Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Story Analogues

In this paper, we describe a computational system that generates story analogues based on previous stories. Unlike many previous works on story generation that attempt to produce a story artifact from a set of high-level specifications, we describe an approach that generates relatively novel stories by transforming existing stories to new contexts. We describe an algorithm for using analogical reasoning to find similarities between story contexts in order to map events from an existing story to a novel context.
Dynamic control for failure recovery and flow reconfiguration in SDN

By separating control and data planes, Software-Defined Networks (SDN) pave the way to a plethora of online routing mechanisms that react instantaneously to sudden traffic variation and network failures. In this paper, we introduce a dynamic routing system where these online mechanisms strive to solve an optimization instance that constantly evolves due to new arrivals and departures as well as network failures and repairs. We model the optimality gap, which is characteristic to the surcharge cost paid by network operators, of such a system as an auto-regressive stochastic process and we show that its evolving average does not diverge. We further propose a control policy that operates on top of the online routing solver to dynamically decide when to reconfigure the network. The policy minimizes the average surcharge cost and guarantees that the network reconfiguration rate does not exceed a threshold set by the operator. Numerical results show that our policy outperforms periodic schemes and approaches that reconfigure constantly the network after any system changes occur.
Crossmodal Audio and Tactile Interaction with Mobile Touchscreens

This article asserts that using crossmodal auditory and tactile interaction can aid mobile touchscreen users in accessing data non-visually and, by providing a choice of modalities, can help to overcome problems that occur in different mobile situations where one modality may be less suitable than another Hoggan, 2010. By encoding data using the crossmodal parameters of audio and vibration, users can learn mappings and translate information between both modalities. In this regard, data may be presented to the most appropriate modality given the situation and surrounding environment.
Rapid variability in TeV blazars: the case of PKS 2155—304

Recent Cherenkov observations of BL Lacertae (BL Lac) objects showed that the TeV (teraelectron volt) flux of PKS 2155−304 changed by a factor of 2 in just 3–5 min. This fast variability can be accounted for if the emitting region is moving with a bulk Lorentz factor Γ∼ 50 and a similar relativistic Doppler factor. If this Γ is adopted, several models can fit the data, but, irrespective of the chosen model, the jet is matter dominated. The Doppler factor requires viewing angles of the order of 1° or less: if the entire jet is as narrow as this, then we have problems with current unification schemes. This suggests that there are small active regions, inside a larger jet, moving faster than the rest of the plasma, occasionally pointing at us. Coordinated X-ray/TeV variability can discriminate between the different scenarios.
Technology-induced Errors

Summary Objective: The objective of this paper is to examine the extent, range and scope to which frameworks, models and theories dealing with technology-induced error have arisen in the biomedical and life sciences literature as indexed by Medline®. Methods: To better understand the state of work in the area of technology-induced error involving frameworks, models and theories, the authors conducted a search of Medline® using selected key words identified from seminal articles in this research area. Articles were reviewed and those pertaining to frameworks, models or theories dealing with technology-induced error were further reviewed by two researchers. Results: All articles from Medline® from its inception to April of 2011 were searched using the above outlined strategy. 239 citations were returned. Each of the abstracts for the 239 citations were reviewed by two researchers. Eleven articles met the criteria based on abstract review. These 11 articles were downloaded for further in-depth review. The majority of the articles obtained describe frameworks and models with reference to theories developed in other literatures outside of healthcare. The papers were grouped into several areas. It was found that articles drew mainly from three literatures: 1) the human factors literature (including human-computer interaction and cognition), 2) the organizational behavior/socio-technical literature, and 3) the software engineering literature. Conclusions: A variety of frameworks and models were found in the biomedical and life sciences literatures. These frameworks and models drew upon and extended frameworks, models and theoretical perspectives that have emerged in other literatures. These frameworks and models are informing an emerging line of research in health and biomedical informatics involving technology-induced errors in healthcare.
In vivo assessment of optimal viewing angles from X-ray coronary angiography.

AIMS To propose and validate a novel approach to determine the optimal angiographic viewing angles for a selected coronary (target) segment from X-ray coronary angiography, without the need to reconstruct the entire coronary tree in three-dimensions (3D), such that subsequent interventions are carried out from the best view. METHODS AND RESULTS The approach starts with standard quantitative coronary angiography (QCA) of the target vessel in two angiographic views. Next, the target vessel is reconstructed in 3D, and in a very simple and intuitive manner, the possible overlap of the target vessel and other vessel segments can be assessed, resulting in the best view with minimum foreshortening and overlap. A retrospective study including 67 patients was set up for the validation. The overlap prediction result was compared with the true overlap on the available angiographic views (TEST views). The foreshortening for the views proposed by the new approach software viewing angle (SVA) and the views used during the stent deployment software viewing angle (EVA) were compared. Two experienced interventional cardiologists visually evaluated the success of SVA with respect to EVA. The evaluation results were graded into five values ranging from -2 to 2. The overlap prediction algorithm successfully predicted the overlap condition for all 235 TEST views. EVA was associated with more foreshortening than SVA (8.9% ± 8.2% vs. 1.6% ± 1.5%, p<0.001). The average evaluated point for the success of SVA was 0.94 ± 0.80 (p <0.001), indicating that the evaluators were in favor of the optimal views determined by the proposed approach versus the views used during the actual intervention. CONCLUSIONS The proposed approach is able to accurately and quickly determine the optimal viewing angles for the online support of coronary interventions.
Evaluation outcomes of a sex education strategy in high schools of Pavia (Italy)

Aim: We aimed to provide process and effectiveness evaluations of a sex education intervention realized with interactive techniques in high schools of Pavia (Italy). Methods: Six public high schools, divided into ‘treated’ and ‘control’ units, voluntarily joined this mixed-methods study. Only second-year classes were enrolled: treated adolescents followed a sex education course, performed by trained ‘near-peer educators’ (undergraduate medical students) with interactive techniques. All adolescents compiled an anonymous effectiveness evaluation questionnaire at baseline (pre-test) and 3 months later (post-test). Sexual knowledge and reported behavioural changes were compared between the two groups through linear mixed-effects models. The process was assessed through a satisfaction questionnaire for treated students, monitoring cards for working group members and cards/diaries for educators. Results: The final sample consisted of 547 treated and 355 control adolescents (mean age = 15.28 ± 0.61 years). Highly significant changes (p < 0.001) from pre-test to post-test, with strong improvements of treated adolescents, were evident for all knowledge items. No significant changes for behavioural items were reported. Process evaluation showed positive results in adolescents’ satisfaction, highlighting the need for more youth gathering places. Working group members and educators generally provided positive evaluations, although difficult communication was perceived. Conclusions: The intervention was effective in improving adolescents’ sexual knowledge. The present work highlighted that in Italy sex education in adolescence is still neglected: this could encourage misinformation and health-risk behaviour. Young people perceive the need for a serious health-promoting action in which they could play an active role, spreading educational messages with organized interactive methods.
Hybrid powertrains analysis for ship propulsion using energy storage.

The ship emission would occupy the sixth position in the world biggest emitters ranking. It happens because the ship operations have a huge demand variation, to reduce the ship emissions is required an intelligent operation of the generators. This work aims at integrating advanced storage systems to the diesel generators operation. The variation of the operation point has a direct interference on the emissions and on the diesel consumption, this variation is allowed through the frequency and voltage control. The use of lithium batteries for various operation points of the generators is analysed. The mainly result obtained was the reduction on diesel consumption and on CO2 emissions when the diesel generators operate for a long time closer to their nominal power. The results of this work can also be applied to isolated power systems equipped with advanced storage systems and diesel generators.
MURS: Mitigating Memory Pressure in Service-Oriented Data Processing System

Although a data processing system often works as a batch processing system, many enterprises deploy such a system as a service, which we call the service-oriented data processing system. It has been shown that in-memory data processing systems suffer from serious memory pressure. The situation becomes even worse for the service-oriented data processing systems due to various reasons. For example, in a service-oriented system, multiple submitted tasks are launched at the same time and executed in the same context in the resources, compared with the batch processing mode where the tasks are processed one by one. Therefore, the memory pressure will affect all submitted tasks, including the tasks that only incur the light memory pressure when they are run alone. In this paper, we find that the reason why memory pressure arises is because the running tasks produce massive long-living data objects in the limited memory space. Our studies further reveal that the long-living data objects are generated by the API functions that are invoked by the in-memory processing frameworks. Based on these findings, we propose a method to classify the API functions based on the memory usage rate. Further, we design a scheduler called MURS to mitigate the memory pressure. We implement MURS in Spark and conduct the experiments to evaluate the performance of MURS. The results show that when comparing to Spark, MURS can 1) decrease the execution time of the submitted jobs by up to 65.8%, 2) mitigate the memory pressure in the server by decreasing the garbage collection time by up to 81%, and 3) reduce the data spilling, and hence disk I/O, by approximately 90%.
AlzPlatform: An Alzheimer’s Disease Domain-Specific Chemogenomics Knowledgebase for Polypharmacology and Target Identification Research

Alzheimer’s disease (AD) is one of the most complicated progressive neurodegeneration diseases that involve many genes, proteins, and their complex interactions. No effective medicines or treatments are available yet to stop or reverse the progression of the disease due to its polygenic nature. To facilitate discovery of new AD drugs and better understand the AD neurosignaling pathways involved, we have constructed an Alzheimer’s disease domain-specific chemogenomics knowledgebase, AlzPlatform (www.cbligand.org/AD/) with cloud computing and sourcing functions. AlzPlatform is implemented with powerful computational algorithms, including our established TargetHunter, HTDocking, and BBB Predictor for target identification and polypharmacology analysis for AD research. The platform has assembled various AD-related chemogenomics data records, including 928 genes and 320 proteins related to AD, 194 AD drugs approved or in clinical trials, and 405 188 chemicals associated with 1 023 137 records of reported bioactivities from 38 284 corresponding bioassays and 10 050 references. Furthermore, we have demonstrated the application of the AlzPlatform in three case studies for identification of multitargets and polypharmacology analysis of FDA-approved drugs and also for screening and prediction of new AD active small chemical molecules and potential novel AD drug targets by our established TargetHunter and/or HTDocking programs. The predictions were confirmed by reported bioactivity data and our in vitro experimental validation. Overall, AlzPlatform will enrich our knowledge for AD target identification, drug discovery, and polypharmacology analyses and, also, facilitate the chemogenomics data sharing and information exchange/communications in aid of new anti-AD drug discovery and development.
Markers of Cutaneous Human Papillomavirus Infection in Individuals with Tumor-Free Skin, Actinic Keratoses, and Squamous Cell Carcinoma

Separately, actinic keratosis (AK) and cutaneous squamous cell carcinoma (SCC) have been associated with cutaneous human papillomavirus (HPV) infections. To further explore the association between HPV infection and SCC development, we determined markers of cutaneous HPV infection within a single population in persons with precursor lesions (AK), cancerous lesions (SCC), and without. Serum and plucked eyebrow hairs were collected from 57 tumor-free controls, 126 AK, and 64 SCC cases. Presence of HPV L1 and E6 seroreactivity and viral DNA were determined for HPV types 5, 8, 15, 16, 20, 24, and 38. Significant positive associations with increasing severity of the lesions (controls, AK, and SCC, respectively) were observed for overall HPV L1 seropositivity (13%, 26%, and 37%) and for HPV8 (4%, 17%, and 30%). In parallel, the proportion of L1 seropositive individuals against multiple HPV types increased from 14% to 39% and 45%. The overall E6 seroreactivity, however, tended to decline with AK and SCC, especially for HPV8 (21%, 11%, and 2%). HPV DNA positivity was most prevalent in the AK cases (54%) compared with the SCC cases (44%) and the tumor-free controls (40%). Among all participants, there was a positive trend between overall HPV DNA positivity and L1 seropositivity, but not E6 seropositivity. Taken together, our data suggest that cutaneous HPV infections accompanied by detectable HPV DNA in eyebrow hairs and HPV L1 seropositivity, but not E6 seropositivity, are associated with an increased risk of AK and SCC. (Cancer Epidemiol Biomarkers Prev 2006;15(3):529–35)
Enhancing State Mapping-Based Cross-Lingual Speaker Adaptation using Phonological Knowledge in a Data-Driven Manner

HMM state mapping with the Kullback-Leibler divergence as a distribution similarity measure is a simple and effective technique that enables cross-lingual speaker adaptation for speech synthesis. However, since this technique does not take any other potentially useful information into account for mapping construction, an approach involving phonological knowledge in a data-driven manner is proposed in order to produce better state mapping rules – state distributions from the input and output languages are clustered according to broad phonetic categories using a decision tree, and mapping rules are constructed only within each resultant leaf node. Apart from this, previous research shows that a regression class tree that follows the decision tree structure for state tying is detrimental to cross-lingual speaker adaptation. Thus it is also proposed to apply this new approach to regression class tree growth – state distributions from the output language are clustered according to broad phonetic categories using a decision tree, which is then directly used as a regression class tree for transform estimation. Experimental results show that the proposed approach can reduce mel-cepstral distortion consistently and produce state mapping rules and regression class trees that generalize to unseen test speakers. The impacts of the phonological/acoustic similarity between input and output languages upon the reliability of state mapping rules and upon the structure of regression class trees are also demonstrated and analyzed.
Variational multiscale method based on the Crank–Nicolson extrapolation scheme for the non-stationary Navier–Stokes equations

In this report, a variational multiscale (VMS) method based on the Crank–Nicolson extrapolation scheme of time discretization for the turbulent flow is analysed. The flow is modelled by the fully evolutionary Navier–Stokes problem. This method has two differences compared to the standard VMS method: (i) For the trilinear term, we use the extrapolation skill to linearize the scheme; (ii) for the projection term, we lag it onto the previous time level to simplify the construction of the projection. These modifications make the algorithm more efficient and feasible. An unconditionally stability and an a priori error estimate are given for a case with rather general linear (cellwise constant) viscosity of the turbulent models. Moreover, numerical tests for both linear viscosity and nonlinear Smagorinsky-type viscosity are performed, they confirm the theoretical results and indicate the schemes are effective.
Adaptive Language and Translation Models for Interactive Machine Translation

We describe experiments carried out with adaptive language and translation models in the context of an interactive computer-assisted translation program. We developed cache-based language models which were then extended to the bilingual case for a cachebased translation model. We present the improvements we obtained in two contexts: in a theoretical setting, we achieved a drop in perplexity for the new models and, in a more practical situation simulating a user working with the system, we showed that fewer keystrokes would be needed to enter a translation.
A bilayer representation of the human atria

Atrial fibrillation is the most commonly encountered clinical arrhythmia. Despite recent advances in treatment by catheter ablation, its origin is still incompletely understood and it may be difficult to treat. Computer modelling offers an attractive complement to experiment. Simulations of fibrillation, however, are computationally demanding since the phenomenon requires long periods of observation. Because the atria are thin walled structures, they are often modelled as surfaces. However, this may not always be appropriate as the crista terminalis and pectinate muscles are discrete fibrous structures lying on the endocardium and cannot be incorporated into the surface. In the left atrium, there are essentially two layers with an abrupt change in fibre orientation between them. We propose a double layer method, using shell elements to incorporate wall thickness, where fibre direction is independent in each layer and layers are electrically linked. Starting from human multi-detector CT (MDCT) images, we extracted surfaces for the atria and manually added a coronary sinus. Propagation of electrical activity was modelled with the monodomain equation. Results indicate that major features are retained while reducing computation cost considerably. Meshes based on the two layer approach will facilitate studies of AF.
Feature-based transfer learning with real-world applications

Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions and/or feature spaces. We can find many novel applications of machine learning and data mining where transfer learning is helpful, especially when we have limited labeled data in our domain of interest. In this thesis, we first survey different settings and approaches of transfer learning and give a big picture of the field. We focus on latent space learning for transfer learning, which aims at discovering a “good” common feature space across domain, such that knowledge transfer becomes possible. In our study, we propose a novel dimensionality reduction framework for transfer learning, which tries to reduce the distance between different domains while preserve data properties as much as possible. This framework is general for many transfer learning problems when domain knowledge is unavailable. Based on this framework, we propose three effective solutions to learn the latent space for transfer learning. We apply these methods to two diverse applications: cross-domain WiFi localization and cross-domain text classification, and achieve promising results. Furthermore, for a specific application area, such as sentiment classification, where domain knowledge is available for encoding to transfer learning methods, we propose a spectral feature alignment algorithm for cross-domain learning. In this algorithm, we try to align domain-specific features from different domains by using some domain independent features as a bridge. Experimental results show that this method outperforms a state-of-the-art algorithm in two real-world datasets on cross-domain sentiment classification.
Forced activation of Stat5 subjects mammary epithelial cells to DNA damage and preferential induction of the cellular response mechanism during proliferation

Parity‐dependent adenocarcinoma tumors developed in postestropausal transgenic mice expressing a constitutively active Stat5 variant (STAT5ca) in their mammary gland. These tumors maintained elevated expression levels of genes regulating the cellular DNA damage response (DDR) mechanism, compared to the intact gland. No correlation with STAT5ca expression was observed for these genes in the established tumors. However, activated Stat5a in individual cells of the rarely and earlier developed hyperplasia was associated with induced Chk2 activity. Deregulated Stat5 may already cause DNA damage during the fertile period. This hypothesis and the specific vulnerable stage were further studied in mammary epithelial cells that were stably transfected with β‐lactoglobulin (BLG)/STAT5ca and exposed to a reproduced reproductive cycle. During the pregnancy‐like proliferative state, STAT5ca expression was induced by the added lactogenic hormones. Production of reactive oxygen species, rather than proliferation, served as the primary mediator of DNA damage and cellular DDR. Differentiated cells expressed higher levels of STAT5ca and retained the DNA nicks. However, the elevated expression of the genes involved in DDR was downregulated. Higher levels of DNA damage were also detected in the mammary gland of transgenic mice expressing the BLG/STAT5ca during pregnancy and lactation. However, the relative number of damaged cells was much lower than that in the reproduced in vitro stages and the insults were generally associated with apoptosis and DDR. This study implicates pregnancy as the vulnerable stage for deregulated Stat5 activity, and demonstrates that DNA insults in viable differentiated mammary epithelial cells are ignored by the DDR mechanism. J. Cell. Physiol. 226: 616–626, 2011. © 2010 Wiley‐Liss, Inc.