Miracle-dz/UniFAR-FTUs huggingface dataset:
Anatomical, Physiological, and Functional Heterogeneity of the Dorsal Raphe Serotonin System

The dorsal raphe (DR) constitutes a major serotonergic input to the forebrain, and modulates diverse functions and brain states including mood, anxiety, and sensory and motor functions. Most functional studies to date have treated DR serotonin neurons as a single, homogeneous population. Using viral-genetic methods, we found that subcortical-vs. cortical-projecting serotonin neurons have distinct cell body distributions within the DR and different degrees of coexpressing a vesicular glutamate transporter. Further, the amygdala-and frontal cortex-projecting DR serotonin neurons have largely complementary whole-brain collateralization patterns, receive biased inputs from presynaptic partners, and exhibit opposite responses to aversive stimuli. Gain-and loss-of-function experiments suggest that amygdala-projecting DR serotonin neurons promote anxiety-like behavior, whereas frontal cortex-projecting neurons promote active coping in face of challenge. These results provide compelling evidence that the DR serotonin system contains parallel sub-systems that differ in input and output connectivity, physiological response properties, and behavioral functions.
Étude Au Laboratoire Du Développement Possible De L'Activité Migratrice Chez Le Criquet Pélerin, Schistocerca Gregaria, Lors Des Invasions Et Des Récessions

[Le passage de periodes de pullulation a des periodes de recession chez le Criquet Pelerin (Schistocerca gregaria) est mime en faisant varier la densite au cours de generations successives : des descendants de criquets, vivant en groupe depuis de nombreuses generations, sont eleves en isolement pendant une a trois generations puis, a nouveau en groupe pendant plusieurs generations. L'activite de vol des generations isolees est tres nettement inferieure a celle des generations maintenues constamment groupees. Le comportement de vol des generations groupees apres isolement augmente pendant deux ou trois generations et depasse largement celui exprime par les generations groupees en permanence. Leur potentiel de vol diminue ulterieurement peu a peu d'une generations a l'autre. Les resultats sont discutes en fonction de facteurs ecologiques. , Le passage de periodes de pullulation a des periodes de recession chez le Criquet Pelerin (Schistocerca gregaria) est mime en faisant varier la densite au cours de generations successives : des descendants de criquets, vivant en groupe depuis de nombreuses generations, sont eleves en isolement pendant une a trois generations puis, a nouveau en groupe pendant plusieurs generations. L'activite de vol des generations isolees est tres nettement inferieure a celle des generations maintenues constamment groupees. Le comportement de vol des generations groupees apres isolement augmente pendant deux ou trois generations et depasse largement celui exprime par les generations groupees en permanence. Leur potentiel de vol diminue ulterieurement peu a peu d'une generations a l'autre. Les resultats sont discutes en fonction de facteurs ecologiques. ]
A Practical Guide of Off-Policy Evaluation for Bandit Problems

Off-policy evaluation (OPE) is the problem of estimating the value of a target policy from samples obtained via different policies. Recently, applying OPE methods for bandit problems has garnered attention. For the theoretical guarantees of an estimator of the policy value, the OPE methods require various conditions on the target policy and policy used for generating the samples. However, existing studies did not carefully discuss the practical situation where such conditions hold, and the gap between them remains. This paper aims to show new results for bridging the gap. Based on the properties of the evaluation policy, we categorize OPE situations. Then, among practical applications, we mainly discuss the best policy selection. For the situation, we propose a meta-algorithm based on existing OPE estimators. We investigate the proposed concepts using synthetic and open real-world datasets in experiments.
Lipid entrapment property of polysaccharide gel (PG) extracted from fruit-hulls of durian (Durio zibethinus Murr. Cv. Mon-Thong)

Tippayakul, C. , Pongsamart, S. and Suksomtip, M. Lipid entrapment property of polysaccharide gel (PG) extracted from fruit-hulls of durian (Durio zibethinus Murr. Cv. Mon-Thong) Songklanakarin J. Sci. Technol. , 2005, 27(2) : 291-300 Lipid entrapment property of polysaccharide gel (PG) extracted from fruit-hulls of durian (Durio zibethinus Murr. Cv. Mon-Thong) was investigated in vitro by semipermeable membrane dialysis technique using both cellulose membrane and gut sacs of disected jejunum of rat. Lipids (cholesterol, oleic acid and stearic acid) were mixed with 0-2%W/V PG in the presence of bile salt as a surface active agent in dialysis membrane. Lipids inside and outside dialysis membrane were analyzed by HPLC method after 4-16 hours of dialysis in Ringer lactate buffer pH7. Increasing PG concentration resulted in increasing lipids trapped inside membrane and decreasing lipids released outside membrane. Two percent PG trapped about 80-90% cholesterol. The result of PG trapping cholesterol in egg york showed that egg york cholesterol released outside membrane was decreased with increasing PG concentration. A significant relationship was found between the decreasing of absorbed cholesterol into everted rat jejunum with respect to increasing concenORIGINAL ARTICLE 1M.Sc. Student in Biomedicinal Chemistry, 2Ph.D. (Medical Chemistry), Assoc. Prof., 3M.Sc. (Medical and Pharmaceutical Research), Department of Biochemistry, Faculty of Pharmaceutical Science, Chulalongkorn University, Phaya thai, Bangkok 10330, Thailand. Corresponding e-mail: maneewan.s@chula.ac. th Received, 14 June 2004 Accepted, 16 September 2004 Songklanakarin J. Sci. Technol. Vol.27 No.2 Mar. Apr. 2005 292 Lipid entrapment property of PG Tippayakul, C. , et al. tration of PG. These results suggested that durian polysaccharide gel is able to entrap lipids and it seems to have potential use as medicinal dietary food for lipid controlling patient. Furthermore, in vitro study using cellulose semipermeable membrane dialysis method may be applied for preliminary evaluation of polysaccharide effecting lipids absorption.
A 2020 Perspective on Research Findings on Alphabetics ( Phoneme Awareness and Phonics ) : Implications for Instruction

For the NRP report, a meta-analysis was conducted to answer a number of questions pertaining to phoneme awareness. The analyses led to the strong conclusion that phoneme awareness can and should be taught: “[Phonemic awareness] training benefi ts not only word reading, but children’s ability to read and spell for months, if not years, after the training has ended” (pp. 2-40). In addition, they reported that “it is essential to teach letters as well as phonemic awareness to beginners” (pp. 2-41). In the past twenty years, the prediction of later literacy performance by early phoneme awareness and letter knowledge has been replicated in several longitudinal studies (e.g., Hulme et al., 2002) and by subsequent reviews of research (e.g., NELP, 2008). Reciprocal benefi ts of phoneme awareness for learning how to read and of phonic skills for improving phoneme awareness noted in the NRP report have been confi rmed (e.g., Clayton et al., 2020). Hulme and colleagues (2012) determined that “the development of children’s word literacy skills is causally infl uenced by children’s early letter knowledge and phoneme awareness” (p. 576), strengthening the case that these two skills should be directly taught to all beginning readers. In sum, the convergent evidence for the importance of phoneme awareness and letter skills for learning to read is indisputable. The explanation for the importance of phoneme awareness and letter knowledge is as follows: The learner of a writing system (an orthography) has to understand that sound units in the spoken language are represented by written symbols. In writing systems that are alphabetic, the beginner fi rst has to become aware of individual phonemes in spoken words and then understand that those phonemes are represented by letters. This sequence provides students with a necessary understanding of how the alphabetic writing system works, referred to as the alphabetic principle.
GEViTRec: Data Reconnaissance Through Recommendation Using a Domain-Specific Prevalence Visualization Design Space

Genomic Epidemiology (genEpi) is a branch of public health that uses many different data types including tabular, network, genomic, and geographic, to identify and contain outbreaks of deadly diseases. Due to the volume and variety of data, it is challenging for genEpi domain experts to conduct data reconnaissance; that is, have an overview of the data they have and make assessments toward its quality, completeness, and suitability. We present an algorithm for data reconnaissance through automatic visualization recommendation, GEViTRec. Our approach handles a broad variety of dataset types and automatically generates coordinated combinations of charts, in contrast to existing systems that primarily focus on singleton visual encodings of tabular datasets. We automatically detect linkages across multiple input datasets by analyzing non-numeric attribute fields, creating an entity graph within which we analyze and rank paths. For each high-ranking path, we specify chart combinations with spatial and color alignments between shared fields, using a gradual binding approach to transform initial partial specifications of singleton charts to complete specifications that are aligned and oriented consistently. A novel aspect of our approach is its combination of domain-agnostic elements with domain-specific information that is captured through a domain-specific visualization prevalence design space. Our implementation is applied to both synthetic data and real data from an Ebola outbreak. We compare GEViTRec's output to what previous visualization recommendation systems would generate, and to manually crafted visualizations used by practitioners. We conducted formative evaluations with ten genEpi experts to assess the relevance and interpretability of our results.
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence

The goal of this paper is to learn dense 3D shape correspondence for topology-varying objects in an unsupervised manner. Conventional implicit functions estimate the occupancy of a 3D point given a shape latent code. Instead, our novel implicit function produces a part embedding vector for each 3D point, which is assumed to be similar to its densely corresponded point in another 3D shape of the same object category. Furthermore, we implement dense correspondence through an inverse function mapping from the part embedding to a corresponded 3D point. Both functions are jointly learned with several effective loss functions to realize our assumption, together with the encoder generating the shape latent code. During inference, if a user selects an arbitrary point on the source shape, our algorithm can automatically generate a confidence score indicating whether there is a correspondence on the target shape, as well as the corresponding semantic point if there is one. Such a mechanism inherently benefits man-made objects with different part constitutions. The effectiveness of our approach is demonstrated through unsupervised 3D semantic correspondence and shape segmentation.
Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images

Diagnosing Brain Tumor with the aid of Magnetic Resonance Imaging (MRI) has gained enormous prominence over the years primarily in the field of medical science. Detection and/or partitioning of brain tumors solely with the aid of MR imaging is achieved at the cost of immense time and effort and demands a lot of expertise from engaged personnel. This substantiates the necessity of fabricating an autonomous model brain tumor diagnosis. Our work involves the implementation of a deep convolutional neural network (DCNN) for diagnosing brain tumor from MR images. The dataset, used in this paper, consists of 253 brain MR images where 155 images are reported to have tumors. Our model can single out the MR images with tumors with an overall accuracy of 96%. The model outperformed the existing conventional methods for the diagnosis of brain tumor in the test dataset (Precision = 0.93, Sensitivity = 1.00, and F1-score = 0.97). Moreover, the average precision-recall score of the proposed model is 0.93, Cohen's Kappa 0.91, and AUC 0.95. Therefore, the proposed model can be helpful for clinical experts to verify whether the patient has a brain tumor and, consequently, accelerate the treatment procedure.
Enhanced Remineralisation of Tooth Enamel Using Casein Phosphopeptide-Amorphous Calcium Phosphate Complex: A Review

The protective effects of milk and milk products against dental caries, due to micellar casein or caseinopeptide derivatives, have been demonstrated in various animal and human in situ studies. Among all the remineralising agents, casein phosphopeptide-amorphous calcium phosphate (CPP-ACP) technology has shown the most promising scientific evidence to support its use in prevention and reversal of carious lesions. CPP-ACP complex acts as a calcium phosphate reservoir and buffer the activities of free calcium and phosphate ions in the plaque. Calcium phosphate stabilized by CPP produces a metastable solution supersaturated with respect to the amorphous and crystalline calcium phosphate phases, thereby depressing enamel demineralisation and enhancing mineralisation. CPP-ACP provides new avenue for the remineralisation of noncavitated caries lesions. The objective of this article was to review the clinical trials of CPP-ACP complex and highlight its evidence based applications in dentistry.
Population Gradients improve performance across data-sets and architectures in object classification

The most successful methods such as ReLU transfer functions, batch normalization, Xavier initialization, dropout, learning rate decay, or dynamic optimizers, have become standards in the field due, particularly, to their ability to increase the performance of Neural Networks (NNs) significantly and in almost all situations. Here we present a new method to calculate the gradients while training NNs, and show that it significantly improves final performance across architectures, data-sets, hyper-parameter values, training length, and model sizes, including when it is being combined with other common performance-improving methods (such as the ones mentioned above). Besides being effective in the wide array situations that we have tested, the increase in performance (e.g. F1) it provides is as high or higher than this one of all the other widespread performance-improving methods that we have compared against. We call our method Population Gradients (PG), and it consists on using a population of NNs to calculate a non-local estimation of the gradient, which is closer to the theoretical exact gradient (i.e. this one obtainable only with an infinitely big data-set) of the error function than the empirical gradient (i.e. this one obtained with the real finite data-set).
Automated Environmental Compliance Monitoring with IoT and Open Government Data

Negative environmental impacts on societies and ecosystems are frequently driven by human activity and amplified by increasing climatic variability. Properly managing these impacts relies on a government's ability to ensure environmental regulatory compliance in the face of increasing uncertainty. Water flow rates are the most widely used evaluation metric for river regulatory compliance. Specifically, compliance thresholds are set by calculating the minimum flow rates required by aquatic species such as fish. These are then designated as the minimum "environmental flows" (eflows) for each river. In this paper, we explore how IoT-generated open government data can be used to enhance the development of an automated IoT-based eflows compliance system. To reduce development and operational costs, the proposed solution relies on routinely collected river monitoring data. Our approach allows for any authority with similar data to rapidly develop, test and verify a scalable solution for eflow regulatory compliance monitoring and evaluation. Furthermore, we demonstrate a real-world application of our system using open government data from Estonia's national river monitoring network. The main novelty of this work is that the proposed IoT-based system provides a simple evaluation tool that re-purposes IoT-generated open government data to evaluate compliance and improve monitoring at a national scale. This work showcases a new paradigm of IoT-based solutions using open government data and provides a real-world example of how the solution can automatically evaluate environmental compliance in increasingly uncertain environments.
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding

Coarse-grained linguistic information, such as named entities or phrases, facilitates adequately representation learning in pre-training. Previous works mainly focus on extending the objective of BERT’s Masked Language Modeling (MLM) from masking individual tokens to contiguous sequences of n tokens. We argue that such contiguously masking method neglects to model the intra-dependencies and inter-relation of coarse-grained linguistic information. As an alternative, we propose ERNIE-Gram, an explicitly n-gram masking method to enhance the integration of coarse-grained information into pre-training. In ERNIE-Gram, n-grams are masked and predicted directly using explicit n-gram identities rather than contiguous sequences of n tokens. Furthermore, ERNIE-Gram employs a generator model to sample plausible n-gram identities as optional n-gram masks and predict them in both coarse-grained and fine-grained manners to enable comprehensive n-gram prediction and relation modeling. We pre-train ERNIE-Gram on English and Chinese text corpora and fine-tune on 19 downstream tasks. Experimental results show that ERNIE-Gram outperforms previous pre-training models like XLNet and RoBERTa by a large margin, and achieves comparable results with state-of-the-art methods. The source codes and pre-trained models have been released at https://github.com/PaddlePaddle/ERNIE.
Maturation experiments reveal bias in the fossil record of feathers

The evolutionary history of birds and feathers is a major focus in palaeobiology and evolutionary biology. Diverse exceptionally preserved birds and feathered dinosaurs from Jurassic and Cretaceous biotas in China have provided pivotal evidence of early feathers and feather-like integumentary features, but the true nature of many of these fossil soft tissues is still debated. Interpretations of feathers at intermediate developmental stages (i.e. Stages II, III and IV) and of simple quill-like (Stage I) feathers are particularly controversial. This reflects key uncertainties relating to the preservation potential of feathers at different evolutionary-developmental stages, and to the relative preservation potential of diagnostic features of Stage I feathers and hair. To resolve these issues, we used high pressure-high temperature autoclave experiments to simulate the effects of burial on modern feathers from the Black Coucal (Centropus grilii) and Common Starling (Sturnus vulgaris), and on human hair. Our results reveal profound differences in the recalcitrance of feathers of different types during maturation: Stage I and Stage V feathers retain diagnostic morphological and ultrastructural details following maturation, whereas other feather types do not. Further, the morphology and arrangement of certain ultrastructural features diagnostic of Stages III and IV, e.g. barbules, are preferentially lost during maturation. These results indicate a pervasive bias in the fossil record of feathers, whereby preservation of feathers at Stages I and V is favored. Critical stages in the evolution of feathers, i.e. Stages II, III and IV, are less likely to be preserved and more likely to be misinterpreted as feathers at earlier developmental stages. Our discovery has major implications for our understanding of the fidelity of the fossil record of feathers and provides a framework for testing the significance of putative examples of fossil feathers at different developmental stages.
Transformer-Based End-to-End Speech Recognition with Local Dense Synthesizer Attention

Recently, several studies reported that dot-product self-attention (SA) may not be indispensable to the state-of-the-art Transformer models. Motivated by the fact that dense synthesizer attention (DSA), which dispenses with dot products and pairwise interactions, achieved competitive results in many language processing tasks, in this paper, we first propose a DSA-based speech recognition, as an alternative to SA. To reduce the computational complexity and improve the performance, we further propose local DSA (LDSA) to restrict the attention scope of DSA to a local range around the current central frame for speech recognition. Finally, we combine LDSA with SA to extract the local and global information simultaneously. Experimental results on the Ai-shell1 Mandarin speech recognition corpus show that the proposed LDSA-Transformer achieves a character error rate (CER) of 6.49%, which is slightly better than that of the SA-Transformer. Meanwhile, the LDSA-Transformer requires less computation than the SA-Transformer. The proposed combination method not only achieves a CER of 6.18%, which significantly outperforms the SA-Transformer, but also has roughly the same number of parameters and computational complexity as the latter. The implementation of the multi-head LDSA is available at https://github.com/mlxu995/multihead-LDSA
Euclid: Impact of non-linear and baryonic feedback prescriptions on cosmological parameter estimation from weak lensing cosmic shear

Upcoming surveys will map the growth of large-scale structure with unprecented precision, improving our understanding of the dark sector of the Universe. Unfortunately, much of the cosmological information is encoded on small scales, where the clustering of dark matter and the effects of astrophysical feedback processes are not fully understood. This can bias the estimates of cosmological parameters, which we study here for a joint analysis of mock Euclid cosmic shear and Planck cosmic microwave background data. We use different implementations for the modelling of the signal on small scales and find that they result in significantly different predictions. Moreover, the different non-linear corrections lead to biased parameter estimates, especially when the analysis is extended into the highly non-linear regime, with the Hubble constant, H0, and the clustering amplitude, σ8, affected the most. Improvements in the modelling of non-linear scales will therefore be needed if we are to resolve the current tension with more and better data. For a given prescription for the non-linear power spectrum, using different corrections for baryon physics does not significantly impact the precision of Euclid, but neglecting these correction does lead to large biases in the cosmological parameters. In order to extract precise and unbiased constraints on cosmological parameters from Euclid cosmic shear data, it is therefore essential to improve the accuracy of the recipes that account for non-linear structure formation, as well as the modelling of the impact of astrophysical processes that redistribute the baryons.