Miracle-dz/UniFAR-FTUs huggingface dataset:
A Three-Stage Training Method for Abstractive Summarization

Abstractive summarization requires the model to understand the input text before generating the summary. The main challenges of abstractive summarization include consistency and ﬂuency. In order to make the generated summary consistent with the input text and ﬂuent, we propose a three-stage training method: general corpus pretraining, summarization-related ﬁne-tuning and end-to-end training. We use the BERT base model as the result of general corpus pretraining. Additionally, we propose a task named summarization language model (SLM) for the second training phase, which consists of two parts: for every input pair of sentences, predicting the masked tokens and whether the second sentence is the summary of the ﬁrst sentence. The effectiveness of SLM is veriﬁed on the encoder-decoder framework. However, the model is very large and contains too many variables, which consumes a lot of computing resources. In order to reduce the number of variables, we propose to use only pretrained transformer encoder to generate the summary with three-stage training and self double-check mechanism. Experimental results on the A Large-Scale Chinese Short Text Summarization Dataset (LC-STS) and the Annotated English Gigaword datasets show that after SLM, the encoder-decoder framework can outperform most of the other works, and a single transformer encoder can achieve nearly the same performance with the pretrained encoder-decoder framework with much fewer variables.
On the Interplay between Requirements, Engineering, and Artificial Intelligence

With this paper, we present our reﬂections on the issues that are faced by the Requirements Engineering academic discipline and practice. The current reality of the Artiﬁcial Intelligence and Machine Learning hype penetrating from research into all industry sectors and all phases of sys-tem design and development is a transformative shift that inﬂuences the way Requirements Engineering is conducted and the nature of the systems that are engineered. We identify two sides of this transformation with regards to the Requirements Engineering discipline: (1) Artiﬁcial Intelligence tools are used more and more during the Requirements Engineering process, (2) the Requirements Engineering process for systems that include Artiﬁcial Intelligence is diﬀerent. By identifying and framing these changes, we pose questions about what it means to engineer requirements. Our analysis asks more questions than it answers. We hope to engage the Requirements Engineering academic community in a larger conversation about the role of Requirements Engineering in the changing world and about a possible new vision of engineering becoming secondary to requirements in Requirements Engineering.
Kranioplastie: Geschichte, Materialien, aktuelle Techniken und Ergebnisse // Cranioplasty: History, materials, recent techniques and results

Cranioplasty: History, materials, recent techniques and results. There is plenty of recent and historical literature in neurosurgery documenting the development and technique of cranioplasty. Besides the autologues bone flap artificial materials are used (polymethylmethacrylate, hydroxyapatite, titanium mesh, alumina ceramics, polyetheretherketone). New indications for decompressive craniectomy were established. Everyone who suffered from a calvari al defect should undergo reconstructive surgery with a predictable stable and an excellent aesthetic result. Cranioplasty nowadays is performed within weeks or a few months after the primary surgery. Furthermore we give a short overview of cranioplasty`s history, discuss the advantages and disadvantages of the used materials and present our own results within the time period from 2005 until 2014 (233 patients). J Neurol Neurochir Psychiatr 2017; 18 (4): 136–42.
Bridging the Gap Between Requirements and Simulink Model Analysis

Formal veriﬁcation and simulation are powerful tools for the veriﬁcation of requirements against complex systems. Requirements are developed in early stages of the software lifecycle and are typically expressed in natural language. There is a gap between such requirements and their software implementation. We present a framework that bridges this gap by supporting a tight integration and feedback loop between high-level requirements and their analysis against software artifacts. Our framework implements an analysis portal within the fret requirements elicitation tool, thus forming an end-to-end, open-source environment where requirements are written in an intuitive, structured natural language, and are veriﬁed automatically against Simulink models.
Unsupervised Learning Optical Flow by Robust Reconstruction and Edge-Aware Smoothing

Optical ﬂow provides good features for many computer vision tasks, such as action recognition, motion estimation and multi-target tracking. Well-performed deep networks are driven by lots of labeled data which is hard to get especially in real scenes. In this work, we present a novel unsupervised method that can learn optical ﬂow by robust image reconstruction based on brightness constancy and structure similarity constraints. To overcome negative eﬀects of occlusion and obtain natural dense pixel ﬂow ﬁelds, we propose an edge-aware loss function for smoothing ﬂow ﬁelds while following original image structure. Experiments conducted on Flying Chairs and KITTI datasets conform the eﬀectiveness and robustness of our method. It also provides a good initialization for supervised ﬁne-tuning.
Application of the Theory of Unpleasant Symptoms in Practice: A Challenge for Nursing

Symptoms are at the heart of nursing care. Much of what nurses do for and with patients is focused on symptoms: measuring them, assessing factors that may inuence changes, developing prevention and management strategies, and helping patients with ongoing monitoring and self-management. e eory of Unpleasant Symptoms (TOUS) was developed to highlight important aspects of the symptom experience in order to improve understanding and help guide nursing research and practice. It is exciting that interest in the theory and its application has been increasing internationally. e TOUS has three major related parts: the symptom(s), factors that inuence the symptom (inuencing factors), and the performance outcome. e theory denes symptoms subjectively, as perceived by the patient. Symptoms are proposed to vary in intensity, timing (e.g., time of onset, duration), the distress that the patient experiences, and quality (how they feel). Two or more symptoms can occur together, a phenomenon currently identied as a symptom cluster. Multiple symptoms occurring together may have the same or different causes, and can have cumulative or multiplicative effects. By categorizing inuencing factors as physiological, psychological, and situational, the TOUS encourages thinking beyond the physical realm of care. Physiological factors include age, gender, and variables related to the illness(es) and treatments. Psychological factors include mood and cognition (for example, knowledge about and understanding of the illness). ey exert strong inuence on symptoms. Situational factors are external to the individual. ey emphasize the potential impact of the physical and social environments on the patient. e outcome of the symptom experience is performance, which is dened broadly to include physical, cognitive, and social role performance (1,2). e TOUS emphasizes the complexity of symptoms, while also implying possible preventive and management strategies. According to the theory, the three related categories of inuencing factors affect the occurrence of one or more symptoms and how they are experienced. e symptom(s), in turn, affects the individual’s performance, which can feed back to inuence the symptom experience and the inuencing factors. e theory does not include explicit interventions. Instead, it is assumed that many of the components and relationships in the TOUS could be targeted for intervention. Several studies have applied the TOUS in practice with positive results. It should be noted that these applications have had multiple components, and all include teaching of the patient and family caregivers. Some of the practice applications that would be consistent with the theory include the following.
Barriers on Hiv Prevention for Female Sex Workers in Lao PDR: a Literature Review (2003-2014)

Although the government of Lao PDR and its development partners have been providing strong political commitment and strengthening the endorsement and implementation on HIV/AIDS prevention programs to end epidemic of HIV/AIDS; nonetheless, HIV/AIDS prevalence and incidence in Lao PDR have been increasing rapidly since 2003. The risk are higher among key affected populations such as sex workers, male who have sex with male, their clients, and injecting drug users. There are numerous causes and barriers that prevent to country to reach the MDGS that set targets of HIV prevalence amongst population aged 15-24 years at less than 1 percent and HIV prevalence amongst female sex workers at less than 5 percent in 2015 such as limited civil society involvement, poor coverage and quality of HIV/AIDS prevention and treatment services, condom use program that is not universally covered, increased in migrant sex workers, low level of education, and unsafe sexual activity, and unaccommodating environment. The objective of the HIV prevention among female sex workers research was identified the barriers on HIV/AIDS interventions in Lao PDR. Literature review was conducted by using existing Lao PDR country progress reports on HIV/AIDS. The HIV prevention programmes for female sex workers not successful too much, because there have many barriers such as: 1) Sexual services are illegal and visible phenomenon in Lao PDR. 2) Number and HIV incidence amongst the female sex worker was increased and also their sexual activities are more hidden and difficult to reach them into HIV prevention programmes. 3) Female sex workers are vulnerable and high risk behaviour. 4) Condom use of female sex workers with the recent client was steadily increased, but with regular partners is still low. 5) The quality of health care services on HIV prevention still low and lack of professional skills. 6) FSWs have sexual intercourse in too young age, so they are higher risks for HIV infection. 7) Female sex workers are remaining perceived stigma and discrimination by society, especially by health care service providers. Stigmatization and discrimination is the one barriers of HIV prevention for female sex workers in Lao PDR.
Smart Logistics: Blockchain trends and applications

The recent increase of new technologies and their involvement into all management processes call into question the smart logistics current models, in which massive amounts of data is collected and controlled. Smart Logistics is considered as fundamental pillar of the 4th industrial revolution 'industry 4.0'. This revolution is based on different concepts including the blockchain technology. Blockchain remains one of the buzzwords in the technological world. So that all sectors are focus on concrete use cases. However, few actors can boast of having devised revolutionary solutions. For good reasons, blockchain technology is still very complex to understand. The purpose of this study is to define the various applications of Blockchain in Smart Logistics, as well as to present concrete examples of these applications. This work was done by classifying the applications according to four clusters: Information, Transport, Finance, and Management, in addition to presenting the applications of each cluster.
Improved Knowledge Base Question Answering System Using Re-ranking Approach

Knowledge Base Question Answering(KBQA) is an important task in NLP domain and equally diﬃcult. Billions of facts can be accessed with KBQA in eﬃcient way. However, it includes the challenging part of mapping a natural language question to a structured KB query. In this paper, we extended a previous work which is based on memory networks with a re-ranking procedure. For carrying out the re-ranking procedure, we used a relation predictor model which we have implemented using deep learning. The new procedure re-ranks the top candidate answers given by the previous KBQA model. After re-ranking, our model outputs the highest scored candidate answer as the output. The experimental results show that our approach achieves the best result on SPADES (Semantic PArsing of DEclarative Sentences) dataset.
Error Correcting Codes for Uncompressed Messages

Most types of messages we transmit (e.g., video, audio, images, text) are not fully compressed, since they do not have known efficient and information theoretically optimal compression algorithms. When transmitting such messages, standard error correcting codes fail to take advantage of the fact that messages are not fully compressed. We show that in this setting, it is sub-optimal to use standard error correction. We consider a model where there is a set of “valid messages” which the sender may send that may not be efficiently compressible, but where it is possible for the receiver to recognize valid messages. In this model, we construct a (probabilistic) encoding procedure that achieves better tradeoffs between data rates and error-resilience (compared to just applying a standard error correcting code). Additionally, our techniques yield improved efficiently decodable (probabilistic) codes for fully compressed messages (the standard setting where the set of valid messages is all binary strings) in the high-rate regime.
t-Distributed Stochastic Neighbor Embedding Spectral Clustering using higher order approximations

This paper introduces a new topological clustering approach to cluster high dimensional datasets based on t -SNE (Stochastic Neighbor Embedding) dimensionality reduction method and Self-Organizing Maps (SOM). The unsupervised learning is often used for clustering data and rarely as a data preprocessing method. However, there are many methods that produce new data representations from unlabeled data. These unsupervised methods are sometimes used as a preprocessing tool for supervised or unsupervised learning models. The t -SNE method which performs good results for visulaization allows a projection of the dataset in low dimensional spaces that make it easy to use for very large datasets. Using t -SNE during the learning process will allow to reduce the dimensionality and to preserve the topology of the dataset by increasing the clustering accuracy. We illustrate the power of this method with several real datasets.
DISTRIBUTION OF PEPTIDE-CONTAINING ENDOCRINE CELLS AND NEURONS IN THE GASTROINTESTINAL TRACT OF THE DOG: IMMUNOHISTOCHEMICAL STUDIES USING ANTISERA TO SOMATOSTATIN, SUBSTANCE P, VASOACTIVE INTESTINAL POLYPEPTIDE, MET-ENKEPHALIN, AND NEUROTENSIN

The distribution of peptide-containing endocrine cells and neurons in the stomach, duodenum, jejunum, ileum, and colon of the dog was studied with the peroxidaseantiperoxidase technique using antisera to somatostatin (SOM), substance P (SP), vasoactive intestinal polypeptide (VIP), Met-enkephalin (M-ENK), and neurotensin (NT). 1) Somatostatin-containing endocrine cells were distributed throughout all parts of the gastrointestinal tract. Met-enkephalin-containing endocrine cells were located only in the pylorus and duodenum. Neurotensin-containing endocrine cells were observed in the duodenum, jejunum, ileum and colon. Neither substance P nor VIP immunoreactivity was detected in gut endocrine cells. 2) Somatostatin, substance P, VIP, and Met-enkephalin immunoreactivity was observed also in neuronal elements throughout the gastrointestinal tract, whereas neurotensin immunoreactivity was detected only in the small intestine. Immunoreactivity to all peptides except neurotensin was found in nerve fibers and in ganglion cells located in both the myenteric and submucous plexus. No neurotensin-containing neurons were seen in any layer of the gut wall. Somatostatin-positive nerve fibers were distributed in the myenteric plexus, circular muscle, submucous plexus, and lamina propria mucosae. A large number of substance Pand VIP—positive nerve fibers were seen in all layers of the gut wall; they formed a dense network especially in the nerve plexus and lamina propria. Met-enkephalin-positive nerve fibers were also demonstrated in all layers and formed a dense network in the myenteric and submucous plexus. Neurotensin-positive nerve fibers were found in the longitudinal muscle, myenteric plexus, and circular muscle, but only a few were seen in the submucous plexus. One of the most exciting findings in recent years has been that many of the hormonal peptides initially isolated in the brain are also present in the gut, and that several gut hormonal peptides 1Present address: First Department of Surgery, Shiga University of Medical Science, Seta Tsukinowacho, Otsu, Shiga 520-21, Japan are also found in the brain (7). In the gut, these peptides, which have been termed brain-gut peptides (7), are located both in endocrine cells and intramural and extramural neurons. The peptides identified in gut endocrine cells include somatostatin (SOM) (1, 3, 21, 25, 26, 32, 34, 44, 46), substance P (SP) (38, 41, 53), vasoactive intestinal polypeptide (VIP) (9, 10, 43),
Ginkgo biloba Prevents Oxidative Stress-Induced Apoptosis Blocking p53 Activation in Neuroblastoma Cells

Oxidative stress has been associated to neuronal cell loss in neurodegenerative diseases. Neurons are post-mitotic cells that are very sensitive to oxidative stress—especially considering their limited capacity to be replaced. Therefore, reduction of oxidative stress, and inhibiting apoptosis, will potentially prevent neurodegeneration. In this study, we investigated the neuroprotective effect of Ginkgo biloba extract (EGb 761) against H2O2 induced apoptosis in SK-N-BE neuroblastoma cells. We analysed the molecular signalling pathway involved in the apoptotic cell death. H2O2 induced an increased acetylation of p53 lysine 382, a reduction in mitochondrial membrane potential, an increased BAX/Bcl-2 ratio and consequently increased Poly (ADP-ribose) polymerase (PARP) cleavage. All these effects were blocked by EGb 761 treatment. Thus, EGb 761, acting as intracellular antioxidant, protects neuroblastoma cells against activation of p53 mediated pathway and intrinsic mitochondrial apoptosis. Our results suggest that EGb 761, protecting against oxidative-stress induced apoptotic cell death, could potentially be used as nutraceutical for the prevention and treatment of neurodegenerative diseases.
Type 2 diabetes in Sri Lanka : Genetic epidemiology and periodontal association.

Prevalence rates of type 2 diabetes and impaired fasting glyceamia (IFG) in Sri Lanka are high and an increasing number of people are succumbing to disease. Identifying people at risk of developing complications is a healthcare priority of the county to prevent morbidity and mortality. Type 2 diabetes is familiarly aggregated and maternal influence for disease transmission has been observed in European countries but not in South India. The severity of periodontitis is reported to be high when diabetes control decreases but data relating to periodontitis as a complication of diabetes is not present in this population. Periodontal associated genotype (PAG), presence of allele 2 of both {ILIA (+4845) & IL1B (+3954)}) is positively associated with periodontal disease in some populations but ruled out in other populations. Heightened levels of TNF-? level have been observed in both patients with type 2 diabetes and periodontitis.This study aimed to determine familial aggregation of type 2 diabetes and parental influence of disease transmission in the Sri Lankan population. The project also aimed to investigate periodontal status in diabetic patients and the effect of glycaemic control on periodontal status. The final aim of the project was to investigate the role of cytokine polymorphisms PAG and TNFA (-308) in association with periodontitis in people with type 2 diabetes.Family history data for one thousand patients was collected and analyzed. Subjects with established diabetes were recruited to the periodontal study (n=285) with an age and sex matched control population (n=72). All subjects underwent both periodontal and general health examination. Their periodontal parameters and metabolic parameters were measured including blood pressure, TG and LDL values. Patients were genotyped by PCR/RFLP method.The results of the study indicated that 59.4% of the diabetic subjects had at least one affected first degree relative in the family. It was also observed that 15.6% of mothers transfer the disease compared to (12.5%) of fathers (p 4mm, PD >5mm and maximum loss of attachment (LOA) scores than the non diabetic population (p0.05). Subjects with periodontitis were significantly older than those with gingivitis or periodontally healthy (p 0.05). The percentage of PAG (12.5%) is low compared to Caucasians but higher compared to Chinese population. TNFA (-308) allele distribution had no significant effect on periodontal status. TNFA (-308) genotypes were significantly correlated with HDL.The results of the present study indicate that type 2 diabetes is familiarly aggregated in the Sri Lankan diabetic population and maternal excess is observed in the transmission pattern. There is a trend towards periodontital status to be higher in the diabetic population. Severity of periodontitis is higher in people with diabetes than those without and there is an indication that periodontitis may induce hyperglycaemia. PAG distribution shows population variation and the TNFA (-308) polymorphism may be associated with diabetes.
HisCoM-PCA: software for hierarchical structural component analysis for pathway analysis based using principal component analysis

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.