Miracle-dz/UniFAR-FTUs huggingface dataset:
“Simultaneous and Systematic Abolition”?: Automated Content Analysis and the 1789 Cahiers de Doléances

Upon reading a selection of the grievance books (“cahiers de doléances”) proffered to King Louis XVI in 1789 by the various regions and estates of France, Alexis de Tocqueville reflected with horror that “what they were asking for was the simultaneous and systematic abolition of all the laws and customs in force throughout the country” (Tocqueville 1856, 131–132). Given that he couldn’t read all 60,000 books, however, to what extent is Tocqueville’s conclusion simply the result of a selection bias? Shapiro and Markoff (1998), the culmination of decades of work manually tagging a subset of these grievance books, was the first step towards an answer to this question. After centuries of interpretive studies, Shapiro and Markoff were able to subject hypotheses concerning the contents, geographic distribution, and socioeconomic covariates of the cahiers to rigorous statistical testing. Not only did their study overturn accepted knowledge from the outset (e.g., Tocqueville’s assertion that “all of the cahiers demand[ed] that corvees be definitively abolished” when in fact only 13% did so), it also provided an extraordinarily rich dataset of tags with sufficient generality to allow future researchers to test a range of intuitions regarding the French Revolution. In this paper, I take quantitative studies of the French Revolution and the cahiers de doléances one step further, by developing a fully-automated (“unsupervised”) content analysis algorithm based on computational topic modeling and sentiment analysis. By eliminating the need for manual labeling, the paper serves as a case study demonstrating the feasibility of obtaining insights in mere minutes which previously required decades of work by a team of research assistants. I show that it is possible to obtain substantive findings regarding the relative concerns of the Nobility, Clergy, and Third Estate using this algorithm, by illustrating the striking degree to which the results of the model comport with both Tocqueville’s and Shapiro and Markoff’s comparative analyses of the cahiers. I then use peasant revolt data from Markoff (1996) to explore the relationship between emphasis on the automatically-discovered “seigneurial rights” grievance topic and variation in “anti-seignurial events” among the peasantry. I conclude with a proposal for a “supervised” learning approach which allows researchers full control over the learned grievance categories via a small set of manually-produced “seed” tags.
Biological explanations of aggression and the medicolegal implications

Introduction Jeffery Landrigan was a difficult child. Adopted at birth, he threw temper tantrums as a toddler, progressing to substance abuse and burglary by the age of 11. At 20, he killed his first victim. Imprisoned, he repeatedly stabbed an inmate and later escaped, committing his second murder. He was then sentenced to death. An inmate noticed a striking resemblance between Landrigan and Darrel Hill, a man on death row in another state. Hill, it transpired, was the biological father of Landrigan – both murderers, sentenced to die. Hill’s father and grandfather had also been criminals. Despite being adopted, Landrigan was a fourth-generation felon. Does this mean that we can inherit behaviours? (Glenn and Raine, 2014)
Structure of the C-terminal sterile a-motif (SAM) domain of human p73a

# 2001 International Union of Crystallography Printed in Denmark ± all rights reserved p73 is a homologue of the tumour suppressor p53 and contains all three functional domains of p53. The -splice variant of p73 (p73 ) contains near its C-terminus an additional structural domain known as the sterile -motif (SAM) that is probably responsible for regulating p53-like functions of p73. Here, the 2.54 AÊ resolution crystal structure of this protein domain is reported. The crystal structure and the published solution structure have the same ®ve-helix bundle fold that is characteristic of all SAM-domain structures, with an overall r.m.s.d. of 1.5 AÊ for main-chain atoms. The hydrophobic core residues are well conserved, yet some large local differences are observed. The crystal structure reveals a dimeric organization, with the interface residues forming a mini four-helix bundle. However, analysis of solvation free energies and the surface area buried upon dimer formation indicated that this arrangement is more likely to be an effect of crystal packing rather than re ̄ecting a physiological state. This is consistent with the solution structure being a monomer. The p73 SAM domain also contains several interesting structural features: a Cys-X-X-Cys motif, a 310-helix and a loop that have elevated B factors, and short tight inter-helical loops including two -turns; these elements are probably important in the normal function of this domain. Received 16 November 2000 Accepted 5 February 2001
Efficient Feature Extraction for Person Re-identification via Knowledge Distillation and Kernel Pruning

— Person re-identification is the task of finding a person of interest across images or videos captured by different cameras. Cameras are usually placed in uncontrolled environments, for instance surveillance cameras in shopping centre, where the quality of the videos is far from ideal and the subjects are not directly facing the camera and might be partially occluded. Person re-identification has recently received increasing attention due to the high performance achieved by new methods based on deep learning. This thesis aims to to develop a deep learning based re-ID system for deployment in a real world task. To guarantee optimal time response, the optimal tradeoff between accuracy and computational cost is studied. Firstly, the generalization ability of different architectures to data captured in different conditions is studied, in order to produce a model robust to data captured in a real-life scenario. Secondly, deep neural network distillation techniques coupled with convolutional kernel pruning are used for producing a smaller model, reducing the cost of the system without affecting its performance. The proposed approach is validated by testing on two different person re-identification datasets captured in real-life scenarios. In the end, the compressed model is three times faster, without a decrease in performance. The dimensionality of the feature vector extracted for each input image is reduced by 8 times and it has 60 times less trainable parameters.
reTHINK framework evaluation through application development

With the goal to be an alternative to the currently dominant walled garden communication networks, the reTHINK project provides a new framework for web application development that handles with governance, security and identity management for the registered users. In addition, this European project aims to offer a global, open and shared identity system enabling dynamic trusted relationships among distributed applications without relying on communication protocols. This document presents an evaluation methodology with the goal of evaluate the reTHINK project from the point of view of ease in developing applications when compared with traditional technologies, testing the benefits and costs in terms of complexity. To accomplish this goal, we developed two versions of the same web application and performed an evaluation considering three different points of view. With this work, we showed the main advantages and disadvantages of using the reTHINK framework for application development, justifying them and providing alternatives to those that need to be improved. At the end and considering all the conclusions, we provide the most important recommendations to make the reTHINK framework more usable, easy to use and, consequently, more well accepted and adopted by the developer community.
Carrier Phase DGPS

Until GPS accuracy stopped being purposefully degraded in 2000, DGPS was used extensively to increase the positioning precision. These algorithms rely on a base station, whose position is known with great accuracy, to deduct the relative position – or baseline – of one or more rover receivers, by use of techniques to get rid of the common error sources. These techniques can be used on their own, or augmented through the use of Kalman Filtering or other algorithms. Receivers used to employ these techniques have to output a raw measurement of the pseudorange to each satellite, not just the position in geodetic or Cartesian coordinates. This type of device is known as a surveying receiver. Higher-end surveying receivers also track and output a measurement of the carrier signal phase sent by the satellite. Using this signal, having a much higher frequency, centimetre-level accuracy can be achieved. The use of carrier phase to implement DGPS is usually referred to as Real Time Kinematics (RTK). In this work, the development of a carrier phase DGPS solution will be presented. The goal is to implement a standalone, low cost system that delivers performance similar to that of commercial systems, using an accessible embedded platform.
Time-Space Earth Surface Gradients for Satellite Monitoring of Ecosystems

The results of Earth surface gradients characteristics calculate based on satellite data are presents. Dynamics structural features of horizontal gradient fields in aquatic objects and land ecosystems by physical, biological parameters are considered. Problems of the parametrization for environment exchanges evaluations with using numerical modeling based on satellite data and software tools are submitted. Spatial-temporal scaling and averaging of gradient components are discussed. Develop improved estimates of Earth surface gradients fields in the different ecosystems is considered.
Transfer Learning for Tumor Mutational Burden (TMB) Prediction from Histopathology Slides in Bladder Cancer

Tumor mutational burden (TMB) is a potential genomic biomarker, which helps in identifying bladder cancer patients likely to beneﬁt from immunotherapy. DNA sequencing has been widely used to determine the TMB. However, DNA sequencing has limited clinical utilities, since many cancer patients are not feasible to have tissue biopsy for DNA sequencing. In addition, the sequencing-based TMB measurement could be limited to take into account the heterogeneous TMB status present in the tumor. In this study, we present a deep learning based whole slide image (WSI) analysis framework to predicts TMB status using digitized histopathology slides. Our method consists of four modules. First, the tumor detector based on convolutional neural networks (CNN) is trained to segment bladder cancer regions in the WSI. Aﬃnity propagation clustering is then applied to select a subset of representative tumor tiles. After that, transfer learning based on Xception model is applied to extract features from representative tumor tiles. The SVM classiﬁer is ﬁnally trained to make patient-level predictions of low or high TMB status. Experiments have been performed on 253 WSIs from The Cancer Genome Atlas (TCGA) bladder cancer cohort. Our results show that the trained CNN-based tumor detector provides over 90% sensitivity and precision in detecting bladder cancer regions in WSIs. The trained SVM classiﬁer for TMB prediction provides over 0.75 AUC value (95% CI, 0.683-0.802) in predicting TMB high/low patients. The spatial analysis of TMB predictions shows heterogeneous TMB high/low status present within the tumor. By integrating TMB prediction with its heterogeneity analysis, our technique could help to identify patients with distinct survival outcomes. Taken together, we show that transfer learning framework for TMB prediction using the WSI could provide novel insights of heterogeneous TMB status within the tumor and its clinical utility for more accurate bladder cancer prognostication
Gateway between sensor networks and an instant messaging system

— This work is focused on sensor networks and aims to create a system able to monitor and command them through an Instant Messaging system. Currently there are multiple sensor technologies, developed by different manufacturers, which are incompatible at the application level. These networks are typically developed on low-resource platforms, leading to solutions designed specifically to each case, to provide the desired levels of functionality and performance. The lack of uniformity is a barrier to the development of this area and hinders the development of generic tools able to access and control any type of network. This dissertation seeks to overcome the limitations described, by adopting the DomoBus model that hides the sensor networks’ peculiarities, and using a gateway that allows its integration with a system that exchanges text messages in real-time among multiple users (instant messaging). The Domobus technology is based on a device’s type abstraction that allows the control of a generic sensor network and provides mechanisms flexible enough to enable an easy development of applications adaptable to the needs of each end-user. Using an instant messaging system allows a simple interaction with sensor and actuator networks. The adopted solution is based on the XMPP standard, allowing remote monitoring and control of these networks using different kinds of hardware and operating systems and avoiding the need to install specific applications, as it can be used through a common browser. All the information processing is done by the gateway developed in this work.
Pompilos, a Model for Augmenting Health Assistant Applications with Social Media Content

Caused by habits such as poor diets, lack of physical activity practice or smoking, non-communicable diseases were elected by the World Health Organization as one of the greatest challenges of the twenty-first century, despite a lot of information produced in social media focused on preventing this type of disease. This paper presents the Pompilos Model, which aims at improving computer-aided social support by suggesting beneficial health resources and revealing what influences other people’s health, so to foster better health behaviors in social relations. In order to evaluate the model’s feasibility, we performed a random experiment during one month and half with two groups to assess the influence of messages related to the prevention of chronic diseases. Those messages presented information on a healthier diet, the practice of physical activities, and ways to lose weight, from monitored Twitter profiles on the habits of health assistant web application’s users. So it would be possible to manage food intake, the practice of physical activities, and weight control. Messages related to the prevention of chronic diseases, such as a healthier diet, the practice of physical activities, and weight loss from monitored Twitter profiles were directed to an intervention group as a way to re-engage users in their care activities. With this information, we found a correlation between message reading and the access to the application history feature among intervention users.
An Entity Resolution Framework for Matching Product Offers with Reviews

Product review sites are objective sources of information for millions of people who rely on unbiased product reviews to purchase products from online comparison shopping sites. Typically, a large number of product oﬀers from multiple vendors are received continually at these shopping sites. Providing potential customers with reviews from such review sites for the appropriate product is the principal problem we address in this paper. We describe a framework that matches product oﬀers with reviews as an instance of the Entity Resolution problem and show some of the potential challenges that occur in the Product review space. We high-light the design & principal components of the system and present a novel feature annotation approach in the Product review space, where key features embedded in the oﬀer ﬁelds are extracted. We also provide detailed experimental results carried on real-world datasets and illustrate the performance of the system using measures for our evaluation.
A Facile Microwave-Assisted Hydrothermal Synthesis of Graphene Quantum Dots for Organic Solar Cell Efficiency Improvement

Carbon-based nanomaterials have successively remained at the forefront of different research fields and applications for years. Understanding of low-dimension carbon material family (CNT, fullerenes, graphene, and graphene quantum dots) has arrived at a certain extension. In this report, graphene quantum dots were synthesized from graphene oxide with a microwave-assisted hydrothermal method. Compared with conventional time-consuming hydrothermal routes, this novel method requires a much shorter time, around ten minutes. Successful formation of quantum dots derived from graphene sheets was verified with microscopic and spectroscopic characterization. Nanoparticles present a diameter of about 2-8 nm, blue emission under ultraviolet excitation, and good dispersion in polar solvents and can be collected in powder form. The synthesized graphene quantum dots were utilized as a hole transport layer in organic solar cells to enhance the cell quantum efficiency. Such quantum dots possess energy levels (Ec and Ev) relevant to HOMO and LUMO levels of conductive polymers. Mixing P3HT:PCBM polymer and graphene quantum dots of sufficient extent notably helps reduce potential difference at interfaces of the two materials. Overall efficiency consequently advances to 1.43%, an increase of more than 44% compared with pristine cells (0.99%).
Correlation via Synthesis: End-to-end Image Generation and Radiogenomic Learning Based on Generative Adversarial Network

Radiogenomic map linking image features and gene expression proﬁles has great potential for non-invasively identifying molecular properties of a particular type of disease. Conventionally, such map is produced in three independent steps: 1) gene-clustering to metagenes, 2) image feature extraction, and 3) statistical correlation between metagenes and image features. Each step is separately performed and relies on arbitrary measurements without considering the correlation among each other. In this work, we investigate the potential of an end-to-end method fusing gene code with image features to generate synthetic pathology image and learn radiogenomic map simultaneously. To achieve this goal, we develop a multi-conditional generative adversarial network (GAN) conditioned on both background images and gene expression code, synthesizing the corresponding image. Image and gene features are fused at different scales to ensure both the separation of pathology part and background, as well as the realism and quality of the synthesized image. We tested our method on non-small cell lung cancer (NSCLC) dataset. Results demonstrate that the proposed method produces realistic synthetic images, and provides a promising way to ﬁnd gene-image relationship in a holistic end-to-end manner.
Milk electrolytes and induction of parturition

Induction of parturition should not be viewed as a tool to make foaling more convenient but as a means by which proper veterinary care can be provided without delay when complications are anticipated. The best way to insure that veterinary assistance will be available when a mare foals is to induce parturition. However, because the equine gestation length is so variable, and inducing parturition before the foal is mature and capable of life outside the womb is an invitation for disaster, assessing fetal readiness for birth is paramount for achieving success. The best method to assess fetal readiness for birth is to measure calcium, sodium and potassium in the mammary secretions. A level of 10 mmol (400 ppm) calcium, in combination with a concentration of potassium that is greater than that of sodium, is indicative of fetal maturity. However, with pregnancies complicated by problems such as placentitis or twins, milk electrolytes are unreliable for assessing fetal readiness for birth. Nevertheless, milk electrolytes may still be helpful in these cases to decide when close supervision is recommended. Regardless, care must be taken to understand the testing method used and thereby interpret the results. Usually, when parturition is induced it is done so because a high probability of complications exists. Therefore it is recommended to place an intravenous catheter prior to the induction procedure. Similarly, planning for possible complications such as dystocia, premature placental separation or neonatal problems before beginning the induction procedure will greatly speed their successful resolution if encountered. Although many methods for inducing parturition have been reported, a single low dose of oxytocin (10 IU, i.v. for a 550 kg mare) is preferred. Provided the criteria for readiness for birth have been met, a single low dose is sufficient to initiate the cascade of events leading to parturition and provides a more natural course of events than higher doses or repeated administration.
Topic-speciﬁc Knowledge Bases: Selecting Relevant Entities through Text, Co-Occurrences and Relations

Topic-speciﬁc knowledge graphs can help to support the work of subject matter experts. Through the topical context, many ambiguities can be easily resolved and spurious relationships can be removed. A crucial step in the construction of topic-speciﬁc knowledge graphs is to identify topically relevant entities from a larger background knowledge graph and a text corpus. To achieve high-recall coverage, it is not suﬃcient to only select entities whose name or abstract matches the topic. Our approach demonstrates the eﬃcacy of using relevant passages to identify relevant entities and relations, outperforming relational information from knowledge graph links and bibliographic coupling. Evaluating diﬀerent approaches on a benchmark of open-domain topics, we ﬁnd that the co-occurrence of entities in relevant text is one of the strongest indicators (0.2534 MAP), and hence should be included when building topic-speciﬁc knowledge bases.
Physiological and Antioxidative Responses of a Halophytic Grass Leptochloa fusca L. Kunth (Kallar grass) to Salinity

In this study, the effects of salt stress on growth and some physiological parameters of halophytic plant, Leptochloa fusca L. Kunth, were investigated. The seedlings were irrigated with half-strength Hoagland solution and then treated with different concentrations of NaCl (0, 100, 300, 500 and 700 mM) for 15 days. The fresh and dry weights of both roots and shoots were unchanged at 100 mM NaCl and decreased at higher concentrations. Relative water content significantly decreased at high NaCl treatments. The significant increase in the contents of chlorophylls and carotenoids due to NaCl stress was observed in all salinity treatment except for100 mM NaCl. Leaf Na/ K ratio increased with increase in medium salinity. Moderate (300 mM) and high (700 mM) salinities dramatically increased proline content as compared to that of the control group. With a progressive increase in salinity, catalase, ascorbat peroxidase and guaiacol peroxidase activities also increased gradually in this species. It seems that antioxidant enzyme activity for scavenging reactive oxygen species and proline accumulation for osmotic adjustment play an essential protective role in L. fusca under salinity stress. In summary, these data indicate that salinity tolerance is well programmed in L. fusca allowing adaptation to harsh environmental conditions in the natural