Miracle-dz/UniFAR-FTUs huggingface dataset:
Human mast cells promote colon cancer growth via bidirectional crosstalk: studies in 2D and 3D coculture models

ABSTRACT Chronic inflammation drives the development of colorectal cancer (CRC), where tumor-infiltrating immune cells interact with cancer cells in a dynamic crosstalk. Mast cells (MC), one of earliest recruited immune cells, accumulate in CRC tissues and their density is correlated with cancer progression. However, the exact contribution of MC in CRC and their interaction with colon cancer cells is poorly understood. Here, we investigated the impact of primary human MC and their mediators on colon cancer growth using 2D and 3D coculture models. Primary human MC were generated from peripheral CD34+ stem cells. Transwell chambers were used to analyze MC chemotaxis to colon cancer. Colon cancer cells HT29 and Caco2 differentially recruited MC by releasing CCL15 or SCF, respectively. Using BrdU proliferation assays, we demonstrated that MC can directly support colon cancer proliferation and this effect was mediated by their cellular crosstalk. 3D coculture models with cancer spheroids further confirmed the pro-tumor effect of MC on colon cancer growth, where direct cell-cell contact is dispensable and increased production of multiple soluble mediators was detected. Moreover, TLR2 stimulation of MC promoted stronger growth of colon cancer spheroids. By examining the transcriptome profile of colon cancer-cocultured MC versus control MC, we identified several MC marker genes, which were deregulated in expression. Our study provides an advanced in vitro model to investigate the role of human MC in cancer. Our data support the detrimental role of MC in CRC development and provide a molecular insight into the cellular crosstalk between MC and colon cancer cells.
Bi-directional Lattice Recurrent Neural Networks for Confidence Estimation

The standard approach to mitigate errors made by an automatic speech recognition system is to use confidence scores associated with each predicted word. In the simplest case, these scores are word posterior probabilities whilst more complex schemes utilise bi-directional recurrent neural network (BiRNN) models. A number of upstream and downstream applications, however, rely on confidence scores assigned not only to 1-best hypotheses but to all words found in confusion networks or lattices. These include but are not limited to speaker adaptation, semi-supervised training and information retrieval. Although word posteriors could be used in those applications as confidence scores, they are known to have reliability issues. To make improved confidence scores more generally available, this paper shows how BiRNNs can be extended from 1-best sequences to confusion network and lattice structures. Experiments are conducted using one of the Cambridge University submissions to the IARPA OpenKWS 2016 competition. The results show that confusion network and lattice-based BiRNNs can provide a significant improvement in confidence estimation.
Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications

Scheduling fast uplink grant transmissions for machine type communications (MTCs) is one of the main challenges of future wireless systems. In this paper, a novel fast uplink grant scheduling method based on the theory of multi-armed bandits (MABs) is proposed. First, a single quality-of-service metric is defined as a combination of the value of data packets, maximum tolerable access delay, and data rate. Since full knowledge of these metrics for all machine type devices (MTDs) cannot be known in advance at the base station (BS) and the set of active MTDs changes over time, the problem is modeled as a sleeping MAB with stochastic availability and a stochastic reward function. In particular, given that, at each time step, the knowledge on the set of active MTDs is probabilistic, a novel probabilistic sleeping MAB algorithm is proposed to maximize the defined metric. Analysis of the regret is presented and the effect of the prediction error of the source traffic prediction algorithm on the performance of the proposed sleeping MAB algorithm is investigated. Moreover, to enable fast uplink allocation for multiple MTDs at each time, a novel method is proposed based on the concept of best arms ordering in the MAB setting. Simulation results show that the proposed framework yields a three-fold reduction in latency compared to a maximum probability scheduling policy since it prioritizes the scheduling of MTDs that have stricter latency requirements. Moreover, by properly balancing the exploration versus exploitation tradeoff, the proposed algorithm selects the most important MTDs more often by exploitation. During exploration, the sub-optimal MTDs will be selected, which increases the fairness in the system, and, also provides a better estimate of the reward of the sub-optimal MTD.
Convolutional Neural Network Quantization using Generalized Gamma Distribution

As edge applications using convolutional neural networks (CNN) models grow, it is becoming necessary to introduce dedicated hardware accelerators in which network parameters and feature-map data are represented with limited precision. In this paper we propose a novel quantization algorithm for energy-efficient deployment of the hardware accelerators. For weights and biases, the optimal bit length of the fractional part is determined so that the quantization error is minimized over their distribution. For feature-map data, meanwhile, their sample distribution is well approximated with the generalized gamma distribution (GGD), and accordingly the optimal quantization step size can be obtained through the asymptotical closed form solution of GGD. The proposed quantization algorithm has a higher signal-to-quantization-noise ratio (SQNR) than other quantization schemes previously proposed for CNNs, and even can be more improved by tuning the quantization parameters, resulting in efficient implementation of the hardware accelerators for CNNs in terms of power consumption and memory bandwidth.
Citations in Scientific Texts: Do Social Relations Matter?

This article presents an investigation of the role of social relations in the writing of scientific articles through the study of in‐text citations. Does the fact that the author of an article knows the author whose work he or she cites have an impact on the context of the citation? Because citations are commonly used as criteria for research evaluation, it is important to question their social background to better understand how it impacts textual features. We studied a collection of science articles (N = 123) from 5 disciplines and interviewed their authors (N = 84) to: (a) identify the social relations between citing and cited authors; and (b) measure the correlation between a set of features related to in‐text citations (N = 6,956) and the identified social relations. Our pioneering work, mixing sociological and linguistic results, shows that social relations between authors can partly explain the variations of citations in terms of frequency, position and textual context.
End-to-end Feedback Loss in Speech Chain Framework via Straight-through Estimator

The speech chain mechanism integrates automatic speech recognition (ASR) and text-to-speech synthesis (TTS) modules into a single cycle during training. In our previous work, we applied a speech chain mechanism as a semi-supervised learning. It provides the ability for ASR and TTS to assist each other when they receive unpaired data and let them infer the missing pair and optimize the model with reconstruction loss. If we only have speech without transcription, ASR generates the most likely transcription from the speech data, and then TTS uses the generated transcription to reconstruct the original speech features. However, in previous papers, we just limited our back-propagation to the closest module, which is the TTS part. One reason is that back-propagating the error through the ASR is challenging due to the output of the ASR being discrete tokens, creating non-differentiability between the TTS and ASR. In this paper, we address this problem and describe how to thoroughly train a speech chain end-to-end for reconstruction loss using a straight-through estimator (ST). Experimental results revealed that, with sampling from ST-Gumbel-Softmax, we were able to update ASR parameters and improve the ASR performances by 11% relative CER reduction compared to the baseline.
Influence of trauma origin site on admission rates of patients submitted to emergency laparotomy

1 Hospital do Trabalhador, Service of General Surgery, Curitiba, PR, Brazil. Brunello Influence of trauma origin site on admission rates of patients submitted to emergency laparotomy. 2 Rev Col Bras Cir 45(5):e1970 public policies. Thus, this study aimed to evaluate the influence of the place of trauma occurrence on the trauma scores (ISS, RTS, TRISS) of patients submitted to emergency exploratory laparotomy in a trauma reference hospital center in the country.
Modélisation du mouvement dynamique d’une machine-outil à l’aide d’un logiciel de simulation dynamique multi-corps – Première approche.

(a) Service de Génie Mécanique – UMONS, Faculté Polytechnique, Place du Parc 20, B 7000 Mons, Belgique – *auteur correspondant – Mails : abdullah.ozcan@umons.ac. be; edouard.riviere@umons.ac. be; enrico.filippi@umons.ac. be (b) Service de Mécanique rationnelle, Dynamique et Vibrations – UMONS, Faculté Polytechnique, Place du Parc 20, B 7000 Mons, Belgique – Mails : hoainam.huynh@umons.ac. be; olivier.verlinden@umons.ac. be
Stratégies de commande pour la navigation autonome d'un drone projectile miniature

De nos jours, l'utilisation des drones miniatures a voilure tournante pour des missions d'observation dans des environnements hostiles est en pleine expansion. Ces appareils, grâce a leurs capacites a combiner le vol de translation avec le vol stationnaire, sont en eff et bien adaptes aux besoins de ces missions. L'etude presentee dans cette these concerne un nouveau concept de drone appele GLMAV (pour Gun Launched Micro Aerial Vehicle), qui consiste a rendre tres rapidement operationnel un vehicule hybride projectile - drone. La di fficulte dans le pilotage de ce type de vehicules est d'assurer de bonnes performances de suivi de trajectoires tout en garantissant une resistance aux perturbations aerodynamiques. Apres une etape de modelisation, le cœur de la these presente plusieurs strategies de commande, aussi bien lineaires que non lineaires, permettant la navigation autonome du drone. Plusieurs approches permettant l'estimation et la prise en compte dans la commande des eff orts parasites lies aux phenomenes aerodynamiques sont egalement detaillees. L'e fficacite de tous les algorithmes de commande est ensuite illustree par de nombreuses simulations numeriques. Du point de vue pratique, une simple loi de commande ne suffi t pas. En eff et, des techniques de filtrage particulieres ou des amenagements specifi ques doivent etre utilises pour reconstruire l'etat du drone. Les performances de l'ensemble de la boucle de commande sont d'abord testees en simulation avant l'implantation sur le prototype du GLMAV developpe par l'Institut franco-allemand de recherches de Saint-Louis.
Merging and Decomposition Variants of Cooperative Particle Swarm Optimization: New Algorithms for Large Scale Optimization Problems

Many optimization algorithms suffer under the curse of dimensionality - a problem that describes a decrease in performance as the number of problem variables increases. Particle swarm optimization (PSO) is no exception to this performance degradation. Cooperative methods have since been introduced for PSO in order to increase its effectiveness for high dimensional problems by following a divide and conquer approach to large dimensional problems. Performance still suffers for such cooperative PSO (CPSO) variants, mostly due to the dependencies among variables. This paper demonstrates the performance of two new variants of CPSO, namely decomposition and merging cooperative swarm optimization, referred to as DCPSO and MCPSO respectively. The goal of these variants is to improve performance for large scale problems with variable dependencies. Empirical results show that DCPSO and MCPSO were able to perform better than standard CPSO-Sk, CPSOHk and a random grouping variant, CCPSO, for specific problem classes. While empirical results show that the best strategy between merging and decomposition is very much problem dependent, the MCPSO generally converged earlier than the DCPSO.
Social Isolation and Cognitive Function in Later Life: A Systematic Review and Meta-Analysis

Background: There is some evidence to suggest that social isolation may be associated with poor cognitive function in later life. However, findings are inconsistent and there is wide variation in the measures used to assess social isolation. Objective: We conducted a systematic review and meta-analysis to investigate the association between social isolation and cognitive function in later life. Methods: A search for longitudinal studies assessing the relationship between aspects of social isolation (including social activity and social networks) and cognitive function (including global measures of cognition, memory, and executive function) was conducted in PsycInfo, CINAHL, PubMed, and AgeLine. A random effects meta-analysis was conducted to assess the overall association between measures of social isolation and cognitive function. Sub-analyses investigated the association between different aspects of social isolation and each of the measures of cognitive function. Results: Sixty-five articles were identified by the systematic review and 51 articles were included in the meta-analysis. Low levels of social isolation characterized by high engagement in social activity and large social networks were associated with better late-life cognitive function (r = 0.054, 95% CI: 0.043, 0.065). Sub-analyses suggested that the association between social isolation and measures of global cognitive function, memory, and executive function were similar and there was no difference according to gender or number of years follow-up. Conclusions: Aspects of social isolation are associated with cognitive function in later life. There is wide variation in approaches to measuring social activity and social networks across studies which may contribute to inconsistencies in reported findings.
Abnormal phasic activity in saliency network, motor areas, and basal ganglia in Parkinson's disease during rhythm perception

Behavioral studies indicate that persons with Parkinson's disease have complexity dependent problems with the discrimination of auditory rhythms. Furthermore, neuroimaging studies show that rhythm processing activates many brain areas that overlap with areas affected by Parkinson's disease (PD). This study sought to investigate the neural correlates of rhythm processing in PD and healthy controls, with a particular focus on rhythmic complexity. We further aimed to investigate differences in brain activation during initial phases of rhythm processing. Functional magnetic resonance imaging was used to scan 15 persons with Parkinson's disease and 15 healthy controls while they listened to musical rhythms with two different levels of complexity. Rhythmic complexity had no significant effect on brain activations, but patients and controls showed differences in areas related to temporal auditory processing, notably bilateral planum temporale and inferior parietal lobule. We found indications of a particular sequential or phasic activation pattern of brain activity, where activity in caudate nucleus in the basal ganglia was time‐displaced by activation in the saliency network—comprised of anterior cingulate cortex and bilateral anterior insula—and cortical and subcortical motor areas, during the initial phases of listening to rhythms. We relate our findings to core PD pathology, and discuss the overall, rhythm processing related hyperactivity in PD as a possible dysfunction in specific basal ganglia mechanisms, and the phasic activation pattern in PD as a reflection of a lack of preparatory activation of task‐relevant brain networks for rhythm processing in PD.
Deadline-Driven Multi-node Mobile Charging

Due to the merit without requiring charging cable, wireless power transfer technologies have drawn rising attention as a new method to replenish energy to Wireless Rechargeable Sensor Networks (WRSNs). In this paper, we study mobile charger scheduling problem for multi-node recharging with deadline-series. Our target is to maximize the overall effective charging utility, and minimize the traveling time as well. Instead of charging only once over a scheduling cycle, we incorporate the multiple charging strategy for multi-node charging with deadline constraint, where charging spots and tour are jointly optimized. Specifically, we formulate the effective charging utility maximization problem as to maximize a monotone submodular function subject to a partition matroid constraint, and propose a simple but effective 1/2 -approximation greedy algorithm. After that, we present the grid-based skip-substitute operation further to save the traveling time, which can increase the charging utility. Finally, we conduct the evaluation for the performance of our scheduling scheme. Comparing to the Early Deadline First scheme, the simulation and field experiment results show that our algorithm outperform EDF by 37.5% and 37.9%, respectively.
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost

Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills, making them an appealing component for general-purpose robotic manipulators. However, such hands pose a major challenge for autonomous control, due to the high dimensionality of their configuration space and complex intermittent contact interactions. In this work, we propose deep reinforcement learning (deep RL) as a scalable solution for learning complex, contact rich behaviors with multi-fingered hands. Deep RL provides an end-to-end approach to directly map sensor readings to actions, without the need for task specific models or policy classes. We show that contact-rich manipulation behavior with multi-fingered hands can be learned by directly training with model-free deep RL algorithms in the real world, with minimal additional assumption and without the aid of simulation. We learn to perform a variety of tasks on two different low-cost hardware platforms entirely from scratch, and further study how the learning can be accelerated by using a small number of human demonstrations. Our experiments demonstrate that complex multi-fingered manipulation skills can be learned in the real world in about 4-7 hours for most tasks, and that demonstrations can decrease this to 2-3 hours, indicating that direct deep RL training in the real world is a viable and practical alternative to simulation and model-based control. https:// sites.google. com/view/deeprl-handmanipulation
Time Reproduction Deficits at Young Adult Follow-Up in Childhood ADHD: The Role of Persistence of Disorder and Executive Functioning

ABSTRACT No studies have examined if time reproduction deficits exist in children with attention deficit hyperactivity disorder (ADHD) by adulthood. We followed 131 ADHD and 71 community control (CC) cases for 20+ years to young adulthood at which time they were given a time reproduction task. The ADHD group made smaller time reproductions and showed greater variability of errors at the longer durations compared to CC cases, whether ADHD was still present or not at follow-up. Nonverbal working memory and design fluency tests were related to timing errors while anxiety and depression were not. Childhood ADHD is associated with timing deficits at adult follow-up.
Incorporating Diversity into Influential Node Mining

Diversity is a crucial criterion in many ranking and mining tasks. In this paper, we study how to incorporate node diversity into influence maximization (IM). We consider diversity as a reverse measure of the average similarity between selected nodes, which can be specified using node embedding or community detection results. Our goal is to identify a set of nodes which are simultaneously influential and diverse. Three most commonly used utilities in economics (i.e., Perfect Substitutes, Perfect Complements, and Cobb-Douglas) are proposed to jointly model influence spread and diversity as two factors. We formulate diversified IM as an optimization problem of these utilities, for which we present two approximation algorithms based on non-monotonic submodular maximization and traditional IM respectively. Experimental results show that our diversified IM framework outperforms other natural heuristics, such as embedding and diversified ranking, both in utility maximization and result diversification.
FGF10 and Human Lung Disease Across the Life Spectrum

Lung diseases impact patients across the lifespan, from infants in the first minutes of life through the aged population. Congenital abnormalities of lung structure can cause lung disease at birth or make adults more susceptible to chronic disease. Continuous inhalation of atmospheric components also requires the lung to be resilient to cellular injury. Fibroblast growth factor 10 (FGF10) regulates multiple stages of structural lung morphogenesis, cellular differentiation, and the response to injury. As a driver of lung airway branching morphogenesis, FGF10 signaling defects during development lead to neonatal lung disease. Alternatively, congenital airway abnormalities attributed to FGF10 mutations increase the risk of chronic airway disease in adulthood. FGF10 also maintains progenitor cell populations in the airway and promotes alveolar type 2 cell expansion and differentiation following injury. Here we review the cellular and molecular mechanisms linking FGF10 to multiple lung diseases, from bronchopulmonary dysplasia in extremely preterm neonates, cystic fibrosis in children, and chronic adult lung disorders. Understanding the connections between FGF10 and lung diseases may lead to exciting new therapeutic strategies.
Geometric Median Shapes

We present an algorithm to compute the geometric median of shapes which is based on the extension of median to high dimensions. The median finding problem is formulated as an optimization over distances and it is solved directly using the watershed method as an optimizer. We show that computing the geometric median of shapes is robust in the presence of outliers and it is superior to the mean shape which can easily be affected by the presence of outliers. The geometric median shape thus faithfully represents the true central tendency of the data, contaminated or not. Our approach can be applied to manifold and non manifold shapes, with connected or disconnected shapes. The application of distance transforms and watershed algorithm, two well established constructs of image processing, lead to an algorithm that can be quickly implemented to generate fast solutions with linear storage requirements. We demonstrate our methods in synthetic and natural shapes and compare median and mean results under increasing contamination by strong outliers.