Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaling up Structured Multi-Label Prediction using Discriminative Mean Field Networks

Multi-label classification is an important task in many modern machine learning applications. Accurate methods model the correlations and relationships between labels, either by assuming a low-dimensional embedding of the labels or a graph structure of label dependencies. While such interactions can be achieved using feed-forward predictors, problems with tight coupling between labels are better posed as structured prediction problems. Unfortunately, prior applications of graphical models to multi-label classification scale poorly. In response, we introduce discriminative mean field networks, an iterative structured prediction technique applicable to substantially larger label sets. We employ a deep architecture to define an energy function of candidate labels, and form predictions using backpropagation to iteratively optimize the energy with respect to the labels. This deep architecture captures dependencies between labels that would lead to completely intractable graphical models, and enables a form of structure learning by automatically learning discriminative features of the structured output. The technique is effective on a variety of benchmarks, and generalizes easily to other structured prediction applications.
High Charge Carrier Mobilities and Lifetimes in Organolead Trihalide Perovskites

Organolead trihalide perovskites are shown to exhibit the best of both worlds: charge-carrier mobilities around 10 cm2 V−1 s−1 and low bi-molecular charge-recombination constants. The ratio of the two is found to defy the Langevin limit of kinetic charge capture by over four orders of magnitude. This mechanism causes long (micrometer) charge-pair diffusion lengths crucial for flat-heterojunction photovoltaics.
Neolithic transition in Europe: the radiocarbon record revisited

Understanding the introduction of farming and the adoption of Neolithic culture continues to be a major research objective in Europe. The authors make use of a new database of radiocarbon dates from Mesolithic and Neolithic sites to map the transition. While the overall effect is still a diffusion into Europe from the south-east, detailed spatial analysis reveals fascinating local variations: in some places change was rapid, and one population replaced another, in others it was gradual and owed to incoming ideas rather than people.
Applying Lyapunov Exponents in heart rate time series to identify the Anaerobic Threshold in healthy men

During the last years a lot of papers have appeared dealing with the applications to physiological time series of some parameters originally found in Dynamical Systems. In this work, we looked for the Largest Lyapunov Exponents (LLE) in heart rate time series of healthy men in order to verify if it was possible to find the Anaerobic Threshold (AT) in a non-invasive way using just the time series and the LLE extracted from them. The work was undertaken in a group of 10 healthy individuals using a conventional electrocardiogram. The series of RR intervals, lasting for twelve minutes, were obtained in rest (supine and seated positions) and in a discontinuous protocol of dynamic exercise in seated position. The LLE were computed using the TISEAN system. The results were compared to those obtained by the measurement of AT using an autoregressive integrated moving-average model (ARIMA) and using the Kolmogorov-Sinai Entropy (KS). The Spearman test (rs) with α = 5% applied to these three methods gave: a) for KS × ARIMA: p <; 0.01, rs = 0.93; b) LLE × KS: p <; 0.01, rs = 0.94 and c) LLE × ARIMA: p <; 0.01, rs = 0.93. These results are very expressive concerning the use of LLE to find the AT.
Adult image content filtering: A statistical method based on Multi-Color Skin Modeling

Automatic skin detection is a key enabler of various imaging applications, such as face detection, human tracking, and adult content filtering. In 1996, the first paper on identifying nude pictures was published. Since then, different researchers argue different color models to be the best choice for skin detection. But, to the best our knowledge, no significant work has been reported previously that attempted to use more than one color model and evaluate the performance for recognizing adult contents. In this paper, a simple statistical framework for recognizing adult images based on an MCSM (Multi-Color Skin Model) is described. From a high level, our approach works in two steps. First, skin regions in an input image are detected using the MCSM. Then these suspected regions are fed into a specialized geometrical analyzer that attempts to assemble a human figure using simple geometric shapes derived from human body structure. Quantitative evaluation shows that our method compares favorably with the state-of-the-art methods in terms of detection rate and false alarm, while reducing the computational complexity by a factor of 1/6 with respect to the Forsyth's method.
African Easterly Wave Variability and Its Relationship to Atlantic Tropical Cyclone Activity

Abstract Automatic tracking of vorticity centers in European Centre for Medium-Range Weather Forecasts analyses has been used to develop a 20-yr climatology of African easterly wave activity. The tracking statistics at 600 and 850 mb confirm the complicated easterly wave structures present over the African continent. The rainy zone equatorward of 15°N is dominated by 600-mb activity, and the much drier Saharan region poleward of 15°N is more dominated by 850-mb activity. Over the Atlantic Ocean there is just one storm track with the 600- and 850-mb wave activity collocated. Based on growth/decay and genesis statistics, it appears that the 850-mb waves poleward of 15°N over land generally do not get involved with the equatorward storm track over the ocean. Instead, there appears to be significant development of 850-mb activity at the West African coast in the rainy zone around (10°N, 10°W), which, it is proposed, is associated with latent heat release. Based on the tracking statistics, it has been shown th.
Monte-Carlo tree search in production management problems

Classical search algorithms rely on the existence of a sufficiently powerful evaluation function for non-terminal states. In many task domains, the development of such an evaluation function requires substantial effort and domain knowledge, or is not even possible. As an alternative in recent years, Monte-Carlo evaluation has been succesfully applied in such task domains. In this paper, we apply a search algorithm based on Monte-Carlo evaluation, MonteCarlo Tree Search, in the task domain of production management problems. These can be defined as single-agent problems which consist of selecting a sequence of actions with side effects, leading to high quantities of one or more goal products. They are challenging and can be constructed with highly variable difficulty. Earlier research yielded an offline learning algorithm that leads to good solutions, but requires a long time to run. We show that Monte-Carlo Tree Search leads to a solution in a shorter period of time than this algorithm, with improved solutions for large problems. Our findings can be generalized to other task domains.
Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner’s Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.
Impact of Soil Heavy Metal Pollution on Food Safety in China

Food safety is a major concern for the Chinese public. This study collected 465 published papers on heavy metal pollution rates (the ratio of the samples exceeding the Grade II limits for Chinese soils, the Soil Environmental Quality Standard-1995) in farmland soil throughout China. The results showed that Cd had the highest pollution rate of 7.75%, followed by Hg, Cu, Ni and Zn, Pb and Cr had the lowest pollution rates at lower than 1%. The total pollution rate in Chinese farmland soil was 10.18%, mainly from Cd, Hg, Cu, and Ni. The human activities of mining and smelting, industry, irrigation by sewage, urban development, and fertilizer application released certain amounts of heavy metals into soil, which resulted in the farmland soil being polluted. Considering the spatial variations of grain production, about 13.86% of grain production was affected due to the heavy metal pollution in farmland soil. These results many provide valuable information for agricultural soil management and protection in China.
A TEST OF PRE-MAIN-SEQUENCE LITHIUM DEPLETION MODELS

Despite the extensive study of lithium depletion during pre-main-sequence (PMS) contraction, studies of individual stars show discrepancies between ages determined from the Hertzsprung–Russell (H–R) diagram and ages determined from lithium depletion, indicating open questions in the PMS evolutionary models. To further test these models, we present high-resolution spectra for members of the β Pictoris Moving Group (BPMG), which is young and nearby. We measure equivalent widths of the 6707.8 Å Li i line in these stars and use them to determine lithium abundances. We combine the lithium abundance with the predictions of PMS evolutionary models in order to calculate a lithium depletion age for each star. We compare this age to the age predicted by the H–R diagram of the same model. We find that the evolutionary models underpredict the amount of lithium depletion for the BPMG given its nominal H–R diagram age of ∼12 Myr, particularly for the mid-M stars, which have no observable Li i line. This results in systematically older ages calculated from lithium depletion isochrones than from the H–R diagram. We suggest that this discrepancy may be related to the discrepancy between measured M-dwarf radii and the smaller radii predicted by evolutionary models.
Summation-type conditions for uniform asymptotic convergence in discrete-time systems: applications in identification

We establish summation-type conditions to ensure the uniform convergence of discrete-time systems, parameterized in the sampling time. The main results are analogous to previous results obtained in the domain of continuous-time systems and that we have referred to as "integral conditions". The sufficient conditions that we present here can also be interpreted as conditions for convergence of series. Our main results are also useful for design of sampled-data controllers via approximate models; for illustration, we present two results on control design reminiscent of common problems arising in identification and adaptive control.
Automatic speech recognition for acoustical analysis and assessment of cantonese pathological voice and speech

This paper describes the application of state-of-the-art automatic speech recognition (ASR) systems to objective assessment of voice and speech disorders. Acoustical analysis of speech has long been considered a promising approach to non-invasive and objective assessment of people. In the past the types and amount of speech materials used for acoustical assessment were very limited. With the ASR technology, we are able to perform acoustical and linguistic analyses with a large amount of natural speech from impaired speakers. The present study is focused on Cantonese, which is a major Chinese dialect. Two representative disorders of speech production are investigated: dysphonia and aphasia. ASR experiments are carried out with continuous and spontaneous speech utterances from Cantonese-speaking patients. The results confirm the feasibility and potential of using natural speech for acoustical assessment of voice and speech disorders, and reveal the challenging issues in acoustic modeling and language modeling of pathological speech.
A Language for the Support of Constraints in Temporal Active Databases

Complex applications in domains such as decision analysis and real time systems require a database that supports active enforcement of constraints with temporal aspects. In this paper we present a temporal active language for the support of constraints in temporal active databases. The language primitives are presented using examples and an EBNF, followed by a discussion of the language properties.
Impact of respiratory motion correction and spatial resolution on lesion detection in PET: a simulation study based on real MR dynamic data

The aim of this study is to investigate the impact of respiratory motion correction and spatial resolution on lesion detectability in PET as a function of lesion size and tracer uptake. Real respiratory signals describing different breathing types are combined with a motion model formed from real dynamic MR data to simulate multiple dynamic PET datasets acquired from a continuously moving subject. Lung and liver lesions were simulated with diameters ranging from 6 to 12 mm and lesion to background ratio ranging from 3:1 to 6:1. Projection data for 6 and 3 mm PET scanner resolution were generated using analytic simulations and reconstructed without and with motion correction. Motion correction was achieved using motion compensated image reconstruction. The detectability performance was quantified by a receiver operating characteristic (ROC) analysis obtained using a channelized Hotelling observer and the area under the ROC curve (AUC) was calculated as the figure of merit. The results indicate that respiratory motion limits the detectability of lung and liver lesions, depending on the variation of the breathing cycle length and amplitude. Patients with large quiescent periods had a greater AUC than patients with regular breathing cycles and patients with long-term variability in respiratory cycle or higher motion amplitude. In addition, small (less than 10 mm diameter) or low contrast (3:1) lesions showed the greatest improvement in AUC as a result of applying motion correction. In particular, after applying motion correction the AUC is improved by up to 42% with current PET resolution (i.e. 6 mm) and up to 51% for higher PET resolution (i.e. 3 mm). Finally, the benefit of increasing the scanner resolution is small unless motion correction is applied. This investigation indicates high impact of respiratory motion correction on lesion detectability in PET and highlights the importance of motion correction in order to benefit from the increased resolution of future PET scanners.
Assessing the Impact of Tissue Target Concentration Data on Uncertainty in In Vivo Target Coverage Predictions

Understanding pharmacological target coverage is fundamental in drug discovery and development as it helps establish a sequence of research activities, from laboratory objectives to clinical doses. To this end, we evaluated the impact of tissue target concentration data on the level of confidence in tissue coverage predictions using a site of action (SoA) model for antibodies. By fitting the model to increasing amounts of synthetic tissue data and comparing the uncertainty in SoA coverage predictions, we confirmed that, in general, uncertainty decreases with longitudinal tissue data. Furthermore, a global sensitivity analysis showed that coverage is sensitive to experimentally identifiable parameters, such as baseline target concentration in plasma and target turnover half‐life and fixing them reduces uncertainty in coverage predictions. Overall, our computational analysis indicates that measurement of baseline tissue target concentration reduces the uncertainty in coverage predictions and identifies target‐related parameters that greatly impact the confidence in coverage predictions.
Living with a Mother with Chronic Depression: To Tell or Not To Tell?

Background: While there is mounting evidence that children of mothers with depression are at risk for increased psychopathology and disturbed family relationships, it is not yet known what the children's lives are like growing up with their mothers' depression. The purpose of the study was to understand children's experiences of living with mothers who have depression: specifically, how these children understand disclosing their experiences to others. Method: Eight interviews were conducted with adult children of mothers with depression. Grounded theory methodology was used to collect and analyze the data. Results: We identified three types of disclosing: self-disclosing, first disclosing, and selective disclosing. As the children grew up and began to understand their mothers' differentness as an illness, then as depression, their ways of disclosing changed, as did what they disclosed, where, when, and for what purposes. Most significantly, what is disclosed changed remarkably over time, depending on the offspring's perceptions of their mother's problem, the safety of disclosing, and the purpose and anticipated consequences of disclosing. Conclusion: The results indicate that disclosing is not a single event but rather occurs in various forms, repeatedly, over time and in response to several factors.
Accuracy of CBCT, Digital Radiography and Cross-Sectioning for the Evaluation of Mandibular Incisor Root Canals

Introduction: The aim of this study was to compare the accuracy of cone-beam computed tomography (CBCT), digital radiography and tooth sectioning in evaluating root canal morphology of mandibular incisors in an in vitro setting. Methods and Materials: A total of 76 samples were imaged using CBCT, and digital radiography in straight and angled views. The samples were then sectioned at different distances from the apex for further visualization under stereomicroscope. The agreement between the observers was statistically analyzed by kappa correlation coefficient and the chi-square test. Results: The results showed that the majority of the samples had a single canal (Vertucci’s Type I). CBCT analysis reported more frequent multi-canal roots in comparison with the other techniques. In pairwise comparisons, the highest agreement was found between digital radiographic imaging and microscopic cross-sectioning both in terms of canal configuration and the number of root canals. Conclusion: None of the used imaging techniques per se could adequately show the exact internal anatomical configuration in accordance with the gold standard.
Living in the present: on-the-fly information processing in scalable web architectures

Today's social web platforms, such as Facebook, Twitter, Google+, and LinkedIn, increasingly have to process large volumes of user-generated data on the fly. As the role of such platforms shifts from being portals for largely historic data towards providing platforms for real-time data analytics, we observe that their architectures incrementally move from storage-centric designs, based on distributed data management technologies, towards event-based models exploiting queueing and stream processing systems. We believe that it is time to rethink fundamentally the software architecture for social web platforms and base them on a content-based communication model, that is explicitly designed to disseminate and partition incoming request flows on a cluster of servers. A content-based publish/subscribe system thus acts as a scalable and elastic, highly responsive data distribution backbone. By focusing on fresh data, such an architecture can optimize the routing of data to match the topology of the data center, dynamically adapt data flows to alleviate hot spots, and elastically scale to more servers when required by computationally expensive on-the-fly data analytics applications.
Responding rapidly to service level violations using virtual appliances

One of the key goals in the data center today is providing storage services with service-level objectives (SLOs) for performance metrics such as latency and throughput. Meeting such SLOs is challenging due to the dynamism observed in these environments. In this position paper, we propose dynamic instantiation of virtual appliances, that is, virtual machines with storage functionality, as a mechanism to meet storage SLOs efficiently. In order for dynamic instantiation to be realistic for rapidlychanging environments, it should be automated. Therefore, an important goal of this paper is to show that such automation is feasible. We do so through a caching case study. Specifically, we build the automation framework for dynamically instantiating virtual caching appliances. This framework identifies sets of interfering workloads that can benefit from caching, determines the cache-size requirements of workloads, non-disruptively migrates the application to use the cache, and warms the cache to quickly return to acceptable service levels. We show through an experiment that this approach addresses SLO violations while using resources efficiently.
Random projections weakly preserving the Hamming distance between words

Random projections in the Euclidean space reduce the dimensionality of the data approximately preserving the distances between points. In the hypercube it holds a weaker property: random projections approximately preserve the distances within a certain range. In this note, we show an analogous result for the metric space 〈 Σ, dH 〉 , where Σ is the set of words of length d on alphabet Σ and dH is the Hamming distance.
Competition, Coevolution and the Game of Tag

Tag is a children’s game based on symmetrical pursuit and evasion. In the experiments described here, control programs for mobile agents (simulated vehicles) are evolved based on their skill at the game of tag. A player’s fitness is determined by how well it performs when placed in competition with several opponents chosen randomly from the coevolving population of players. In the beginning, the quality of play is very poor. Then slightly better strategies begin to exploit the weaknesses of others. Through evolution, guided by competitive fitness, increasingly better strategies emerge over time.