Miracle-dz/UniFAR-FTUs huggingface dataset:
SEB-Net: Revisiting Deep Encoder-Decoder Networks for Scene Understanding

As a research area of computer vision and deep learning, scene understanding has attracted a lot of attention in recent years. One major challenge encountered is obtaining high levels of segmentation accuracy while dealing with the computational cost and time associated with training or inference. Most current algorithms compromise one metric for the other depending on the intended devices. To address this problem, this paper proposes a novel deep neural network architecture called Segmentation Efficient Blocks Network (SEB-Net) that seeks to achieve the best possible balance between accuracy and computational costs as well as real-time inference speed. The model is composed of both an encoder path and a decoder path in a symmetric structure. The encoder path consists of 16 convolution layers identical to a VGG-19 model, and the decoder path includes what we call E-blocks (Efficient Blocks) inspired by the widely popular ENet architecture's bottleneck module with slight modifications. One advantage of this model is that the max-unpooling in the decoder path is employed for expansion and projection convolutions in the E-Blocks, allowing for less learnable parameters and efficient computation (10.1 frames per second (fps) for a 480x320 input, 11x fewer parameters than DeconvNet, 52.4 GFLOPs for a 640x360 input on a TESLA K40 GPU device). Experimental results on two outdoor scene datasets; Cambridge-driving Labeled Video Database (CamVid) and Cityscapes, indicate that SEB-Net can achieve higher performance compared to Fully Convolutional Networks (FCN), SegNet, DeepLabV, and Dilation8 in most cases. What's more, SEB-Net outperforms efficient architectures like ENet and LinkNet by 16.1 and 11.6 respectively in terms of Instance-level intersection over Union (iLoU). SEB-Net also shows better performance when further evaluated on the SUNRGB-D, an indoor scene dataset
Adaptive evolution at mRNA editing sites in soft-bodied cephalopods

The bulk of variability in mRNA sequence arises due to mutation – change in DNA sequence which is heritable if it occurs in the germline. However, variation in mRNA can also be achieved by post-translational modification including mRNA editing, changes in mRNA nucleotide sequence that mimic the effect of mutations. Such modifications are not inherited directly; however, as the processes affecting them are encoded in the genome, they have a heritable component, and therefore can be shaped by selection. In soft-bodied cephalopods, adenine-to-inosine RNA editing is very frequent, and much of it occurs at nonsynonymous sites, affecting the sequence of the encoded protein. Here, we show that mRNA editing of individual nonsynonymous sites in cephalopods originates in evolution through substitutions at regions adjacent to these sites. As such substitutions mimic the effect of the substitution at the edited site itself, we hypothesize that they are favored by selection if the inosine is selectively advantageous to adenine at the edited position. Consistent with this hypothesis and with observations on Drosophila and human editing sites, we show that edited adenines are more frequently substituted with guanine, an informational analogue of inosine, in the course of evolution than their unedited counterparts, and for heavily edited adenines, these transitions are favored by positive selection. Thus, our study shows that coleoid editing sites may enhance adaptation, which, together with distinct evolutionary features of Drosophila and human editing sites, points at a general role of RNA editing in the molecular evolution of metazoans.
Joint Policy-Value Learning for Recommendation

Conventional approaches to recommendation often do not explicitly take into account information on previously shown recommendations and their recorded responses. One reason is that, since we do not know the outcome of actions the system did not take, learning directly from such logs is not a straightforward task. Several methods for off-policy or counterfactual learning have been proposed in recent years, but their efficacy for the recommendation task remains understudied. Due to the limitations of offline datasets and the lack of access of most academic researchers to online experiments, this is a non-trivial task. Simulation environments can provide a reproducible solution to this problem. In this work, we conduct the first broad empirical study of counterfactual learning methods for recommendation, in a simulated environment. We consider various different policy-based methods that make use of the Inverse Propensity Score (IPS) to perform Counterfactual Risk Minimisation (CRM), as well as value-based methods based on Maximum Likelihood Estimation (MLE). We highlight how existing off-policy learning methods fail due to stochastic and sparse rewards, and show how a logarithmic variant of the traditional IPS estimator can solve these issues, whilst convexifying the objective and thus facilitating its optimisation. Additionally, under certain assumptions the value- and policy-based methods have an identical parameterisation, allowing us to propose a new model that combines both the MLE and CRM objectives. Extensive experiments show that this "Dual Bandit" approach achieves state-of-the-art performance in a wide range of scenarios, for varying logging policies, action spaces and training sample sizes.
Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivity

The human microbiota has a close relationship with human disease and it remodels components of the glycocalyx including heparan sulfate (HS). Studies of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) spike protein receptor binding domain suggest that infection requires binding to HS and angiotensin converting enzyme 2 (ACE2) in a codependent manner. Here, we show that commensal host bacterial communities can modify HS and thereby modulate SARS-CoV-2 spike protein binding and that these communities change with host age and sex. Common human-associated commensal bacteria whose genomes encode HS-modifying enzymes were identified. The prevalence of these bacteria and the expression of key microbial glycosidases in bronchoalveolar lavage fluid (BALF) was lower in adult COVID-19 patients than in healthy controls. The presence of HS-modifying bacteria decreased with age in two large survey datasets, FINRISK 2002 and American Gut, revealing one possible mechanism for the observed increase in COVID-19 susceptibility with age. In vitro, bacterial glycosidases from unpurified culture media supernatants fully blocked SARS-CoV-2 spike binding to human H1299 protein lung adenocarcinoma cells. HS-modifying bacteria in human microbial communities may regulate viral adhesion, and loss of these commensals could predispose individuals to infection. Understanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility. Graphical Abstract. Diagram of hypothesis for bacterial mediation of SARS-CoV-2 infection through heparan sulfate (HS). It is well known that host microbes groom the mucosa where they reside. Recent investigations have shown that HS, a major component of mucosal layers, is necessary for SARS-CoV-2 infection. In this study we examine the impact of microbial modification of HS on viral attachment.
GATA4 regulates epithelial morphogenesis in the developing mouse stomach to promote establishment of a glandular columnar epithelium

The transcription factor GATA4 is broadly expressed in nascent foregut endoderm. As development progresses, GATA4 is lost in the domain giving rise to the stratified squamous epithelium of the esophagus and forestomach (FS), while it is maintained in the domain giving rise to the simple columnar epithelium of the hindstomach (HS). Differential GATA4 expression within these domains coincides with the onset of distinct tissue morphogenetic events, suggesting a role for GATA4 in diversifying foregut endoderm into discrete esophageal/FS and HS tissues. By eliminating GATA4 in the developing HS or maintaining GATA4 in the developing FS, we identified GATA4 as an essential, principal regulator of simple columnar epithelium morphogenesis within the developing HS. GATA4- deficient HS epithelium adopted FS-like fate, and conversely, GATA4- expressing FS epithelium adopted HS-like fate. Underlying structural changes in these epithelia were broad changes in gene expression networks attributable to GATA4 directly activating or repressing expression of HS or FS defining transcripts. Our data implicate GATA4 as having a primary role in suppressing an esophageal/FS transcription factor network during HS development to promote a columnar epithelium. Moreover, GATA4-dependent phenotypes in developmental mutants reflected changes associated with Barrett’s esophagus, suggesting that developmental biology can provide insight into human disease mechanisms.
Wing damage affects flight kinematics but not flower tracking performance in hummingbird hawkmoths

ABSTRACT Wing integrity is crucial to the many insect species that spend distinct portions of their life in flight. How insects cope with the consequences of wing damage is therefore a central question when studying how robust flight performance is possible with such fragile chitinous wings. It has been shown in a variety of insect species that the loss in lift-force production resulting from wing damage is generally compensated by an increase in wing beat frequency rather than amplitude. The consequences of wing damage for flight performance, however, are less well understood, and vary considerably between species and behavioural tasks. One hypothesis reconciling the varying results is that wing damage might affect fast flight manoeuvres with high acceleration, but not slower ones. To test this hypothesis, we investigated the effect of wing damage on the manoeuvrability of hummingbird hawkmoths (Macroglossum stellatarum) tracking a motorised flower. This assay allowed us to sample a range of movements at different temporal frequencies, and thus assess whether wing damage affected faster or slower flight manoeuvres. We show that hummingbird hawkmoths compensate for the loss in lift force mainly by increasing wing beat amplitude, yet with a significant contribution of wing beat frequency. We did not observe any effects of wing damage on flight manoeuvrability at either high or low temporal frequencies. Highlighted Article: Hawkmoths compensate for wing damage when tracking flowers in hovering flight: they beat their wings faster and with larger amplitudes, without an impairment in tracking precision.
Spontaneous activity competes externally evoked responses in sensory cortex

The functional role of spontaneous brain activity, especially in relation to external events, is a longstanding key question in neuroscience. Intrinsic and externally-evoked activities were suggested to be anticorrelated, yet inferring an antagonistic mechanism between them remains a challenge. Here, we used beta-band (15-30 Hz) power as a proxy of spontaneous activity in the rat somatosensory cortex during a detection task. Beta-power anticorrelated with sensory-evoked-responses, and high rates of spontaneously occurring beta-bursts predicted reduced detection. By applying a burst-rate detection algorithm in real-time and trial-by-trial stimulus-intensity adjustment, this influence could be counterbalanced. Mechanistically, bursts in all bands indicated transient synchronization of cell assemblies, but only beta-bursts were followed by a reduction in firing-rate. Our findings reveal that spontaneous beta-bursts reflect a dynamic state that competes with external stimuli.
EPOC outside the shield: comparing the performance of a consumer-grade EEG device in shielded and unshielded environments

Low-cost, portable electroencephalography (EEG) devices have become commercially available in the last 10 years. One such system, Emotiv’s EPOC, has been modified to allow event-related potential (ERP) research. Although the EPOC has been shown to provide data comparable to research-grade equipment and has been used in real-world settings, how EPOC performs without the electrical shielding, commonly used in research-grade laboratories, is yet to be systematically tested. In the current article we address this gap by conducting a simple EEG experiment in shielded and unshielded contexts. Participants (n = 13, mean age = 23.2 years, SD = 7.9) monitored the presentation of human versus wristwatch faces, responding whether the images were inverted or not. This method elicited the face-sensitive N170 ERP. In both shielded and unshielded contexts, the N170 amplitude was larger when participants viewed human faces and peaked later when a human face was inverted. More importantly, Bayesian analysis showed no difference in the N170 measured in the shielded and unshielded contexts. Further, the signal recorded in both contexts was highly correlated. The EPOC appears to reliably record EEG signals without a purpose-built electrically-shielded room.
USAD: UnSupervised Anomaly Detection on Multivariate Time Series

The automatic supervision of IT systems is a current challenge at Orange. Given the size and complexity reached by its IT operations, the number of sensors needed to obtain measurements over time, used to infer normal and abnormal behaviors, has increased dramatically making traditional expert-based supervision methods slow or prone to errors. In this paper, we propose a fast and stable method called UnSupervised Anomaly Detection for multivariate time series (USAD) based on adversely trained autoencoders. Its autoencoder architecture makes it capable of learning in an unsupervised way. The use of adversarial training and its architecture allows it to isolate anomalies while providing fast training. We study the properties of our methods through experiments on five public datasets, thus demonstrating its robustness, training speed and high anomaly detection performance. Through a feasibility study using Orange's proprietary data we have been able to validate Orange's requirements on scalability, stability, robustness, training speed and high performance.
Humanoid Robot Control System Based on AR-SSVEP

In the brain computer interface (BCI), steady-state visual evoked potential (SSVEP) is a relatively common input signal of human-computer interaction systems. However, it often requires a fixed computer screen as a visual stimulator, which limits the flexibility of its application. In this research, HoloLens glasses are used as visual stimulators in a BCI system based on augmented reality to control the humanoid robot NAO to recognize and grasp objects. The system uses augmented reality device to induce steady-state visual evoked potential. The user does not need to perform visual stimulation at a fixed position, which can enhance the applicability in complex environments, thereby achieving more natural human-computer interaction. In order to achieve grasping, this study uses robot monocular vision recognition and establish forward and inverse kinematics models of the robot arms. EEG experiments have been performed to verify the accuracy of the system, it is more flexible and convenient for using augmented reality as stimulators in a humanoid robot control system based on SSVEP-BCI.
A Self-Evolving Mutually-Operative Recurrent Network-based Model for Online Tool Condition Monitoring in Delay Scenario

With the increasing demand of product supply, manufacturers are in urgent need of online tool condition monitoring (TCM) without compromising with the maintenance cost in terms of time as well as man-power requirement. However, the existing machine learning models for TCM are mostly offline and not suitable for the non-stationary environment of the machining settings. Moreover, the access of the ground truth always imposes a shutdown of the machining process and the existing models are severely affected by such delay in receiving labelled samples. In order to tackle these issues, we propose SERMON as a novel learning model based on a pair of self-evolving mutually-operative recurrent neural networks. The proposed SERMON is well-equipped with features for automated and real-time monitoring of machine fault status even in the finite/infinite label delay scenario. The experimental evaluation of SERMON using real-world dataset on 3D-printing process demonstrates its effectiveness in online fault detection under non-stationary as well as delayed label context of the machining process. Additional comparative study on large-scale benchmark streaming datasets further exhibits the scalability power of SERMON.
How to Calibrate your Neural Network Classifier: Getting True Probabilities from a Classification Model

Research in Machine Learning (ML) for classification tasks has been primarily guided by metrics that derive from a confusion matrix (e.g. accuracy, precision and recall). Several works have highlighted that this has lead to training practices that produce over-confident models and void the assumption that the model learns a probability distribution over the classification targets; this is referred to as miscalibration. Consequently, modern ML architectures struggle to perform in applications where a probabilistic forecaster is needed. Research efforts on calibration techniques have explored the possibility of recovering probability distributions from traditional architectures. This tutorial covers the key concepts required to understand the motivations behind calibration and aims at providing participants with the tools that they require assess the calibration of ML models and calibrate them when required.
Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping

Background This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to achieve desired selection accuracy for breeders and quality for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. However, genomic selection requires a large initial investment, which limits the adoption of genomic selection for some breeding programmes. Methods We simulated a case-study of a small dairy population with a number of scenarios under equal available resources. The conventional progeny testing scenario had 11 phenotype records per lactation. In genomic scenarios, we reduced phenotyping to between 10 and 1 phenotype records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without initial training population for genomic selection. Results Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing investment in genotyping, despite reduced phenotyping, and with high-genotyping scenarios not even using all the available resources. Compared to the conventional scenario, genomic scenarios also increased accuracy for young non-phenotyped male and female candidates, and cows. Conclusions This study shows that breeding programmes should optimize investment into phenotyping and genotyping to maximise return on investment. Our results suggest that any dairy breeding programme using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.
Pest Management In Cotton Farms: An AI-System Case Study from the Global South

Nearly 100 million families across the world rely on cotton farming for their livelihood. Cotton is particularly vulnerable to pest attacks, leading to overuse of pesticides, lost income for farmers, and in some cases farmer suicides. We address this problem by presenting a new solution for pesticide management that uses deep learning, smartphone cameras, inexpensive pest traps, existing digital pipelines, and agricultural extension-worker programs. Although generic, the platform is specifically designed to assist smallholder farmers in the developing world. In addition to outlining the solution, we consider the set of unique constraints this context places on it: data diversity, annotation challenges, shortcomings with traditional evaluation metrics, computing on low-resource devices, and deployment through intermediaries. This paper summarizes key lessons learned while developing and deploying the proposed solution. Such lessons may be applicable to other teams interested in building AI solutions for global development.
Microbial community composition interacts with local abiotic conditions to drive colonization resistance in human gut microbiome samples

Biological invasions can alter ecosystem stability and function, and predicting what happens when a new species or strain arrives remains a major challenge in ecology. In the mammalian gastrointestinal tract, susceptibility of the resident microbial community to invasion by pathogens has important implications for host health. However, at the community level, it is unclear whether susceptibility to invasion depends mostly on resident community composition (which microbes are present), or also on local abiotic conditions (such as nutrient status). Here, we used a gut microcosm system to disentangle some of the drivers of susceptibility to invasion in microbial communities sampled from humans. We found resident microbial communities inhibited an invading E. coli strain, compared to community-free control treatments, sometimes excluding the invader completely (colonization resistance). These effects were stronger at later time points, coinciding with shifts in microbial community composition and nutrient availability. By separating these two components (microbial community and abiotic environment), we found taxonomic composition played a crucial role in suppressing invasion, but this depended critically on local abiotic conditions (adapted communities were more suppressive in nutrient-depleted conditions). This helps predict when resident communities will be most susceptible to invasion, with implications for optimizing treatments based around microbiota management.
Museomics of a rare taxon: placing Whalleyanidae in the Lepidoptera Tree of Life

Museomics is a valuable tool that utilises the diverse biobanks that are natural history museums. The ability to sequence genomes from old specimens has expanded not only the variety of interesting taxa available to study but also the scope of questions that can be investigated in order to further knowledge about biodiversity. Here we present whole genome sequencing results from the enigmatic genus Whalleyana, as well as the families Callidulidae and Hyblaeidae. Library preparation was carried out on four museum specimens and one existing DNA extract and sequenced with Illumina short reads. De novo assembly resulted in highly fragmented genomes with the N50 ranging from 317 – 2,078 bp. Mining of a manually curated gene set of 332 genes from these draft genomes had an overall gene recovery rate of 64 – 90%. Phylogenetic analysis places Whalleyana as sister to Callidulidae, and Hyblaea as sister to Pyraloidea. Since the former sister-group relationship turns out to be also supported by ten morphological synapomorphies, we propose to formally assign the Whalleyanidae to the superfamily Calliduloidea. These results highlight the usefulness of not only museum specimens, but also existing DNA extracts, for whole genome sequencing and gene mining for phylogenomic studies.