Miracle-dz/UniFAR-FTUs huggingface dataset:
THE DEVELOPMENT OF ENFORCING RECEIVABLES IN THE CZECH REPUBLIC Luboš Smrčka –

This paper is focused on a enforcing receivables process in the Czech Republic. There are two basic possibilities how to enforce receivables when the debtor does not want or is not able to repay. The first one is an individual enforcing where on one side there is a affected creditor and on the other side there is a debtor. When the individual enforcing process is organised there are also other related entities as court enforcing agents, lawyers etc. The second type is a collective enforcing when the debtor has more creditors who are joined together in insolvency proceedings. This paper will compare data available for individual and collective enforcing. It will show if there are differences in the development of enforcing receivables. This time development will be compared with the country economic development, especially presented by the gross domestic product. The used methods will be based on a current state description and analysis using statistic methods as descriptive statistics and regression analysis. The limitation of this research (or its broadness) is data availability.
Human Resources for Commune Health Centers as per National Standards: The Case of Vietnam

Vietnam is among a few countries which have established a model of Commune Health Centers (CHCs) to serve primary health care for all people. Using quota sampling technique, 30 CHCs per each among nine provinces nationwide were equally selected for data collection to assess human resources for CHCs as national standards in Vietnam. A total of 239 CHCs finally completed the survey. Overall, the average number of Health Care Workers (HCWs) in each CHC was 6.3 ± 1.7, which met the national standards (at least five HCWs per CHC). However, only 24.4% of CHCs had 5 required job positions, and 59.7% of CHCs had at least one doctor. These shortages were more emerging in mountainous and remote areas. Further, mountainous and remote areas have been underrepresented of female HCWs, as compared to those in rural and urban areas. Our data provide a quite strong case for policy makers and health managers to improve human resources structure for CHCs.
The Impact of Context on Affective Norms: A Case of Study With Suspense

The emotional response to a stimulus is typically measured in three variables called valence, arousal and dominance. Based on such dimensions, Bradley and Lang (1999) published the Affective Norms for English Words (ANEW), a corpus of affective ratings for 1,034 non-contextualized words. Expanded and adapted to many languages, ANEW provides a corpus to evaluate and to predict human responses to different stimuli, and it has been used in a number of studies involving analysis of emotions. However, ANEW seems not to appropriately predict affective responses to concepts when these are contextualized in certain situational backgrounds, in which words can have different connotations from those in non-contextualized scenarios. These contextualized affective norms have not been sufficiently contrasted yet because the literature does not provide a corpus of the ANEW list in specific contexts. On this basis, this paper reports on the creation of a new corpus of affective norms for the original 1,034 ANEW words in a particular context (a fictional scene of suspense). An extensive quantitative data analysis comparing both corpora was carried out, confirming that the affective ratings are highly influenced by the context. The corpus can be downloaded as Supplementary Material.
Learning Disentangled Representations via Independent Subspaces

Image generating neural networks are mostly viewed as black boxes, where any change in the input can have a number of globally effective changes on the output. In this work, we propose a method for learning disentangled representations to allow for localized image manipulations. We use face images as our example of choice. Depending on the image region, identity and other facial attributes can be modified. The proposed network can transfer parts of a face such as shape and color of eyes, hair, mouth, etc.directly between persons while all other parts of the face remain unchanged. The network allows to generate modified images which appear like realistic images. Our model learns disentangled representations by weak supervision. We propose a localized resnet autoencoder optimized using several loss functions including a loss based on the semantic segmentation, which we interpret as masks, and a loss which enforces disentanglement by decomposition of the latent space into statistically independent subspaces. We evaluate the proposed solution w.r. t. disentanglement and generated image quality. Convincing results are demonstrated using the CelebA dataset.
Clinical outcomes of endoscopic balloon dilatation of intestinal strictures in patients with Crohn's disease

Abstract Intestinal strictures are common complications of Crohn's disease (CD). Endoscopic balloon dilatation (EBD) constitutes an alternative therapy to surgery, but associated factors of procedure success are inconclusive. Therefore, we aimed to evaluate the EBD success rate and its associated factors in CD patients. This is a retrospective cohort study of consecutive EBDs that were conducted between 2006 and 2014 among patients with CD with lower gastrointestinal tract strictures. Patients’ and stricture characteristics, short term procedure success and related complications at 1 week follow-up, and long-term clinical endpoints were documented. A total of 138 dilatations were performed on 64 CD patients. The overall dilatation success rate was 84.8%, with no difference between primary or anastomotic strictures, or between first or recurrent dilatation procedures. Long strictures (≥4 cm) were negatively associated with successful EBDs, but not with perforations. A multivariate analysis adjusting for age, sex, smoking, and disease duration revealed that a maximal dilatation diameter of ≥15 mm was positively associated with a successful EBD, while an inflamed stricture was negatively associated with procedure success. Strictures which were both long and inflamed were associated with the lowest EBD success rates compared with other strictures. Only 32.8% of patients required surgery during the follow-up period. Long-term prevention of surgery was negatively associated with stricture length and with a successful EBD. EBD is highly successful in treating intestinal strictures and in prevention of surgery in CD patients. Although EBD of long strictures is safe, it will not prevent surgery in the majority of cases.
Consistent Classification with Generalized Metrics

We propose a framework for constructing and analyzing multiclass and multioutput classification metrics, i.e., involving multiple, possibly correlated multiclass labels. Our analysis reveals novel insights on the geometry of feasible confusion tensors -- including necessary and sufficient conditions for the equivalence between optimizing an arbitrary non-decomposable metric and learning a weighted classifier. Further, we analyze averaging methodologies commonly used to compute multioutput metrics and characterize the corresponding Bayes optimal classifiers. We show that the plug-in estimator based on this characterization is consistent and is easily implemented as a post-processing rule. Empirical results on synthetic and benchmark datasets support the theoretical findings.
Integrated Information Theory and Isomorphic Feed-Forward Philosophical Zombies

Any theory amenable to scientific inquiry must have testable consequences. This minimal criterion is uniquely challenging for the study of consciousness, as we do not know if it is possible to confirm via observation from the outside whether or not a physical system knows what it feels like to have an inside—a challenge referred to as the “hard problem” of consciousness. To arrive at a theory of consciousness, the hard problem has motivated development of phenomenological approaches that adopt assumptions of what properties consciousness has based on first-hand experience and, from these, derive the physical processes that give rise to these properties. A leading theory adopting this approach is Integrated Information Theory (IIT), which assumes our subjective experience is a “unified whole”, subsequently yielding a requirement for physical feedback as a necessary condition for consciousness. Here, we develop a mathematical framework to assess the validity of this assumption by testing it in the context of isomorphic physical systems with and without feedback. The isomorphism allows us to isolate changes in Φ without affecting the size or functionality of the original system. Indeed, the only mathematical difference between a “conscious” system with Φ>0 and an isomorphic “philosophical zombie” with Φ=0 is a permutation of the binary labels used to internally represent functional states. This implies Φ is sensitive to functionally arbitrary aspects of a particular labeling scheme, with no clear justification in terms of phenomenological differences. In light of this, we argue any quantitative theory of consciousness, including IIT, should be invariant under isomorphisms if it is to avoid the existence of isomorphic philosophical zombies and the epistemological problems they pose.
Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation

Recent developments in NLP have been accompanied by large, expensive models. Knowledge distillation is the standard method to realize these gains in applications with limited resources: a compact student is trained to recover the outputs of a powerful teacher. While most prior work investigates student architectures and transfer techniques, we focus on an often-neglected aspect---student initialization. We argue that a random starting point hinders students from fully leveraging the teacher expertise, even in the presence of a large transfer set. We observe that applying language model pre-training to students unlocks their generalization potential, surprisingly even for very compact networks. We conduct experiments on 4 NLP tasks and 24 sizes of Transformer-based students; for sentiment classification on the Amazon Book Reviews dataset, pre-training boosts size reduction and TPU speed-up from 3.1x/1.25x to 31x/16x. Extensive ablation studies dissect the interaction between pre-training and distillation, revealing a compound effect even when they are applied on the same unlabeled dataset.
Multi-Omics Analysis of Fatty Alcohol Production in Engineered Yeasts Saccharomyces cerevisiae and Yarrowia lipolytica

Fatty alcohols are widely used in various applications within a diverse set of industries, such as the soap and detergent industry, the personal care, and cosmetics industry, as well as the food industry. The total world production of fatty alcohols is over 2 million tons with approximately equal parts derived from fossil oil and from plant oils or animal fats. Due to the environmental impact of these production methods, there is an interest in alternative methods for fatty alcohol production via microbial fermentation using cheap renewable feedstocks. In this study, we aimed to obtain a better understanding of how fatty alcohol biosynthesis impacts the host organism, baker’s yeast Saccharomyces cerevisiae or oleaginous yeast Yarrowia lipolytica. Producing and non-producing strains were compared in growth and nitrogen-depletion cultivation phases. The multi-omics analysis included physiological characterization, transcriptome analysis by RNAseq, 13Cmetabolic flux analysis, and intracellular metabolomics. Both species accumulated fatty alcohols under nitrogen-depletion conditions but not during growth. The fatty alcohol–producing Y. lipolytica strain had a higher fatty alcohol production rate than an analogous S. cerevisiae strain. Nitrogen-depletion phase was associated with lower glucose uptake rates and a decrease in the intracellular concentration of acetyl–CoA in both yeast species, as well as increased organic acid secretion rates in Y. lipolytica. Expression of the fatty alcohol–producing enzyme fatty acyl–CoA reductase alleviated the growth defect caused by deletion of hexadecenal dehydrogenase encoding genes (HFD1 and HFD4) in Y. lipolytica. RNAseq analysis showed that fatty alcohol production triggered a cell wall stress response in S. cerevisiae. RNAseq analysis also showed that both nitrogen-depletion and fatty alcohol production have substantial effects on the expression of transporter encoding genes in Y. lipolytica. In conclusion, through this multi-omics study, we uncovered some effects of fatty alcohol production on the host metabolism. This knowledge can be used as guidance for further strain improvement towards the production of fatty alcohols.
Mocycle-GAN: Unpaired Video-to-Video Translation

Unsupervised image-to-image translation is the task of translating an image from one domain to another in the absence of any paired training examples and tends to be more applicable to practical applications. Nevertheless, the extension of such synthesis from image-to-image to video-to-video is not trivial especially when capturing spatio-temporal structures in videos. The difficulty originates from the aspect that not only the visual appearance in each frame but also motion between consecutive frames should be realistic and consistent across transformation. This motivates us to explore both appearance structure and temporal continuity in video synthesis. In this paper, we present a new Motion-guided Cycle GAN, dubbed as Mocycle-GAN, that novelly integrates motion estimation into unpaired video translator. Technically, Mocycle-GAN capitalizes on three types of constrains: adversarial constraint discriminating between synthetic and real frame, cycle consistency encouraging an inverse translation on both frame and motion, and motion translation validating the transfer of motion between consecutive frames. Extensive experiments are conducted on video-to-labels and labels-to-video translation, and superior results are reported when comparing to state-of-the-art methods. More remarkably, we qualitatively demonstrate our Mocycle-GAN for both flower-to-flower and ambient condition transfer.
Verifying Bit-vector Invertibility Conditions in Coq (Extended Abstract)

This work is a part of an ongoing effort to prove the correctness of invertibility conditions for the theory of fixed-width bit-vectors, which are used to solve quantified bit-vector formulas in the Satisfiability Modulo Theories (SMT) solver CVC4. While many of these were proved in a completely automatic fashion for any bit-width, some were only proved for bit-widths up to 65, even though they are being used to solve formulas over arbitrary bit-widths. In this paper we describe our initial efforts in proving a subset of these invertibility conditions in the Coq proof assistant. We describe the Coq library that we use, as well as the extensions that we introduced to it.
Exact Fit to the Swaption Volatility Matrix Using Semidefinite Programming ∗

This paper addresses the problem of parameterizing jointly to all swaptions in the framework of the lognormal Libor market model (LLMM). First we refine some previous work showing that swaprates are nearly lognormal with a deterministic volatility. Then we formulate the fitting problem in terms of covariance matrices rather than the usual two variable volatility function. Semidefinite programming, in which the variables are positive semidefinite matrices (covariance matrices), minimizes a linear function of the variables subject to linear constraints on the variables. Exact fits to the swaption volatility matrix are imposed through linear constraints on covariance matrices. Various objectives can be used to get close to a historical covariance matrix. The method is illustrated by pricing fixed maturity Bermudan options.
A Digital Collection Study and Framework Exploration – Applying Textual Analysis on Source Code Collection

There are many technical procedures to analyze and evaluate raw source code of any software. However, most of them focus on productivity and efficiency of the code rather than the concealed historical value, especially with the legacy software and programs. On the other hand, preservation of digital and technological collection has increasingly been one of the major concerns in certain places like museum and science center. Such activity, therefore, includes the exploration of digital collection and extraction of the undiscovered or disappearing knowledge. This study displays an effort to experimentally use topic modeling approaches to extract interesting facts and knowledge within a special kind of digital-born collection, software’s source code. By exercising multiple techniques with selected subjects, this study also estimates the possibility to create a primitive framework to investigate digital collection, such as codebase, for further use in museum context.
Acupoint-tuina therapy promotes lactation in postpartum women with insufficient milk production who underwent caesarean sections

Abstract Breast milk is recognized and strongly recommended by the World Health Organization (WHO) as the optimal feeding for all babies. Breastfeeding is associated with better nutritional and non-nutritional outcomes when compared to formula feeding, and has proven health benefits to both infants and their mothers. This clinical research is to examine the feasibility and efficacy of Acupoint-Tuina therapy in treating postpartum women who underwent C-sections and suffered from insufficient milk production. The patients in the control group received standard medical care, while the patients in the Tuina group received Tuina therapy during the next 48 hours in addition to standard care, given once daily for 2 days. To evaluate the efficacy of Tuina therapy, patients of both groups were assessed for surface temperature of breasts, volume of breasts, volume of breast milk production, serum PRL level, and uterus recovery at various time points. Tuina therapy significantly increased the milk production when compared to the control group, for as much as 13-fold and 10-fold of that in the control group on the third and fourth postpartum days. In addition, Tuina therapy also significantly increased the full breast enlargement and the serum PRL level change, and decreased the breast surface temperature rise. Last but not the least, Tuina therapy also accelerated the post-surgery recovery of uterus. During the early postpartum days, Tuina therapy increases the milk production and promotes other physiological changes supporting lactation for postpartum women with C-section delivery and insufficient breast milk production. The novel intervention is warranted for further investigation and validation.
CoinNet: Deep Ancient Roman Republican Coin Classification via Feature Fusion and Attention

We perform classification of ancient Roman Republican coins via recognizing their reverse motifs where various objects, faces, scenes, animals, and buildings are minted along with legends. Most of these coins are eroded due to their age and varying degrees of preservation, thereby affecting their informative attributes for visual recognition. Changes in the positions of principal symbols on the reverse motifs also cause huge variations among the coin types. Lastly, in-plane orientations, uneven illumination, and a moderate background clutter further make the task of classification non-trivial and challenging. To this end, we present a novel network model, CoinNet, that employs compact bilinear pooling, residual groups, and feature attention layers. Furthermore, we gathered the largest and most diverse image dataset of the Roman Republican coins that contains more than 18,000 images belonging to 228 different reverse motifs. On this dataset, our model achieves a classification accuracy of more than \textbf{98\%} and outperforms the conventional bag-of-visual-words based approaches and more recent state-of-the-art deep learning methods. We also provide a detailed ablation study of our network and its generalization capability.
Beyond acute stroke: Rate of stroke transfers to a tertiary centre following the implementation of a dedicated inpatient teleneurology network

Introduction This study evaluated the impact of establishing an inpatient teleneurology consultation service alongside an already established telestroke network on the stroke transfers to the hub. The study also aimed to assess the financial impact of establishing this network. Methods Prospectively collected data on all stroke patients evaluated through our telestroke and teleneurology networks between January 2008 and March 2018 were interrogated. For all spokes (eight sites) that had both teleneurology and telestroke services, we compared the rate of transfers to the hub before and after the establishment of the teleneurology network in August 2014. The cost reduction was estimated using the Medicare 5% standard analytic files. Results A total of 4296 stroke patients were evaluated during the study period. Of these, 2493 were seen before and 1803 were seen after the implementation of the teleneurology network at the included sites. Patients in the pre-teleneurology group were older (66.4 years (SD = 14.7 years) vs. 67.8 years (SD = 15.1 years); p = 0.002). Otherwise, there were no differences in baseline characteristics. Patients in the pre-teleneurology group were more likely to be transferred to the telestroke hub (29.4% vs. 20.2%; p < 0.001). The estimated mean cost reduction for each one minus the cost of transfer was estimated to be US$4997. Discussion The implementation of an inpatient teleneurology network was associated with a significant reduction in the transfer rate of stroke patients to hospitals with a higher level of care and could lead to a significant cost reduction.
“Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer

Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information. In this work we introduce the Generative Style Transformer (GST) - a new approach to rewriting sentences to a target style in the absence of parallel style corpora. GST leverages the power of both, large unsupervised pre-trained language models as well as the Transformer. GST is a part of a larger ‘Delete Retrieve Generate’ framework, in which we also propose a novel method of deleting style attributes from the source sentence by exploiting the inner workings of the Transformer. Our models outperform state-of-art systems across 5 datasets on sentiment, gender and political slant transfer. We also propose the use of the GLEU metric as an automatic metric of evaluation of style transfer, which we found to compare better with human ratings than the predominantly used BLEU score.