Miracle-dz/UniFAR-FTUs huggingface dataset:
3-D Velocity and Volume Flow Measurement  $In~Vivo$  Using Speckle Decorrelation and 2-D High-Frame-Rate Contrast-Enhanced Ultrasound

Being able to measure 3-D flow velocity and volumetric flow rate effectively in the cardiovascular system is valuable but remains a significant challenge in both clinical practice and research. Currently, there has not been an effective and practical solution to the measurement of volume flow using ultrasound imaging systems due to challenges in existing 3-D imaging techniques and high system cost. In this study, a new technique for quantifying volumetric flow rate from the cross-sectional imaging plane of the blood vessel was developed by using speckle decorrelation (SDC), 2-D high-frame-rate imaging with a standard 1-D array transducer, microbubble contrast agents, and ultrasound imaging velocimetry (UIV). Through SDC analysis of microbubble signals acquired with a very high frame rate and by using UIV to estimate the two in-plane flow velocity components, the third and out-of-plane velocity component can be obtained over time and integrated to estimate volume flow. The proposed technique was evaluated on a wall-less flow phantom in both steady and pulsatile flow. UIV in the longitudinal direction was conducted as a reference. The influences of frame rate, mechanical index (MI), orientation of imaging plane, and compounding on velocity estimation were also studied. In addition, an in vivo trial on the abdominal aorta of a rabbit was conducted. The results show that the new system can estimate volume flow with an averaged error of 3.65% ± 2.37% at a flow rate of 360 mL/min and a peak velocity of 0.45 m/s, and an error of 5.03% ± 2.73% at a flow rate of 723 mL/min and a peak velocity of 0.8 m/s. The accuracy of the flow velocity and volumetric flow rate estimation directly depend on the imaging frame rate. With a frame rate of 6000 Hz, a velocity up to 0.8 m/s can be correctly estimated. A higher mechanical index (MI = 0.42) is shown to produce greater errors (up to 21.78±0.49%, compared to 3.65±2.37% at MI = 0.19). An in vivo trial, where velocities up to 1 m/s were correctly measured, demonstrated the potential of the technique in clinical applications.
Strengthening Global Public Health Surveillance through Data and Benefit Sharing

Equitable sharing of public health surveillance data can help prevent or mitigate the effect of infectious diseases. Equitable data sharing includes working toward more equitable sharing of the public health benefits that data sharing brings and requires the engagement of those providing the data, those interpreting and using the data generated by others, those facilitating the data-sharing process, and those deriving and contributing to the benefit. An expert consultation conducted by Chatham House outlined 7 principles to encourage the process of equitable data sharing: 1) building trust; 2) articulating the value; 3) planning for data sharing; 4) achieving quality data; 5) understanding the legal context; 6) creating data-sharing agreements; and 7) monitoring and evaluation. Sharing of public health surveillance data is best done taking into account these principles, which will help to ensure data are shared optimally and ethically, while fulfilling stakeholder expectations and facilitating equitable distribution of benefits.
SyMIL: MinMax Latent SVM for Weakly Labeled Data

Designing powerful models able to handle weakly labeled data are a crucial problem in machine learning. In this paper, we propose a new multiple instance learning (MIL) framework. Examples are represented as bags of instances, but we depart from standard MIL assumptions by introducing a symmetric strategy (SyMIL) that seeks discriminative instances in positive and negative bags. The idea is to use the instance the most distant from the hyper-plan to classify the bag. We provide a theoretical analysis featuring the generalization properties of our model. We derive a large margin formulation of our problem, which is cast as a difference of convex functions, and optimized using concave-convex procedure. We provide a primal version optimizing with stochastic subgradient descent and a dual version optimizing with one-slack cutting-plane. Successful experimental results are reported on standard MIL and weakly supervised object detection data sets: SyMIL significantly outperforms competitive methods (mi/MI/Latent-SVM), and gives very competitive performance compared to state-of-the-art works. We also analyze the selected instances of symmetric and asymmetric approaches on weakly supervised object detection and text classification tasks. Finally, we show complementarity of SyMIL with recent works on learning with label proportions on standard MIL data sets.
Linkages between measures of biodiversity and community resilience in Pacific Island agroforests

Designing agroecosystems that are compatible with the conservation of biodiversity is a top conservation priority. However, the social variables that drive native biodiversity conservation in these systems are poorly understood. We devised a new approach to identify social–ecological linkages that affect conservation outcomes in agroecosystems and in social‐ecological systems more broadly. We focused on coastal agroforests in Fiji, which, like agroforests across other small Pacific Islands, are critical to food security, contain much of the country's remaining lowland forests, and have rapidly declining levels of native biodiversity. We tested the relationships among social variables and native tree species richness in agroforests with structural equation models. The models were built with data from ecological and social surveys in 100 agroforests and associated households. The agroforests hosted 95 native tree species of which almost one‐third were endemic. Fifty‐eight percent of farms had at least one species considered threatened at the national or international level. The best‐fit structural equation model (R2 = 47.8%) showed that social variables important for community resilience—local ecological knowledge, social network connectivity, and livelihood diversity—had direct and indirect positive effects on native tree species richness. Cash‐crop intensification, a driver of biodiversity loss elsewhere, did not negatively affect native tree richness within parcels. Joining efforts to build community resilience, specifically by increasing livelihood diversity, local ecological knowledge, and social network connectivity, may help conservation agencies conserve the rapidly declining biodiversity in the region.
Persistent actin depolarization caused by ethanol induces the formation of multiple small cortical septin rings in yeast

ABSTRACT Short-term exposure to severe ethanol stress has adverse effects on yeast cells. However, limited information is available on the effects of long-term exposure to severe ethanol stress. In this study, we examined the effects of a long-term treatment with a high ethanol concentration [10% (v/v)] on yeast morphology. We found that long-term severe ethanol stress induced the continuous depolarization of the actin cytoskeleton and hypertrophy in yeast cells, accompanied by the aberrant localization of septins, which formed multiple small cortical rings (MSCRs). The formation of MSCRs was also induced by the continuous depolarization of the actin cytoskeleton caused by a treatment with latrunculin-A, an effective inhibitor of actin polymerization. Unlike the formation of conventional septin rings, the formation of MSCRs did not require Cdc42 and its effectors, Gic1, Gic2 and Cla4. These results provide novel insights into the effects of persistent actin depolarization caused by long-term exposure to severe ethanol stress on yeast cytomorphology. Summary: Long-term severe ethanol stress induces the formation of a unique high-ordered structure (multiple small cortical rings) of septin, accompanied by a depolarized actin cytoskeleton in yeast.
Unification of MAP Estimation and Marginal Inference in Recurrent Neural Networks

Numerous experimental data show that human brain can represent probability distributions and perform Bayesian inference. However, it remains unclear how the brain implements probabilistic inference in the form of neural circuits. Several models have been proposed that aim at explaining how the network of neurons carry out maximum a posterior inference (MAP) estimation and marginal inference, but they are all task specific in that they treat MAP estimation and marginal inference separately. In this brief, we propose that human brain could implement MAP estimation and marginal inference in the same network of neurons. We illustrate our result in hidden Markov models and prove that a recurrent neural network (RNN) implementation of belief propagation can be tuned to perform approximate Bayesian inference (to provide posterior or conditional distribution over the latent causes of observations) or identify the MAP or peak of the joint distribution. The key tuning parameter is a temperature parameter that controls the precision of probability distributions that are optimized. Theoretical analyses and experimental results demonstrate that RNNs can carry out near-optimal MAP estimation and marginal inference.
Hidden features: exploring the non-canonical functions of metabolic enzymes

ABSTRACT The study of cellular metabolism has been rigorously revisited over the past decade, especially in the field of cancer research, revealing new insights that expand our understanding of malignancy. Among these insights is the discovery that various metabolic enzymes have surprising activities outside of their established metabolic roles, including in the regulation of gene expression, DNA damage repair, cell cycle progression and apoptosis. Many of these newly identified functions are activated in response to growth factor signaling, nutrient and oxygen availability, and external stress. As such, multifaceted enzymes directly link metabolism to gene transcription and diverse physiological and pathological processes to maintain cell homeostasis. In this Review, we summarize the current understanding of non-canonical functions of multifaceted metabolic enzymes in disease settings, especially cancer, and discuss specific circumstances in which they are employed. We also highlight the important role of subcellular localization in activating these novel functions. Understanding their non-canonical properties should enhance the development of new therapeutic strategies for cancer treatment. Summary: This Review summarizes recent findings about multifaceted metabolic enzymes with non-canonical activities outside their core biochemical functions, and how they may provide new therapeutic strategies for cancers.
Adding Semantics To Comics Using A Crowdsourcing Approach

With over 85 million print units sold last year only for the top 300 comic book titles, the comics industry is reaching a new high for the first time since 2007 (before the economical crisis). And this excludes the increasingly popular graphic novels or the increasingly more accessible digital comics. Digital comics however find themselves at a point where the difficulty of producing high quality content is directly related to the lack of semantic information linked to it. In this short paper, we propose a crowdsourcing approach for annotating comic books for the purpose of producing XML-based encodings to assist publishers, researchers or collection curators.
Multiclass Classification of Word Imagination Speech With Hybrid Connectivity Features

<italic>Objective:</italic> In this study, electroencephalography data of imagined words were classified using four different feature extraction approaches. Eight subjects were recruited for the recording of imagination with five different words, namely; “go,” “back,” “left,” “right,” and “stop. ” <italic>Methods:</italic> One hundred trials for each word were recorded for both imagination and perception, although this study utilized only imagination data. Two different connectivity methods were applied, namely; a covariance-based and a maximum linear cross-correlation-based connectivity measure. These connectivity measures were further computed to extract the phase-only data as an additional method of feature extraction. In addition, four different channel selections were used. The final connectivity matrix from each of the four methods was vectorized and used as the feature vector for the classifier. To classify EEG data, a sigmoid activation function-based linear extreme learning machine was used. <italic>Result and Significance:</italic> We achieved a maximum classification rate of 40.30% (<italic>p</italic> <inline-formula><tex-math notation="LaTeX">$<$ </tex-math></inline-formula> 0.007) and 87.90% (<italic>p</italic> <inline-formula><tex-math notation="LaTeX"> $<$</tex-math></inline-formula> 0.003) in multiclass (five classes) and binary settings, respectively. Thus, our results suggested that EEG responses to imagined speech could be successfully classified using an extreme learning machine. <italic>Conclusion:</italic> This study involving the classification of imagined words can be a milestone contribution toward the development of practical brain–computer interface systems using silent speech.
Hierarchical Consensus Problem via Group Information Exchange

This paper presents a hierarchical structure to solve the consensus problem of multiagent systems. The new scheme divides the agents into several groups, with each group containing a value concerning all of the intragroup agents’ states, which we call group information. For each single agent, it receives not only the agent information from its intragroup neighbors, but also the group information from its neighboring groups. It is then shown that global consensus can be achieved under the proposed scheme in both discrete time and continuous time. Moreover, a sufficient condition to achieve average consensus is provided. This hierarchical model can be well used in the PageRank algorithm to reduce the communication loads, and to reveal the attractors for Boolean networks by reducing the computational complexity.
A Distance-Based Weighted Undersampling Scheme for Support Vector Machines and its Application to Imbalanced Classification

A support vector machine (SVM) plays a prominent role in classic machine learning, especially classification and regression. Through its structural risk minimization, it has enjoyed a good reputation in effectively reducing overfitting, avoiding dimensional disaster, and not falling into local minima. Nevertheless, existing SVMs do not perform well when facing class imbalance and large-scale samples. Undersampling is a plausible alternative to solve imbalanced problems in some way, but suffers from soaring computational complexity and reduced accuracy because of its enormous iterations and random sampling process. To improve their classification performance in dealing with data imbalance problems, this work proposes a weighted undersampling (WU) scheme for SVM based on space geometry distance, and thus produces an improved algorithm named WU-SVM. In WU-SVM, majority samples are grouped into some subregions (SRs) and assigned different weights according to their Euclidean distance to the hyper plane. The samples in an SR with higher weight have more chance to be sampled and put to use in each learning iteration, so as to retain the data distribution information of original data sets as much as possible. Comprehensive experiments are performed to test WU-SVM via 21 binary-class and six multiclass publically available data sets. The results show that it well outperforms the state-of-the-art methods in terms of three popular metrics for imbalanced classification, i.e., area under the curve, F-Measure, and G-Mean.
Climate Negotiation Analysis

Text analysis methods based on word co-occurrence have yielded useful results in humanities and social sciences research. Whereas these methods provide a useful overview of a corpus, they cannot determine the predicates relating co-occurring elements with each other. For instance, if France and the phrase "binding commitments" co-occur within a sentence, how are both elements related? Is France in favour of, or against binding commitments? Different natural language processing (NLP) technologies can identify related elements in text, and the predicates relating them. We are developing a workflow to analyze the Earth Negotiations Bulletin, which summarizes international climate negotiations. A sentence in this corpus can contain several verbal or nominal predicates indicating support and opposition. Results were uneven when applying Open Relation Extraction tools to this corpus. To address these challenges, we developed a workflow with a domain model, and analysis rules that exploit annotations for semantic roles and pronominal anaphora, provided by an NLP pipeline.
Unsupervised Domain Adaptation for Facial Expression Recognition Using Generative Adversarial Networks

In the facial expression recognition task, a good-performing convolutional neural network (CNN) model trained on one dataset (source dataset) usually performs poorly on another dataset (target dataset). This is because the feature distribution of the same emotion varies in different datasets. To improve the cross-dataset accuracy of the CNN model, we introduce an unsupervised domain adaptation method, which is especially suitable for unlabelled small target dataset. In order to solve the problem of lack of samples from the target dataset, we train a generative adversarial network (GAN) on the target dataset and use the GAN generated samples to fine-tune the model pretrained on the source dataset. In the process of fine-tuning, we give the unlabelled GAN generated samples distributed pseudolabels dynamically according to the current prediction probabilities. Our method can be easily applied to any existing convolutional neural networks (CNN). We demonstrate the effectiveness of our method on four facial expression recognition datasets with two CNN structures and obtain inspiring results.
Browsing, Sharing, Learning and Reviewing the Haine du théâtre Corpus through Insightful Island

In a participatory perspective, new tools are required to share the contents and results in the scientific community in general, and in the DH community in particular. Furthermore, the users of corpora are waiting for tools that help them to understand, to seek information and to memorize. In this paper, we show how to use Memory Island (MI) technique to help to create an insightful visualization for the Haine du theâtre (HdT) corpus. More generally, we discuss the design of systems based on the Memory Island technique for sharing, learning and reviewing the humanities and literary corpora.
An Efficient Nondominated Sorting Algorithm for Large Number of Fronts

Nondominated sorting is a key operation used in multiobjective evolutionary algorithms (MOEA). Worst case time complexity of this algorithm is <inline-formula> <tex-math notation="LaTeX">${O(MN^{2})}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> is the number of solutions and <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> is the number of objectives. For stochastic algorithms like MOEAs, it is important to devise an algorithm which has better average case performance. In this paper, we propose a new algorithm that makes use of faster scalar sorting algorithm to perform nondominated sorting. It finds partial orders of each solution from all objectives and use these orders to skip unnecessary solution comparisons. We also propose a specific order of objectives that reduces objective comparisons. The proposed method introduces a weighted binary search over the fronts when the rank of a solution is determined. It further reduces total computational effort by a large factor when there is large number of fronts. We prove that the worst case complexity can be reduced to <inline-formula> <tex-math notation="LaTeX">${\Theta }({MNC}_{{max}}\mathrm {log}_{{2}} {(F+1)})$ </tex-math></inline-formula>, where the number of fronts is <inline-formula> <tex-math notation="LaTeX">${F}$ </tex-math></inline-formula> and the maximum number of solutions per front is <inline-formula> <tex-math notation="LaTeX">${C}_{\mathrm {max}}$ </tex-math></inline-formula>; however, in general cases, our worst case complexity is still <inline-formula> <tex-math notation="LaTeX">${O(MN^{2})}$ </tex-math></inline-formula>. Our best case time complexity is <inline-formula> <tex-math notation="LaTeX">${O}({MN}\mathrm {log} {N})$ </tex-math></inline-formula>. We also achieve the best case complexity <inline-formula> <tex-math notation="LaTeX">${O}({MN}\mathrm {log} {N+N^{2}})$ </tex-math></inline-formula>, when all solutions are in a single front. This method is compared with other state-of-the-art algorithms—efficient nondomination level update, deductive sort, corner sort, efficient nondominated sort and divide-and-conquer sort—in four different datasets. Experimental results show that our method, namely, bounded best order sort, is computationally more efficient than all other competing algorithms.
Mapping the Bentham Corpus

University College London (UCL) owns a large corpus of the philosopher and social reformer Jeremy Bentham (1748-1832). Until recently, these papers were for the most part untranscribed, so that very few people had access to the corpus to evaluate its content and its value. The corpus is now being digitized and transcribed thanks to a large number of volunteers recruited through a crowd-sourcing initiative called Transcribe Bentham (Causer and Terras, 2014a, 2014b). The problem researchers are facing with such a corpus is clear: how to access the content, how to structure these 30,000 files, and how to get relevant access to this mass of data? Our goal has thus been to produce an automatic analysis procedure aiming at providing a general characterization of the content of the corpus. We are more specifically interested in identifying the main topics and their structure so as to provide meaningful static and dynamic representations of their evolution over time.
Reliability evaluation of UML/DAM software architectures under parameter uncertainty

Model-based evaluation of software reliability in the architecture design stage helps designers make objective decisions about design trade-offs. A challenging problem is how to deal with uncertainties in model parameters, e.g. usage profile. In this study, an approach based on evidence theory is proposed to handle the uncertainties in model parameters. In this approach, UML is used for modelling software architectures, and the DAM profile is used for specifying reliability parameters in the UML model. The constructed UML/DAM model is transformed to a fault tree to evaluate reliability. A software tool is developed to automate the transformation and evaluation procedures, and a case study is presented to demonstrate the applicability of the method.