Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Di erence in Di erences & Fuzzy Change in Change∗

Di erence in Di erences (DID) and Change in Change (CIC) require perfect compliance : treatment rate should be 0% in the control group and during period 0 (no always takers ) and 100% in the treatment group in period 1 (no never takers ). In many instances, the treatment rate increases more in the treatment than in the control group but there are never or always takers. This paper derives identi cation results which apply to such fuzzy DID and fuzzy CIC settings. Its rst contribution is that its fuzzy DID identi cation results only require one common trend assumption on the outcome (Y ) whereas the standard instrumental variable (IV) result usually invoked in such settings relies on a supplementary common trend assumption on treatment rate and on a no de ers assumption. When there are never takers but no always takers, common trend on Y is su cient to identify an ATT as with standard DID. When there are always takers, it is no longer su cient but partial identi cation is still possible provided Y is bounded. It is also possible to derive a second and narrower identi cation region under the supplementary assumption that treatment e ects do not change between the two periods in the control group. I use those ndings to measure the e cacy of a new pharmacotherapy for smoking cessation. Its second contribution is that it is the rst paper which considers extending the CIC model to applications with imperfect compliance. The CIC assumptions are not su cient for identi cation when the perfect compliance assumption is violated. One important exception is when there are no always takers, in which case ∗I am extremely thankful to Xavier d'Haultfoeuille for his numerous advice and encouragements. I am very grateful to Andrew Clark, Joseph Doyle, Pauline Givord, Marc Gurgand, Edwin Leuven, Thierry Magnac, Eric Maurin, Thomas Piketty, Roland Rathelot, participants at the European Conference in Econometrics (EC), seminar participants at the Paris School of Economics and at CREST for their helpful comments. †Paris School of Economics, 48 boulevard Jourdan 75014 Paris France, chaisemartin@pse.ens. fr ‡CREST, 15 boulevard Gabriel Péri 92245 Malako France
Civil Conflict, Internal Migration, and Monetary Transfers: A Case Study in Colombia

The purpose of this paper is to provide an initial exploration of how conflict shapes internal migration and the dynamics of domestic monetary transfers. Studies on remittances focus on international migrants and mostly on traditional migrants, while the remittance behavior of forced migrants – refugees and internally displaced persons – is largely unexplored. Nonetheless, remittances can play an important role by supporting households left behind in conflict regions or by providing monetary resources for the settlement process undergone by forced migrants. This paper studies the case of Colombia, a country with the largest number of internally displaced persons worldwide; it uses recent data from a longitudinal survey that tracks migrants before and after migration and collects detailed information on conflict dynamics. The ability to identify statistically significant effects is limited by the small sample size of households that migrated, were exposed to direct violence, or fled communities under the presence of nonstate armed actors, and also sent and received monetary transfers. However, we are able to observe how conflict and displacement dynamics are associated with difficulties in sending and receiving transfers. Although victims of violence who are forced to migrate, as well as those who are unable to migrate and thus remain in conflict-torn regions, could use transfers to cope with the socioeconomic consequences of violence, conflict dynamics also seem to undermine the flows of transfers to and from these households.
Generalized approach to sentiment analysis of short text messages in natural language processing

Introduction: Sentiment analysis is a complex problem whose solution essentially depends on the context, field of study andamount of text data. Analysis of publications shows that the authors often do not use the full range of possible data transformationsand their combinations. Only a part of the transformations is used, limiting the ways to develop high-quality classification models.Purpose: Developing and exploring a generalized approach to building a model, which consists in sequentially passing throughthe stages of exploratory data analysis, obtaining a basic solution, vectorization, preprocessing, hyperparameter optimization, andmodeling. Results: Comparative experiments conducted using a generalized approach for classical machine learning and deeplearning algorithms in order to solve the problem of sentiment analysis of short text messages in natural language processinghave demonstrated that the classification quality grows from one stage to another. For classical algorithms, such an increasein quality was insignificant, but for deep learning, it was 8% on average at each stage. Additional studies have shown that theuse of automatic machine learning which uses classical classification algorithms is comparable in quality to manual modeldevelopment; however, it takes much longer. The use of transfer learning has a small but positive effect on the classificationquality. Practical relevance: The proposed sequential approach can significantly improve the quality of models under developmentin natural language processing problems.
Formaldehyde Carcinogenicity Research: 30 Years and Counting for Mode of Action, Epidemiology, and Cancer Risk Assessment

Formaldehyde is a widely used chemical, with global production of over 20 million tons per year (International Agency for Research on Cancer, 2012). It is utilized in industrial and consumer products and is also produced as a byproduct of fires, cigarette smoke and automotive exhaust. Furthermore, it is released from products used in building materials such as particle board and carpet. It has long been used as a fixation product for pathology specimens and in embalming. However, what is often not realized is that formaldehyde is also one of the oldest chemicals in the world. Life started with one carbon chemistry and formaldehyde was one of the earliest chemicals formed and utilized in cells. There are numerous sources of endogenous formaldehyde, including the one carbon pool, amino acid metabolism, methanol metabolism, lipid peroxidation, and p450 dependent demethylation (O-, N-, and S-methyl).
Mineralization of organic matter in boreal lake sediments: Rates, pathways and nature of the fermenting substrates

The complexity of organic matter (OM) degradation mechanisms represents a significant challenge for developing biogeochemical models to quantify the role of aquatic sediments in the climate system. The common representation of OM by carbohydrates formulated as CH2O in models comes with the assumption that its degradation by fermentation produces equimolar amounts of methane (CH4) and dissolved inorganic carbon (DIC). To test the validity of this assumption, we modelled using reaction-transport equation vertical profiles of the concentration and isotopic composition (δ13C) of CH4 and DIC in the top 25 cm of the sediment column from two lake basins, one whose hypolimnion is perennially oxygenated and one with seasonal anoxia. Furthermore, we modelled solute porewater profiles reported in the literature for four other seasonally anoxic lake basins. A total of 17 independent porewater datasets are analyzed. CH4 and DIC production rates associated with methanogenesis at the five seasonally anoxic sites collectively show that the fermenting OM has a mean (±SD) carbon oxidation state (COS) value of −1.4± 0.3. This value is much lower than the value of zero expected from carbohydrate fermentation. We conclude that carbohydrates do not adequately represent the fermenting OM in hypolimnetic sediments and propose to include the COS in the formulation of OM fermentation in models applied to lake sediments to better quantify sediment CH4 outflux. This study highlights the potential of mass balancing the products of OM mineralization to characterize labile substrates undergoing fermentation in sediments.
LSWT Linear Shockwave Therapy for Erectile Dysfunction Clinical Data and Reports June 2019

Purpose To look at the evidence base for LISWT as a treatment modality for vasculogenic erectile dysfunction, focusing on the long-term outcomes at over 6 months following treatment. Methods A systematic literature search was conducted utilising MEDLINE and Scopus databases from 2010 to September 2018 by two independent reviewers. Outcome measures extracted for longterm efficacy included International Index of Erectile Function scores and Erection Hardness Scores. Subgroup analysis for LISWT effectiveness included age, PDE5i responsiveness, presence of vascular co-morbidities and smoking status. Results The search identified eleven studies, representing a total of 799 patients. Nine studies found a significant improvement in erectile function after LISWT at 6-month follow-up (median IIEFEF improvement in 5.3 at 6 months). However, of five studies assessing erectile function at 12 months; two identified a plateauing of results, with three a deterioration (IIEFEF score changes of − 2 to 0.1 from 6 months). Erectile function did, however, remain above baseline results in all of these studies. Subgroup analysis revealed increasing age to reduce the response to LISWT treatment. Whilst ED severity, PDE5i responsiveness and co-morbidities potentially influence effectiveness, results are still inconsistent. Conclusions LISWT may be a safe and acceptable potential ED treatment with demonstrated benefits at 6 months. There is some question regarding efficacy deterioration beyond this, but there is still a demonstrated benefit seen even at 12 months post treatment. However, quality of evidence remains low with larger multiinstitutional studies required, standardising confounders such as shockwave administration and oral medication use.
8 The social context of chimpanzee tool use

Although several animal species exhibit some form of tool use (Shumaker et al., 2011), there are relatively few animals which flexibly use a diverse repertoire of implements on a regular basis within their natural environments. As shown in this volume, hominins, chimpanzees, orangutans, some capuchins and corvids are the exceptions. Van Schaik et al. (1999) have suggested that the evolution of material culture in primates is dependent upon the intersection of four primary factors: manipulative skills, cognitive abilities, suitable ecological niches and social tolerance. Although one cannot entirely dismiss the possibility of differences in manipulative skills and cognitive abilities within species, there are intriguing differences in diversity and types of tool use among populations of wild chimpanzees (Pan troglodytes) which are not entirely explicable by environmental circumstances, and so have been attributed to social influences (Whiten et al., 1999, 2001;Möbius et al., 2008). With the exception of a few developmental studies, the social context of tool use remains largely unexplored in these apes. Primates show differing degrees of social cohesion, and varying responses to fluctuations in the availability of resources in their environment. The abundance and distribution of important food resources dictates not only population density, but also social tolerance. Social tolerance varies across species, but may also differ within species (among populations, groups and even individuals). Resource scarcity may incite feeding competition among conspecifics, which could cause primates to avoid spending time in close proximity, whereas bountiful resources may attract conspecifics to forage at the same site. Social tolerance in gregarious foraging could enhance social learning by allowing primates to forage in close proximity to each other, providing a relaxed social atmosphere in which attention may be focused on a task, and enabling subordinate individuals to participate in close proximity foraging without risk of theft or aggression by conspecifics (van Schaik, 2003). The “opportunities for social learning hypothesis” predicts that higher degrees of social tolerance should result in a larger number of customary technical skills exhibited by primates (van Schaik, 2003). An extension of the opportunities for social learning hypothesis is that higher degrees of
EmpiriST Corpus 2.0: Adding Manual Normalization, Lemmatization and Semantic Tagging to a German Web and CMC Corpus

The EmpiriST corpus (Beißwenger et al., 2016) is a manually tokenized and part-of-speech tagged corpus of approximately 23,000 tokens of German Web and CMC (computer-mediated communication) data. We extend the corpus with manually created annotation layers for word form normalization, lemmatization and lexical semantics. All annotations have been independently performed by multiple human annotators. We report inter-annotator agreements and results of baseline systems and state-of-the-art off-the-shelf tools.
Soware Heritage: Why and How to Preserve Soware Source Code∗

Soware is now a key component present in all aspects of our society. Its preservation has aracted growing aention over the past years within the digital preservation community. We claim that source code—the only representation of soware that contains human readable knowledge—is a precious digital object that needs special handling: it must be a rst class citizen in the preservation landscape and we need to take action immediately, given the increasingly more frequent incidents that result in permanent losses of source code collections. In this paper we present Soware Heritage, an ambitious initiative to collect, preserve, and share the entire corpus of publicly accessible soware source code. We discuss the archival goals of the project, its use cases and role as a participant in the broader digital preservation ecosystem, and detail its key design decisions. We also report on the project road map and the current status of the Soware Heritage archive that, as of early 2017, has collected more than 3 billion unique source code les and 700 million commits coming from more than 50 million soware development projects. ACM Reference format: Roberto Di Cosmo and Stefano Zacchiroli. 2016. Soware Heritage: Why and How to Preserve Soware Source Code. In Proceedings of 14th International Conference on Digital Preservation, Kyoto, Japan, September 2017 (iPRES2017), 10 pages. DOI: 10.1145/nnnnnnn.nnnnnnn
GC-MS analysis of Abelmoschus manihot (L.) Medik (Malvaceae) leaves

Plants are a rich source of bioactive phytochemicals which provide health benefits for humans further than those attributed to macronutrients and micronutrients. The phytochemical compounds isolated and purified are employed in a wide range of applications. In the present study, the bioactive compounds of Abelmoschus manihot benzene, chloroform, methanol and ethanol leaf extracts via GC-MS was analyzed and its biological properties being available in pure form, being nontoxic with a wide spectrum of biological functions, may find its application in the formation of various medicinal products.
A Parallel and Distributed Approach for Diversified Top-k Best Region Search

Given a set of points, the Best Region Search problem finds the optimal location of a rectangle of a specified size such that the value of a user-defined scoring function over its enclosed points is maximized. A recently proposed top-k algorithm for this problem returns results progressively, while also incorporating additional constraints, such as taking into consideration the overlap between the set of selected top-k rectangles. However, the algorithm is designed for a centralized setting and does not scale to very large datasets. In this paper, we overcome this limitation by enabling parallel and distributed computation of the results. We first propose a strategy that employs multiple rounds to progressively collect partial top-k results from each node in the cluster, while a coordinator handles the aggregation of the global top-k list, dealing with overlapping results. We then devise a single-round strategy, where the algorithm executed by each node is enhanced with additional conditions that anticipate potential overlapping solutions from neighboring nodes. Additional optimizations are proposed to further increase performance. Our experiments on real-world datasets indicate that our proposed algorithms are efficient and scale to millions of points.
PHYLOGEOGRAPHY OF THE SOCIALLY POLYMORPHIC SWEAT BEE

The evolution of sociality in insects holds a central place in evolutionary theory. By examining the phylogenetic patterns of solitary and social behavior and how they correlate with ecological variables, we may identify factors important in the evolution of sociality. In this study, we investigated historical and biogeographical patterns of sociality in a socially polymorphic bee species (one that demonstrates both social and solitary nesting behavior). This unique system allows for a more powerful examination of evolutionary transitions in sociality than interspecific studies of obligately social and solitary species. We conducted a phylogenetic analysis among populations of the halictine bee Halictus rubicundus and then identified relationships among mitochondrial DNA sequence data, sociality, environmental conditions at the nesting site, and geographic location of populations of this species. Within North America, populations of H. rubicundus expressing social and solitary behavior belong to different genetic lineages. Sociality is also correlated with at least one environmental variable used in this study. Taken together, the results support the predictions for genetic control of sociality, but they are still consistent with social behavior at some level being determined by the environmental conditions at the nesting site.
Deep Learning for PET Imaging : From Denoising to Learned Primal-Dual Reconstruction

One of the main issues of PET imaging is the high level of noise that characterizes the reconstructed image. During this project we implemented several algorithms with the aim of improving the reconstruction of PET images exploiting the power of Neural Networks. We developed a simple Denoiser, then two Neural Network based iterative reconstruction algorithms and finally, we used the most promising approach to reconstruct images from data acquired with the KTH MTH microCT - miniPET.
Integrating navigated transcranial magnetic stimulation motor mapping in hypofractionated and single-dose gamma knife radiosurgery: A two-patient case series and a review of literature

Background: The aim of the study was to demonstrate the feasibility of integrating navigated transcranial magnetic stimulation (nTMS) in preoperative gamma knife radiosurgery (GKRS) planning of motor eloquent brain tumors. Case Description: The first case was a 53-year-old female patient with metastatic breast cancer who developed focal epileptic seizures and weakness of the left hand. The magnetic resonance imaging (MRI) scan demonstrated a 30 mm metastasis neighboring the right precentral gyrus and central sulcus. The lesion was treated with adaptive hypofractionated GKRS following preoperative nTMS-based motor mapping. Subsequent follow-up imaging (up to 12 months) revealed next to complete tumor ablation without toxicity. The second case involved a previously healthy 73-year-old male who similarly developed new left-handed weakness. A subsequent MRI demonstrated a 26 mm metastatic lesion, located in the right postcentral gyrus and 5 mm from the hand motor area. The extracranial screening revealed a likely primary lung adenocarcinoma. The patient underwent preoperative nTMS motor mapping prior to treatment. Perilesional edema was noted 6 months postradiosurgery; nevertheless, long- term tumor control was demonstrated. Both patients experienced motor function normalization shortly after treatment, continuing to final follow-up. Conclusion: Integrating preoperative nTMS motor mapping in treatment planning allowed us to reduce dose distributions to perilesional motor fibers while achieving salvage of motor function, lasting seizure freedom, and tumor control. These initial data along with our review of the available literature suggest that nTMS can be of significant assistance in brain radiosurgery. Prospective studies including larger number of patients are still warranted.
Development of Bi-Directional English To Yoruba Translator for Real-Time Mobile Chatting

Machine translation (MT) is a subfield of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. Translating between English language and Yoruba language comes with some computational complexities such as syntactic and grammatical differences in the language pair. This paper aims at exploring a multi-layer hybridized language translation approach, which combines the Corpus-based and Rule-based approaches of machine translation to generate its outputs. A parallel corpus was built with texts from English and Yoruba languages and stored in My Structured Query Language (MySQL) database. One hundred and forty seven computational rules were manually formulated and also stored in MySQL database for generating sentences in both languages. A di-bilingual dictionary was developed, one of which stored words in English with their corresponding Yoruba counterparts and their equivalent parts of speech while the other dictionary stored words in Yoruba with their corresponding English counterparts and their equivalent parts of speech. A real time mobile chatting interface was developed for users’ interactions with themselves and the system. The research model was implemented using PHP for server-side scripting, JSON for data interchange and Java programming language for user interfaces accessible on users’ mobile phones. The Java programming language was coded in Android Studio 3.0 Integrated Development Environment. Two hundred and eleven sentences from Contemporary English Grammar were used for system testing and the result shows 95% accuracy compare with Google Translate.
Do I Belong in a Makerspace?: Investigating Student Belonging and Non-verbal Cues in a University Makerspace

Dr. Kimberly G. Talley is an assistant professor in the Department of Engineering Technology, Maker Space Co-Director and Senior Research Fellow for the LBJ Institute for STEM Education and Research at Texas State University, and a licensed Professional Engineer. She received her Ph.D. and M.S.E. from the University of Texas at Austin in Structural Engineering. Her undergraduate degrees in History and in Construction Engineering and Management are from North Carolina State University. Dr. Talley teaches courses in the Construction Science and Management Program, and her research focus is in student engagement and retention in engineering and engineering technology education. Contact: talley@txstate.edu