Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust parameter extraction for decision support using multimodal intensive care data

Digital information flow within the intensive care unit (ICU) continues to grow, with advances in technology and computational biology. Recent developments in the integration and archiving of these data have resulted in new opportunities for data analysis and clinical feedback. New problems associated with ICU databases have also arisen. ICU data are high-dimensional, often sparse, asynchronous and irregularly sampled, as well as being non-stationary, noisy and subject to frequent exogenous perturbations by clinical staff. Relationships between different physiological parameters are usually nonlinear (except within restricted ranges), and the equipment used to measure the observables is often inherently error-prone and biased. The prior probabilities associated with an individual's genetics, pre-existing conditions, lifestyle and ongoing medical treatment all affect prediction and classification accuracy. In this paper, we describe some of the key problems and associated methods that hold promise for robust parameter extraction and data fusion for use in clinical decision support in the ICU.
The supportive distance learning environment: A study on the learning support needs of Malaysian online learners

This paper reports the process and findings of a qualitative research designed to explore the perception of a supportive distance learning environment and the needs of learners of an online distance learning Finance course offered by Universiti Teknologi MARA (UiTM), Malaysia. The focus of the research was the learners' own reflection of their personal, academic and social needs as they trudged into the online learning environment. Using a phenomenological approach on seven participants, this article demonstrates how online learners were able to explicate their needs through their narratives on their initial experience, the difficulties they faced and what they believe to be a supportive learning environment. It further reports the email correspondence that took place during which the data was continuously subjected to content analysis and participant validation. Building upon existing theories of adult learning (andragogy), and online learning, this research demonstrates how seven adult learners perceived their learning needs as online learners. The results shows the emerging concept of a supportive learning environment and themes of needs that revolve around (1)the instructor, (2) structured learning resources and (3) regulation of learning activities.
Cutting the Cord: A Corrective for World Navels in Cartography and Science

ABSTRACT A map is not its territory. Taking a map too seriously may lead to pernicious reification: map and world are conflated. As one family of cases of such reification, I focus on maps exuding the omphalos syndrome, whereby a centred location on the map is taken to be the world navel of, for instance, an empire. I build on themes from my book When Maps Become the World, in which I analogize scientific theories to maps, and develop the tools of assumption archaeology and integration platforms. Here I argue that excavating assumptions helps fill cartographic silences, showing the limitations of perspectives often at war. Furthermore, integrating perspectives permits resisting imperial central or master images. A worthwhile future project would be a repository of world-navel maps, critically annotated with cultural context and imperial information. Mutual understanding may result from such an integration platform, perhaps implemented online or in a museum.
Word Sense Induction and Disambiguation Using Principal Component Analysis and Latent Semantic Indexing

In this paper we present a statistical method using principal component analysis and latent semantic indexing to solve the problem of word sense induction and use the generated sense inventory to perform word sense disambiguation. We use standard co-occurrence graph algorithms and word dependency matrices (context words, dependency relations) and map them to a matrix. Then, we apply non-negative matrix factorization and principal component analysis on dimensions (latent factors) obtained from the training set. The intuition behind this is to merge dimensions that overlap in the semantic space (like computers and processors) and hence reinforcing their effect mutually to improve the disambiguation step. We work on the idea that each sense obtained in the induction process corresponds to a topical dimension. We extend this idea for each word to obtain a word’s most dominant sense. The framework is tested on the standard SEM-EVAL 2010 for WSI/WSD on which it produces state of the art results.
Task-Specific Performance Evaluation of UGVs: Case Studies at the IVFC

Performance evaluation is considered as an important part of the unmanned ground vehicle (UGV) development; it helps to discover research problems and improves driving safety. In this paper, a task-specific performance evaluation model of UGVs applied in the Intelligent Vehicle Future Challenge (IVFC) annual competitions is discussed. It is defined in functional levels with a formal evaluation process, including metrics analysis, metrics preprocessing, weights calculation, and a technique for order of preference by similarity to ideal solution and fuzzy comprehensive evaluation methods. IVFC 2012 is selected as a case study and overall performances of five UGVs are evaluated with specific analyzed autonomous driving tasks of environment perception, structural on-road driving, unstructured zone driving, and dynamic path planning. The model is proved to be helpful in IVFC serial competition UGVs performance evaluation.
A pruning algorithm for optimal diversified search

Given a number of possible sub-intents (also called subtopics) for a certain query and their corresponding search results, diversified search aims to return a single result list that could satisfy as many users' intents as possible. Previous studies have demonstrated that finding the optimal solution for diversified search is NP-hard. Therefore, several algorithms have been proposed to obtain a local optimal ranking with greedy approximations. In this paper, a pruned exhaustive search algorithm is proposed to decrease the complexity of the optimal search for the diversified search problem. Experimental results indicate that the proposed algorithm can decrease the computation complexity of exhaustive search without any performance loss.
Experiments on cross-language acoustic modeling

With the distribution of speech products all over the world, the portability to new target languages becomes a practical concern. As a consequence our research focuses on rapid transfer of LVCSR systems to other languages. In former studies we evaluated the performance if limited adaptation data is available. Particularly for very time constrained tasks and minority languages, it is even reasonable that no training data is available at all. In this paper we examine what performance can be expected in this scenario. All experiments are run in the framework of the GlobalPhone project which investigates LVCSR systems in 15 languages.
An eutrophication index for lowland sandy rivers in Mediterranean coastal climatic regions of Southern Africa

The eutrophication of waterways has become an endemic global problem. Nutrient enrichment from agriculture activities and waste water treatment plants are major drivers, but it remains unclear how lowland sandy rivers respond to eutrophication. The objective of this study was the development and verification of eutrophication index for sandy rivers (EISR) to prioritize nutrient enrichment river stretches caused by different land use activities that include point and nonpoint sources of nutrient enrich water. The Berg River drainage system in South Africa served as a case study area for this purpose during the dry seasons (December and January) of 2015 and 2016. In the initial EISR development phase, periphyton, benthic biomass (chl‐a mg m−2), and macroinvertebrate families were employed as benthic bioindicators of river bedforms, whereas in the second phase, physicochemical and abiotic variables were used as target indicator. Using a weight of support approach, the site receiving sewage effluent was categorize as heavily polluted whereas sites impacted by agriculture land use activities were polluted. The EISR that focuses strongly on benthic bioindicators, which are close to the transfer of nutrients and energy in the food web, showed a distinct difference between river bedform impacted by sewage effluent and agriculture none point source. A maximum benthic algae biomass of 110 mg m−2 chl‐a was recorded with higher sediment orthophosphate concentration at sewage‐impacted sites. The outcome of the proposed EISR showed that it can be employed as a decision support tool for eutrophication management of sandy rivers.
Sequential Adaptation of Radial Basis Function Neural Networks and its Application to Time-series Prediction

We develop a sequential adaptation algorithm for radial basis function (RBF) neural networks of Gaussian nodes, based on the method of successive F-Projections. This method makes use of each observation efficiently in that the network mapping function so obtained is consistent with that information and is also optimal in the least L2-norm sense. The RBF network with the F-Projections adaptation algorithm was used for predicting a chaotic time-series. We compare its performance to an adaptation scheme based on the method of stochastic approximation, and show that the F-Projections algorithm converges to the underlying model much faster.
Discrimination of low-frequency magnetic fields by honeybees: biophysics and experimental tests.

It has been shown repeatedly over the past 20 years that honeybees are able to detect weak, earth-strength magnetic fields. Table I shows a summary of these known geomagnetic effects on honeybee behavior, as well as the independent attempts to replicate them. We know of no attempts to replicate these effects that were not eventually successful (some apparently took practice). Towne and Gould (1985) provide a thorough and critical review of this literature prior to 1985 (effects 1-4 in Table I). Of these unconditioned responses, the horizontal dance experiment of Lindauer and Martin (1972) and Martin and Lindauer (1977) (effect 2 in Table I) has proven to be particularly easy to replicate.
Serum LncRNAs Profiles Serve as Novel Potential Biomarkers for the Diagnosis of HBV-Positive Hepatocellular Carcinoma

Background Hepatocellular carcinoma (HCC) is a common malignancy that has a poor prognosis because there is lack of methods for early diagnosis. We aimed to utilize two serum long non-coding RNAs (lncRNAs), uc001ncr and AX800134, to diagnose hepatitis B virus (HBV)–positive HCC. Methods lncRNA microarrays were utilized to measure the differential expression of lncRNAs between tumor tissues and corresponding non-tumor tissues in HBV-positive hapatocellular carcinoma. uc001ncr and AX800134 were selected as candidate lncRNAs and detected in three independent cohorts containing a total of 684 participants (healthy individuals and chronic HBV patients and HBV-positive HCC patients) who were recruited between March 2011 and December 2012. A logistic regression model was constructed using a training cohort (n = 353) and validated using an independent cohort (n = 181). The area under the receiver operating characteristic curve (AUC) was utilized to evaluate the diagnostic accuracy. Results We determined that a panel based on the expression of uc001ncr and AX800134 accurately diagnosed HBV-positive HCC (AUC values of 0.9494 and 0.9491 for the training and validation cohorts, respectively). The diagnostic performance of the panel remained high in patients with AFP≤400 ng/ml (AUC values of 0.9371 and 0.9527 for the training and validation cohorts, respectively). The panel also diagnosed early HCC (AUC values of 0.9450 and 0.9564 for the training and validation cohorts, respectively). Conclusion Our results indicated that the serum expression of uc001ncr and AX800134 has potential as novel potential biomarker for the diagnosis of HCC, especially in patients with AFP≤400 ng/ml or early-stage disease (BCLC 0+A).
Traumatic Brain Injury and Peripheral Immune Suppression: Primer and Prospectus

Nosocomial infections are a common occurrence in patients following traumatic brain injury (TBI) and are associated with an increased risk of mortality, longer length of hospital stay, and poor neurological outcome. Systemic immune suppression arising as a direct result of injury to the central nervous system (CNS) is considered to be primarily responsible for this increased incidence of infection, a view strengthened by recent studies that have reported novel changes in the composition and function of the innate and adaptive arms of the immune system post-TBI. However, our knowledge of the mechanisms that underlie TBI-induced immune suppression is equivocal at best. Here, after summarizing our current understanding of the impact of TBI on peripheral immunity and discussing CNS-mediated regulation of immune function, we propose roles for a series of novel mechanisms in driving the immune suppression that is observed post-TBI. These mechanisms, which have never been considered before in the context of TBI-induced immune paresis, include the CNS-driven emergence into the circulation of myeloid-derived suppressor cells and suppressive neutrophil subsets, and the release from injured tissue of nuclear and mitochondria-derived damage associated molecular patterns. Moreover, in an effort to further our understanding of the mechanisms that underlie TBI-induced changes in immunity, we pose throughout the review a series of questions, which if answered would address a number of key issues, such as establishing whether manipulating peripheral immune function has potential as a future therapeutic strategy by which to treat and/or prevent infections in the hospitalized TBI patient.
BRCA-1 Gene Expression and Comparative Proteomic Profile of Primordial Follicles from Young and Adult Buffalo (Bubalus bubalis) Ovaries

ABSTRACT In our previous study, we demonstrated that the repair efficiency of DNA double-strand breaks declines with increasing age in rat primordial follicles. In the present study, we extended our studies to buffalo (Bubalus bubalis) wherein we studied the expression of BRCA-1 related DNA repair genes in primordial follicles of young (12 months-22 months) and adult (72–96 months) buffaloes. The relative expression of selected genes, as determined by RT-PCR, revealed a significant (p < 0.05) decrease in mRNA levels of BRCA1, MRE11, RAD51, ATM, and H2AX in adult primordial follicles as compared to the young. Western blot analysis revealed a significant (p < 0.05) decrease in the expression of phosphorylated protein levels of BRCA1 and H2AX in adult buffalo primordial follicles. The protein expression profile of young and adult buffalo primordial follicles revealed differential expression of proteins involved in mitochondrial function, cell survival and cell metabolism. Similar to reports from aging rodent and human primordial follicles, our findings support the fact that impairment of DNA repair may be an universal mechanism involved in oocyte aging.
Morphological Studies on the Heart Ventricle of African Catfish (Clarias gariepinus)

Summary The histological and ultrastructural characteristics of the heart ventricle in Clarias gariepinus (African catfish) has been studied by light microscopy and transmission electron microscopy. The ventricle of the heart has a saccular shape and the myocardial wall consists of an outer thin compact myocardium and an inner well‐developed spongy myocardium. The myocardial layer has small myocytes, interstitial spaces and blood vessels. The myocytes are the major constituents of the ventricular wall. They are long cells, with large nuclei, and predominantly euchromatin. The sarcoplasmic reticulum of the ventricular myocytes consists of a network of tubules and subsarcolemmal cisternae oriented mainly along the longitudinal axis of the myofibrils. In contrast to the ventricular structure of other fish species described in the literature ( Greer‐Walker et al., 1985 ; Santer, 1985 ; Sánchez‐Quintana et al., 1995, 1996 ), the African catfish, a freshwater sedentary fish recently introduced in neotropical climatic environments, showed a saccular ventricle that consisted of two muscle layers, a thin compact layer with large vessels and a developed spongy layer. The ultrastructure of the ventricular myocardium of C.gariepinus is similar to that of other teleosts, inclusive that of fish with other swimming habits.
Level-shifter-less approach for multi-VDD design to use body bias control in FD-SOI

Level shifters to convert signal swings from low-voltage (VDDL) to high-voltage (VDDH) are required at the boundary of voltage domains in SoC employing multiple supply voltages. However, they cost delay, power and area in addition to increasing the complexity of physical design. This paper proposes a level-shifter-less (LSL) approach to use a reverse body bias (RBB) in the VDDH domain and superior threshold-voltage modulation capability of FD-SOI devices. Simulation results and measurements of a fabricated chip demonstrated that the chip applying the LSL approach correctly operates at VDDL=0.6V and VDDH=1.2V under RBB of 2V for pMOS transistors while suppressing the static dc current in the VDDH domain.
Kernel adaptive filtering under generalized Maximum Correntropy Criterion

Owing to their universal approximation capability and online learning manner, kernel adaptive filters have been widely used in nonlinear systems modeling. Under Gaussian assumption, traditional kernel adaptive algorithms utilize the well-known mean square error(MSE) as a cost function to get optimal solutions. For non-Gaussian situations, MSE will not properly represent the statistics of the error, and hence degrade the performance. In recent years, an information theoretic learning(ITL) based criterion called Maximum Correntropy Criterion(MCC) has been proposed and applied in robust adaptive filtering. The correntropy is a generalized correlation measure in kernel space, which uses Gaussian kernel as a default kernel function. Of course, Gaussian kernel is not always the best choice. Recently, a more flexible definition of correntropy, called generalized correntropy, has been proposed. With a proper shape parameter, the generalized correntropy may get better performance than original correntropy with Gaussian kernel. In this paper, we take advantages of both kernel methods and generalized correntropy to develop a new kernel adaptive algorithm called Generalized Kernel Maximum Correntropy(GKMC) algorithm. We analyze theoretically the stability and steady-state performance of the new algorithm. In addition, we propose a Quantized GKMC(QGKMC) algorithm to curb the growth of the network size in GKMC while maintaining the performance. Simulation results confirm the theoretical expectations and show superior performance compared with existing methods.
The HMM-based speech synthesis system (HTS) version 2.0

A statistical parametric speech synthesis system based on hidden Markov models (HMMs) has grown in popularity over the last few years. This system simultaneously models spectrum, excitation, and duration of speech using context-dependent HMMs and generates speech waveforms from the HMMs themselves. Since December 2002, we have publicly released an open-source software toolkit named HMM-based speech synthesis system (HTS) to provide a research and development platform for the speech synthesis community. In December 2006, HTS version 2.0 was released. This version includes a number of new features which are useful for both speech synthesis researchers and developers. This paper describes HTS version 2.0 in detail, as well as future release plans.
Throwing Out the Baby with the Bath Water: A Comment on Green, Kim, and Yoon

Donald P. Green, Soo Yeon Kim, and David H. Yoon argue that many findings in quantitative international relations that use the dyad-year design are flawed. In particular, they argue that the effect of democracy on both trade and conflict has been vastly overstated, that researchers have ignored unobserved heterogeneity between the various dyads, and that heterogeneity can be best modeled by “fixed effects,” that is, a model that includes a separate dummy for each dyad. We argue that the use of fixed effects is almost always a bad idea for dyad-year data with a binary dependent variable like conflict. This is because conflict is a rare event, and the inclusion of fixed effects requires us to not analyze dyads that never conflict. Thus while the 90 percent of dyads that never conflict are more likely to be democratic, the use of fixed effects gives democracy no credit for the lack of conflict in these dyads. Green, Kim, and Yoon's fixed-effects logit can tell us little, if anything, about the pacific effects of democracy. Their analysis of the impact of democracy on trade is also flawed. The inclusion of fixed effects almost always masks the impact of slowly changing independent variables; the democracy score is such a variable. Thus it is no surprise that the inclusion of dyadic dummy variables in their model completely masks the relationship between democracy and trade. We show that their preferred fixed-effects specification does not outperform a model with no effects (when that model is correctly specified in other ways). Thus there is no need to include the masking fixed effects, and so Green, Kim, and Yoon's findings do not overturn previous work that found that democracy enhanced trade. We agree with Green, Kim, and Yoon that modeling heterogeneity in time-series cross-section data is important. We mention a number of alternatives to their fixed-effects approach, none of which would have the pernicious consequences of using dyadic dummies in their two reanalyses.
Semantic and geometric enrichment of 3D geo-spatial models with captioned photos and labelled illustrations

There are many 3D digital models of buildings with cultural heritage interest, but most of them lack semantic annotation that could be used to inform users of mobile and desktop applications about their origins and architectural features. We describe methods in an ongoing project for enriching 3D models with generic annotation, derived from examples of images of building components and from labelled plans and diagrams, and with object-specific descriptions obtained from photo captions. This is the first stage of research that aims to annotate 3D models with facts extracted from the text of authoritative architectural guides.
A study on the frequency-domain Primary-ambient extraction for stereo audio signals

Primary-ambient extraction (PAE) has been playing an important role in spatial audio analysis-synthesis. Based on the spatial features, PAE decomposes a signal into primary and ambient components, which are then rendered separately. PAE is performed in subband domain for complex input signals having multiple point-like sound sources. However, the performance of PAE approaches and their key influences for such signals have not been well-studied so far. In this paper, we conducted a study on frequency-domain PAE using principal component analysis (PCA) in the case of multiple sources. We found that the partitioning of the frequency bins is very critical in PAE. Simulation results reveal that the proposed top-down adaptive partitioning method achieves superior performance as compared to the conventional partitioning methods.