Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images

Segmenting aerial images is of great potential in surveillance and scene understanding of urban areas. It provides a mean for automatic reporting of the different events that happen in inhabited areas. This remarkably promotes public safety and traffic management applications. After the wide adoption of convolutional neural networks methods, the accuracy of semantic segmentation algorithms could easily surpass 80% if a robust dataset is provided. Despite this success, the deployment of a pretrained segmentation model to survey a new city that is not included in the training set significantly decreases accuracy. This is due to the domain shift between the source dataset on which the model is trained and the new target domain of the new city images. In this paper, we address this issue and consider the challenge of domain adaptation in semantic segmentation of aerial images. We designed an algorithm that reduces the domain shift impact using generative adversarial networks (GANs). In the experiments, we tested the proposed methodology on the International Society for Photogrammetry and Remote Sensing (ISPRS) semantic segmentation dataset and found that our method improves overall accuracy from 35% to 52% when passing from the Potsdam domain (considered as source domain) to the Vaihingen domain (considered as target domain). In addition, the method allows efficiently recovering the inverted classes due to sensor variation. In particular, it improves the average segmentation accuracy of the inverted classes due to sensor variation from 14% to 61%.
From Vineyard Soil to Wine Fermentation: Microbiome Approximations to Explain the “terroir” Concept

Wine originally emerged as a serendipitous mix of chemistry and biology, where microorganisms played a decisive role. From these ancient fermentations to the current monitored industrial processes, winegrowers and winemakers have been continuously changing their practices according to scientific knowledge and advances. A new enology direction is emerging and aiming to blend the complexity of spontaneous fermentations with industrial safety of monitored fermentations. In this context, wines with distinctive autochthonous peculiarities have a great acceptance among consumers, causing important economic returns. The concept of terroir, far from being a rural term, conceals a wide range of analytical parameters that are the basis of the knowledge-based enology trend. In this sense, the biological aspect of soils has been underestimated for years, when actually it contains a great microbial diversity. This soil-associated microbiota has been described as determinant, not only for the chemistry and nutritional properties of soils, but also for health, yield, and quality of the grapevine. Additionally, recent works describe the soil microbiome as the reservoir of the grapevine associated microbiota, and as a contributor to the final sensory properties of wines. To understand the crucial roles of microorganisms on the entire wine making process, we must understand their ecological niches, population dynamics, and relationships between ‘microbiome- vine health’ and ‘microbiome-wine metabolome. ’ These are critical steps for designing precision enology practices. For that purpose, current metagenomic techniques are expanding from laboratories, to the food industry. This review focuses on the current knowledge about vine and wine microbiomes, with emphasis on their biological roles and the technical basis of next-generation sequencing pipelines. An overview of molecular and informatics tools is included and new directions are proposed, highlighting the importance of –omics technologies in wine research and industry.
Multimodal Semantic Attention Network for Video Captioning

Inspired by the fact that different modalities in videos carry complementary information, we propose a Multimodal Semantic Attention Network(MSAN), which is a new encoder-decoder framework incorporating multimodal semantic attributes for video captioning. In the encoding phase, we detect and generate multimodal semantic attributes by formulating it as a multi-label classification problem. Moreover, we add auxiliary classification loss to our model that can obtain more effective visual features and high-level multimodal semantic attribute distributions for sufficient video encoding. In the decoding phase, we extend each weight matrix of the conventional LSTM to an ensemble of attribute-dependent weight matrices, and employ attention mechanism to pay attention to different attributes at each time of the captioning process. We evaluate algorithm on two popular public benchmarks: MSVD and MSR-VTT, achieving competitive results with current state-of-the-art across six evaluation metrics.
On recoverability of finite traces of square-summable sequences

The paper investigates recoverability of infinite sequences (discrete time signals) from incomplete observations. It is shown that under very mild restrictions on the location of the observed and missed data, recoverability of finite traces of signals can be achieved for wide classes of signals that everywhere dense in the space of square-summable signals.
Deep Landscape Forecasting for Real-time Bidding Advertising

The emergence of real-time auction in online advertising has drawn huge attention of modeling the market competition, i.e., bid landscape forecasting. The problem is formulated as to forecast the probability distribution of market price for each ad auction. With the consideration of the censorship issue which is caused by the second-price auction mechanism, many researchers have devoted their efforts on bid landscape forecasting by incorporating survival analysis from medical research field. However, most existing solutions mainly focus on either counting-based statistics of the segmented sample clusters, or learning a parameterized model based on some heuristic assumptions of distribution forms. Moreover, they neither consider the sequential patterns of the feature over the price space. In order to capture more sophisticated yet flexible patterns at fine-grained level of the data, we propose a Deep Landscape Forecasting (DLF) model which combines deep learning for probability distribution forecasting and survival analysis for censorship handling. Specifically, we utilize a recurrent neural network to flexibly model the conditional winning probability w.r. t. each bid price. Then we conduct the bid landscape forecasting through probability chain rule with strict mathematical derivations. And, in an end-to-end manner, we optimize the model by minimizing two negative likelihood losses with comprehensive motivations. Without any specific assumption for the distribution form of bid landscape, our model shows great advantages over previous works on fitting various sophisticated market price distributions. In the experiments over two large-scale real-world datasets, our model significantly outperforms the state-of-the-art solutions under various metrics.
An Alternative Proof of the Largest Number of Maximal Independent Sets in Connected Graphs Having at Most Two Cycles

G. C. Ying, Y. Y. Meng, B. E. Sagan, and V. R. Vatter [1] found the maximum number of maximal independent sets in connected graphs which contain at most two cycles. In this paper, we give an alternative proof to determine the largest number of maximal independent sets among all connected graphs of order n ≥ 12, which contain at most two cycles. We also characterize the extremal graph achieving this maximum value.
Situation and social awareness-based personalized recommendation service in pervasive computing environment

Recently, many mobile techniques such as sensor networks or various types of mobile devices make it possible to provide smart services at any time, and anywhere. In despite of these remarkable advances of techniques, there are few personalized mobile recommendation services which fully consider user's current situation. Proposed recommendation algorithm efficiently defines user's current situation with situational data captured from various smartphone sensors. Also, the algorithm uses user's social network for efficiently filtering valuable items which are considered as authorities. To verify the usefulness of proposed technique, we implement a prototype of the personalized music recommendation service in which proposed recommendation technique is applied. Additionally, through the demonstration of implemented prototype, we investigate the effect of incorporating smartphone sensor data and social data to collaborative filtering algorithms.
The Generalized Nonlinear Ohm's Law: How a Strong Electric Field Influences Nonideal MHD Effects in Dusty Protoplanetary Disks

The magnetohydrodynamics (MHD) of protoplanetary disks are strongly subject to the nonideal MHD effects arising from the low ionization fraction of the disk gas. A strong electric field induced by gas motions can heat ionized gas particles and can thereby affect the ionization balance in the disks. Our previous studies revealed that in dusty protoplanetary disks, the ohmic conductivity decreases with increasing electric field strength until the electrical breakdown of the disk gas occurs. In this study, we extend our previous work to more general cases where both electric and magnetic fields affect the motion of plasma particles, allowing us to study the impacts of plasma heating on all nonideal MHD effects: ohmic, Hall, and ambipolar diffusion. We find that the upper limit on the electric current we previously derived applies even in the presence of magnetic fields. Although the Hall and ambipolar resistivities can either increase or decrease with electric field strength depending on the abundance of charged dust grains, the ohmic resistivity always increases with electric field strength. An order-of-magnitude estimate suggests that a large-scale electric current generated by gas motions in the inner part of protoplanetary disks could exceed the upper limit. This implies that MHD motions of the inner disk, such as the motion driven by the Hall-shear instability, could either get suppressed or trigger electrical breakdown (lightning discharge). This may have important implications for gas accretion and chondrule formation in the inner part of protoplanetary disks.
On age-specific selection and extensive lifespan beyond menopause

Standard evolutionary theory of ageing predicts weaker purifying selection on genes critical to later life stages. Prolonged post-reproductive lifespan (PPRLS), observed only in a few species like humans, is likely a result of disparate relaxation of purifying selection on survival and reproduction in late life stages. While the exact origin of PPRLS is under debate, many researchers agree on hypotheses like mother-care and grandmother-care, which ascribe PPRLS to investment into future generations—provision to one’s descendants to enhance their overall reproductive success. Here, we simulate an agent-based model, which properly accounts for age-specific selection, to examine how different investment strategies affect the strength of purifying selection on survival and reproduction. We observed in the simulations that investment strategies that allow a female individual to remain contributive to its own descendants (infants and adults) at late life stages may lead to differential relaxation of selection on survival and reproduction, and incur the adaptive evolution of PPRLS.
Optimal Randomness in Swarm-based Search

Lévy flights is a random walk where the step-lengths have a probability distribution that is heavy-tailed. It has been shown that Lévy flights can maximize the efficiency of resource searching in uncertain environments and also the movements of many foragers and wandering animals have been shown to follow a Lévy distribution. The reason mainly comes from the fact that the Lévy distribution has an infinite second moment and hence is more likely to generate an offspring that is farther away from its parent. However, the investigation into the efficiency of other different heavy-tailed probability distributions in swarm-based searches is still insufficient up to now. For swarm-based search algorithms, randomness plays a significant role in both exploration and exploitation or diversification and intensification. Therefore, it is necessary to discuss the optimal randomness in swarm-based search algorithms. In this study, cuckoo search (CS) is taken as a representative method of swarm-based optimization algorithms, and the results can be generalized to other swarm-based search algorithms. In this paper, four different types of commonly used heavy-tailed distributions, including Mittag-Leffler distribution, Pareto distribution, Cauchy distribution, and Weibull distribution, are considered to enhance the searching ability of CS. Then four novel CS algorithms are proposed and experiments are carried out on 20 benchmark functions to compare their searching performances. Finally, the proposed methods are used to system identification to demonstrate the effectiveness.
Allostery and conformational changes upon binding as generic features of proteins: a high-dimension geometrical approach

A growing number of experimental evidence shows that it is general for a ligand binding protein to have a potential for allosteric regulation and for further evolution. In addition, such proteins generically change their conformation upon binding. O. Rivoire has recently proposed an evolutionary scenario that explains these properties as a generic byproduct of selection for exquisite discrimination between very similar ligands. The initial claim was supported by two classes of basic examples: continuous protein models with small numbers of degrees of freedom, on which the development of a conformational switch was established, and a 2-dimensional spin glass model supporting the rest of the statement. This work aimed to clarify the implication of the exquisite discrimination for smooth models with large number of degrees of freedom, the situation closer to real biological systems. With the help of differential geometry, jet-space analysis, and transversality theorems, it is shown that the claim holds true for any generic flexible system that can be described in terms of smooth manifolds. The result suggests that, indeed, evolutionary solutions to the exquisite discrimination problem, if exist, are located near a codimension-1 subspace of the appropriate genotypical space. This constraint, in turn, gives rise to a potential for the allosteric regulation of the discrimination via generic conformational changes upon binding.
Decision Making with Machine Learning and ROC Curves

The Receiver Operating Characteristic (ROC) curve is a representation of the statistical information discovered in binary classification problems and is a key concept in machine learning and data science. This paper studies the statistical properties of ROC curves and its implication on model selection. We analyze the implications of different models of incentive heterogeneity and information asymmetry on the relation between human decisions and the ROC curves. Our theoretical discussion is illustrated in the context of a large data set of pregnancy outcomes and doctor diagnosis from the Pre-Pregnancy Checkups of reproductive age couples in Henan Province provided by the Chinese Ministry of Health.
Tasks, Techniques, and Tools for Genomic Data Visualization

Genomic data visualization is essential for interpretation and hypothesis generation as well as a valuable aid in communicating discoveries. Visual tools bridge the gap between algorithmic approaches and the cognitive skills of investigators. Addressing this need has become crucial in genomics, as biomedical research is increasingly data‐driven and many studies lack well‐defined hypotheses. A key challenge in data‐driven research is to discover unexpected patterns and to formulate hypotheses in an unbiased manner in vast amounts of genomic and other associated data. Over the past two decades, this has driven the development of numerous data visualization techniques and tools for visualizing genomic data. Based on a comprehensive literature survey, we propose taxonomies for data, visualization, and tasks involved in genomic data visualization. Furthermore, we provide a comprehensive review of published genomic visualization tools in the context of the proposed taxonomies.
Sparse Multiresolution Representations With Adaptive Kernels

Reproducing kernel Hilbert spaces (RKHSs) are key components of non-parametric toolsused in signal processing, statistics, and machine learning. In this work, we aim to address three issues of the classical RKHS-based techniques. First, they require the RKHS to be known a priori, which is unrealistic in many applications. Furthermore, the choice of RKHS affects the shape and smoothness of the solution, thus impacting its performance. Second, RKHSs are ill-equipped to deal with heterogeneous degrees of smoothness, i.e., with functions that are smooth in some parts of their domain but vary rapidly in others. Finally, the computational complexity of evaluating the solution of these methods grows with the number of data points in the training sample, rendering these techniques infeasible for many applications. Although kernel learning, local kernel adaptation, and sparsity have been used to address these issues, many of these approaches are computationally intensive or forgo optimality guarantees. We tackle these problems by leveraging a novel integral representation of functions in RKHSs that allows for arbitrary centers and different kernels at each center. To address the complexity issue, we then write the function estimation problem as a sparse functional program that explicitly minimizes the support of the representation leading to low complexity solutions. Despite their non-convexity and infinite dimensionality, we show these problems can be solved exactly and efficiently by leveraging duality, and illustrate this new approach in simulated and real data.
Atypical Mycobacterial Infections in Children: The Case for Early Diagnosis

Background & Aims Atypical Mycobacteria (ATB) are a miscellaneous collection of Mycobacteriaceae which also includes M. tuberculosis, M. bovis and M. leprae. In the paediatric population, ATB infections present with non-tender unilateral lymphadenopathy in a systemically well child. Initially the disease may be mistaken for a staphylococcal or streptococcal abscess. Inappropriate surgical incision and drainage is often performed and specimens may be sent for routine histopathology and bacteriology analysis only without considering Mycobacterial infection. The simple incision and drainage procedures can complicate the management and may result in a poor cosmetic outcome. ATB can go undiagnosed until the initial medical management has failed, these surgical interventions performed and the child remains symptomatic. We wish to highlight the importance of considering ATB infection in the differential diagnosis of a child with painless lymphadenitis. Methods An illustrative case report is described. A review of the paediatric data from the Mycobacterial laboratories in Northern Ireland over the last 14 years was performed to ascertain disease trends and prevalence of species. Results Overall an upward trend in the number of cases of cervical lymphadenitis caused by ATB infections in children was demonstrated. Organisms isolated in our population were M avium intracellulare, M malmoense and M interjectum. Conclusions We would like to present this data and a literature review, illustrated by case report, on the optimal management of these infections. We suggest that early definitive surgery is the management of choice, performed ideally by a surgeon with experience of this condition. A heightened awareness of these infections is essential to ensure appropriate early management.
Electrical distribution system state estimation: measurement issues and challenges

The distribution grid is the infrastructure that transports electrical energy generated by large plants that are long distances away to the final user. A typical distribution grid consists of High-to-Medium Voltage (HV/MV) transformation centers; the MV grid; Medium-to-Low Voltage (MV/LV) transformation centers; and the LV grid, both three-phase and single-phase. Residential and commercial customers are mainly connected to the LV, while large industrial facilities are connected to the MV.
A comparison of the metabolism of eighteen-carbon 13C-unsaturated fatty acids in healthy women Published, JLR Papers in Press, December 16, 2003. DOI 10.1194/jlr.M300304-JLR200

Altered use of different dietary fatty acids may contribute to several chronic diseases, including obesity, noninsulin-dependent diabetes mellitus, and cardiovascular disease. However, few comparative data are available to support this link, so the goal of the present study was to compare the metabolism of [13C]oleate, [13C]α-linolenate, [13C]elaidate, and [13C]linoleate through oxidation and incorporation into plasma lipid fractions and adipose tissue. Each tracer was given as a single oral bolus to six healthy women. Samples were collected over 8 days, and 13C was analyzed using isotope ratio mass spectrometry. At 9 h postdose, cumulative oxidation was similar for [13C]elaidate, [13C]oleate, and [13C]α-linolenate (19 ± 1%, 20 ± 4%, and 19 ± 3% dose, respectively). Significantly lower oxidation of [13C]linoleate (12 ± 4% dose; P < 0.05) was accompanied by its higher incorporation into plasma phospholipids and cholesteryl esters. Abdominal adipose tissue was enriched with [13C]α-linolenate, [13C]elaidate, or [13C]linoleate within 6 h. The percentage linoleate in plasma phospholipids correlated positively with [13C]linoleate and [13C]elaidate oxidation, indicating a potential role of background diet. Conversion of [13C]linoleate and [13C]α-linolenate to longer chain polyunsaturates was a quantitatively minor route of utilization.
Bayesian Optimization for Polynomial Time Probabilistically Complete STL Trajectory Synthesis

In recent years, Signal Temporal Logic (STL) has gained traction as a practical and expressive means of encoding control objectives for robotic and cyber-physical systems. The state-of-the-art in STL trajectory synthesis is to formulate the problem as a Mixed Integer Linear Program (MILP). The MILP approach is sound and complete for bounded specifications, but such strong correctness guarantees come at the price of exponential complexity in the number of predicates and the time bound of the specification. In this work, we propose an alternative synthesis paradigm that relies on Bayesian optimization rather than mixed integer programming. This relaxes the completeness guarantee to probabilistic completeness, but is significantly more efficient: our approach scales polynomially in the STL time-bound and linearly in the number of predicates. We prove that our approach is sound and probabilistically complete, and demonstrate its scalability with a nontrivial example.
Blitz-starting QUIC Connections

In this paper, we revisit the idea to remove Slow Start from congestion control. To do so, we build upon the newly gained freedom of transport protocol extendability offered by QUIC to hint bandwidth estimates from a typical web client to a server. Using this bandwidth estimate, we bootstrap congestion windows of new connections to quickly utilize available bandwidth. This custom flow initialization removes the common early exit of Slow Start and thus fuels short flow fairness with long-running connections. Our results indicate that we can drastically reduce flow completion time accepting some losses and thereby an inflated transmission volume. For example, for a typical DSL client, loading a 2 MB YouTube video chunk is accelerated by nearly 2x. In the worst case, we find an inflation of the transfer volume by 12% due to losses.