Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure Communications in a Unified Non-Orthogonal Multiple Access Framework

This paper investigates the impact of physical layer secrecy on the performance of a unified non-orthogonal multiple access (NOMA) framework, where both external and internal eavesdropping scenarios are examined. The spatial locations of legitimate users (LUs) and eavesdroppers are modeled by invoking stochastic geometry. To characterize the security performance, new exact and asymptotic expressions of secrecy outage probability (SOP) are derived for both code-domain NOMA (CD-NOMA) and power-domain NOMA (PD-NOMA), in which imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) are taken into account. For the external eavesdropping scenario, the secrecy diversity orders by a pair of LUs (the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-th user and <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-th user) for CD/PD-NOMA are obtained. Analytical results make known that the diversity orders of the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-th user with ipSIC/pSIC for CD-NOMA and PD-NOMA are equal to zero/<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> and zero/one, respectively. The diversity orders of the <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-th user are equal to <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>/one for CD/PD-NOMA. For the internal eavesdropping scenario, we examine the analysis of secrecy diversity order and observe that the <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-th user to wiretap the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-th user with ipSIC/pSIC for CD-NOMA and PD-NOMA provide the diversity orders of zero/<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> and zero/one, respectively, which is consistent with external eavesdropping scenario. Numerical results are present to confirm the accuracy of the analytical results developed and show that: i) The secrecy outage behavior of the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-th user is superior to that of the <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-th user; ii) By increasing the number of subcarriers, CD-NOMA is capable of achieving a larger secrecy diversity gain compared to PD-NOMA.
Effect of Casuarina Plantations Inoculated with Arbuscular Mycorrhizal Fungi and Frankia on the Diversity of Herbaceous Vegetation in Saline Environments in Senegal

Land salinization is a major constraint for the practice of agriculture in the world. Considering the extent of this phenomenon, the rehabilitation of ecosystems degraded by salinization has become a priority to guarantee food security in semi-arid environments. The mechanical and chemical approaches for rehabilitating salt-affected soils being expensive, an alternative approach is to develop and utilize biological systems utilizing salt-tolerant plant species. Casuarina species are naturally halotolerant, but this tolerance has been shown to be improved when they are inoculated with arbuscular mycorrhizal fungi (AMF) and/or nitrogen-fixing bacteria (Frankia). Furthermore, Casuarina plantations have been proposed to promote the development of plant diversity. Thus, the aim of the current study was to evaluate the impact of a plantation comprising the species Casuarina inoculated with AMF and Frankia on the diversity of the sub-canopy and adjacent vegetation. Work was conducted on a plantation comprising Casurina equisetifolia and C. glauca variously inoculated with Frankia and Rhizophagus fasciculatus prior to field planting. The experimental area of 2500 m2 was divided into randomized blocks and vegetation sampling was conducted below and outside of the Casuarina canopy in 32 m2 plots. A total of 48 samples were taken annually over 3 years, with 24 taken from below the Casuarina canopy and 24 from outside the canopy. The results obtained show that co-inoculation with Frankia and Rhizophagus fasciculatus improves the height and survival rate of both species. After 4–5 years, there was greater species diversity and plant biomass in the sub-canopy environment compared with that of the adjacent environments. Our results suggest that inoculation of beneficial microbes can improve growth of Casuarina species and that planting of such species can improve the diversity of herbaceous vegetation in saline environments.
Machine Learning, Big Data, And Smart Buildings: A Comprehensive Survey

Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contains valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.
Cooperative Embeddings for Instance, Attribute and Category Retrieval

The goal of this paper is to retrieve an image based on instance, attribute and category similarity notions. Different from existing works, which usually address only one of these entities in isolation, we introduce a cooperative embedding to integrate them while preserving their specific level of semantic representation. An algebraic structure defines a superspace filled with instances. Attributes are axis-aligned to form subspaces, while categories influence the arrangement of similar instances. These relationships enable them to cooperate for their mutual benefits for image retrieval. We derive a proxy-based softmax embedding loss to learn simultaneously all similarity measures in both superspace and subspaces. We evaluate our model on datasets from two different domains. Experiments on image retrieval tasks show the benefits of the cooperative embeddings for modeling multiple image similarities, and for discovering style evolution of instances between- and within-categories.
Transcriptomic, proteomic, and biochemical analyses reveal a novel neuritogenesis mechanism of Naja naja venom α‐elapitoxin post binding to TrkA receptor of rat pheochromocytoma cells

This is the first report showing unique neuritogenesis potency of Indian Cobra N. naja venom long‐chain α‐neurotoxin (Nn‐α‐elapitoxin‐1) exhibiting no sequence similarity to conventional nerve growth factor, by high‐affinity binding to its tyrosine kinase A (TrkA) receptor of rat pheochromocytoma (PC‐12) cells without requiring low‐affinity receptor p75NTR. The binding residues between Nn‐α‐elapitoxin‐1 and mammalian TrkA receptor are predicted by in silico analysis. This binding results in a time‐dependent internalization of TrkA receptor into the cytoplasm of PC‐12 cells. The transcriptomic analysis has demonstrated the differential expression of 445 genes; 38 and 32 genes are up‐regulated and down‐regulated, respectively in the PC‐12 cells post‐treatment with Nn‐α‐elapitoxin‐1. Global proteomic analysis in concurrence with transcriptomic data has also demonstrated that in addition to expression of a large number of common intracellular proteins in the control and Nn‐α‐elapitoxin‐1‐treated PC‐12 cells, the latter cells also showed the expression of uniquely up‐regulated and down‐regulated intracellular proteins involved in diverse cellular functions. Altogether, the data from transcriptomics, proteomics, and inhibition of downstream signaling pathways by specific inhibitors, and the immunoblot analysis of major regulators of signaling pathways of neuritogenesis unambiguously demonstrate that, similar to mouse 2.5S‐nerve growth factor, the activation of mitogen activated protein kinase/extracellular signal‐regulated kinase is the major signaling pathway for neuritogenesis by Nn‐α‐elapitoxin‐1. Nonetheless, fibroblast growth factor signaling and heterotrimeric G‐protein signaling pathways were found to be uniquely expressed in Nn‐α‐elapitoxin‐1‐treated PC‐12 cells and not in mouse 2.5S‐nerve growth factor ‐treated cells. The TrkA binding region of Nn‐α‐elapitoxin‐1 may be developed as a peptide‐based drug prototype for the treatment of major central neurodegenerative diseases.
A stable finite element method for an open, inextensible, viscoelastic rod

We present and analyse a numerical method for understanding the dynamics of an open, inextensible viscoelastic rod - a long and thin three dimensional object. Our model allows for both elastic and viscous, bending and twisting deformations and describes the evolution of the midline curve of the rod as well as an orthonormal frame which full determines the rod's three dimensional geometry. The numerical method is based on using a combination of piecewise linear and piecewise constant functions based on a novel rewriting of the model equations. We derive a stability estimate for the semi-discrete scheme and show that at the fully discrete level that we have good control over the length element and preserve the frame orthonormality conditions up to machine precision. Numerical experiments demonstrate both the good properties of the method as well as the applicability of the method for simulating locomotion of the microscopic nematode Caenorhabditis elegans.
The Impact of Extraneous Variables on the Performance of Recurrent Neural Network Models in Clinical Tasks

Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies. Identifying which variables are useful in predicting clinical outcomes can be challenging. Advanced algorithms such as deep neural networks were designed to process high-dimensional inputs containing variables in their measured form, thus bypass separate feature selection or engineering steps. We investigated the effect of extraneous input variables on the predictive performance of Recurrent Neural Networks (RNN) by including in the input vector extraneous variables randomly drawn from theoretical and empirical distributions. RNN models using different input vectors (EMR variables; EMR and extraneous variables; extraneous variables only) were trained to predict three clinical outcomes: in-ICU mortality, 72-hour ICU re-admission, and 30-day ICU-free days. The measured degradations of the RNN's predictive performance with the addition of extraneous variables to EMR variables were negligible.
The UV spectral slope β and stellar population of most active star-forming galaxies at z ∼ 4

We investigate the stellar population of star-forming galaxies at $z \sim 4$ by focusing on their slope of rest-frame ultraviolet (UV) continuum, $\beta$ where $f_{\lambda} \propto \lambda^{\beta}$. We investigate the sample of bright Lyman Break Galaxies (LBGs) with $i' \leq 26.0$ in the Subaru/XMM-Newton Deep Survey field by using the SED fitting analysis. We find that the apparently redder ($\beta_{\mathrm{obs}} > -1.73$) LBGs tend to be dusty ($\mathrm{Av} > 1.0$), have the young stellar population ($\beta_{\mathrm{int}} < -2.42$), and the intrinsically active star-forming galaxies (SFR $\gtrsim$ a few $\times 10^{2}$ $\mathrm{M_{\odot}} \mathrm{yr^{-1}}$). It means that a significant fraction of the UV-selected LBGs at $z \sim 4$ is on-going active and dust obscured star-forming galaxies. We compare the IR to UV luminosity ratio assuming the dust attenuation laws with the sub-millimeter observations from previous works. The result suggests that the Calzetti-like dust attenuation law is preferable for the active and dusty star-forming LBGs at $z = 4$. We also find that an extrapolation of the $\beta_{\mathrm{int}}$-$M_{\mathrm{UV,int}}$ relation toward the fainter magnitude range below our sample magnitude limit intersects the $\beta_{\mathrm{obs}}$-$M_{\mathrm{UV,obs}}$ relation previously obtained in the deeper narrow-area observations at $M_{\mathrm{UV}} = -18.9$ and $\beta = -1.94$, which coincides with the break point of $\beta_{\mathrm{obs}}$-$M_{\mathrm{UV,obs}}$ relation observed so far. The coincidence suggest that we see the almost dust-free population at $M_{\mathrm{UV,obs}} \gtrsim -18.9$.
Biomechanical modeling and computer simulation of the brain during neurosurgery

Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine Laboratory and its collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics‐based modeling approach and describe the brain deformation in mechanical terms (such as displacements, strains, and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading, and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time‐accurate and steady‐state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment.
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition

Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data. Recently, there is a trend of using very deep feedforward neural networks to model the 3D coordinates of joints without considering the computational efficiency. In this paper, we propose a simple yet effective semantics-guided neural network (SGN) for skeleton-based action recognition. We explicitly introduce the high level semantics of joints (joint type and frame index) into the network to enhance the feature representation capability. In addition, we exploit the relationship of joints hierarchically through two modules, i.e., a joint-level module for modeling the correlations of joints in the same frame and a framelevel module for modeling the dependencies of frames by taking the joints in the same frame as a whole. A strong baseline is proposed to facilitate the study of this field. With an order of magnitude smaller model size than most previous works, SGN achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets.
Pragmatically Informative Text Generation

We improve the informativeness of models for conditional text generation using techniques from computational pragmatics. These techniques formulate language production as a game between speakers and listeners, in which a speaker should generate output text that a listener can use to correctly identify the original input that the text describes. While such approaches are widely used in cognitive science and grounded language learning, they have received less attention for more standard language generation tasks. We consider two pragmatic modeling methods for text generation: one where pragmatics is imposed by information preservation, and another where pragmatics is imposed by explicit modeling of distractors. We find that these methods improve the performance of strong existing systems for abstractive summarization and generation from structured meaning representations.
Easy Transfer Learning By Exploiting Intra-Domain Structures

Transfer learning aims at transferring knowledge from a well-labeled domain to a similar but different domain with limited or no labels. Unfortunately, existing learning-based methods often involve intensive model selection and hyperparameter tuning to obtain good results. Moreover, cross-validation is not possible for tuning hyperparameters since there are often no labels in the target domain. This would restrict wide applicability of transfer learning especially in computationally-constraint devices such as wearables. In this paper, we propose a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance. By exploiting intra-domain structures, EasyTL is able to learn both non-parametric transfer features and classifiers. Extensive experiments demonstrate that, compared to state-of-the-art traditional and deep methods, EasyTL satisfies the Occam's Razor principle: it is extremely easy to implement and use while achieving comparable or better performance in classification accuracy and much better computational efficiency. Additionally, it is shown that EasyTL can increase the performance of existing transfer feature learning methods.
The quantum geometric tensor from generating functions

We introduce a new method to compute the Quantum Geometric Tensor, this procedure allows us to compute the Quantum Information Metric and the Berry curvature perturbatively for a theory with an arbitrary interaction Hamiltonian. The calculation uses the generating function method, and it is illustrated with the harmonic oscillator with a linear and a quartic perturbation.
Life-Cycle Inequality: Blacks and Whites Differentials in Life Expectancy, Savings, Income, and Consumption

Life expectancy for Blacks is about 8 year shorter than for Whites. A shorter life expectancy, in line with the theoretical prediction of a simple model, determines a much lower amount of savings and wealth accumulation and therefore a lower degree of insurance. This, in turn, contributes to persistent racial differentials in life-cycle consumption. Starting from the same position in the consumption distribution Blacks end up in a lower percentile than Whites after a few decades. This is particularly marked for those Blacks who start at the top of the consumption distribution, where Whites are much more persistent. We document these facts using 40 years of PSID data (1981-2017).
MalPaCA: Malware Packet Sequence Clustering and Analysis

Malware family characterization is a challenging problem because ground-truth labels are not known. Anti-virus solutions provide labels for malware samples based on their static analysis. However, these labels are known to be inconsistent, causing the evaluation of analysis methods to depend on unreliable ground truth labels. These analysis methods are often black-boxes that make it impossible to verify the assigned family labels. To support malware analysts, we propose a whitebox method named MalPaCA to cluster malware's attacking capabilities reflected in their network traffic. We use sequential features to model temporal behavior. We also propose an intuitive, visualization-based cluster evaluation method to solve interpretability issues. The results show that clustering malware's attacking capabilities provides a more intimate profile of a family's behavior. The identified clusters capture various attacking capabilities, such as port scans and reuse of C\&C servers. We discover a number of discrepancies between behavioral clusters and traditional malware family designations. In these cases, behavior within a family group was so varied that many supposedly related malwares had more in common with malware from other families than within their family designation. We also show that sequential features are better suited for modeling temporal behavior than statistical aggregates.
A Review of Critical Infrastructure Protection Approaches: Improving Security through Responsiveness to the Dynamic Modelling Landscape

As new technologies such as the Internet of Things (IoT) are integrated into Critical National Infrastructures (CNI), new cybersecurity threats emerge that require specific security solutions. Approaches used for analysis include the modelling and simulation of critical infrastructure systems using attributes, functionalities, operations, and behaviours to support various security analysis viewpoints, recognising and appropriately managing associated security risks. With several critical infrastructure protection approaches available, the question of how to effectively model the complex behaviour of interconnected CNI elements and to configure their protection as a system-of-systems remains a challenge. Using a systematic review approach, existing critical infrastructure protection approaches (tools and techniques) are examined to determine their suitability given trends like IoT, and effective security modelling and analysis issues. It is found that empirical-based, agent-based, system dynamics-based, and network-based modelling are more commonly applied than economic-based and equation-based techniques, and empirical-based modelling is the most widely used. The energy and transportation critical infrastructure sectors reflect the most responsive sectors, and no one Critical Infrastructure Protection (CIP) approach - tool, technique, methodology or framework -- provides a fit-for-all capacity for all-round attribute modelling and simulation of security risks. Typically, deciding factors for CIP choices to adopt are often dominated by trade-offs between complexity of use and popularity of approach, as well as between specificity and generality of application in sectors.
Multitask Soft Option Learning

We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference. MSOL extends the concept of options, using separate variational posteriors for each task, regularized by a shared prior. This ''soft'' version of options avoids several instabilities during training in a multitask setting, and provides a natural way to learn both intra-option policies and their terminations. Furthermore, it allows fine-tuning of options for new tasks without forgetting their learned policies, leading to faster training without reducing the expressiveness of the hierarchical policy. We demonstrate empirically that MSOL significantly outperforms both hierarchical and flat transfer-learning baselines.
BCMA-ES: A Bayesian Approach to CMA-ES

This paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm. Assuming the parameters of the multi variate normal distribution for the minimum follow a conjugate prior distribution, we derive their optimal update at each iteration step. Not only provides this Bayesian framework a justification for the update of the CMA-ES algorithm but it also gives two new versions of CMA-ES either assuming normal-Wishart or normal-Inverse Wishart priors, depending whether we parametrize the likelihood by its covariance or precision matrix. We support our theoretical findings by numerical experiments that show fast convergence of these modified versions of CMA-ES.