Miracle-dz/UniFAR-FTUs huggingface dataset:
PNS1100001 162..170

Liquids have been shown to have a low satiating efficiency. The may be related to the high rate of consumption for liquids which may be higher than 200 g/min. In a number of studies, we showed that the positive relationship between eating rate and energy intake is mediated by oro-sensory exposure time. Longer sensory exposure times are consistently associated with lower food intakes. This observation maybe linked to the role of cephalic phase responses to foods. Cephalic phase responses are a set of physiological responses, which are conceived to prepare the digestive system for the incoming flow of nutrients after ingestion, with the aim of maintaining homeostasis. Results from various studies suggest that cephalic phase responses are much smaller (absent) for liquids compared to solids. It is hypothesised that the absence of cephalic phase responses to liquid foods may be one of the causes why liquid energies enter the body undetected and lead to weak energy intake compensation. This idea fits with the concept of the taste system as a nutrient-sensing system that informs the brain and the gastro-intestinal system about what is coming into our body. With liquids, this system is bypassed. Slower eating may help the human body to associate the sensory signals from food with their metabolic consequences. Foods that are eaten quickly may impair this association, and may therefore lead to overconsumption of energy, and ultimately to weight gain.
A rationalized definition of general tumor suppressor microRNAs excludes miR-34a

While several microRNAs (miRNAs) have been proposed to act as tumor suppressors, a consensual definition of tumor suppressing miRNAs is still missing. Similarly to coding genes, we propose that tumor suppressor miRNAs must show evidence of genetic or epigenetic inactivation in cancers, and exhibit an anti-tumorigenic (e.g., anti-proliferative) activity under endogenous expression levels. Here we observe that this definition excludes the most extensively studied tumor suppressor candidate miRNA, miR-34a. In analyzable cancer types, miR-34a does not appear to be down-regulated in primary tumors relatively to normal adjacent tissues. Deletion of miR-34a is occasionally found in human cancers, but it does not seem to be driven by an anti-tumorigenic activity of the miRNA, since it is not observed upon smaller, miR-34a-specific alterations. Its anti-proliferative action was observed upon large, supra-physiological transfection of synthetic miR-34a in cultured cells, and our data indicates that endogenous miR-34a levels do not have such an effect. Our results therefore argue against a general tumor suppressive function for miR-34a, providing an explanation to the lack of efficiency of synthetic miR-34a administration against solid tumors.
Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere

Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants’ large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3 in size with as good as 5 μm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement of Bacillus subtilis populations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point “first contact”, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.
Opposing mechanisms of competition determine species invasions and functional diversity

Understanding how species’ phenotypic differences affect competition is key to explaining community assembly and predicting biodiversity responses. Many studies overlook the variable effects that species’ trait differences can have on the direction of competitive exclusion, which reverses depending on the specific mechanism at play. We performed a comprehensive trait-based study of an ant invasion integrating morphological, dietary, physiological and behavioral analyses. We found that trait differences between invasive and resident species were not only associated with niche differences which promoted the coexistence of dissimilar species, but also competitive ability differences which acted in the opposite fashion. Furthermore, competition along separate trait axes led to complex and contrasting patterns in the invaded assemblages, where species were at once similar (clustered) in some traits but also dissimilar (overdispersed) in others. Our results reveal that different aspects of phenotype may distinctly modulate the effect of competition in structuring ecological communities and functional diversity.
The effect of resource depletion on the thermal response of mosquito population fitness

The temperature-dependencies of life history traits are increasingly being used to predict how climatic warming will affect vector-borne disease dynamics, partially by affecting the abundance dynamics of the vector population. Such predictions generally arise from mathematical models that incorporate the temperature dependence of traits measured under laboratory conditions. These temperature-trait relationships are typically estimated from juvenile populations reared under optimal resource conditions, even though natural populations experience intermittent resource depletion. Using laboratory experiments on the mosquito Aedes aegypti, combined with a stage-structured population model, we show that resource depletion in the juvenile habitat can significantly depress the vector’s maximal population growth rate (rm) across the entire temperature range, cause it to peak at a lower temperature, and narrow its thermal niche width. Our results provide compelling evidence for future studies to consider resource depletion when predicting the effects of global change on vector-borne disease transmission, disease vectors and other arthropods.
Survey on connectivity and cloud computing technologies: State-of-the-art applied
 to Agriculture 4.0

In recent years, agriculture has faced many challenges, from a growing global population to be fed, the work power evasion in the sector, to sustainability requirements and environmental constraints. To satisfy the increasingly demanding stakeholders, the agricultural sector has looked for new ways to tackle these issues. In this context, Information and Communications Technologies (ICTs) have been applied to help the agricultural sector overcome these challenges. This article investigates how two ICTs — connectivity and cloud computing — can leverage and traverse other ICTs, such as Internet of Things and artificial intelligence, enabling the entire productive sector to be supported by decision-making systems, which in turn are based on data-driven models. Moreover, a successful case study on how cloud computing has helped one of SiDi’s biggest customers — a global company — improve its operational performance by obtaining insights from its data is presented.
Attribute-Modulated Generative Meta Learning for Zero-Shot Learning

Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to semantically related unseen classes, which are absent during training. The promising strategies for ZSL are to synthesize visual features of unseen classes conditioned on semantic side information and to incorporate meta-learning to eliminate the model’s inherent bias towards seen classes. While existing meta generative approaches pursue a common model shared across task distributions, we aim to construct a generative network adaptive to task characteristics. To this end, we propose an Attribute-Modulated generAtive meta-model for Zero-shot learning (AMAZ). Our model consists of an attribute-aware modulation network, an attribute-augmented generative network, and an attribute-weighted classifier. Given unseen classes, the modulation network adaptively modulates the generator by applying task-specific transformations so that the generative network can adapt to highly diverse tasks. The weighted classifier utilizes the data quality to enhance the training procedure, further improving the model performance. Our empirical evaluations on four widely-used benchmarks show that AMAZ outperforms state-of-the-art methods by 3.8% and 3.1% in ZSL and generalized ZSL settings, respectively, demonstrating the superiority of our method. Our experiments on a zero-shot image retrieval task show AMAZ’s ability to synthesize instances that portray real visual characteristics.
Topological Simplifications of Hypergraphs

We study hypergraph visualization via its topological simplification. We explore both vertex simplification and hyperedge simplification of hypergraphs using tools from topological data analysis. In particular, we transform a hypergraph into its graph representations known as the line graph and clique expansion. A topological simplification of such a graph representation induces a simplification of the hypergraph. In simplifying a hypergraph, we allow vertices to be combined if they belong to almost the same set of hyperedges, and hyperedges to be merged if they share almost the same set of vertices. Our proposed approaches are general, mathematically justifiable, and put vertex simplification and hyperedge simplification in a unifying framework.
Universal Horn Sentences and the Joint Embedding Property

The finite models of a universal sentence $\Phi$ in a finite relational signature are the age of a structure if and only if $\Phi$ has the joint embedding property. We prove that the computational problem whether a given universal sentence $\Phi$ has the joint embedding property is undecidable, even if $\Phi$ is additionally Horn and the signature of $\Phi$ only contains relation symbols of arity at most two.
An Examination of Demographic Differences in Obtaining Investment and Financial Planning Information

Financial literacy and financial education are important components of modern life. The importance of financial literacy is increasing for financial consumers because of the weakening of both government and employer-based retirement systems. Unfortunately, empirical research shows that financial consumers are not fully informed and are not able to make proper choices even when appropriate information is available. More research is needed as to how financial consumers obtain investment and financial planning information. A primary data study was conducted to understand the differences between the demographic categories of gender, age, education-level, and income-level with the means of obtaining investment and financial planning information. In this research study, which selected a population from the LinkedIn platform, statistical differences between gender, age, education-level, and income-level were confirmed. These differences helped to confirm prior research in this field of study. Practical opportunities for commercial outreach to specific populations became evident through this type of research. Providers of investment and financial planning information can access their targeted audience more effectively by understanding the demographic profile of the audience, as well as the propensity of the demographic profile of the audience to respond. As this type of research is relatively easy to construct and administer, commercial outreach for providers of investment and financial planning information can be conducted in a cost-efficient and effective manner.
Self-optimizing loop sifting and majorization for 3D reconstruction

Visual simultaneous localization and mapping (vSLAM) and 3D reconstruction methods have gone through impressive progress. These methods are very promising for autonomous vehicle and consumer robot applications because they can map large-scale environments such as cities and indoor environments without the need for much human effort. However, when it comes to loop detection and optimization, there is still room for improvement. vSLAM systems tend to add the loops very conservatively to reduce the severe influence of the false loops. These conservative checks usually lead to correct loops rejected, thus decrease performance. In this paper, an algorithm that can sift and majorize loop detections is proposed. Our proposed algorithm can compare the usefulness and effectiveness of different loops with the dense map posterior (DMP) metric. The algorithm tests and decides the acceptance of each loop without a single user-defined threshold. Thus it is adaptive to different data conditions. The proposed method is general and agnostic to sensor type (as long as depth or LiDAR reading presents), loop detection, and optimization methods. Neither does it require a specific type of SLAM system. Thus it has great potential to be applied to various application scenarios. Experiments are conducted on public datasets. Results show that the proposed method outperforms state-of-the-art methods.
Elastic analysis of irregularly or sparsely sampled curves

We provide statistical analysis methods for samples of curves in two or more dimensions, where the image, but not the parameterization of the curves, is of interest and suitable alignment/registration is thus necessary. Examples are handwritten letters, movement paths, or object outlines. We focus in particular on the computation of (smooth) means and distances, allowing, for example, classification or clustering. Existing parameterization invariant analysis methods based on the elastic distance of the curves modulo parameterization, using the square‐root‐velocity framework, have limitations in common realistic settings where curves are irregularly and potentially sparsely observed. We propose using spline curves to model smooth or polygonal (Fréchet) means of open or closed curves with respect to the elastic distance and show identifiability of the spline model modulo parameterization. We further provide methods and algorithms to approximate the elastic distance for irregularly or sparsely observed curves, via interpreting them as polygons. We illustrate the usefulness of our methods on two datasets. The first application classifies irregularly sampled spirals drawn by Parkinson's patients and healthy controls, based on the elastic distance to a mean spiral curve computed using our approach. The second application clusters sparsely sampled GPS tracks based on the elastic distance and computes smooth cluster means to find new paths on the Tempelhof field in Berlin. All methods are implemented in the R‐package “elasdics” and evaluated in simulations.
Chandra Observations of Excess Fe Kα Line Emission in Galaxies with High Star Formation Rates: X-Ray Reflection on Galaxy Scales?

In active galactic nuclei (AGNs), fluorescent Fe Kα (iron) line emission is generally interpreted as originating from obscuring material around a supermassive black hole on the scale of a few parsecs. However, recent Chandra studies indicate the existence of iron line emission extending to kiloparsec scales in the host galaxy. The connection between iron line emission and large-scale material can be spatially resolved directly only in nearby galaxies, but could be inferred in more distant AGNs by a connection between line emission and star-forming gas and dust that is more extended than the parsec-scale torus. Here we present the results from a stacking analysis and X-ray spectral fitting performed on sources in the Chandra Deep Field South (CDFS) 7 Ms observations. From the deep stacked spectra, we select sources with stellar mass at 0.5 < z < 2, obtaining 25 sources with high-infrared (IR) luminosity (star formation rate, SFRFIR ≥ 17 M ☉ yr−1) and 32 sources below this threshold. We find that the equivalent width (EW) of the iron line EW(Fe) is a factor of three higher with 3σ significance for high-IR luminosity measured from Herschel observations, indicating a connection between iron line emission and star-forming material on galaxy scales. We show that there is no significant dependence of the EW(Fe) on M * or X-ray luminosity, suggesting that the reflection of AGN X-ray emission over large scales in their host galaxies may be widespread.
Building Bilingual and Code-Switched Voice Conversion with Limited Training Data Using Embedding Consistency Loss

Building cross-lingual voice conversion (VC) systems for multiple speakers and multiple languages has been a challenging task for a long time. This paper describes a parallel non-autoregressive network to achieve bilingual and code-switched voice conversion for multiple speakers when there are only mono-lingual corpora for each language. We achieve cross-lingual VC between Mandarin speech with multiple speakers and English speech with multiple speakers by applying bilingual bottleneck features. To boost voice cloning performance, we use an adversarial speaker classifier with a gradient reversal layer to reduce the source speaker's information from the output of encoder. Furthermore, in order to improve speaker similarity between reference speech and converted speech, we adopt an embedding consistency loss between the synthesized speech and its natural reference speech in our network. Experimental results show that our proposed method can achieve high quality converted speech with mean opinion score (MOS) around 4. The conversion system performs well in terms of speaker similarity for both in-set speaker conversion and out-set-of one-shot conversion.
On the weak-lensing masses of a new sample of galaxy groups

Galaxy group masses are important to relate these systems with the dark matter halo hosts. However, deriving accurate mass estimates is particularly challenging for low-mass galaxy groups. Moreover, calibration of observational mass-proxies using weak-lensing estimates have been mainly focused on massive clusters. We present here a study of halo masses for a sample of galaxy groups identified according to a spectroscopic catalogue, spanning a wide mass range. The main motivation of our analysis is to assess mass estimates provided by the galaxy group catalogue derived through an abundance matching luminosity technique. We derive total halo mass estimates according to a stacking weak-lensing analysis. Our study allows to test the accuracy of mass estimates based on this technique as a proxy for the halo masses of large group samples. Lensing profiles are computed combining the groups in different bins of abundance matching mass, richness and redshift. Fitted lensing masses correlate with the masses obtained from abundance matching. However, when considering groups in the lowand intermediatemass ranges, masses computed according to the characteristic group luminosity tend to predict higher values than the determined by the weak-lensing analysis. The agreement improves for the low-mass range if the groups selected have a central early-type galaxy. Presented results validate the use of mass estimates based on abundance matching techniques which provide good proxies to the halo host mass in a wide mass range.
Pri3D: Can 3D Priors Help 2D Representation Learning?

Recent advances in 3D perception have shown impressive progress in understanding geometric structures of 3D shapes and even scenes. Inspired by these advances in geometric understanding, we aim to imbue image-based perception with representations learned under geometric constraints. We introduce an approach to learn view-invariant, geometry-aware representations for network pre-training, based on multi-view RGB-D data, that can then be effectively transferred to downstream 2D tasks. We propose to employ contrastive learning under both multi-view image constraints and image-geometry constraints to encode 3D priors into learned 2D representations. This results not only in improvement over 2D-only representation learning on the image-based tasks of semantic segmentation, instance segmentation and object detection on real-world indoor datasets, but moreover, provides significant improvement in the low data regime. We show significant improvement of 6.0% on semantic segmentation on full data as well as 11.9% on 20% data against baselines on ScanNet. Our code is open sourced at https://github.com/Sekunde/Pri3D.
TeraMIMO: A Channel Simulator for Wideband Ultra-Massive MIMO Terahertz Communications

Following recent advances in terahertz (THz) technology, there is a consensus on the crucial role of THz communications in the next generation of wireless systems. Aiming at catalyzing THz communications research, we propose TeraMIMO, an accurate stochastic MATLAB simulator of statistical THz channels. We simulate ultra-massive multiple-input multiple-output antenna configurations as critical infrastructure enablers that overcome the limitation in THz communications distances. We consider both line-of-sight and multipath components and propose frequency- and delay-domain implementations for single- and multi-carrier paradigms in both time-invariant and time-variant scenarios. We implement exhaustive molecular absorption computations based on radiative transfer theory alongside alternative sub-THz approximations. We further model THz-specific constraints, including wideband beam split effects, spherical wave propagation, and misalignment fading. We verify TeraMIMO by analogy with measurement-based channel models in the literature and ergodic capacity analysis. We introduce a graphical user interface and a guide for using TeraMIMO in THz channel generation and analyses.
Close contact risk assessment for SARS-CoV-2 infection

Although close contact represents an important contagion route, the mechanism of exposure to exhaled droplets remains insufficiently characterized. In this study, an integrated risk assessment is presented for SARS-CoV-2 close contact exposure between a speaking infectious subject and a susceptible subject. It is based on a three-dimensional transient numerical model for the description of exhaled droplet spread once emitted by a speaking person, coupled with a recently proposed SARS-CoV-2 emission approach. Particle image velocimetry measurements were conducted to validate the numerical model. The contribution of large droplets to infection risk is dominant for distances < 0.6 m, whereas for longer distances, the exposure risk depends only on airborne droplets. In particular, for short exposures (10 s) a minimum safety distance of 0.75 m should be maintained to lower the risk below 0.1%;for exposures of 1 and 15 min this distance increases to about 1.0 and 1.5 m, respectively. Based on the interpersonal distances across countries reported as a function of interacting individuals, cultural differences, and environmental and sociopsychological factors, the approach presented here revealed that, in addition to intimate and personal distances, particular attention must be paid to exposures longer than 1 min within social distances (of about 1 m).