Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep optical observations of the supernova remnants G 126.2+1.6, G 59.8+1.2 and G 54.4-0.3

Optical CCD imaging and spectroscopic observations of three supernova remnants are presented. Optical emission from G 54.4−0.3 and G 59.8+1.2 is detected for the first time, while the first flux calibrated CCD images of the supernova remnant G 126.2+1.6 were performed in the optical emission lines of Hα + [N ], [O  ]a nd [S]. A mixture of filamentary and diffuse structures is observed in G 54.4−0.3 and G 59.8+1.2, mainly in Hα + [N ], while the deep optical images of G 126.2+1.6 reveal several new filamentary and diffuse structures inside the extent of the remnant as defined by its known radio emission. In all cases, the radio emission is found to be well correlated with the optical filaments. [O ] emission was not detected at G 54.4−0.3 and G 59.8+1.2 while in G 126.2+1.6, significant morphological differences between the low and medium ionization images are present suggesting incomplete shock structures. Deep long-slit spectra were taken at different positions of the remnants. Both the flux calibrated images and the long-slit spectra clearly show that the emission originates from shock-heated gas, while some spectra of G 126.2+1.6 are characterized by large [O ]/Hβ ratios. This remnant’s [O ] flux suggests shock velocities into the interstellar “clouds” between 100 and 120 km s −1 , while the [O ] absence in the other two remnants indicates slower shock velocities. For all remnants, the [S ]λλ 6716/6731 ratio indicates electron densities below 600 cm −3 with particularly low densities for G 54.4−0.3 (below 50 cm −3 ). Finally, the Hα emission has been measured to be between 3.0 to 15.2 × 10 −17 erg s −1 cm −2 arcsec −2 ,3 . 2× 10 −17 erg s −1 cm −2 arcsec −2 and between 6.5 to 16.8 × 10 −17 erg s −1 cm −2 arcsec −2 for G 54.4−0.3, G 59.8+1.2 and G 126.2+1.6, respectively.
Analogical-based Bayesian Optimization

Some real-world problems revolve to solve the optimization problem \max_{x\in\mathcal{X}}f\left(x\right) where f\left(.\right) is a black-box function and X might be the set of non-vectorial objects (e.g., distributions) where we can only define a symmetric and non-negative similarity score on it. This setting requires a novel view for the standard framework of Bayesian Optimization that generalizes the core insightful spirit of this framework. With this spirit, in this paper, we propose Analogical-based Bayesian Optimization that can maximize black-box function over a domain where only a similarity score can be defined. Our pathway is as follows: we first base on the geometric view of Gaussian Processes (GP) to define the concept of influence level that allows us to analytically represent predictive means and variances of GP posteriors and base on that view to enable replacing kernel similarity by a more genetic similarity score. Furthermore, we also propose two strategies to find a batch of query points that can efficiently handle high dimensional data.
(Ferrocenylmethyl)trimethylammonium perchlorate

The asymmetric unit of the title complex, [Fe(C5H5)(C9H15N)]ClO4, contains one discrete (ferrocenylmethyl)trimethylammonium cation and one perchlorate anion. The anion is disordered over two sets of sites, with refined occupancies of 0.776 (8) and 0.224 (8). The distances from the Fe atom to the centroids of the unsubstituted and substituted cyclopentadienyl (Cp) rings are 1.650 (1) and 1.640 (1) Å, respectively. The Cp rings form a dihedral angle of 2.66 (3)°.
Automated support for combinational creativity in requirements engineering

Requirements engineering (RE), framed as a creative problem solving process, plays a key role in innovating more useful and novel requirements and improving a software system's sustainability. Existing approaches, such as creativity workshops and feature mining from web services, facilitate creativity by exploring a search space of partial and complete possibilities of requirements. To further advance the literature, we support creativity from a combinational perspective, i.e., making unfamiliar connections between familiar possibilities of requirements. In particular, we propose a novel framework that extracts familiar ideas from the requirements and stakeholders' comments using topic modeling and applies part-of-speech tagging to obtain unfamiliar idea combinations. We apply our framework on two large open source software systems and further report a human subject evaluation. The results show that our framework complements existing approaches by generating original and relevant requirements in an automated manner.
Is Demography Destiny? Application of Machine Learning Techniques to Accurately Predict Population Health Outcomes from a Minimal Demographic Dataset

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.
Impact of Inner Parameters and Overlay Structure on the Performance of BitTorrent

In this paper we adopt a simulation approach to study the performance of the BitTorrent protocol in terms of the entropy that qualifies a torrent and the structure of the overlay used to distribute the content. We find that the entropy of a torrent, defined as the diversity that characterizes the distribution of pieces of the content, plays an important role for the system to achieve optimal performance. We then relate the performance of BitTorrent with the characteristics of the distribution overlay built by the peers taking part in the torrent. Our results show that the number of connections a given peer maintains with other peers and the fraction of those connections initiated by the peer itself are key factors to sustain a high entropy, hence an optimal system performance. Those results were obtained for a realistic choice of torrent sizes and system parameters, under the assumption of a flash-crowd peer arrival pattern.
Five-year change of periodontal diseases in the Study of Health in Pomerania (SHIP).

AIM To assess the extent of remission and progression of periodontal diseases among adults in a Northeast German Region. MATERIALS AND METHODS The Study of Health in Pomerania (SHIP) is a population-based cohort study with baseline examinations conducted in 1997-2001 and 5-year-follow-up in 2002-2006. The study comprised 2558 subjects aged 20-81 years. Periodontal status was assessed by probing depth (PD) and attachment level (AL), measured at four surfaces per tooth, half-mouth. An event was defined as AL progression or remission of ≥3 mm on ≥2 sites. RESULTS Average change in mean AL (mean PD) was 0.10 mm (-0.05 mm) during follow-up, equalling 0.02 mm/year (-0.01 mm/year). Adjusted for gender and follow-up time, change in mean AL was significantly higher in 20-29- and 60-69-year-old patients compared with 50-59-year-old patients (p < 0.05). 6.5 (7.5) events of progression (remission) in AL per 100 person-years were observed. In multivariate models, risk factors for progression comprised current smoking, middle or low education, being single or divorced and diabetes (p < 0.05). Enhanced progressive attachment loss seen in 20-29- and 60-81-year-old patients was explained through unfavourable risk factors distributions. CONCLUSIONS Periodontal progression was moderate. Periodontal prophylaxis and motivation to reduce risk factors should especially be addressed to patients having unfavourable risk factor distributions.
Lab-on-chips for manipulation of small-scale organisms to facilitate imaging of neurons and organs

Caenorhabditis elegans (C. elegans) and Drosophila melanogaster (D. melanogaster) are widely-used model organisms for neurological and cardiac studies due to their simple neuronal (302 neurons in C. elegans) and cardiac (simple tubular organ in D. melanogaster) systems. However, their small sizes and continuous mobility impede their precise and timely manipulation, hence, limiting the assays that can be done using conventional manual methods. This has resulted in a need for technologies that allow multidirectional manipulation of model organisms to enable studies on target neurons and organs throughout the body. By integration of rotatable glass capillaries with pneumatic suction into microfluidic devices, we propose novel Lab-on-Chips for multi-directional manipulation and imaging of small organisms. These hybrid Lab-on-Chips can facilitate the processes of animal handling and stimuli control, using modules for single-organism selection, orientation, imaging and chemical stimulation. We show the applications of these hybrid microdevices in manipulating C. elegans for neuronal imaging (neuron-level assay) or D. melanogaster for heart screening (organ level assay). These devices can enhance the throughput of biological assays on whole-organisms and find their applications in drug discovery and toxicology.
Disambiguation of imprecise input with one-dimensional rotational text entry

We introduce a distinction between disambiguation supporting continuous versus discrete ambiguous text entry. With continuous ambiguous text entry methods, letter selections are treated as ambiguous due to expected imprecision rather than due to discretized letter groupings. We investigate the simple case of a one-dimensional character layout to demonstrate the potential of techniques designed for imprecise entry. Our rotation-based sight-free technique, Rotext, maps device orientation to a layout optimized for disambiguation, motor efficiency, and learnability. We also present an audio feedback system for efficient selection of disambiguated word candidates and explore the role that time spent acknowledging word-level feedback plays in text entry performance. Through a user study, we show that despite missing on average by 2.46--2.92 character positions, with the aid of a maximum a posteriori (MAP) disambiguation algorithm, users can average a sight-free entry speed of 12.6wpm with 98.9% accuracy within 13 sessions (4.3 hours). In a second study, expert users are found to reach 21wpm with 99.6% accuracy after session 20 (6.7 hours) and continue to grow in performance, with individual phrases entered at up to 37wpm. A final study revisits the learnability of the optimized layout. Our modeling of ultimate performance indicates maximum overall sight-free entry speeds of 29.0wpm with audio feedback, or 40.7wpm if an expert user could operate without relying on audio feedback.
Hibernation during Hypoxia in Cardiomyocytes

During myocardial hibernation, decreases in coronary perfusion elicit inhibition of contraction, suggesting that energy demand is attenuated. We previously found an inhibition of contraction and O2 consumption during hypoxia (3% O2; PO2 = 20 torr for >2 h) in cardiomyocytes, which was reversible after reoxygenation. This study sought to determine whether mitochondria function as cellular O2 sensors mediating this response. Embryonic cardiomyocytes were studied under controlled O2 conditions. Hypoxia produced no acute decrease in mitochondrial potential as assessed using tetramethylrhodamine ethylester (TMRE). Cellular [ATP] was preserved throughout hypoxia, as assessed using the probe Magnesium Green. Thus, ATP synthesis and utilization remained closely coupled. Cells adapted to hypoxia for >2 h exhibited a 4% increase in mitochondrial potential upon reoxygenation, suggesting that a partial inhibition of cytochrome c oxidase had existed. To test whether the oxidase serves as an O2sensor, azide was administered (1 mm) to simulate the effects of hypoxia by lowering the V max of the oxidase. The effects of azide on contraction and mitochondrial potential mimicked the response to hypoxia. We conclude that partial inhibition of cytochrome oxidase during hypoxia allows mitochondria to function as the O2 sensor mediating the decreases in ATP utilization and O2 consumption during hypoxia.
Combining Dynamic Reward Shaping and Action Shaping for Coordinating Multi-agent Learning

Coordinating multi-agent reinforcement learning provides a promising approach to scaling learning in large cooperative multi-agent systems. It allows agents to learn local decision policies based on their local observations and rewards, and, meanwhile, coordinates agents' learning processes to ensure the global learning performance. One key question is that how coordination mechanisms impact learning algorithms so that agents' learning processes are guided and coordinated. This paper presents a new shaping approach that effectively integrates coordination mechanisms into local learning processes. This shaping approach uses two-level agent organization structures and combines reward shaping and action shaping. The higher-level agents dynamically and periodically produce the shaping heuristic knowledge based on the learning status of the lower-level agents. The lower-level agents then uses this knowledge to coordinate their local learning processes with other agents. Experimental results show our approach effectively speeds up the convergence of multi-agent learning in large systems.
A Case Report of Primary Pericardial Malignant Mesothelioma Treated with Pemetrexed and Cisplatin

Primary pericardial malignant mesothelioma (PPM) is a very rare malignancy, with an incidence of less than 0.002% and represents less than 5% of all mesotheliomas. The cause of pericardial mesothelioma is uncertain that differ from pleural mesothelioma which is associated with asbestos exposure. This malignancy is terribly aggressive and has very poor prognosis with less than six months of overall survival. We present a case of a 71-year-old woman who was diagnosed with cardiac tamponade caused by PPM and received chemotherapy with pemetrexed and cisplatin for six months. During two years she was alive without disease progression. To better understand the clinical, pathologic features and treatment outcome of this entity, we reviewed 23 cases described in the English literature from 2009, together with our case, provided a total of 24 cases. Based on this review, we suggest that PPM must be considered in patients who have unexplained massive pericardial effusion and recommend chemotherapy with pemetrexed and cisplatin for the better outcome of PPM.
Genomics, morphogenesis and biophysics: Triangulation of Purkinje cell development

The cerebellar Purkinje cells (P-cells) comprise an organelle that is suitable for combined analysis by morphology and genomics, using biophysical tools. In some unknown way, genomic information specifies the development of P-cells. One of us (AJP) has previously proposed that fractal processes associated with DNA are in a causal relation to the fractal properties of organelles such as P-cells (FractoGene, 2002, patent pending). This fractal postulate predicts that the dendritic arborization of P-cells will be less complex in lower order vertebrates. The prediction can be tested by systematic comparative neuroanatomy of the P-cell in species for which genome sequences permit inter-species comparison. TheFugu rubripes (Fugu),Danio rerio (Danio) and other species are lower order vertebrates for which genome sequences are available and tests could be conducted. Consistent with the fractal prediction, P-cell dendritic arbor is primitive in Fugu, being much less complex than inMus musculus and inHomo sapiens. Genomic analysis readily identified PEP19/Pcp4, Calbindin-D28k, and GAD67 genes in Fugu and in Danio that are closely associated with P-cells inCanis familiaris, Rattus norvegicus, Mus musculus andHomo sapiens. Gene L7/Pcp2 exhibits strongest association with P-cells in higher vertebrates. L7/Pcp2 shows strong protein residue homology with genes greater than 600 residues and including 2–3 GoLoco domains, designated as having G protein signaling modulator function (AGS3-like proteins). Fugu has a short gene with a single GoLoco domain, but it has greatest homology with the AGS3-like proteins. No similar short gene is present in Danio or in Xenopus. Classical L7/Pcp2 is only detected in higher vertebrates, suggesting that it may be a marker of more recent evolutionary development of cerebellar P-cells. We expect that a new generation of data mining tools will be required to support recursive fractal geometrical, combinatorial, and neural network models of the genomic basis of morphogenesis.
A Study of Language Modeling for Chinese Spelling Check

Chinese spelling check (CSC) is still an open problem today. To the best of our knowledge, language modeling is widely used in CSC because of its simplicity and fair predictive power, but most systems only use the conventional n-gram models. Our work in this paper continues this general line of research by further exploring different ways to glean extra semantic clues and Web resources to enhance the CSC performance in an unsupervised fashion. Empirical results demonstrate the utility of our CSC system.
Recognition of prokaryotic promoters based on a novel variable-window Z-curve method

Transcription is the first step in gene expression, and it is the step at which most of the regulation of expression occurs. Although sequenced prokaryotic genomes provide a wealth of information, transcriptional regulatory networks are still poorly understood using the available genomic information, largely because accurate prediction of promoters is difficult. To improve promoter recognition performance, a novel variable-window Z-curve method is developed to extract general features of prokaryotic promoters. The features are used for further classification by the partial least squares technique. To verify the prediction performance, the proposed method is applied to predict promoter fragments of two representative prokaryotic model organisms (Escherichia coli and Bacillus subtilis). Depending on the feature extraction and selection power of the proposed method, the promoter prediction accuracies are improved markedly over most existing approaches: for E. coli, the accuracies are 96.05% (σ70 promoters, coding negative samples), 90.44% (σ70 promoters, non-coding negative samples), 92.13% (known sigma-factor promoters, coding negative samples), 92.50% (known sigma-factor promoters, non-coding negative samples), respectively; for B. subtilis, the accuracies are 95.83% (known sigma-factor promoters, coding negative samples) and 99.09% (known sigma-factor promoters, non-coding negative samples). Additionally, being a linear technique, the computational simplicity of the proposed method makes it easy to run in a matter of minutes on ordinary personal computers or even laptops. More importantly, there is no need to optimize parameters, so it is very practical for predicting other species promoters without any prior knowledge or prior information of the statistical properties of the samples.
HARMONI: Context-aware Filtering of Sensor Data for Continuous Remote Health Monitoring

A promising architecture for remote healthcare monitoring involves the use of a pervasive device (such as a cellular phone), which aggregates data from multiple body-worn medical sensors and transmits the data to the backend. Unfortunately, the volume of data generated by increasingly sophisticated continuously- active sensors can overwhelm the resources on the mobile device. We propose imbuing the mobile device with the intelligence to perform context-aware filtering of sensor data streams in order to reduce transmissions in cases where the observed data corresponds to the norm expected by the system in a given context. To investigate the efficacy of this technique, we implemented the HARMONI middleware on a mobile device, and used it to collect real sensor data from users. Our experiments demonstrate that context-aware filtering can reduce the uplink bandwidth requirements of the system by up to 72%.
Discontinuation of antidepressant medication after mindfulness-based cognitive therapy for recurrent depression: randomised controlled non-inferiority trial

Background Mindfulness-based cognitive therapy (MBCT) and maintenance antidepressant medication (mADM) both reduce the risk of relapse in recurrent depression, but their combination has not been studied. Aims To investigate whether MBCT with discontinuation of mADM is non-inferior to MBCT+mADM. Method A multicentre randomised controlled non-inferiority trial (ClinicalTrials.gov: NCT00928980). Adults with recurrent depression in remission, using mADM for 6 months or longer (n = 249), were randomly allocated to either discontinue (n = 128) or continue (n = 121) mADM after MBCT. The primary outcome was depressive relapse/recurrence within 15 months. A confidence interval approach with a margin of 25% was used to test non-inferiority. Key secondary outcomes were time to relapse/recurrence and depression severity. Results The difference in relapse/recurrence rates exceeded the non-inferiority margin and time to relapse/recurrence was significantly shorter after discontinuation of mADM. There were only minor differences in depression severity. Conclusions Our findings suggest an increased risk of relapse/recurrence in patients withdrawing from mADM after MBCT.
High Performance Query Answering over DL-Lite Ontologies

Current techniques for query answering over DL-Lite ontologies have severe limitations in practice, since they either produce complex queries that are inefficient during execution, or require expensive data pre-processing. In light of this, we present two complementary sets of results that aim at improving the overall peformance of query answering systems. We show how to create ABox repositories that are complete w.r. t. a significant portion of DL-Lite TBoxes, including those expressed in RDFS, but where the data is not explicitly expanded. Second, we show how to characterize ABox completeness by means of dependencies, and how to use these and equivalence to optimize DL-Lite TBoxes. These results allow us to reduce the cost of query rewriting, often dramatically, and to generate highly efficient queries. We have implemented a novel system for query answering over DL-Lite ontologies that incorporates these techniques, and we present a series of data-intensive evaluations that show their effectiveness.
Quiet Planting in the Locked Constraint Satisfaction Problems

We study the planted ensemble of locked constraint satisfaction problems. We describe the connection between the random and planted ensembles. The use of the cavity method is combined with arguments from reconstruction on trees and the first and second moment considerations. Our main result is the location of the hard region in the planted ensemble. In a part of that hard region, instances have with high probability a single satisfying assignment.