Miracle-dz/UniFAR-FTUs huggingface dataset:
Tandem connectionist feature extraction for conventional HMM systems

Hidden Markov model speech recognition systems typically use Gaussian mixture models to estimate the distributions of decorrelated acoustic feature vectors that correspond to individual subword units. By contrast, hybrid connectionist-HMM systems use discriminatively-trained neural networks to estimate the probability distribution among subword units given the acoustic observations. In this work we show a large improvement in word recognition performance by combining neural-net discriminative feature processing with Gaussian-mixture distribution modeling. By training the network to generate the subword probability posteriors, then using transformations of these estimates as the base features for a conventionally-trained Gaussian-mixture based system, we achieve relative error rate reductions of 35% or more on the multicondition Aurora noisy continuous digits task.
Measurement of the membrane curvature preference of phospholipids reveals only weak coupling between lipid shape and leaflet curvature

In biological processes, such as fission, fusion and trafficking, it has been shown that lipids of different shapes are sorted into regions with different membrane curvatures. This lipid sorting has been hypothesized to be due to the coupling between the membrane curvature and the lipid's spontaneous curvature, which is related to the lipid's molecular shape. On the other hand, theoretical predictions and simulations suggest that the curvature preference of lipids, due to shape alone, is weaker than that observed in biological processes. To distinguish between these different views, we have directly measured the curvature preferences of several lipids by using a fluorescence-based method. We prepared small unilamellar vesicles of different sizes with a mixture of egg-PC and a small mole fraction of N-nitrobenzoxadiazole (NBD)-labeled phospholipids or lysophospholipids of different chain lengths and saturation, and measured the NBD equilibrium distribution across the bilayer. We observed that the transverse lipid distributions depended linearly on membrane curvature, allowing us to measure the curvature coupling coefficient. Our measurements are in quantitative agreement with predictions based on earlier measurements of the spontaneous curvatures of the corresponding nonfluorescent lipids using X-ray diffraction. We show that, though some lipids have high spontaneous curvatures, they nevertheless showed weak curvature preferences because of the low values of the lipid molecular areas. The weak curvature preference implies that the asymmetric lipid distributions found in biological membranes are not likely to be driven by the spontaneous curvature of the lipids, nor are lipids discriminating sensors of membrane curvature.
Protective role of tuftsin fragment 1‐3 in an animal model of intracerebral hemorrhage

Intracerebral hemorrhage (ICH) causes morbidity and mortality and commonly follows the reperfusion after an ischemic event. Tissue plasminogen activator (tPA), a fibrinolytic serine protease, is routinely given for the treatment of stroke. However, tPA also can promote neuronal death, suggesting that caution should be exercised when using it. Furthermore, tPA upon brain injury mediates microglial activation and modulates neuronal survival. To investigate the role of tPA and microglia during brain hemorrhage, we induced experimentally ICH by intracerebral injection of collagenase. Seven days after the introduction of ICH, it persisted in tPA‐deficient (tPA−/−) mice but is drastically reduced in size in wild‐type mice. Three weeks after ICH, there are still red blood cells in tPA−/− but not in wild‐type animals. Activated microglia persist around the injury site. When microglial activation is inhibited by tuftsin fragment 1‐3 macrophage/microglial inhibitory factor (MIF), the stroke injury volume is significantly reduced, and the neurobehavioral deficits exhibited by the mice are improved. Our results suggest that endogenous tPA assists in the clearance of intracerebral hemorrhage, presumably by affecting microglial activation, and MIF could be a valuable neuroprotective agent for the treatment of ICH. Ann Neurol 2003;54:655–664
Systematic sequencing of the Escherichia coli genome: analysis of the 0- 2.4 min region

A contiguous 111,402-nucleotide sequence corresponding to the 0 to 2.4 min region of the E. coli chromosome was determined as a first step to complete structural analysis of the genome. The resulting sequence was used to predict open reading frames and to search for sequence similarity against the PIR protein database. A number of novel genes were found whose predicted protein sequences showed significant homology with known proteins from various organisms, including several clusters of genes similar to those involved in fatty acid metabolism in bacteria (e.g., betT, baiF) and higher organisms, iron transport (sfuA, B, C) in Serratia marcescens, and symbiotic nitrogen fixation or electron transport (fixA, B, C, X) in Azorhizobium caulinodans. In addition, several genes and IS elements that had been mapped but not sequenced (e.g., leuA, B, C, D) were identified. We estimate that about 90 genes are represented in this region of the chromosome with little spacer.
Stability and Capacity of Wireless Networks with Probabilistic Receptions : Part II — Regular Networks

We study the stability and capacity problems inregular wireless networks. In Part I, we characterized the stability and the capacity regions, and introduced policies sufficient to achieve every rate in these regions. In this part, we obtainclosed form expressions for the capacity of Manhattan (two-dimensional grid) and ring networks (circular array of nodes). We also find the optimal (i.e., capacity achieving) medium access and routing policies. Our objective in analyzing regular networks is to provide insights and design guidelines for general networks. The knowledge of the exact capacity enables us to quantify the performance loss due to suboptimal protocols (such as slotted ALOHA and random walk), and the effects of link fading and increasing connectivity.
New developments in the study of the microbiota of raw-milk, long-ripened cheeses by molecular methods: the case of Grana Padano and Parmigiano Reggiano

Microorganisms are an essential component of cheeses and play important roles during both cheese manufacture and ripening. Both starter and secondary flora modify the physical and chemical properties of cheese, contributing and reacting to changes that occur during the manufacture and ripening of cheese. As the composition of microbial population changes under the influence of continuous shifts in environmental conditions and microorganisms interactions during manufacturing and ripening, the characteristics of a given cheese depend also on microflora dynamics. The microbiota present in cheese is complex and its growth and activity represent the most important, but the least controllable steps. In the past, research in this area was dependent on classical microbiological techniques. However, culture-dependent methods are time-consuming and approaches that include a culturing step can lead to inaccuracies due to species present in low numbers or simply uncultivable. Therefore, they cannot be used as a unique tool to monitor community dynamics. For these reasons approaches to cheese microbiology had to change dramatically. To address this, in recent years the focus on the use of culture-independent methods based on the direct analysis of DNA (or RNA) has rapidly increased. Application of such techniques to the study of cheese microbiology represents a rapid, sound, reliable, and effective way for the detection and identification of the microorganisms present in dairy products, leading to major advances in understanding this complex microbial ecosystem and its impact on cheese ripening and quality. In this article, an overview on the recent advances in the use of molecular methods for thorough analysis of microbial communities in cheeses is given. Furthermore, applications of culture-independent approaches to study the microbiology of two important raw-milk, long-ripened cheeses such as Grana Padano and Parmigiano Reggiano, are presented.
Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem

One advantage of decision tree based methods like random forests is their ability to natively handle categorical predictors without having to first transform them (e.g., by using feature engineering techniques). However, in this paper, we show how this capability can lead to an inherent "absent levels" problem for decision tree based methods that has never been thoroughly discussed, and whose consequences have never been carefully explored. This problem occurs whenever there is an indeterminacy over how to handle an observation that has reached a categorical split which was determined when the observation in question's level was absent during training. Although these incidents may appear to be innocuous, by using Leo Breiman and Adele Cutler's random forests FORTRAN code and the randomForest R package (Liaw and Wiener, 2002) as motivating case studies, we examine how overlooking the absent levels problem can systematically bias a model. Furthermore, by using three real data examples, we illustrate how absent levels can dramatically alter a model's performance in practice, and we empirically demonstrate how some simple heuristics can be used to help mitigate the effects of the absent levels problem until a more robust theoretical solution is found.
Highly efficient paddy classification using UAV-based orthorectified image

Recently, Unmanned Aerial Vehicle (UAV) has been applied into the precision agriculture [1, 2]. Comparing to satellite-based remote sensing, low-altitude UAV could collect data in cloudy weather with much higher spatial resolution. Thus, in addition to spectral information, UAV images provide more detailed shape and texture information about land use and cover.
Probiotics and prebiotic fiber for constipation associated with Parkinson disease

Objectives: Our objective was to evaluate the efficacy of probiotics and prebiotics in patients with Parkinson disease (PD) and constipation. Methods: We conducted a tertiary setting, randomized, double-blind, placebo-controlled trial in patients with PD with Rome III–confirmed constipation based on 2-week stool diary data at baseline. Patients (n = 120) were randomly assigned (2:1) to either a fermented milk, containing multiple probiotic strains and prebiotic fiber, or placebo, once daily for 4 weeks. The primary efficacy endpoint was the increase in the number of complete bowel movements (CBMs) per week. The key secondary endpoints were 3 or more CBMs and an increase by one or more CBMs per week during weeks 3 and 4. Results: For the primary endpoint, the consumption of a fermented milk containing probiotics and prebiotics resulted in a higher increase in the number of CBMs (mean 1.2, 95% confidence interval [CI] 0.8–1.6) than placebo (0.1, 95% CI −0.4% to 0.6%) (mean difference 1.1, 95% CI 0.4–1.8; p = 0.002). For the key secondary endpoints, a higher number of patients in the probiotics–prebiotics group vs the placebo group reported 3 or more CBMs (p = 0.030; 58.8% vs 37.5%; odds ratio = 2.4, 95% CI 1.1–5.2) and an increase by one or more CBMs (p = 0.004; 53.8% vs 25.0%; odds ratio = 3.5, 95% CI 1.8–8.1) during weeks 3 and 4. Conclusions: The consumption of a fermented milk containing multiple probiotic strains and prebiotic fiber was superior to placebo in improving constipation in patients with PD. ClinicalTrials.gov identifier: NCT02459717. Classification of evidence: This study provides Class I evidence that for patients with PD who have constipation, fermented milk containing probiotics and prebiotics increases the frequency of CBMs.
Coral Reef Ecosystems under Climate Change and Ocean Acidification

Coral reefs are found in a wide range of environments, where they provide food and habitat to a large range of organisms as well as other ecological goods and services. Warm-water coral reefs, for example, occupy shallow sunlit, warm and alkaline waters in order to grow and calcify at the high rates necessary to build and maintain their calcium carbonate structures. At deeper locations (40 – 150 m), “mesophotic” (low light) coral reefs accumulate calcium carbonate at much lower rates (if at all in some cases) yet remain important as habitat for a wide range of organisms, including those important for fisheries. Finally, even deeper, down to 2000 m or more, the so-called ‘cold-water’ coral reefs are found in the dark depths. Despite their importance, coral reefs are facing significant challenges from human activities including pollution, over-harvesting, physical destruction, and climate change. In the latter case, even lower greenhouse gas emission scenarios (such as Representative Concentration Pathway RCP 4.5) are likely drive the elimination of most warm-water coral reefs by 2040-2050. Cold-water corals are also threatened by warming temperatures and ocean acidification although evidence of the direct effect of climate change is less clear. Evidence that coral reefs can adapt at rates which are sufficient for them to keep up with rapid ocean warming and acidification is minimal, especially given that corals are long-lived and hence have slow rates of evolution. Conclusions that coral reefs will migrate to higher latitudes as they warm are equally unfounded, with the observations of tropical species appearing at high latitudes ‘necessary but not sufficient’ evidence that entire coral reef ecosystems are shifting. On the contrary, coral reefs are likely to degrade rapidly over the next 20 years, presenting fundamental challenges for the 500 million people who derive food, income, coastal protection, and a range of other services from coral reefs. Unless rapid advances to the goals of the Paris Climate Change Agreement occur over the next decade, hundreds of millions of people are likely to face increasing amounts of poverty and social disruption, and, in some cases, regional insecurity.
Nonlinear Metric Learning with Kernel Density Estimation

Metric learning, the task of learning a good distance metric, is a key problem in machine learning with ample applications. This paper introduces a novel framework for nonlinear metric learning, called kernel density metric learning (KDML), which is easy to use and provides nonlinear, probability-based distance measures. KDML constructs a direct nonlinear mapping from the original input space into a feature space based on kernel density estimation. The nonlinear mapping in KDML embodies established distance measures between probability density functions, and leads to accurate classification on datasets for which existing linear metric learning methods would fail. It addresses the severe challenge to distance-based classifiers when features are from heterogeneous domains and, as a result, the Euclidean or Mahalanobis distance between original feature vectors is not meaningful. We also propose two ways to determine the kernel bandwidths, including an adaptive local scaling approach and an integrated optimization algorithm that learns the Mahalanobis matrix and kernel bandwidths together. KDML is a general framework that can be combined with any existing metric learning algorithm. As concrete examples, we combine KDML with two leading metric learning algorithms, large margin nearest neighbors (LMNN) and neighborhood component analysis (NCA). KDML can naturally handle not only numerical features, but also categorical ones, which is rarely found in previous metric learning algorithms. Extensive experimental results on various datasets show that KDML significantly improves existing metric learning algorithms in terms of classification accuracy.
In Situ Chemical Reduction (ISCR) Technologies: Significance of Low Eh Reactions

Zero valent iron (ZVI; Fe(0)) has been successfully employed for the transformation and subsequent detoxification of a wide range of environmental contaminants, including chlorinated organics, heavy metals, nitroaromatics and, to some degree, perchlorate. The combined use of ZVI plus controlled release carbon has been shown to generate environmental conditions defined herein as in situ chemical reduction (ISCR) that facilitate the microbiological, chemical and/or physical destruction of various contaminants. In this context, the salient features of ISCR are: (i) its low redox potential; and (ii) its propensity to produce H2. In this paper we discuss the thermodynamics of these characteristics (with special emphasis on ZVI reactions) and provide representative case studies documenting the safe and effective use of ISCR technology to rapidly remove halogenated organics from impacted subsurface environments.
Analysis of Sagittal Spinal Alignment in 181 Asymptomatic Children

Study Design: A cross-sectional study. Objectives: To determine the “normal” radiographic parameters of the sagittal profile of the spine in asymptomatic children. Summary of Background Data: There was consensus that cervical kyphosis is pathologic, but we suspected that the cervical kyphosis or loss of cervical lordosis is abnormal in asymptomatic children and adolescents. And we measured the pediatric sagittal profiles including the cervical lordosis for asymptomatic subjects. Materials and Methods: Analysis of 181 children without spinal pathology was performed. Radiographic measurements consisted of the following: cervical lordosis; thoracic kyphosis; thoracolumbar sagittal angle; thoracic apex; lumbar apex; lumbar lordosis; sacral inclination; sacral slope; pelvic tilt; and sagittal vertebral axis. Results: The mean cervical lordosis was −4.8±12.0 degrees (negative=lordotic), sagittal vertebral axis −2.1±2.4 cm, thoracic kyphosis +33.2±9.0 degrees, thoracolumbar sagittal angle 5.6±8.4 degrees, lumbar lordosis −48.8±9.0 degrees, sacral inclination 43.9±7.6 degrees, sacral slope 34.9±6.6 degrees, and pelvic tilt 9.4±6.1 degrees. One hundred nine (60.2%) patients had hypolordotic cervical spine (≥−5 degrees). Cervical kyphosis was present in 80 (44.2%) patients. Conclusions: There is significant variability in sagittal profile of the cervical spine in asymptomatic children. Cervical kyphosis was found in approximately 40% of our study cohort.
Dietary Intake and Nutritional Status of Young Children in Families Practicing Mixed Home Gardening in Northeast Thailand

One current initiative to assist rural Thai families to increase home food production and security is the implementation of home gardens that produce fish, small animals, and vegetables. This paper presents the results of an investigation comparing seasonal dietary intake and nutritional status among northeastern Thai children in mixed-gardening and nongardening families (n = 30 for each group). Assignment to the gardening group was based on the presence of a mixed garden, whereas nongardening subjects were randomly selected and matched for comparison. Statistical analysis (paired t-test) indicated that there was no significant difference in the observed biochemical variables (serum retinol, ferritin, hemoglobin) between groups at the p ≤ . 05 level. Nutritional status in regard to height-for-weight, weight-for-age, and weight-for-height Z scores was better among children of gardening families, although the differences were not significant. the small sample size and reported results indicate that the relationship between the practice of mixed home gardening and dietary intake and nutritional status needs further investigation.
B2P2: A Scalable Big Bioacoustic Processing Platform

Many application areas such as biodiversity monitoring and speech recognition produce several gigabytes of audio data per day, think of weeks, months of accumulated data available to analyse. However, current methods and frameworks can only process a few megabytes at a time. Moreover, these methods require a lot of manual processing before they can give relevant results for biodiversity researchers. In this paper, we present a novel scalable framework called B2P2 for handling large audio files and processing them in an efficient manner. The proposed framework can process and handle large audio data that can indicate whether there are sounds of potential interest throughout large audio recordings. We implemented the proposed framework using BigData platforms such as Spark and HDFS. Experimental results demonstrate an increase in speed-up and performance of processing audio files when increasing nodes and data sizes on cloud computing.
Looking for Sources of Error in Predicting Student's Knowledge

Recent research has focused on detecting the “gaming” behavior of students while using an intelligent tutoring system. For instance, Baker, Corbett & Koedinger (2004) reported detecting “gaming” by students, and argued that it explained lower learning results for these students. In this paper, we report that while our computer system’s correlation with a student’s actual state test score is well correlated (r=.7), we found that we were systematically under-predicting their scores. We wondered if that underprediction had to do with students engaging in some form of gaming. In this paper, we look to see if some of the online metrics (e.g. rate of asking for hints) Baker et al reported correlated with our under-prediction of student’s scores. We report results from the Assistment Project’s data set of about 70 students collected from May, 2004. We performed a stepwise regression to predict what metrics help to explain our poor prediction of state exam scores. We conclude that while none of the metrics we used were statistically significant, several of the metrics were correlated with our under-prediction, suggesting that there is information in these signals but that it might be too weak for the small sample size we have. For future work, we need to replicate this method with the dataset we are collecting this year that has 600 students using the system for 10 times as long.
Models of gaze control for manipulation tasks

Human studies have shown that gaze shifts are mostly driven by the current task demands. In manipulation tasks, gaze leads action to the next manipulation target. One explanation is that fixations gather information about task relevant properties, where task relevance is signalled by reward. This work presents new computational models of gaze shifting, where the agent imagines ahead in time the informational effects of possible gaze fixations. Building on our previous work, the contributions of this article are: (i) the presentation of two new gaze control models, (ii) comparison of their performance to our previous model, (iii) results showing the fit of all these models to previously published human data, and (iv) integration of a visual search process. The first new model selects the gaze that most reduces positional uncertainty of landmarks (Unc), and the second maximises expected rewards by reducing positional uncertainty (RU). Our previous approach maximises the expected gain in cumulative reward by reducing positional uncertainty (RUG). In experiment ii the models are tested on a simulated humanoid robot performing a manipulation task, and each model's performance is characterised by varying three environmental variables. This experiment provides evidence that the RUG model has the best overall performance. In experiment iii, we compare the hand-eye coordination timings of the models in a robot simulation to those obtained from human data. This provides evidence that only the models that incorporate both uncertainty and reward (RU and RUG) match human data.