Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Reward Functions for Optimal Highway Merging

In this paper, we explore highway merging for autonomous vehicles as a Markov decision process (MDP). Two different reward function formulations are studied, one which encodes safety and mobility objectives and the other which assumes a simple polynomial form based on state and action values at each time step. Solving this MDP for both reward functions with a variety of transition dynamics prescribing faster or slower driving behavior for another vehicle on the road enables a comparison of Pareto optimal curves for each reward function over an iterative sweep of reward function weights. We find that the assumption-free, polynomial reward function significantly outperforms the reward function that encodes prior knowledge for both safety and mobility objectives. A formal inverse reinforcement learning (IRL) approach to this problem may enable a more rigorous computation of reward functions that best explain optimal merging policies.
Wisdom of Crowds; Human Computer Interaction CROWD EFFECTS IN UNFAMILIAR FACE MATCHING

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Group Influence on Blogs Design Behavior GROUP INFLUENCE ON BLOGS DESIGN BEHAVIOUR

Issues of national culture influence on web design behaviour have been rampant and stimulating on static web pages across the globe. The emergence of a new breed of publicationtype web page brought about by the advancement of web technology however, saw a different species of online communication groups. Bloggers as these groups are called; used blogs as their communication and publication tool to distinguish themselves from other websites and online social media users. Since bloggers are groups that are recognised and credited to cultivate their own culture, the idea that national culture has an influence on blogs design behaviour and preferences may have been weakened. Bloggers groups themselves would be the influential factor that determines design preferences of bloggers in a network of blogs. To address the issue, this paper has conducted an assessment on blogs from six countries using content analysis method, national culture traits and SIDE model to ascertain design features characteristics and behaviour. Results from both the global and local blogs in each country showed that blogs design preferences in one country differ between both the global and local bloggers. Furthermore, global bloggers design preferences in countries under observation are found to be similar to one another.
Interactive comment on “ Measurement of atmospheric nitrous acid at Blodgett Forest during BEARPEX 2007 ” by X

In the paper by Ren et al. very interesting data on the contribution of HONO to the HOx production of the rural forested atmosphere is described. In contrast to all other studies in similar environment, the contribution was found to be quite low and some explanation is presented to explain the differences. In addition, a promising intercomparison of the aqueous sampling HPLC technique and the CIMS technique for the detection of HONO is presented.
Improving High – Bandwidth TLS in the FreeBSD kernel

In our 2015 paper, “Optimizing TLS for High– Bandwidth Applications in FreeBSD”, we demonstrated the cost of TLS encryption on high–bandwidth video serving on Netflix’s OpenConnect Appliance (OCA [1]) and explored methods for reducing that cost via in–kernel encryption. The results showed that such a concept is feasible, but only a small performance gain was achieved. In this paper we describe the improvements made since then to reduce the cost of encryption. We also compare the performance of several different bulk encryption implementations.
MANAGEMENTOF CONGLOMERATES IN THAILAND

Managing innovation is to understand how the innovation process can be successfully led and resulted in output and outcome. Considering important factors before managing innovation will help firms avoid failures in innovation projects. The aim of this paper is therefore to proposea model of innovation management and the contextual factors influencing the management in conglomerates in Thailand.This paper conducted literature review of innovation management (IM) models, and analyzed the contextual factors by empirical studies on successful companies and thereby describing how they organized innovation to propose the conceptual IM model. Exemplar innovation management practices from the largest four conglomerates in Thailand that were driven by innovation policy were studied through in-depth interviews and observations. The factors were criticized and weighted on whether they are relevant and critical to the innovation management.Similar and important factors of innovation management are six groups of factors which are external factors, organizational stance factors, resource allocated factors, innovation management system, build on experience factors and the measurement of outputs; profit,sales,growth and outcomes; new customer, customer satisfaction and corporate image. While comparing both the proposed IM model and company’s practice, additional attention is paid to several dimensions of innovation searching for patterns that assist in the practical use of the findings.
PPA with in-vivo cortical amyloidosis and Pick ' s disease pathology

The role of biomarkers in predicting pathological findings in the frontotemporal dementia (FTD) clinical spectrum disorders is still being explored. We present comprehensive, prospective longitudinal data for a 66 year old, right-handed female whomet current criteria for the nonfluent/agrammatic variant of primary progressive aphasia (nfvPPA). Shefirst presentedwith a 3year history of progressive speech and language impairment mainly characterized by severe apraxia of speech. Neuropsychological and general motor functions remained relatively spared throughout the clinical course. Voxel-based morphometry (VBM) showed selective cortical atrophy of the left posterior inferior frontal gyrus (IFG) and underlying insula that worsened over time, extending along the left premotor strip. Five years after her first evaluation, she developed mild memory impairment and underwent PET-FDG and PiB scans that showed left frontal hypometabolism and cortical amyloidosis. Three years later (11 years from first symptom), post-mortem histopathological evaluation revealed Pick’s disease, with severe degeneration of left IFG, mid-insula, and precentral gyrus. Alzheimer’s disease (AD) (CERAD frequent/Braak Stage V) was also detected. This patient demonstrates that biomarkers indicating brain amyloidosis should not be considered conclusive evidence that AD pathology accounts for a typical FTD clinical/anatomical syndrome.
Deep Concrete Inspection Using Unmanned Aerial Vehicle Towards CSSC Database

Concrete spalling and crack inspection is a labor intensive and routine task. However, it plays an important role in structure health monitoring (SHM) of civil infrastructures. Autonomous inspection with robots has been regarded as one of the best ways to reduce both error and cost. This paper presents an automated approach using Unmanned Aerial Vehicle(UAV) and towards a Concrete Structure Spalling and Crack database (CSSC), which is by far the first released database for deep learning inspection. We aim locate the spalling and crack regions to assist 3D registration and visualization. For deep inspection, we provide a complete procedure of data searching, labeling, training, and post processing. We further present a visual Simultaneously Localization and Mapping(SLAM) approach for localization and reconstruction. Comparative experiments and field tests are illustrated, results show that we can achieve an accuracy over 70% for field tests, and more than 93% accuracy with CSSC database.
Modelling Dynamic Functional Brain Connectivity

Functional brain connectivity, the statistical dependence between activity in segregated brain regions, has been studied extensively over the last two decades. Most models that describe functional connectivity have parameters in the model that do not change over time, implicitly assuming that functional connectivity is temporally static. Recent research shows that a wealth of information can be gained by modeling functional connectivity in a dynamic setting, i.e. that the brain can be in different states throughout an experiment. We investigated two different non-parametric Bayesian models of dynamic functional connectivity, one simple model with relatively few parameters, and another more complex model with relatively many parameters. Both were based on the infinite hidden Markov model to model transitions between brain states. We investigated how model complexity can affect the number of states extracted from data, and how that affects our interpretation of dynamic functional connectivity. We first conducted several synthetic experiments, generating data from the two models considered and afterwards ran inference by Markov chain Monte Carlo on the same data. The aim of this was to study the behaviour of the models in a setting where there was a clear model-mismatch. Furthermore, we investigated whether the models were able to characterize task and resting state functional magnetic resonance imaging (fMRI) data from the Danish Research Center for Magnetic Resonance (DRCMR) and from the Human Connectome Project (HCP). On synthetic data we showed that the simple model found many states on data generated from the complex model, but that the complex model was able to find the true number of states in data from the simple model. We found that the more complex model with only one state could characterize real-world data better than the simple model that found evidence for multiple states. The fact that the complex model only found one state in real-world data contradicts our intuition that multiple brain states should be present, but this could be explained by the dimensionality reduction carried out in this project. The results of this thesis indicate that one must always interpret dynamics in functional connectivity in terms of the model used and especially its limitations. We suspect that preprocessing and dimensionality reduction has a huge impact on the conclusions that can be drawn. This should be investigated further.
Metamodel-based Optimization and Full Characterization of Vertical Axis Wind Turbines

In Germany the energy transition is already well underway and it is only a question of time before this scenario is adopted by others nations. The energy transition is revolutionizing the production of electricity in favour of renewable energy sources. In the future, solar and wind energy are likely to become the most important sources of energy. In this context, it is hardly surprising that small, decentralized wind turbines are becoming increasingly important.
Learning Efficient Representations for Sequence Retrieval

Background In many domains, the most natural representation for data is as sequences of feature vectors. For example, in speech recognition, recorded utterances are typically transformed into series of vectors which describe the frequency content over short periods of time [1]. Similarly, in natural language processing tasks, sentences are often represented as sequences of vectors where each word corresponds to a unique vector [2]. Many off-the-shelf machine learning approaches assume that feature vectors are independent, so modeling the sequential nature of these representations often necessitates special treatment.
Specification-Driven Predictive Business Process Monitoring ( Technical Report )

Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Thus, different from previous studies, our approach enables us to deal with various kinds of prediction tasks based on the given specification. A prototype implementing our approach has been developed and experiments using a real-life event log have been conducted.
Gliosarcoma with Prominent Leiomyosarcomatous Differentiation : Case Report

4 Objective and importance: Gliosarcoma is a rare variant of glioblastoma multiforme that calls for the simultaneous presence of a gliomatous differentiation and a sarcomatous differentiation. A gliosarcoma with a sarcomatous differentiation that corresponds to a leiomyosarcoma has not previously been presented. This report presents an exceptional gliosarcoma with a prominent leiomyosarcomatous differentiation. Clinical presentation: A 56 year-old man presented with weakness on the right arm and right leg that had progressed over one week. His physical examination revealed marked right-sided hemiparesis. A left-sided mass lesion was observed on magnetic resonance imaging that involved the surface of the parasagittal aspect of the left frontal lobe of the brain and that was associated with intense edema. Intervention: The patient underwent a craniotomy with resection of the mass lesion and was diagnosed as having a gliosarcoma with a prominent leiomyosarcomatous differentiation. Based on the imminent risk of relapse, the decision was made to proceed with radiation therapy. He suffered a relapse at nine months following the diagnosis, declined salvage treatment and died within one month. Conclusion: Although an exceptional gliosarcoma with a prominent leiomyosarcomatous differentiation might be treated with surgery and radiation therapy, the outcome remains dismal.
Electrospun 3 D multi-scale fibrous scaffold for enhanced human dermal fibroblasts infiltration

Electrospun polymeric nanofibrous scaffold possesses significant potential in the field of tissue engineering due to its extracellular matrix mimicking topographical features that modulate a variety of key cellular activities. However, traditional two-dimensional (2D) electrospun scaffolds are generally close-packed fiber mats which prohibit cell infiltration and proliferation. Consequently, the applications of electrospun scaffolds in regenerative medicine are limited. In this study, we detail the use of a needle collector to fabricate three-dimensional (3D) electrospun poly-ε-caprolactone (PCL) scaffolds with multi-scale fiber dimensions. The resultant pore size is 4 times larger than conventional 2D electrospun scaffolds with interweaving micro (3.3 ± 0.6 μm) and nano (240 ± 50 nm) fibers. The scaffold was surface modified by grafting with gelatin molecules. It was found that surface modification significantly improved human dermal fibroblasts (HDFs) cell infiltration throughout the 3D multi-scale scaffold. Even after an extended culture period of up to 28 days, cell proliferation was well supported in the surface-modified 3D multi-scale scaffold as confirmed by Ki67 staining. Extracellular matrix proteins secreted by the HDFs was evident on the 3D multi-scale PCL scaffold showing promising potential to facilitate tissue regeneration, in particular dermal tissue engineering.
Working Paper 01 / 2010 DYNAMIC CORRELATION ANALYSIS OF FINANCIAL SPILLOVER TO ASIAN AND LATIN AMERICAN MARKETS IN GLOBAL FINANCIAL TURMOIL Prepared by

This paper investigates the spillover of financial crises by studying the dynamics of correlation between eleven Asian and six Latin American stock markets vis-à-vis the US stock market. A regional factor that drives common movements of stock markets in each region is identified for the period from 1993 to early 2009. We then estimate the time-varying volatility correlation between the regional factor and the US stock market by an asymmetric dynamic conditional correlation model. We find that there is a significant rise in the estimated time-varying correlation in the period from August 2007 to March 2009, suggesting evidence of contagion from the US stock market to markets in the two regions during the global financial turmoil. The magnitude of the contagion effect to both regions in the global financial crisis is very similar, albeit their different economic, political and institutional characteristics. On the other hand, we find no evidence of having contagion from the US to the Asian region during the Asian financial crisis in 1997 and 1998 as expected, since the crisis was originated locally. JEL Classification Numbers: F30; G01; G15; G12
Aflatoxin B 1-induced mitotic recombination in L 5178 Y mouse lymphoma cells

Verena Preisler, William J.Caspary2,3, Florian Hoppe, Rudolf Hagen and Helga Stopper1 Department of Oto-Rhinolaryngology and 1Department of Toxicology, University of Würzburg, Versbacher Straβe 9, 97078 Würzburg, Germany and 2National Institutes of Health, Research Triangle Park, NC 27709, USA Aflatoxin B1 is a human hepatocarcinogen. It is also a known point mutagen in bacteria and mammalian cells. This mutagenic activity may be at least partly responsible for its carcinogenic activity. However, recent studies show that aflatoxin B1 induces mitotic recombination in the yeast Saccharomyces cerevisiae. Because numerous reports have implicated mitotic recombination in mechanisms leading to carcinogenesis and because no one has shown that aflatoxin B1 induces recombination in mammalian cells, we decided to examine the ability of aflatoxin B1 to induce recombination in a mammalian cell line. We used a combination of methods, analysis for loss of heterozygosity and whole chromosome in situ hybridization, to identify mechanisms of chromosome mutation, including mitotic recombination in the mammalian L5178Y mouse lymphoma cell system. Our experiments revealed that mitotic recombination caused ~60% or more of the aflatoxin B1-induced mutagenic lesions in this cell system. Thus, mitotic recombination plays an important role in aflatoxin B1-induced mutagenesis in mammalian cells and possibly in chemically induced mutagenesis and carcinogenesis. This work suggests that multiple genetic lesions may be involved in aflatoxin B1-induced pathology.
Keyphrase Extraction with Multi-Objective Genetic Algorithms

Keyphrases have been used extensively in IR systems to facilitate information exchange, organize information and assist information retrieval. Automation of keyphrase generation is essential for the timely creation of keyphrases for large repositories in new domains where previous thesauri do not exist or for metacollections in which keyphrases that are meaningful across disparate collections are needed. In this paper we propose an automated keyphrase extraction algorithm using a nondominated sorting multi-objective genetic algorithm. The “clumping” property of keyphrases is used to judge the appropriateness of a phrase and is quantified by a condensation clustering measure proposed by Bookstein. The objective is to find the smallest phrase set that has the best precision, as measured by average condensation clustering. Keyphrases were retrieved from a collection of design conference papers and the results were presented to domain experts for evaluation. Ninety percent of the generated phrases were deemed appropriate for use in a thesaurus for engineering design.
Population Synthesis Meets Deep Generative Modelling

Agent-based transport models depend to a high degree on the formation of the underlying population. Models and methods for generating such populations, possibly under constraints to reflect future margins, are commonly referred to as “population synthesis” models. Historically, different approaches have been proposed, ranging from deterministic approaches, such as the Iterative Proportional Fitting (IPF) algorithm (Deming and Stephan, 1940; Rich and Mulalic, 2012), to simulation based approaches of the Markov Chain Monte Carlo (MCMC) type (Farooq et al., 2013). Often, matrix fitting methods such as the IPF has been used in combination with postsimulation based methods in order to translate prototypical individuals into true micro agents. While the existing models are capable of producing acceptable results for agents with relative few socioeconomic and spatial characteristics, these methods do not scale well when the dimensionality of the underlying distribution becomes large. As a result, in many cases, these methods are not able to accommodate the increasing need for more dimensions that result from, e.g. smaller zones, the combination of household-based and individual-based synthesis and more detailed variables in general. In this paper, we propose a different approach to population synthesis based on generative models from the deep learning framework. Contrarily to existing methods, these new methods are scalable and can handle a very large number of both numerical and categorical attributes at the same time.
Multi-level Context Response Matching in Retrieval-Based Dialog Systems

We present our work on the Dialog System Technology Challenges 7 (DSTC7). We participated in Track 1 on sentence selection which evaluates response retrieving in dialog systems on more realistic test scenarios compared to the stateof-the-art evaluations. Our proposed dialog system matches the context with the best response by computing their semantic similarity on the word and sequence levels. Evaluation results on the provided datasets show the effectiveness of our system by achieving higher performance compared to the provided baseline system. Our system enjoys the advantages of its simple and end-to-end architecture making its training and adaptation to other domains easier.
Structural and functional evidence of high specificity of Cbf 5 for ACA trinucleotide

Cbf5 is the catalytic subunit of the H/ACA small nucleolar/Cajal body ribonucleoprotein particles (RNPs) responsible for site specific isomerization of uridine in ribosomal and small nuclear RNA. Recent evidence from studies on archaeal Cbf5 suggests its second functional role in modifying tRNA U55 independent of guide RNA. In order to act both as a stand-alone and a RNP pseudouridine synthase, Cbf5 must differentiate features in H/ACA RNA from those in tRNA or rRNA. Most H/ACA RNAs contain a hallmark ACA trinucleotide downstream of the H/ACA motif. Here we challenged an archaeal Cbf5 (in the form of a ternary complex with its accessory proteins Nop10 and Gar1) with T-stem–loop RNAs with or without ACA trinucleotide in the stem. Although these substrates were previously shown to be substrates for the bacterial stand-alone pseudouridine synthase TruB, the Cbf5-Nop10-Gar1 complex was only able to modify those without ACA trinucleotide. A crystal structure of Cbf5Nop10-Gar1 trimer bound with an ACA-containing T-stem–loop revealed that the ACA trinucleotide detracted Cbf5 from the stand-alone binding mode, thereby suggesting that the H/ACA RNP-associated function of Cbf5 likely supersedes its stand-alone