Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations

Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods by learning the distribution of healthy images and identifying anomalies as outliers. In presence of an additional dataset of unlabelled data containing also anomalies, the task can be framed as a semi-supervised task with negative and unlabelled sample points. Recently, in Albu et al., 2020, we have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder, trained on unlabelled data. The dissimilarity is computed between the encoding of the image and the encoding of its reconstruction obtained through a different autoencoder trained only on healthy images. In this paper we present novel and improved results for our method, obtained by training the Variational AutoEncoders on a subset of the HCP and BRATS-2018 datasets and testing on the remaining individuals. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines, which employ a single VAE trained on healthy individuals. As expected, the performance of our method increases with the size of the threshold used to determine the presence of an anomaly.
Molecular Systematics of the Least Darter (Percidae: Etheostoma microperca): Historical Biogeography and Conservation Implications

A repeated pattern in North American fishes is species having geographic ranges consisting of two disjunct, widely separated areas of distribution, one in previously glaciated northern areas and one in the unglaciated Ozarks. We used phylogenetic analyses of mitochondrial DNA (ND2 and cyt b) and a nuclear DNA sequence (S7 intron 1) to examine the historical biogeography of one such species, the Least Darter, Etheostoma microperca. Uniquely for Ozark fishes, populations in western Ozark tributaries of the Arkansas River represent most of the evolutionary legacy of the species, with three markedly divergent cyt b clades. Populations in the Illinois River (Arkansas) and Shoal Creek (Missouri) appear to have been isolated for about 5 and 2 my, respectively, and potentially represent undescribed species. A wide-ranging northern clade (Hudson Bay, Great Lakes, and Ohio and upper Mississippi river systems) was sister to a clade comprising haplotypes from Blue River and all Ozark haplotypes exclusive of the Illinois River and Shoal Creek clades. The northern clade appears to have survived Illinoian and Wisconsin glaciations in one or more northern refuges and exhibits molecular genetic signals of population expansion during the Sangamon interglacial. The Blue River and western Ozark populations are especially susceptible to local extirpation with little opportunity for replacement. Persistence of these populations will require maintenance of springflows via regional groundwater management.
Counting and locating people in outdoor environments: a comparative experimental study using WiFi-based passive methods

WiFi-based passive methods are becoming a common tool to count, estimate, and/or locate people. One area of applicability is the development of intelligent control system for traffic management in urban areas, so that these systems are able to take into account not only vehicles’ behaviors but also pedestrians’, as important actors in the road scenario. In this work, we present the performance evaluation in terms of accuracy of a WiFi-based passive method used to identify pedestrians, classify them as moving pedestrians or static pedestrians, and for the latter, to locate them in a traffic intersection. The proposed algorithm is implemented in a low-cost development board and tested through several experiments in a real outdoor scenario. Our proposal is compared with several classic Machine Learning (ML) algorithms, specifically with Binary Logistic Regression, Support Vector Classification, Gaussian Naive Bayes, Random Forest, and k-Nearest Neighbors. Results show that despite the simplicity of our method, the outcomes are similar or better than most of the ML techniques, without the expected complexity or computational requirements that the latter required.
Non-Hamiltonian actions with fewer isolated fixed points

In an earlier paper, the second author resolved a question of McDuff by constructing a non-Hamiltonian symplectic circle action on a closed, connected six-dimensional symplectic manifold with exactly 32 fixed points. In this paper, we improve on this example by reducing the number of fixed points. More concretely, we construct a non-Hamiltonian symplectic circle action on a closed, connected six-dimensional symplectic manifold with exactly $2k$ fixed points for any $k \geq 5$.
MPC-enabled Privacy-Preserving Neural Network Training against Malicious Attack

In the past decades, the application of secure multiparty computation (MPC) to machine learning, especially privacy-preserving neural network training, has attracted tremendous attention from both academia and industry. MPC enables several data owners to jointly train a neural network while preserving their data privacy. However, most previous works focus on semi-honest threat model which cannot withstand fraudulent messages sent by malicious participants. In this work, we propose a construction of efficient $n$-party protocols for secure neural network training that can secure the privacy of all honest participants even when a majority of the parties are malicious. Compared to the other designs that provides semi-honest security in a dishonest majority setting, our actively secured neural network training incurs affordable efficiency overheads. In addition, we propose a scheme to allow additive shares defined over an integer ring $\mathbb{Z}_N$ to be securely converted to additive shares over a finite field $\mathbb{Z}_Q$. This conversion scheme is essential in correctly converting shared Beaver triples in order to make the values generated in preprocessing phase to be usable in online phase, which may be of independent interest.
Cross-validation Confidence Intervals for Test Error

This work develops central limit theorems for cross-validation and consistent estimators of its asymptotic variance under weak stability conditions on the learning algorithm. Together, these results provide practical, asymptotically-exact confidence intervals for $k$-fold test error and valid, powerful hypothesis tests of whether one learning algorithm has smaller $k$-fold test error than another. These results are also the first of their kind for the popular choice of leave-one-out cross-validation. In our real-data experiments with diverse learning algorithms, the resulting intervals and tests outperform the most popular alternative methods from the literature.
Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles

To improve the driving mobility and energy efficiency of connected autonomous electrified vehicles, this paper presents an integrated longitudinal speed decision-making and energy efficiency control strategy. The proposed approach is a hierarchical control architecture, which is assumed to consist of higher-level and lower-level controls. As the core of this study, model predictive control and reinforcement learning are combined to improve the powertrain mobility and fuel economy for a group of automated vehicles. The higher-level exploits the signal phase and timing and state information of connected autonomous vehicles via vehicle to infrastructure and vehicle to vehicle communication to reduce stopping at red lights. The higher-level outputs the optimal vehicle velocity using model predictive control technique and receives the power split control from the lower-level con-troller. These two levels communicate with each other via a controller area network in the real vehicle. The lower-level utilizes a model-free reinforcement learning method to improve the fuel economy for each connected autonomous vehicle. Numerical tests illustrate that vehicle mobility can be noticeably improved (traveling time reduced by 30%) by reducing red-light idling. The effectiveness and performance of the proposed method are validated via comparison analysis among different energy efficiency controls (fuel economy promoted by 13%).
Quasi-Degradation Probability of Two-User NOMA Over Rician Fading Channels

Non-orthogonal multiple access (NOMA) has a great potential to offer a higher spectral efficiency of multi-user wireless networks than orthogonal multiples access (OMA). Previous work has established the condition, referred to quasi-degradation (QD) probability, under which NOMA has no performance loss compared to the capacity-achieving dirty paper coding for the two-user case. Existing results assume Rayleigh fading channels without line-of-sight (LOS). In many practical scenarios, the channel LOS component is critical to the link quality where the channel gain follows a Rician distribution instead of a Rayleigh distribution. In this work, we analyze the QD probability over multi-input and single-output (MISO) channels subject to Rician fading. The QD probability heavily depends on the angle between two user channels, which involves a matrix quadratic form in random vectors and a stochastic matrix. With the deterministic LOS component, the distribution of the matrix quadratic form is non-central that dramatically complicates the derivation of the QD probability. To remedy this difficulty, a series of approximations is proposed that yields a closed-form expression for the QD probability over MISO Rician channels. Numerical results are presented to assess the analysis accuracy and get insights into the optimality of NOMA over Rician fading channels.
Genomics of root development.

Because of the different roles the root system plays in overall plant growth, root architecture is a fundamental aspect of plant growth and development. The root system especially acquires water and nutrients from the soil, anchors the plant in the substrate, synthesizes hormones and metabolites, interacts with symbiotic microorganisms, and insures storage functions. In light of these characteristics, more and more breeders turn their attention to this underground organ in order to increase yield. This requires a better understanding of the relation of this part of the plant with the environment and of its highly adaptive behavior (Lynch 2007; Gewin 2010; Den Herder et al. 2010). Within the angiosperms, major differences in root architecture between dicotyledonous and monocotyledonous plants exist. Dicots develop a tap root system composed of a main primary root, already formed during embryogenesis, which grows vertically into the soil and gives rise to the emergence of numerous lateral roots extending the surface area. Monocots have a fibrous root system in which the embryonic primary root is only important for the early development of the plant (Feix et al. 2002) and in which an extensive postembryonic shoot-born root system is formed later on. Very little is known about the genetic and molecular mechanisms involved in the development and architecture of the root system in major crop species, generally monocotyledonous plants. Lack of insight is certainly a consequence of the difficulty to access and observe this organ in its natural habitat, namely the soil. Moreover, and probably because of this hidden character, the root has been neglected for a long time in crop improvement and in agricultural approaches aiming at increasing shoot biomass. Nevertheless, while most of the work has been done on Arabidopsis thaliana, the awareness of the importance of the root system in modulating plant growth, together with progress in sequencing and new molecular techniques, has caused renewed interest in understanding molecular mechanisms in crop species (Hochholdinger and Zimmermann 2009; Coudert et al. 2010). In the scope of root development and its interaction with the soil, in this chapter, we propose to focus on the mechanisms involved in root branching, which is a major determinant of root system architecture. The plasticity of the root system represents indeed an important potential for plants, being sessile organisms, to adapt to the heterogeneity of their environment. The soil is a complex mixture of solid, gaseous, and liquid phases, wherein nutrients are unequally distributed. Plants have therefore developed a highly sophisticated regulatory system to control their root architecture, in
Free Air Temperature Increase (FATI): a new tool to study global warming effects on plants in the field

A new technique, called Free Air Temperature Increase (FATI), was developed to artificially induce increased canopy temperature in field conditions without the use of enclosures. This acronym was chosen in analogy with FACE (Free Air CO 2 Enrichment), a technique which produces elevated CO 2 concentrations [CO 2 ] in open field conditions. The FATI system simulates global warming in small ecosystems of limited height, using infrared heaters from which all radiation below 800nm is removed by selective cut-off filters to avoid undesirable photomorpho-genetic effects. An electronic control circuit tracks the ambient canopy temperature in an unheated reference plot with thermocouples, and modulates the radiant energy from the lamps to produce a 2.5°C increment in the canopy temperature of an associated heated plot (continuously day and night). This pre-set target differential is relatively constant over time due to the fast response of the lamps and the use of a proportional action controller (the standard deviation of this increment was <1°C in a 3 week field study with 1007 measurements). Furthermore, the increase in leaf temperature does not depend on the vertical position within the canopy or on the height of the stand. Possible applications and alternative designs are discussed.
A chemical study of carbon fractionation in external galaxies

In the interstellar medium carbon exists in the form of two stable isotopes 12C and 13C and their ratio is a good indicator of nucleosynthesis in galaxies. However, chemical fractionation can potentially significantly alter this ratio and in fact observations of carbon fractionation within the same galaxy has been found to vary from species to species. In this paper, we theoretically investigate the carbon fractionation for selected abundant carbon-bearing species in order to determine the conditions that may lead to a spread of the 12C/13C ratio in external galaxies. We find that carbon fractionation is sensitive to almost all the physical conditions we investigated, it strongly varies with time for all species but CO, and shows pronounced differences across species. Finally, we discuss our theoretical results in the context of the few observations of the 12C/13C in both local and higher redshift galaxies.
Timely Status Update in Internet of Things Monitoring Systems: An Age-Energy Tradeoff

We consider an Internet of Things (IoT) monitoring system, in which an IoT device monitors a physical process and transmits randomly generated status updates to its associated access point (AP) as timely as possible. The timeliness of the status updates is characterized by a recently introduced metric, termed the age of information (AoI), which is defined as the time elapsed since the generation of the last successfully received status update. The channel between the IoT device and the AP is considered to be error-prone and thus the status updates suffer from packet loss. Assuming that the AP provides no feedback to the IoT device, we adopt a practical truncated automatic repeat request (TARQ) scheme: the IoT device keeps transmitting the current status update repeatedly until the maximum allowable transmission times is reached or a new status update is generated. We characterize the inherent age-energy tradeoff for the considered IoT monitoring system. Specifically, a larger value of the maximum allowable transmission times reduces the average AoI, at the cost of incurring higher average energy consumption at the IoT device. Based on the evolution of AoI, we derive the closed-form expressions of the average AoI, the average peak AoI, and the average energy consumption. We then minimize the average AoI by optimizing the transmit power of the IoT device and the maximum allowable transmission times under an average transmit power constraint. Simulations validate the theoretical analysis and reveal that under the same average transmit power constraint, the adopted TARQ scheme achieves a lower average AoI than the classical ARQ scheme that allows an infinite number of retransmission times.
Sparsifying Dictionary Learning for Beamspace Channel Representation and Estimation in Millimeter-Wave Massive MIMO

Millimeter-wave (mmWave) massive multiple-input-multiple-output (mMIMO) is reported as a key enabler in fifth-generation communication and beyond. It is customary to use a lens antenna array to transform a mmWave mMIMO channel into a beamspace where the channel exhibits sparsity. This beamspace transformation is equivalent to performing a Fourier transformation of the channel. Still, a Fourier transformation is not necessarily optimal for many reasons. For example, it can cause a power leakage problem. Accordingly, this paper proposes using a learned sparsifying dictionary as the transformation operator leading to another beamspace for channel representation. Since a dictionary is obtained by training over actual channel measurements in an end-to-end manner, this transformation is shown to yield two immediate advantages. First is enhancing channel sparsity, thereby leading to more efficient pilot reduction. Second is improving the channel representation quality, thus reducing the underlying power leakage phenomenon. Consequently, this allows for improved channel estimation and facilitates beam selection in mmWave mMIMO. In addition, a learned dictionary is used as the channel estimation operator for the same reasons. Extensive simulations under various operating scenarios and environments validate the added benefits of using learned dictionaries in improving the channel estimation quality and beam selectivity, thus improving spectral efficiency.
Convergence Rates of Inertial Primal-Dual Dynamical Methods for Separable Convex Optimization Problems

In this paper, we propose a second-order continuous primal-dual dynamical system with time-dependent positive damping terms for a separable convex optimization problem with linear equality constraints. By the Lyapunov function approach, we investigate asymptotic properties of the proposed dynamical system as the time $t\to+\infty$. The convergence rates are derived for different choices of the damping coefficients. We also show that the obtained results are robust under external perturbations.
Analysis of solar wind energy and the Akasofu parameter for energy dynamics assessment during Supersubstorm

Solar activities have significant impact on the member of the solar systems including earth. As earth is a planet with its own magnetic field, solar emissions with magnetic fields do interact with the earth's own magnetic field to create geo-magnetic disturbances. Substorms are geo-magnetic disturbances of shorter duration (few hours or less) which are mainly concentrated in the auroral region and originates due to the ionospheric current injection at high latitudes. Supersubstorm are intense substorms as identified by peak activities in geo-magnetic indices. In this thesis, we have investigated the energy dynamics during the supersubstorm and analyzed the relation of the energy components with different geo-magnetic indices and interplanetary magnetic field (IMF) components. In particular, the solar wind energy levels and the energy coupled into the magnetosphere as captured by the Akasofu parameter are studied. A qualitative analysis of the computed energy and its relationship with different geo-magnetic indices is presented. Correlation and cross-correlation analysis have been used to get insights into the nature of the relationship between the energy components and indices. This for example uncovered the negative relationship between the solar wind energy and the Akasofu parameter. A summary analysis of the supersubstorm event in comparison with the quiet phases are made. This has led to insights for example showing the higher energy levels during supersubstorm and different energy ratios (Akasofu parameter divided by the solar wind energy) existing for different supersubstorm.
Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines (SVM), artificial neural networks (ANN), regression tree, random forest (RF), extreme gradient boosting (XGBoost), and conventional deep neural network (DNN) for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 14.9% of SOC spatial variability followed by the normalized difference vegetation index (12.5%), day temperature index of moderate resolution imaging spectroradiometer (10.6%), multiresolution valley bottom flatness (8.7%) and land use (8.2%), respectively. Based on 10-fold cross-validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 0.59%, a root mean squared error of 0.75%, a coefficient of determination of 0.65, and Lin’s concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 3.71%, followed by the aquic (2.45%) and xeric (2.10%) classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN (hidden layers = 7, and size = 50) is a promising algorithm for handling large numbers of auxiliary data at a province-scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC base-line map and minimal uncertainty.
Impact of Vermicompost on Vegetable Transplant Quality

Vermicomposting is a promising method of transforming unwanted and virtually unlimited supplies of organic wastes into usable substrates. In this process, the digestive tracts of certain earthworm species (e.g., Eisenia fetida) are used to stabilize organic wastes. The fi nal product is an odorless peat-like substance, which has good structure, moisture- holding capacity, relatively large amounts of available nutrients, and microbial metabolites that may act as plant growth regulators. For these reasons, vermicompost has the potential to make a valuable contribution to soilless potting media. The objective of this study was to evaluate the transplant quality and fi eld performance of vegetable transplants grown in vermicompost. Tomato (Lycopersicon esculentum Mill.), eggplant (Solanum melongena L.), and pepper (Capsicum annuum L.) transplants were grown in a commercial soilless mix including 0%, 10%, or 20% (v/v) worm-worked cattle manure. Growth of vegetable transplants was positively affected by addition of vermicompost, perhaps by altering the nutritional balance of the medium. Transplant quality was improved in peppers and eggplants while tomato transplant quality was slightly reduced. There were no signifi cant differences in fi eld performance. Alternative substrate amendments for soil-