Miracle-dz/UniFAR-FTUs huggingface dataset:
An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics.

We describe an "embarrassingly parallel" method for Bayesian phylogenetic inference, annealed Sequential Monte Carlo (SMC), based on recent advances in the SMC literature such as adaptive determination of annealing parameters. The algorithm provides an approximate posterior distribution over trees and evolutionary parameters as well as an unbiased estimator for the marginal likelihood. This unbiasedness property can be used for the purpose of testing the correctness of posterior simulation software. We evaluate the performance of phylogenetic annealed SMC by reviewing and comparing with other computational Bayesian phylogenetic methods, in particular, different marginal likelihood estimation methods. Unlike previous SMC methods in phylogenetics, our annealed method can utilize standard Markov chain Monte Carlo (MCMC) tree moves and hence benefit from the large inventory of such moves available in the literature. Consequently, the annealed SMC method should be relatively easy to incorporate into existing phylogenetic software packages based on MCMC algorithms. We illustrate our method using simulation studies and real data analysis. [Marginal likelihood; phylogenetics; Sequential Monte Carlo. ].
Development of Wheelchair Dance Game using Wheel Rotation Speed as Input

A wheelchair is often caught as a tool for substitution to walk and to move; however in recent years, another type of wheelchair superior in design to fascinate onlookers appears. Following and pushing forward this tendency, we aim to create a wheelchair that provides an amusing riding experience - not only for moving -- for changing perspectives for disability. To realize this concept, we developed a dance game system using the wheel rotation as input, referencing a game design of music video game. Players can behave as if they are dancing just by moving according to the music (Figure 1). For concept proof, in exhibition "Super Welfare Expo 2016" in Shibuya, Japan, we held the experience meeting of this dance game for both actual wheelchair users and healthy persons and get the feedback.
Contraceptive use among migrant women with a history of induced abortion in Finland

Abstract Background: Women’s contraceptive choices may change after an induced abortion, due to contraceptive counselling or a behavioural change prompted by the experience. The effect may vary between women; sociocultural background, for example, may affect their subsequent reproductive choices. Objective: We examined whether women’s current contraceptive use was differently associated with a history of induced abortion among immigrant groups in Finland (Russian, Kurdish and Somali) and the general Finnish population. Methods: We analysed data from two surveys, the Migrant Health and Wellbeing study and the Health 2011 study, linked to the Finnish register of induced abortions. Propensity score weighted logistic regression was used to analyse the data. Results: The likelihood of using contraceptives after an abortion varied depending on women’s sociocultural background. A history of induced abortion increased contraceptive use among all groups, except Russian women, in whom there was no effect. The effect was particularly strong for Kurdish women. Conclusion: Sociocultural background was an important determinant of post-abortion contraceptive use. Some immigrants may struggle to navigate the Finnish health care system due to language or literacy issues. Attention should be paid to improving access to family planning among these groups.
Current computational trends in polyanionic cathode materials for Li and Na batteries

A long-standing effort has been devoted for the development of high energy density cathodes both for Li- and Na-ion batteries (LIBs and SIBs). The scientific communities in battery research primarily divide the Li- and Na-ion cathode materials into two categories: layered oxides and polyanionic compounds. Researchers are trying to improve the energy density of such materials through materials screening by mixing the transition metals or changing the concentration of Li or Na in the polyanionic compounds. Due to the fact that there is more stability in the polyanionic frameworks, batteries based on these materials mostly provide a prolonged cycling life as compared to the layered oxide materials. Nevertheless, the bottleneck for such compounds is the weight penalty from polyanionic groups that results into the lower capacity. The anion engineering could be considered as an essential way out to design such polyanionic compounds to resolve this issue and to fetch improved cathode performance. In this topical review we present a systematic overview of the polyanionic cathode materials used for LIBs and SIBs. We will also present the computational methodologies that have become significantly relevant for battery research. We will make an attempt to provide the theoretical insight with a current development in sulfate (SO4), silicate (SiO4) and phosphate (PO4) based cathode materials for LIBs and SIBs. We will end this topical review with the future outlook, that will consist of the next generation organic electrode materials, mainly based on conjugated carbonyl compounds.
InDUS: Incremental Document Understanding System Focus on Document Classification

Our objective is to propose a Document Understanding System for Digital Mailroom application which can cope with three challenges: (1) process a full workflow with high accuracy, with the constraint of a partial training; (2) minimal requirement for configuration work from expert users; (3) adapt incrementally the system in quasi real-time to continuously maximize the recall. We describe an end-to-end system based on existing incremental algorithms for both document classification and field extraction. But in this paper, we really focus on the document classification issue. The main contribution is to adapt the Incremental Growing Neural Gas (A2ING) with a dynamic incremental feature vector. Moreover, a generic Framework automatically selects textual descriptors relying on performance. The quality assessment converges the A2ING and controls the system accuracy.
A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study

Background A major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic. Objective To investigate a deep learning approach to retrieve scientifically sound treatment studies from the biomedical literature. Methods We trained a Convolutional Neural Network using a noisy dataset of 403,216 PubMed citations with title and abstract as features. The deep learning model was compared with state-of-the-art search filters, such as PubMed’s Clinical Query Broad treatment filter, McMaster’s textword search strategy (no Medical Subject Heading, MeSH, terms), and Clinical Query Balanced treatment filter. A previously annotated dataset (Clinical Hedges) was used as the gold standard. Results The deep learning model obtained significantly lower recall than the Clinical Queries Broad treatment filter (96.9% vs 98.4%; P<.001); and equivalent recall to McMaster’s textword search (96.9% vs 97.1%; P=.57) and Clinical Queries Balanced filter (96.9% vs 97.0%; P=.63). Deep learning obtained significantly higher precision than the Clinical Queries Broad filter (34.6% vs 22.4%; P<.001) and McMaster’s textword search (34.6% vs 11.8%; P<.001), but was significantly lower than the Clinical Queries Balanced filter (34.6% vs 40.9%; P<.001). Conclusions Deep learning performed well compared to state-of-the-art search filters, especially when citations were not indexed. Unlike previous machine learning approaches, the proposed deep learning model does not require feature engineering, or time-sensitive or proprietary features, such as MeSH terms and bibliometrics. Deep learning is a promising approach to identifying reports of scientifically rigorous clinical research. Further work is needed to optimize the deep learning model and to assess generalizability to other areas, such as diagnosis, etiology, and prognosis.
Chronic social stress induces DNA methylation changes at an evolutionary conserved intergenic region in chromosome X

ABSTRACT Chronic stress resulting from prolonged exposure to negative life events increases the risk of mood and anxiety disorders. Although chronic stress can change gene expression relevant for behavior, molecular regulators of this change have not been fully determined. One process that could play a role is DNA methylation, an epigenetic process whereby a methyl group is added onto nucleotides, predominantly cytosine in the CpG context, and which can be induced by chronic stress. It is unknown to what extent chronic social defeat, a model of human social stress, influences DNA methylation patterns across the genome. Our study addressed this question by using a targeted-capture approach called Methyl-Seq to investigate DNA methylation patterns of the dentate gyrus at putative regulatory regions across the mouse genome from mice exposed to 14 days of social defeat. Findings were replicated in independent cohorts by bisulfite-pyrosequencing. Two differentially methylated regions (DMRs) were identified. One DMR was located at intron 9 of Drosha, and it showed reduced methylation in stressed mice. This observation replicated in one of two independent cohorts. A second DMR was identified at an intergenic region of chromosome X, and methylation in this region was increased in stressed mice. This methylation difference replicated in two independent cohorts and in Major Depressive Disorder (MDD) postmortem brains. These results highlight a region not previously known to be differentially methylated by chronic social defeat stress and which may be involved in MDD.
Analytical intra-system EMI model using dipole moments and reciprocity

The dipole moment-based reciprocity to model intra-system electromagnetic interference (EMI) in wireless devices is proposed in this paper. In this model, the decomposition method based on reciprocity is used to calculate the coupling voltage on the victim antenna. The original noise source is replaced by equivalent dipole moments in the forward problem while electromagnetic fields on the locations of dipole moments are scanned in the reverse problem. The proposed model has been validated with full-wave simulations and measurements; it provides not only convenience to estimate radio-frequency interference (RFI), but also physical insight to understand the intra-system EMI problem.
Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements

In recent years, spaceborne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here, we employ an automatic sea ice classification algorithm on two sets of spatially and temporally near coincident fully polarimetric acquisitions from the ALOS-2, Radarsat-2, and TerraSAR-X/TanDEM-X satellites. Overlapping coincident sea ice freeboard measurements from airborne laser scanner data are used to validate the classification results. The automated sea ice classification algorithm consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. Next, the resulting feature vectors are ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. Coherency matrix-based features that require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix-based features, which makes coherency matrix-based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant-based features (geometric intensity, scattering diversity, and surface scattering fraction). Classification results show that 100% of the open water is separated from the surrounding sea ice and that the sea ice classes have at least 96.9% accuracy. This analysis reveals analogous results for both X-band and C-band frequencies and slightly different for the L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected sea ice when compared with high-resolution airborne measurements.
Research on the island intelligent monitoring system based on Beidou

The natural resources stored in the islands and its adjacent seas are precious assets for the survival and development of the Chinese nation. It is necessary to establish an intelligent monitoring system for Islands. The paper introduces the system composition and working principle of the island intelligent monitoring system based on Beidou; And the paper introduces the key processing algorithms and key modules of the system. The system realizes the remote intelligent collection and transmission of Island video data and ecological data.
Oxytocin‐like signaling in ants influences metabolic gene expression and locomotor activity

Ants are emerging model systems to study cellular signaling because distinct castes possess different physiologic phenotypes within the same colony. Here we studied the functionality of inotocin signaling, an insect ortholog of mammalian oxytocin (OT), which was recently discovered in ants. In Lasius ants, we determined that specialization within the colony, seasonal factors, and physiologic conditions down‐regulated the expression of the OT‐like signaling system. Given this natural variation, we interrogated its function using RNAi knockdowns. Next‐generation RNA sequencing of OT‐like precursor knock‐down ants highlighted its role in the regulation of genes involved in metabolism. Knock‐down ants exhibited higher walking activity and increased self‐grooming in the brood chamber. We propose that OT‐like signaling in ants is important for regulating metabolic processes and locomotion. —Liutkevičiūtė, Z. , Gil‐Mansilla, E. , Eder, T. , Casillas‐Pérez, B. , Di Giglio, M. G. , Muratspahić, E. , Grebien, F. , Rattei, T. , Muttenthaler, M. , Cremer, S. , Gruber, C. W. Oxytocin‐like signaling in ants influences metabolic gene expression and locomotor activity. FASEB J. 32, 6808–6821 (2018). www.fasebj. org
DRAM refresh improvement with bank reordering

Due to few cells in memory that have lower retention time, DRAM have to raise the refresh frequency to keep the data integrity, and produce unnecessary refresh for the other normal cells, which result in large refresh power consumption and the delay of memory access. In this paper, we propose a DRAM bank reordering technique to considering the randomness of weak row distribution. We resolve this problem by the mixed integer linear programming methodology to achieve the best bank order that have the lowest refresh overhead. Experimental results show that with the bank reordering technique, we can reduce more refresh power and get even performance improvement than without applying it.
Cerebral Aneurysm Computer-Aided Detection System by combing MSER, SURF and SIFT descriptors

Computer-Assisted Detection (CAD) systems for detecting Cerebral Aneurysms (CA) present an important influence in the prevention of Intracranial HSA (Subarachnoid Hemorrhage), their usefulness haves been reported. We propose in this paper, a CAD to detect CA in DSA angiographic images by combining different methods for robust features/interest points detector which are Maximally Stable Extremal Regions (MSER), Speed Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT). The results on the proposed CAD over the provided benchmark are very encouraging.
Descending pathways generate perception of and neural responses to weak sensory input

Natural sensory stimuli frequently consist of a fast time-varying waveform whose amplitude or contrast varies more slowly. While changes in contrast carry behaviorally relevant information necessary for sensory perception, their processing by the brain remains poorly understood to this day. Here, we investigated the mechanisms that enable neural responses to and perception of low-contrast stimuli in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus. We found that fish reliably detected such stimuli via robust behavioral responses. Recordings from peripheral electrosensory neurons revealed stimulus-induced changes in firing activity (i.e., phase locking) but not in their overall firing rate. However, central electrosensory neurons receiving input from the periphery responded robustly via both phase locking and increases in firing rate. Pharmacological inactivation of feedback input onto central electrosensory neurons eliminated increases in firing rate but did not affect phase locking for central electrosensory neurons in response to low-contrast stimuli. As feedback inactivation eliminated behavioral responses to these stimuli as well, our results show that it is changes in central electrosensory neuron firing rate that are relevant for behavior, rather than phase locking. Finally, recordings from neurons projecting directly via feedback to central electrosensory neurons revealed that they provide the necessary input to cause increases in firing rate. Our results thus provide the first experimental evidence that feedback generates both neural and behavioral responses to low-contrast stimuli that are commonly found in the natural environment.
In vivo imaging of the pathophysiological changes and neutrophil dynamics in influenza virus-infected mouse lungs

Significance We used a state-of-the-art in vivo imaging system and fluorescent influenza viruses (Color-flu) to determine in real time the pathophysiological changes in the lungs of infected mice. We found that influenza virus infections reduced blood flow speed and decreased neutrophil motility. More significantly, infection with a prototypic “bird flu” strain, a highly pathogenic H5N1 influenza virus, caused higher pulmonary permeability than did infection with a mouse-adapted human influenza virus. This in vivo imaging system with quantitative analyses allowed us to reveal the progression of the disease at the cellular level and to perform a multiparameter analysis that is not possible by using conventional histopathology. The pathophysiological changes that occur in lungs infected with influenza viruses are poorly understood. Here we established an in vivo imaging system that combines two-photon excitation microscopy and fluorescent influenza viruses of different pathogenicity. This approach allowed us to monitor and correlate several parameters and physiological changes including the spread of infection, pulmonary permeability, pulmonary perfusion speed, number of recruited neutrophils in infected lungs, and neutrophil motion in the lungs of live mice. Several physiological changes were larger and occurred earlier in mice infected with a highly pathogenic H5N1 influenza virus compared with those infected with a mouse-adapted human strain. These findings demonstrate the potential of our in vivo imaging system to provide novel information about the pathophysiological consequences of virus infections.
In-Situ Instructions Exceed Side-by-Side Instructions in Augmented Reality Assisted Assembly

Driven by endeavors towards Industry 4.0, there is increasing interest in augmented reality (AR) as an approach for assistance in areas like picking, assembly and maintenance. In this work our focus is on AR-based assistance in manual assembly. The design space for AR instructions in this context includes, e.g., side-by-side, 3D or projected 2D presentations. In previous research, the low quality of the AR devices available at the respective time had a significant impact on performance evaluations. Today, a proper and up-to-date comparison of different presentation approaches is missing. This paper presents an improved 3D in-situ instruction and compares it to previously presented techniques. All instructions are implemented on up-to-date AR hardware, namely the Microsoft HoloLens. To support reproducible research, the comparison is made using a standardized benchmark scenario. The results show, contrary to previous research, that in-situ instructions on state-of-the-art AR glasses outperform side-by-side instructions in terms of errors made, task completion time, and perceived task load.
Prediction model of the unemployment rate for nanyang in henan province based on BP neural network

A prediction model for the unemployment rate of Nanyang in Henan province based on BP (Back Propagation) neural network has been built in this paper. The training samples are from the data in Nanyang statistical yearbook, and the prediction model has been simulated by the MATLAB software. It's concluded that it is fully feasible to adopt the BP neural network to predict unemployment rate from the results, which provides some employment guidance for early warning.
A Bayesian Bandit Approach to Adaptive Delay-based Congestion Control

Adapting the transmission rate of video telephony Internet applications in order to guarantee the maximal communication quality is still an open and extremely challenging problem. The congestion control algorithm, which is the algorithm responsible for adjusting the transmission rate according to the network conditions, should typically be able to reach the largest possible rate, in order to achieve a high video quality, at the minimum possible delay, in order to guarantee a good interactivity. At the same time, it should also guarantee a fair share of the network resources when competing with other communication protocols, in particular loss-based congestion protocols. These two objectives actually conflict with each other: whereas, in order to achieve the largest rate with the minimum delay, the delay-based congestion control should be extremely sensitive to delay variations, it should also be ideally immune to delay variations to have perfect coexistence with loss-based protocols. In order to achieve this double objective we propose a learning-based adaptive controller that tunes the delay sensitivity of an underlying delay-based congestion control according to the estimated network conditions. We first define a simple low-dimensional model for the network response. We then formulate a bayesian bandit problem for the selection of the delay sensitivity of the congestion control algorithm. By solving the bandit problem using an optimal learning method we are able to maximize effectively the long term utility provided to the user. Finally, we provide simulation results to demonstrate the operation of the proposed method and its effective ability to adapt to different network scenarios in order to maximize the communication quality.
Effect of human recreation on bird anti-predatory response

Wildlife perceive humans as predators, and therefore normally flushes. Flight initiation distance (FID) is the distance a human can approach an animal at a steady pace until it flushes. Recently, several studies showed differences in within-species FID according to human presence by comparing urban and rural habitats, with urban birds showing reduced FIDs. However, urban and rural habitats also differ in structure, which might affect FID. Therefore, in order to understand the real effect of human presence, we investigated whether differences in FID are also present in natural habitats (forests), differing only in the intensity of human use for recreation. We found that human frequentation had a distinct effect on bird escape responses, with shorter FIDs in forests more-heavily frequented by humans than in forests rarely visited by humans. Whether this finding is driven by non-random spatial distribution of personalities (shy vs. bold) or phenotypic plasticity (habituation to humans) cannot be assessed with our data. Studies relying on FIDs should also incorporate human recreation intensity, as this affects the measurements strongly.