Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Machine Learning for 5G Communications Security

Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization. As communication systems become smarter with cognitive radio capabilities empowered by machine learning to perform critical tasks such as spectrum awareness and spectrum sharing, they also become susceptible to new vulnerabilities due to the attacks that target the machine learning applications. This paper identifies the emerging attack surface of adversarial machine learning and corresponding attacks launched against wireless communications in the context of 5G systems. The focus is on attacks against (i) spectrum sharing of 5G communications with incumbent users such as in the Citizens Broadband Radio Service (CBRS) band and (ii) physical layer authentication of 5G User Equipment (UE) to support network slicing. For the first attack, the adversary transmits during data transmission or spectrum sensing periods to manipulate the signal-level inputs to the deep learning classifier that is deployed at the Environmental Sensing Capability (ESC) to support the 5G system. For the second attack, the adversary spoofs wireless signals with the generative adversarial network (GAN) to infiltrate the physical layer authentication mechanism based on a deep learning classifier that is deployed at the 5G base station. Results indicate major vulnerabilities of 5G systems to adversarial machine learning. To sustain the 5G system operations in the presence of adversaries, a defense mechanism is presented to increase the uncertainty of the adversary in training the surrogate model used for launching its subsequent attacks.
Understanding the Error in Evaluating Adversarial Robustness

Deep neural networks are easily misled by adversarial examples. Although lots of defense methods are proposed, many of them are demonstrated to lose effectiveness when against properly performed adaptive attacks. How to evaluate the adversarial robustness effectively is important for the realistic deployment of deep models, but yet still unclear. To provide a reasonable solution, one of the primary things is to understand the error (or gap) between the true adversarial robustness and the evaluated one, what is it and why it exists. Several works are done in this paper to make it clear. Firstly, we introduce an interesting phenomenon named gradient traps, which lead to incompetent adversaries and are demonstrated to be a manifestation of evaluation error. Then, we analyze the error and identify that there are three components. Each of them is caused by a specific compromise. Moreover, based on the above analysis, we present our evaluation suggestions. Experiments on adversarial training and its variations indicate that: (1) the error does exist empirically, and (2) these defenses are still vulnerable. We hope these analyses and results will help the community to develop more powerful defenses.
SuperMat: construction of a linked annotated dataset from superconductors-related publications

ABSTRACT A growing number of papers are published in the area of superconducting materials science. However, novel text and data mining (TDM) processes are still needed to efficiently access and exploit this accumulated knowledge, paving the way towards data-driven materials design. Herein, we present SuperMat (Superconductor Materials), an annotated corpus of linked data derived from scientific publications on superconductors, which comprises 142 articles, 16052 entities, and 1398 links that are characterised into six categories: the names, classes, and properties of materials; links to their respective superconducting critical temperature (Tc); and parametric conditions such as applied pressure or measurement methods. The construction of SuperMat resulted from a fruitful collaboration between computer scientists and material scientists, and its high quality is ensured through validation by domain experts. The quality of the annotation guidelines was ensured by satisfactory Inter Annotator Agreement (IAA) between the annotators and the domain experts. SuperMat includes the dataset, annotation guidelines, and annotation support tools that use automatic suggestions to help minimise human errors.
Homonym Identification using BERT - Using a Clustering Approach

Homonym identification is important for WSD that require coarse-grained partitions of senses. The goal of this project is to determine whether contextual information is sufficient for identifying a homonymous word. To capture the context, BERT embeddings are used as opposed to Word2Vec, which conflates senses into one vector. SemCor is leveraged to retrieve the embeddings. Various clustering algorithms are applied to the embeddings. Finally, the embeddings are visualized in a lower-dimensional space to understand the feasibility of the clustering process.
Associated Spatio-Temporal Capsule Network for Gait Recognition

It is a challenging task to identify a person based on her/his gait patterns. State-of-the-art approaches rely on the analysis of temporal or spatial characteristics of gait, and gait recognition is usually performed on single modality data (such as images, skeleton joint coordinates, or force signals). Evidence has shown that using multi-modality data is more conducive to gait research. Therefore, we here establish an automated learning system, with an associated spatio-temporal capsule network (ASTCapsNet) trained on multi-sensor datasets, to analyze multimodal information for gait recognition. Specifically, we first design a low-level feature extractor and a high-level feature extractor for spatio-temporal feature extraction of gait with a novel recurrent memory unit and a relationship layer. Subsequently, a Bayesian model is employed for the decision-making of class labels. Extensive experiments on several public datasets (normal and abnormal gait) validate the effectiveness of the proposed ASTCapsNet, compared against several state-of-the-art methods.
Desenvolvimento e estudos de estabilidade preliminares de emulsões Desenvolvimento e estudos de estabilidade preliminares de emulsões Desenvolvimento e estudos de estabilidade preliminares de emulsões

Development and Preliminary Stability Evaluations of O/W emulsion containing Ketoconazole 2.0% . The objective of this work was the development of O/W emulsions containing Ketoconazole 2.0% and to evaluate their preliminary stability by analyzing physical-chemical characteristics such as homogeneity, aggregation formation, flocculation, creaming and coalescence. The emulsions were formulated using different self-emulsifying bases, composed of cetearyl alcohol, ethoxyl alcohol, higher fatty alcohol, stearic acid, lanolin and others. The O/W emulsions were evaluated by centrifugation test, thermal stress test, and freezing/defrosting cycles, and its organoleptic and physical-chemistry characteristics were analyzed before and after each assay. All samples maintained their homogeneity after the centrifugation test, but only the systems prepared with self emulsifying composed of higher fatty alcohol (Polawax NF ® ) and ammonium acryloyl dimethyl taurate copolymer, Trilauryl 4 Phosphate, methyl glucose Sesquistearate, Summer flower oleum and Glycerin tetradibutyl pentaerythrityl hydroxyhydrocinnamate (Hostacerin NCB ® ) remained stable after the thermal stress test
Active learning for object detection in high-resolution satellite images

In machine learning, the term active learning regroups techniques that aim at selecting the most useful data to label from a large pool of unlabelled examples. While supervised deep learning techniques have shown to be increasingly efficient on many applications, they require a huge number of labelled examples to reach operational performances. Therefore, the labelling effort linked to the creation of the datasets required is also increasing. When working on defense-related remote sensing applications, labelling can be challenging due to the large areas covered and often requires military experts who are rare and whose time is primarily dedicated to operational needs. Limiting the labelling effort is thus of utmost importance. This study aims at reviewing the most relevant active learning techniques to be used for object detection on very high resolution imagery and shows an example of the value of such techniques on a relevant operational use case: aircraft detection.
Distributed Quantum Computing and Network Control for Accelerated VQE

Interconnecting small quantum computers will be essential in the future for creating large-scale, robust quantum computers. Methods for distributing monolithic quantum algorithms efficiently are, thus, needed. In this article, we consider an approach for distributing the accelerated variational quantum eigensolver algorithm over arbitrary sized—in terms of number of qubits—distributed quantum computers. We consider approaches for distributing qubit assignments of the Ansatz states required to estimate the expectation value of Hamiltonian operators in quantum chemistry in a parallelized computation and provide a systematic approach to generate distributed quantum circuits for distributed quantum computing. Moreover, we propose an architecture for a distributed quantum control system in the context of centralized and decentralized network control.
Single Shot Multitask Pedestrian Detection and Behavior Prediction

Detecting and predicting the behavior of pedestrians is extremely crucial for self-driving vehicles to plan and interact with them safely. Although there have been several research works in this area, it is important to have fast and memory efficient models such that it can operate in embedded hardware in these autonomous machines. In this work, we propose a novel architecture using spatial-temporal multi-tasking to do camera based pedestrian detection and intention prediction. Our approach significantly reduces the latency by being able to detect and predict all pedestrians' intention in a single shot manner while also being able to attain better accuracy by sharing features with relevant object level information and interactions.
Disentangling homophily, community structure and triadic closure in networks

Network homophily, the tendency of similar nodes to be connected, and transitivity, the tendency of two nodes being connected if they share a common neighbor, are conflated properties in network analysis, since one mechanism can drive the other. Here we present a generative model and corresponding inference procedure that are capable of distinguishing between both mechanisms. Our approach is based on a variation of the stochastic block model (SBM) with the addition of triadic closure edges, and its inference can identify the most plausible mechanism responsible for the existence of every edge in the network, in addition to the underlying community structure itself. We show how the method can evade the detection of spurious communities caused solely by the formation of triangles in the network, and how it can improve the performance of edge prediction when compared to the pure version of the SBM without triadic closure.
Measurement of Global Value Chain (GVC) Participation in World Development Report 2020

As we can understand with the spread of GVCs, a lot of new questions emerge regarding the measurement of participation and positioning in the globalised production process. The World Development Report (WDR) 2020 explains the GVC phenomenon and then focus on participation and the prospects especially in a world of change in technology. From the overview section, we can figure out that nowadays, goods and services flow across borders as intermediate inputs rather than final goods. In traditional trade, we need two countries with the notions of export and import. However, in GVC trade, the goods and services cross borders multiple times requiring more than two countries. Remarkable improvements in information, communication, and transport technologies have made it possible to fragment production across national boundaries. So the question is: how to conceptualise this type of new trade to justify the measurement of participation.
Model-Free Assortment Pricing with Transaction Data

We study the problem when a firm sets prices for products based on the transaction data, that is, which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers’ valuations and only assumes, instead, that purchase choices satisfy incentive-compatible constraints. The uncertainty set of potential valuations of each past customer can then be encoded as a polyhedral set, and our approach maximizes the worst case revenue, assuming that new customers’ valuations are drawn from the empirical distribution implied by the collection of such polyhedra. We study the single-product case analytically and relate it to the traditional model-based approach. Then, we show that the optimal prices in the general case can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. We further design three approximation strategies that are of low computational complexity and interpretable. In particular, the cutoff pricing heuristic has a competent provable performance guarantee. Comprehensive numerical studies based on synthetic and real data suggest that our pricing approach is uniquely beneficial when the historical data has a limited size or is susceptible to model misspecification. This paper was accepted by Omar Besbes, revenue management and market analytics. Funding: This work was supported by the Rotman School of Management (TD-MDAL Research Grant) and the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757, RGPIN-2020-04038, RGPIN-2020-06054, RGPIN-2021-04295]. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022. 4651 .
Uncertainties in galaxy rotation curves

Assessing the likelihood that the rotation curve of a galaxy matches predictions from galaxy formation simulations requires that the uncertainties in the circular speed as a function of radius derived from the observational data be statistically robust. Few uncertainties presented in the literature meet this requirement. In this paper we present a new standalone tool, makemap, that estimates the fitted velocity at each pixel from Gauss-Hermite fits to a 3D spectral data cube, together with its uncertainty obtained from a modified bootstrap procedure. We apply this new tool to neutral hydrogen spectra for 18 galaxies from the THINGS sample, and present new velocity maps with uncertainties. We propagate the estimated uncertainties in the velocity map into our previously-described model fitting tool DiskFit to derive new rotation curves. The uncertainties we obtain from these fits take into account not only the observational errors, but also uncertainties in the fitted systemic velocity, position of the rotation centre, inclination of the galaxy to the line of sight, and forced non-circular motion. They are therefore much better-defined than values that have previously been available. Our estimated uncertainties on the circular speeds differ from previous estimates by factors ranging up to of five, being smaller in some cases and larger in others. We conclude that kinematic models of well-resolved HI datasets vary widely in their precision and reliability, and therefore potentially in their value for comparisons with predictions from cosmological galaxy formation simulations.
Effect of Hypovitaminosis D on Lipid Profile in Hypothyroid Patients in Saudi Arabia

Background Hypothyroidism is believed to be associated with dyslipidemia and is considered a risk factor for the development of atherosclerotic cardiovascular diseases (ASCVD). Vitamin D, due to its steroid hormone action, retains cell function and controls the metabolism of lipids. Therefore, the present study was carried out to show the association of the risk factors of ASCVD and deficiency of thyroid hormones and vitamin D levels since no previous studies have been performed on Saudi patients before. Methodology. A retrospective cohort study was carried out on 400 hypothyroid patients. Medical records of those patients were followed up and were classified as normal and hypothyroid patients according to their thyroid-stimulating hormone levels. TSH, vitamin D, and lipid profiles were determined using the ELISA technique. Result Total cholesterol, triglyceride, and low-density lipoprotein cholesterol levels were significantly higher in hypothyroid patients than those in the normal group. We have found a significant correlation between TSH levels and the risk factors of ASCVD (total cholesterol, triglycerides, and LDL-C). Moreover, a significant correlation between vitamin D levels and the risk factors of ASCVD (total cholesterol, triglycerides, and LDL-C) has been found. In addition, there is a correlation between deficiency of Vit D and low-TSH levels (95% CI 1.092–4.05) indicating a higher risk for the development of ASCVD among those patients. Conclusion Hypothyroid and vitamin D-deficient patients must be screened regularly at an early stage to predict and also to prevent cardiovascular diseases. Moreover, an adequate supply of vitamin D and TH should be given to those patients to prevent cardiovascular diseases at an early stage.
Effect of Crosslink on Properties of Thermoplastics Dialdehyde Sweet Potato Starch

In this study, Dialdehyde sweet potato starch (DASS) with various carbonyl contents had been prepared by sodium periodate in the neutral condition and applied to improve the comprehensive properties of thermoplastic starch. Thermoplastic dialdehyde sweet potato starches (TPDASS) was prepared by thermal press when glycerol was added as a plasticizer. The thermal properties, moisture adsorption, mechanical and biodegradable properties of TPDASS were investigated. The results illustrated that at higher carbonyl content, the moisture adsorption and biodegradable ratio decreased with the increasing Tg of TPDASS, which may attribute to the partial cross-linking of DASS. The highlight properties of TPDASS95 were that: its tensile strength could reach 12.9 MPa even water content was 13.5%.
Probiotic Potential, Iron and Zinc Bioaccessibility, and Sensory Quality of Uapaca kirkiana Fruit Jam Fermented with Lactobacillus rhamnosus Yoba

Uapaca kirkiana is an underutilised indigenous fruit tree (IFT) found in the miombo ecological zone in sub-Saharan Africa. Furthermore, sub-Saharan Africa is home to many nutritionally insecure people who suffer from micronutrient deficiency. The incorporation of probiotic strains in jams as a possible way of enhancing mineral accessibility, food quality, and health is limited in Africa. This study monitored the probiotic potential, bioaccessible iron and zinc, and organoleptic properties of U. kirkiana fruit jam fermented with L. rhamnosus yoba. U. kirkiana fruits were collected from semiarid rural areas of Zimbabwe. The L. rhamnosus yoba strain was obtained from the Yoba for Life Foundation, Netherlands. Mineral and biochemical properties of the probiotic jam were analysed using AOAC standard methods. The U. kirkiana fruit tree was ranked first as a food resource by most rural populations in Zimbabwe. The probiotic jam formulation had 55% (wt/vol) U. kirkiana fruit pulp, 43% (wt/vol) sugar, 1.25% (wt/vol) pectin, 0.5% (wt/vol) citric acid, and 0.25% (wt/vol) L. rhamnosus yoba strain. The probiotic jam had 6.2 ± 0.2 log CFU/mL viable L. rhamnosus yoba cells. Iron and zinc content (mg/100 g w.b.) was 4.13 ± 0.22 and 0.68 ± 0.02 with pH 3.45 ± 0.11, respectively. Nutrient content was g/100 g w.b. , carbohydrate 66 ± 4.1, fat 0.1 ± 0.01, crude protein 0.2 ± 0.01, ash 0.7 ± 0.02, and crude fiber 0.3 ± 0.01. Bioaccessible iron and zinc were 6.55 ± 0.36% and 16.1 ± 0.50% and increased by 4% and 2% in the probiotic jam, respectively. Mineral bioaccessibility and nutrient content were significantly different (p < 0.05) in jam with 0.25% L. rhamnosus yoba. Jam acceptance rating was 83%. The probiotic jam can be used as a sustainable food containing probiotic with potential nutritional and health benefits.
In vitro biocompatibility and osteoblast differentiation of an injectable chitosan/nano-hydroxyapatite/collagen scaffold

The purpose of this study was to evaluate the in vitro cell biocompatibility of an in situ forming composite consisting of chitosan (CS), nano-hydroxyapatite and collagen (nHAC), which has a complex hierarchical structure similar to natural bone. MC3T3- E1 mouse calvarial preosteoblasts were cultured on the surface of the injectable CS/nHAC and CS scaffold. The proliferations of seeded MC3T3-E1 were investigated for 10 days. Cytotoxicity, cell proliferation, and cell expression of osteogenic markers such as alkaline phosphatase (ALP), type 1 collagen (COL-1), RUNX-2, and osteocalcin (OCN) were examined by biochemical assay and reverse transcription polymerase chain reaction. Cell viability and total cellularity (measured by dsDNA) were similar between the two scaffold groups. However, ALP, COL-1, OCN, and RUNX-2 production were significantly greater when osteoblasts were cultured on CS/nHAC scaffolds. The increase in osteogenic markers production on CS/nHAC scaffolds indicated that these scaffolds were superior to chitosan-only scaffolds in facilitating osteoblast mineralization. These results demonstrate the potential of the CS/nHAC scaffolds to be used in bone tissue engineering.
Ion channel profiling of the Lymnaea stagnalis ganglia via transcriptome analysis

Background The pond snail Lymnaea stagnalis (L. stagnalis) has been widely used as a model organism in neurobiology, ecotoxicology, and parasitology due to the relative simplicity of its central nervous system (CNS). However, its usefulness is restricted by a limited availability of transcriptome data. While sequence information for the L. stagnalis CNS transcripts has been obtained from EST libraries and a de novo RNA-seq assembly, the quality of these assemblies is limited by a combination of low coverage of EST libraries, the fragmented nature of de novo assemblies, and lack of reference genome. Results In this study, taking advantage of the recent availability of a preliminary L. stagnalis genome, we generated an RNA-seq library from the adult L. stagnalis CNS, using a combination of genome-guided and de novo assembly programs to identify 17,832 protein-coding L. stagnalis transcripts. We combined our library with existing resources to produce a transcript set with greater sequence length, completeness, and diversity than previously available ones. Using our assembly and functional domain analysis, we profiled L. stagnalis CNS transcripts encoding ion channels and ionotropic receptors, which are key proteins for CNS function, and compared their sequences to other vertebrate and invertebrate model organisms. Interestingly, L. stagnalis transcripts encoding numerous putative Ca 2+ channels showed the most sequence similarity to those of Mus musculus , Danio rerio , Xenopus tropicalis , Drosophila melanogaster , and Caenorhabditis elegans , suggesting that many calcium channel-related signaling pathways may be evolutionarily conserved. Conclusions Our study provides the most thorough characterization to date of the L. stagnalis transcriptome and provides insights into differences between vertebrates and invertebrates in CNS transcript diversity, according to function and protein class. Furthermore, this study provides a complete characterization of the ion channels of Lymnaea stagnalis , opening new avenues for future research on fundamental neurobiological processes in this model system.