Miracle-dz/UniFAR-FTUs huggingface dataset:
What is the role of Brain derived neurotrophic factor in Multiple Sclerosis neuroinflammation?

Multiple Sclerosis (MS) is a chronic, inflammatory and degenerative disease of the central nervous system (CNS) with an unknown etiology. The MS pathophysiology is due to altered bidirectional interactions between several immune cell types in the periphery (such as T and B cells, myeloid cells) and resident CNS cells (such as microglia and astrocytes). It is also known that inflammatory responses have both detrimental and neuroprotective effects. The release of brain derived neurotrophic factor (BDNF) by immune cells, in both peripheral blood and into inflammatory lesions in MS, but also by microglia and astrocytes, into the CNS, seems to be a possible mechanism for this neuroprotective effect. So far, the link between BDNF and neuroinflammation has been poorly investigated. A better understanding of this link could help in the development of new therapeutic strategies for MS. In this review, the role of BDNF in MS will be discussed as well as its possible alternative as an innovative therapeutic
On the Distribution , Behaviour and Seasonal Variation of Irrawaddy Dolphins ( Orcaella brevirostris ) in the Kep Archipelago , Cambodia

Irrawaddy dolphins (Orcaella brevirostris) are a globally Endangered cetacean species found in rivers, lakes, estuaries, and coastal waters across Southeast Asia. Whilst much attention has concentrated on understanding freshwater populations of the species, marine populations have received less research attention, with the majority of marine studies focusing on determining abundance and distribution. As part of The Cambodian Marine Mammal Conservation Project, the current study utilises a combination of year-long land and boat survey techniques to identify seasonal critical habitats for the species in Cambodia’s Kep Archipelago, as well as fill knowledge gaps on the species’ behavioural ecology, to contribute to the design of effective and tailored regional conservation strategies. Results showed Irrawaddy dolphins to be present in the Kep Archipelago in all seasons, with the highest encounter rates in Summer Monsoon (May–September) and Post-Monsoon (October–November) seasons, and the lowest encounter rates in Pre-Monsoon season (March–April). Juveniles were present in all seasons, suggesting the region represents an important nursing ground for the population. Foraging was the most commonly observed behaviour, with significant associations found between certain behavioural states and events, group sizes and seasons, group sizes and juvenile presence, and swim styles and juvenile presence.
Variants of double robust estimators for two-stage dynamic treatment regimes

Certain conditions and illnesses may necessitate multiple stages of treatment and thus require unique study designs to compare the efficacy of these interventions. Such studies are characterized by two or more stages of treatment punctuated by decision points where intermediate outcomes inform the choice for the next stage of treatment. The algorithm that dictates what treatments to take based on intermediate outcomes is referred to as a dynamic regime. This paper describes an efficient method of building double robust estimators of the treatment effect of different regimes. A double robust estimator utilizes both modeling of the outcome and weighting based on the modeled probability of receiving treatment in such a way that the resulting estimator is a consistent estimate of the desired population parameter under the condition that at least one of those models is correct. This new and more efficient double robust estimator is compared to another double robust estimator as well as classical regression and inverse probability weighted estimators. The methods are applied to estimate the regime effects in the STAR*D anti-depression treatment trial.
Optimal vector Quantization: from signal processing to clustering and numerical probability

Optimal vector quantization has been originally introduced in Signal processing as a discretization method of random signals, leading to an optimal trade-off between the speed of transmission and the quality of the transmitted signal. In machine learning, similar methods applied to a dataset are the historical core of unsupervised classification methods known as “clustering”. In both case it appears as an optimal way to produce a set of weighted prototypes (or codebook) which makes up a kind of skeleton of a dataset, a signal and more generally, from a mathematical point of view, of a probability distribution. Quantization has encountered in recent years a renewed interest in various application fields like automatic classification, learning algorithms, optimal stopping and stochastic control, Backward SDEs and more generally numerical probability. In all these various applications, practical implementation of such clustering/quantization methods more or less rely on two procedures (and their countless variants): the Competitive Learning Vector Quantization (CLV Q) which appears as a stochastic gradient descent derived from the so-called distortion potential and the (randomized) Lloyd’s procedure (also known as k-means algorithm, nuées dynamiques) which is but a fixed point search procedure. Batch version of those procedures can also be implemented when dealing with a dataset (or more generally a discrete distribution). In a more formal form, if μ is probability distribution on an Euclidean space Rd, the optimal quantization problem at level N boils down to exhibiting an N -tuple (x1, . . . , x ∗ N ), solution to
Training Deep Convolutional Networks with Unlimited Synthesis of Musical Examples for Multiple Instrument Recognition

Deep learning has yielded promising results in music information retrieval and other domains compared to machine learning algorithms trained on hand-crafted feature representations, but is often limited by the availability of data and vast hyper-parameter space. It is difﬁcult to obtain large amounts of annotated recordings due to prohibitive labelling costs and copyright restrictions. This is especially true when the MIR task is low-level in nature such as instrument recognition and applied to wide ranges of world instruments, causing most MIR techniques to focus on recovering easily veriﬁable metadata such as genre. We tackle this data availability problem using two techniques: generation of synthetic recordings using MIDI ﬁles and synthesizers, and by adding noise and ﬁlters to the generated samples for data augmentation purposes. We investigate the application of deep synthetically trained models to two related low-level MIR tasks of frame-level polyphony detection and instrument classiﬁcation in polyphonic recordings, and empirically show that deep models trained on synthetic recordings augmented with noise can outperform a majority class baseline on a dataset of polyphonic recordings labeled with predominant instruments.
Rationalizing Context-Dependent Preferences: Divisive Normalization and Neurobiological Constraints on Decision-Making

Biology places constraints on the form of neural computation that ultimately characterizes choice behaviour. We identify a canonical neural computation, divisive normalization, which predicts that the composition and size of the choice set will adversely influence choice behaviour, including increased stochasticity, novel violations of the axiom of Independence of Irrelevant Alternatives, and a decreased likelihood of choosing a higher valued alternative. Evidence for such context-dependent choice behaviour is provided from two behavioural experiments, and these violations of IIA are more accurately captured by a choice model incorporating normalization than alternative econometric specifications. Finally, we address the normative implications of divisive normalization in choice behaviour. We demonstrate that normalization implements an efficient means for the brain to represent valuations given the constraints of a neural system, yielding the fewest choice errors possible. These results emphasize both the positive advances offered by developing choice models grounded in neuroscience, and the normative role neurobiological constraints can play in the study of decision-making. ∗Center for Neuroeconomics, NYU, 4 Washington Pl. , NY, NY, 10003, webbr@nyu.edu. This research was funded by the Army Research Office (W911ND-11-1-0482), and the experiment was approved by an IRB. The authors wish to thank A. Caplin, N.G. Martineau and T. LoFaro for helpful comments, as well as seminar participants at Harvard University, the University of Zurich, and Maastricht University. A recent version can be found at http://www.cns. nyu.edu/%7Ewebbr/WebbJMP.pdf
Deep Learning and Domain Transfer for Orca Vocalization Detection

In this paper, we study the difficulties of domain transfer when training deep learning models, on a specific task that is orca vocalization detection. Deep learning appears to be an answer to many sound recognition tasks in human speech analysis as well as in bioacoustics. This method allows to learn from large amounts of data, and find the best scoring way to discriminate between classes (e.g. orca vocalization and other sounds). However, to learn the perfect data representation and discrimination boundaries, all possible data configurations need to be processed. This causes problems when those configurations are ever changing (e.g. in our experiment, a change in the recording system happened to considerably disturb our previously well performing model). We thus explore approaches to compensate on the difficulties faced with domain transfer, with two convolutionnal neural networks (CNN) architectures, one that works in the time-frequency domain, and one that works directly on the time domain.
Using remote sensing to quantify albedo of roofs in seven California cities

Cool roofs reflect sunlight and therefore can reduce cooling energy use in buildings. Further, since roofs cover about 20 – 25% of most cities, widespread deployment of cool roofs could mitigate the urban heat island effect and partially counter urban temperature increases associated with global scale climate change. The magnitude of these potential benefits for a given city depends on the increase in albedo that can be achieved using reflective roofs. Assessing this increase requires knowledge of roof albedo at the cityscale, which until now has been unknown due to a lack of reflectance data with sufficient spatial coverage, spatial resolution, and spectral information. In this work we use multiband aerial imagery to derive the albedos of individual roofs in seven California cities: Los Angeles, Long Beach, San Diego, Bakersfield, Sacramento, San Francisco, and San Jose. The radiometrically calibrated, remotely sensed imagery has high spatial resolution (1 m) and four narrow band reflectances: blue, green, red, and near-infrared. First, we locate roof pixels within GIS building outlines. Next, we use laboratory measurements of the solar spectral reflectances of 190 roofing products to empirically relate solar reflectance (albedo) to reflectances in the four narrow bands; the empirical relationship well predicts albedo as indicated by a low root-mean-square of the residuals of 0.016. Albedos computed from remotely sensed reflectances are calibrated to ground measurements of roof albedo in each city. The error (accuracy) at 90% confidence interval of the calibrated albedos is found to vary by city from 0.00 – 0.01 at low albedo and 0.06 – 0.14 at high albedo. The fraction of urban area covered by roofs ranged by city from 10 to 25%. City-wide average roof albedo ranged from 0.17 ± 0.08 to 0.20 ± 0.11 (mean ± standard deviation) for five of the cities; values were higher in Sacramento (0.24 ± 0.11) and San Diego (0.29 ± 0.15). Buildings with small roofs were found to constitute a large fraction of city roof area and to have low mean albedos. This suggests that efforts to increase urban albedo through the use of reflective roofs should include small roofs, which are presumably mostly residential. Roof albedos derived for Bakersfield were used in a regional climate model (WRF) to estimate temperature changes attainable by converting the current stock of roofs to “cool” high albedo roofs. It was found that seasonal mean afternoon (15:00 LST) temperatures could be reduced by up to 0.2 °C during both the summer and winter. Changes in precipitation were not significant at the 95% confidence level.
Effect of Pearl River closed fishing on Megalobrama hoffmanni recruitment stock

After closed fishing had been implemented in the main river systems in China,it was carried out for the first time in Pearl River on April 1st,2011. Guangdong black bream( Megalobrama hoffmanni) w as one of the important economic fishes. For analyzing the effect of closed fishing on Megalobrama hoffmanni recruitment stock,the fish larvae w ere sampled by Jiang net at Zhaoqing section in low er reaches of Pearl River before( 2006—2010) and after( 2011—2012) closed fishing. The results show ed that Megalobrama hoffmanni larvae usually occured from mid-April to mid-October,and duration w as( 189 ± 11) days before closed fishing. And the peak density of Megalobrama hoffmanni larvae w as from June to Auguest every year. After closed fishing,the starting time of Megalobrama hoffmanni larvae w as w ithout any major changes,but the end time w as sooner than before closed fishing,and duration w as a little shortened. The drift density of Megalobrama hoffmanni larvae increased obviously w ithin the banned fishing periods from April to May,and the biomass and the relative abundance also rose greatly. For analyzing the effect of closed fishing on Megalobrama hoffmanni recruitment stock,the regression model w as built,and the logarithmic equation w as LgY = 0. 988Lnx- 4. 932( R2= 0. 365,P 0. 01),and regression effect w as significant. Based on the logarithmic equation,the fitted value of Megalobrama hoffmanni larvae w as calculated,and the closed fishing effect w as estimated combining actual monitored value at Zhaoqing sampling station. The increment of Megalobrama hoffmanni laevae caused by closed fishing w as 9. 43 × 108ind. in 2011 and 651. 81 × 108 ind. in 2012 respectively. The result indicates the present regime of closed fishing can effectively increase the Megalobrama hoffmanni recruitment stock based on the abundant discharge.
Text Analyzer for Competitive Examination

Competitive examination provide a platform to the user for gauging their verbal and literature skills. The tools available currently only provide some simple feature regarding text processing such as spelling correction and providing different synonyms of the selected words. A complete assessment is not done for the user’s abilities and relevant details related to the context are not taken entirely into consideration. The following paper proposes a way to implement Natural Language Processing on text to provide feedback to the user for their competitive examinations. The assessment of the text will be done according to the parameter such as grammar, vocabulary; relevance to the context. Some applications for web and mobile platform are available to offer assessment of English language essay but limited academic research available to validate research work in this domain. This work is effort to address requirement of text analyzer for English language evaluation methods incorporating natural language processing.
Learning Representations for Safe Autonomous Movement

Mobile robots have become an increasingly common presence in our homes and on our roads. To move safely within these shared spaces, autonomous agents must understand how other dynamic actors behave and how such behavior influences the navigability of the surrounding scene. Towards this goal, we propose two data-driven methods that learn representations from large, self-supervised corpora of data for 1) motion planning in hazardous, dynamic environments and 2) motion forecasting in autonomous driving settings. The first method active affordance learning (A2L) advocates for a modular approach to autonomous navigation that combines learned spatial representations with traditional geometry-based maps and classical planning algorithms. By learning to predict a spatial affordance map (that encodes what parts of a scene are navigable) throug‘h active self-supervised experience gathering, we show that A2L is more sample efficient, generalizable, and interpretable than existing state-of-the-art reinforcement learning-based (RL) approaches. The second method what-if motion prediction (WIMP) proposes a recurrent graph-based attentional approach for data-driven trajectory forecasting in autonomous driving settings. To the best of our knowledge, this is the first approach to demonstrate counterfactual motion forecasting based on topological queries such as mapbased goals and social contexts. We demonstrate the benefits of our proposed framework on the challenging Argoverse vehicle motion forecasting dataset, outperforming previous birds-eye-view (BEV) representation-based methods and setting a new benchmark for prediction quality.
Nitrogen and phosphorus transport as a criterion for soil categorisation

Water pollution, both surface and underground, by agricultural activity is a relatively serious problem. The pollutants are transported from the soil to the water. The aim of this paper is to evaluate the soil transport function on the basis of present knowledge of soil nutrient (nitrogen and phosphorus) transport and soil and local properties. The west Slovak lowlands, south Slovak basins and east Slovak highlands were selected due to their different climate and soil parameters. Soil nutrient transport was classifi ed as (1) very weak, (2) weak, (3) moderate, (4) strong and (5) very strong. Accordingly, we defi ned the area of individual categories of soil transport function in agricultural soils in three observed areas in Slovakia. Thus we could determine the origin of the water pollution potential risk. Moreover, this is very important when it comes to observing of the Nitrate Directive as farmers can harmonise nitrogen and phosphorus fertilisation to soil categories. In the case of nitrogen, its transport is very dependent on soil nitrifi cation intensity with nitrogen, and the most fertile soils (on Žitný ostrov) are very susceptible to water pollution. Almost 88% of agricultural soil in this area are in the strong and very strong nitrogen transport categories. On the other hand, only 2.1% of soils in hilly and cold landscapes (Ondavská vrchovina) feature water nitrogen pollution. Phosphorus transport is very strong on the hilly landscape with shallow soils (less than 0.30 m) and/or with gravel high content (Ondavská vrchovina). Especially in these locations, an increase of water body phosphorus content and thus a decrease in water quality (almost 50% of total agricultural soil area) is possible. Keywords
SECL: Separated Embedding and Correlation Learning for Demographic Prediction in Ubiquitous Sensor Scenario

Knowing exact demographic attributes of users is crucial for human-computer interaction, intelligent marketing and automatic advertising. Ubiquitous sensor devices yield massive volumes of temporal data which hide a lot of valuable demographic information. In this paper, we bridge the gap between sensor data and demographic prediction to obtain real attributes of users from popular sensor devices: pedometer, which is widely used in mobile devices. We propose a novel model named Separated Embedding and Correlation Learning (SECL) for demographic prediction. Specifically, SECL first process the input data with a separated embedding layer to disentangle task-specific features for interference eliminating, and then capture the hidden correlations between different tasks via a correlation learning layer, finally the refined task-specific features are fed into a multi-task prediction layer to predict demographic attributes. Experimental results show impressive performance of our model on a real-world pedometer dataset, which is made publicly available on https://github.com/deepdeed/SECL.
Forecasting method of multidimensional time series based on Neuro-Fuzzy Cognitive Temporal Models

In the article there are Neuro-Fuzzy Cognitive Temporal Models (NFCTM) described. Those provide accounting of indirect and indirect mutual impact of all the multidimensional time series (MTS) components with their temporary delays relative to each other and are oriented on forecasting of multidimensional time series. Neuro-Fuzzy Cognitive Temporal Componental Models, which provide the formation of forecasted values of the MTS components with the temporary delays demanded, are used in NFCTM concepts in order to accomplish the temporal transformation. There is the way of NFCTM coordinated training described, which consists in Neuro-Fuzzy Componental Temporary Models for each of the NFCTM component and also in coherence of these Neuro-Fuzzy Componental Temporary Models (NFCTM) between each other. There is an MTS forecasting method offered in condition of unreliability the nonlinearity of the interaction, partial inconsistency and interdependence of the MTS components, that is based on NFCTM. There are experimental studies conducted and the results of using the proposed method are presented on the example of the problem of multidimensional forecasting of the state of the urban environment in Moscow. The use of the proposed method may be in demand to provide reliable forecasting of the state of the urban environment in various regions of Russia and other countries, including into account the complex epidemiological situation.
Evaluation of Antidiarrhoeal Activity of Hydroalcoholic Extract of Ageratum Conyzoides Linn

The leaves of Ageratum conyzoides are used in traditional medicine for the treatment of diarrhoea. Thus the hydroalcoholic extract of leaves Ageratum conyzoides (Asteraceae) was investigated for its antidiarrhoeal property to substantiate folkloric claim. The hydroalcoholic extract of Ageratum conyzoides, at graded dose (250 and 500 mg/kg body weight) was investigated for antidiarrhoeal activity in castor oil and magnesium sulphate induced diarrhoea. Results were comparable to that of standard drug loperamide (3 mg/kg body weight). A single oral dose of Ageratum conyzoides extract of 500 mg/kg body weight produced a significant decrease in the severity of diarrhoea. To understand the mechanism of its antidiarrhoeal activity, its effect was further evaluated on intestinal transit and castor oil induced intestinal fluid accumulation (enteropooling). Extract produced profound decrease in intestinal transit (39.66–58.17%) and significantly inhibited castor oil induced enter pooling comparable to that of intraperitonial injection of standard drug atropine sulphate. The results showed that the hydroalcoholic extract of Ageratum conyzoides have a significant antidiarrhoeal activity and supports its traditional uses in herbal medicine.