estimate user's/target indoor position using a multi phase statistical Fingerprint and DL disruptive approach. In addition, a Gaussian outlier filter is applied to the position estimation model output to further reduce the error in the estimation. The set of experiments performed shows that Fingerprint positioning accuracy estimation can be improved up to 45% resulting in a final estimation error that outperforms related literature.
Fe3O4@SiO2 mesoporous spheres as Fe(II) donors loaded with artemisinin and a photosensitizer to alleviate tumor hypoxia in PDT for enhanced anticancer therapy
Year: 2019

Photodynamic therapy (PDT) is an emerging acknowledged alternative treatment for multiple cancers. However, the efficiency of PDT is lower in tumors with imperfect vascular systems under hypoxia because anticancer therapy mediated by reactive oxygen species (ROS) is highly dependent on an adequate and sustained O2 supply. In this paper, Rattle type Fe3O4@SiO2 mesoporous spheres are prepared using colloidal carbon spheres as templates to load artemisinin (ART) and the chlorophyll photosensitizer HNPa for localized ROS generating therapy. HNPa was firstly reported by our group previously as a highly soluble drug and an excellent tissue penetrant. ART was considered to generate ROS based on a pH mediated Fenton like process. Our research shows that Fe2+ can be easily released from the Fe3O4@SiO2 nanocarrier in the tumor microenvironment by weak acidic etching, and the reaction of ART with Fe2+ from Fe3O4@SiO2 ART HNPa can continue to perform curative functions even in the hypoxic tumor environment after PDT. Moreover, it was found that NIR light irradiation can partly enhance the production of ROS for enhanced anticancer therapy. All these results show the promise of this smart strategy to conquer the existing barriers of PDT.
Steroid Responsive Atypical Marginal Zone Hyperplasia of the Lip in a Child
Venue: The American Journal of dermatopathology
Year: 2015

Atypical marginal zone hyperplasia (AMZH) is a recently described disease entity seen mainly in children. AMZH most commonly affects tonsils and appendices. Cutaneous AMZH is rare. The authors report here a recurrent AMZH in the lip of a 9 year old child who presented originally with a lip swelling for approximately 3 months. The lip lesion recurred after each incomplete excision for 4 times. Pathologically, the lesion demonstrated marginal zone B cell hyperplasia with kappa monoclonality by flow cytometry and immunohistochemistry studies. Lymphoepithelial lesions were noted with involvement of minor salivary glands. Polymerase chain reaction for immunoglobulin heavy chain gene rearrangement has been repeatedly negative. Polymerase chain reaction for Borrelia species DNA was negative on both paraffin embedded tissue and plasma. Serum antibodies IgG and IgM for Helicobacter Pylori were positive. A diagnosis of AMZH was made. Two courses of anti H. Pylori therapy did not improve the lip lesion, which completely regressed after a course of prednisone therapy. With differential diagnosis of cutaneous marginal zone lymphoma, the case illustrated diagnostic challenges, especially with recurrent lesions. This is the first case of recurrent cutaneous AMZH that has uncharacteristic kappa light chain restriction. AMZH should be considered in children with mucocutaneous lesions with features of marginal zone lymphoma.
Teorias implicitas sobre escritura academica y su ensenanza: contrastes entre el ingreso, la transicion y el egreso universitarios
Venue: Universitas Psychologica
Year: 2019

La educacion superior incluye la enculturacion de los estudiantes en las formas esperadas de comunicarse disciplinarmente, pero este proceso puede verse obstaculizado por ciertas teorias implicitas sobre la escritura (transcripcion, talento, inspiracion individual, habilidad basica, vocabulario y ortografia, objeto no ensenable) que difieren de su caracter de actividad retorica compleja, situada y epistemica. Esta investigacion reconstruye y contrasta la presencia de estas teorias en tres etapas formativas (ingreso, transicion y egreso) a partir de las respuestas de 8192 estudiantes universitarios. Los resultados, evaluados con prueba chi cuadrado de Pearson de bondad de ajuste, muestran que los estudiantes ingresan con ciertas teorias implicitas (escritura como ortografia y transcripcion del pensamiento, aprendizaje mediante la practica y a traves del curriculum y niveles formativos) pero no con otras (escritura como habilidad basica) ademas, la formacion universitaria transforma parcialmente algunas (escritura como inspiracion individual y transcripcion del pensamiento) pero no otras (importancia relativa de la adecuacion situacional y la estructura textual) e incorpora expectativas sobre tareas (posicionamiento propio en ensayos, uso de citas en explicaciones, contrastes en comparaciones) Comprender la distribucion y los grados de estabilidad o transformacion de teorias implicitas sobre la escritura no solo hace aportes al conocimiento cientifico, sino que tambien informa iniciativas de alfabetizacion academica, de escritura a traves del curriculum y de apoyo y orientacion de los aprendizajes.
RANCANGAN TAPAK PENGOLAHAN LIMBAH CAIR PABRIK MINYAK KAYU PUTIH (Melaleuca cajuputi subsp. Cajuputi Powell) DENGAN REAKTOR HIBRID ANAEROB (Kasus Pabrik Minyak Kayu Putih Gelaran, Gunung Kidul)
Year: 2020

This research is aimed to study the contamination level of Gelaran Cajuput Oil Plant wastewater, to obtain the design of the treatment site plan, and to recommend the determination of the site plan location. The phases covered pilot field survey to determine the location, measuring the volume and collecting the sample, analizing the process and deciding the treatment system. The results showed the contamination level parameters were acid pH, and high BOD and COD. The appropriate length, width, and depth of chamber units' dimension and specifications of the treatment yielded were respectively as follows: a) 1.5 m, 1.0 m, and 1.0 m of equalisation; b) 1.5 m, 1.0 m, and 1.0 m of neutralization; c) 1.25 m, 1.06 m, and 1.25 m of anaerobic hybrid reactor; d) 1.5 m, 1.0 m, and 1.0 m of stabilization, and was located on downward to facilitate the effluent flowing by gravitational force merely.  Cajuput oil, waste water treatment, site plan, anaerobic hybrid reactor