Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory Prediction using Generative Adversarial Network in Multi-Class Scenarios

Predicting traffic agents' trajectories is an important task for auto-piloting. Most previous work on trajectory prediction only considers a single class of road agents. We use a sequence-to-sequence model to predict future paths from observed paths and we incorporate class information into the model by concatenating extracted label representations with traditional location inputs. We experiment with both LSTM and transformer encoders and we use generative adversarial network as introduced in Social GAN to learn the multi-modal behavior of traffic agents. We train our model on Stanford Drone dataset which includes 6 classes of road agents and evaluate the impact of different model components on the prediction performance in multi-class scenes.
Study on Hydration of Illite in K+, Na+, Ca2+, Mg2+, and Al3+ Electrolyte Solutions

Abstract The hydration of clay particles in aqueous solutions plays an important role in the scientific and industrial fields. In this study, the hydration properties of fine illite particles in K+, Na+, Ca2+, Mg2+, and Al3+ electrolyte solutions were investigated through the relative viscosity method based on Einstein’s viscosity equation. During the experiments, the hydration index (I) was measured using a rheometer to analyze the hydration layers formed on the illite surfaces in different aqueous electrolyte solutions, and it was found that the index I was the highest in Al3+ followed by that in Mg2+, Ca2+, Na2+, and K+ in descending order. It was also observed that the index increased as the electrolyte concentration increased until the solution reached an adsorption equilibrium. When electrolytes were added, the effect of electroviscosity on the calculated value of I became weaker until it could eventually be neglected. Based on these results, we concluded that the electroviscosity should be considered when calculating the hydration index of a suspension of fine charged particles with low conductivity.
Hamiltonian formalism for cosmological perturbations: the separate-universe approach

The separate-universe approach provides an effective description of cosmological perturbations at large scales, where the universe can be described by an ensemble of independent, locally homogeneous and isotropic patches. By reducing the phase space to homogeneous and isotropic degrees of freedom, it greatly simplifies the analysis of large-scale fluctuations. It is also a prerequisite for the stochastic-inflation formalism. In this work, we formulate the separate-universe approach in the Hamiltonian formalism, which allows us to analyse the full phase-space structure of the perturbations. Such a phase-space description is indeed required in dynamical regimes which do not benefit from a background attractor, as well as to investigate quantum properties of cosmological perturbations. We find that the separate-universe approach always succeeds in reproducing the same phase-space dynamics for homogeneous and isotropic degrees of freedom as the full cosmological perturbation theory, provided that the wavelength of the modes under consideration are larger than some lower bound that we derive. We also compare the separate-universe approach and cosmological perturbation theory at the level of the gauge-matching procedure, where the agreement is not always guaranteed and requires specific matching prescriptions that we present.
Statistical discrimination in learning agents

Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics. One primary example is \textit{statistical discrimination} -- selecting social partners based not on their underlying attributes, but on readily perceptible characteristics that covary with their suitability for the task at hand. We present a theoretical model to examine how information processing influences statistical discrimination and test its predictions using multi-agent reinforcement learning with various agent architectures in a partner choice-based social dilemma. As predicted, statistical discrimination emerges in agent policies as a function of both the bias in the training population and of agent architecture. All agents showed substantial statistical discrimination, defaulting to using the readily available correlates instead of the outcome relevant features. We show that less discrimination emerges with agents that use recurrent neural networks, and when their training environment has less bias. However, all agent algorithms we tried still exhibited substantial bias after learning in biased training populations.
Multifilter photometry of Solar System objects from the SkyMapper Southern Survey

Context. The populations of small bodies of the Solar System (asteroids, comets, Kuiper Belt objects) are used to constrain the origin and evolution of the Solar System. Both their orbital distribution and composition distribution are required to track the dynamical pathway from their regions of formation to their current locations. Aims. We aim at increasing the sample of Solar System objects (SSOs) that have multi-filter photometry and compositional taxonomy. Methods. We search for moving objects in the SkyMapper Southern Survey. We use the predicted SSO positions to extract photometry and astrometry from the SkyMapper frames. We then apply a suite of filters to clean the catalog for false-positive detections. We finally use the near-simultaneous photometry to assign a taxonomic class to objects. Results. We release a catalog of 880,528 individual observations, consisting of 205,515 known and unique SSOs. The catalog completeness is estimated to be about 97% down to V=18 mag and the purity to be above 95% for known SSOs. The near-simultaneous photometry provides either three, two, or a single color that we use to classify 117,356 SSOs with a scheme consistent with the widely used Bus-DeMeo taxonomy. Conclusions. The present catalog contributes significantly to the sample of asteroids with known surface properties (about 40% of main-belt asteroids down to an absolute magnitude of 16). We will release more observations of SSOs with future SkyMapper data releases.
Off-Dynamics Inverse Reinforcement Learning from Hetero-Domain

We propose an approach for inverse reinforcement learning from hetero-domain which learns a reward function in the simulator, drawing on the demonstrations from the real world. The intuition behind the method is that the reward function should not only be oriented to imitate the experts, but should encourage actions adjusted for the dynamics difference between the simulator and the real world. To achieve this, the widely used GAN-inspired IRL method is adopted, and its discriminator, recognizing policy-generating trajectories, is modified with the quantification of dynamics difference. The training process of the discriminator can yield the transferable reward function suitable for simulator dynamics, which can be guaranteed by derivation. Effectively, our method assigns higher rewards for demonstration trajectories which do not exploit discrepancies between the two domains. With extensive experiments on continuous control tasks, our method shows its effectiveness and demonstrates its scalability to high-dimensional tasks.
Active learning for imbalanced data under cold start

Modern systems that rely on Machine Learning (ML) for predictive modelling, may suffer from the cold-start problem: supervised models work well but, initially, there are no labels, which are costly or slow to obtain. This problem is even worse in imbalanced data scenarios, where labels of the positive class take longer to accumulate. We propose an Active Learning (AL) system for datasets with orders of magnitude of class imbalance, in a cold start streaming scenario. We present a computationally efficient Outlier-based Discriminative AL approach (ODAL) and design a novel 3-stage sequence of AL labeling policies where ODAL is used as warm-up. Then, we perform empirical studies in four real world datasets, with various magnitudes of class imbalance. The results show that our method can more quickly reach a high performance model than standard AL policies without ODAL warm-up. Its observed gains over random sampling can reach 80% and be competitive with policies with an unlimited annotation budget or additional historical data (using just 2% to 10% of the labels).
The capsaicin receptor

The landmark paper discussed in this chapter is ‘The capsaicin receptor: A heat activated ion channel in the pain pathway’, published by Caterina et al. in 1997. The identification of the molecular basis for the sensitivity of a major proportion of nociceptive primary sensory neurons for capsaicin, the pungent agent in chilli pepper, was undoubtedly one of the most significant pain-related discoveries in the twentieth century, for at least three reasons. First, the mechanism for capsaicin-induced responses could unequivocally be explained. Second, the discovery heralded the starting point for the development of a highly promising, mechanism-based means of analgesia. Third, the discovery also sparked studies which resulted in the discovery of the major cation channel family, the transient receptor potential (TRP) ion channel family, several members of which have also become putative targets for the development of analgesics.
Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach

We compared the prognostic value of myocardial perfusion imaging (MPI) by conventional- (C-) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride- (CZT-) SPECT in a cohort of patients with suspected or known coronary artery disease (CAD) using machine learning (ML) algorithms. A total of 453 consecutive patients underwent stress MPI by both C-SPECT and CZT-SPECT. The outcome was a composite end point of all-cause death, cardiac death, nonfatal myocardial infarction, or coronary revascularization procedures whichever occurred first. ML analysis performed through the implementation of random forest (RF) and k-nearest neighbors (KNN) algorithms proved that CZT-SPECT has greater accuracy than C-SPECT in detecting CAD. For both algorithms, the sensitivity of CZT-SPECT (96% for RF and 60% for KNN) was greater than that of C-SPECT (88% for RF and 53% for KNN). A preliminary univariate analysis was performed through Mann-Whitney tests separately on the features of each camera in order to understand which ones could distinguish patients who will experience an adverse event from those who will not. Then, a machine learning analysis was performed by using Matlab (v. 2019b). Tree, KNN, support vector machine (SVM), Naïve Bayes, and RF were implemented twice: first, the analysis was performed on the as-is dataset; then, since the dataset was imbalanced (patients experiencing an adverse event were lower than the others), the analysis was performed again after balancing the classes through the Synthetic Minority Oversampling Technique. According to KNN and SVM with and without balancing the classes, the accuracy (p value = 0.02 and p value = 0.01) and recall (p value = 0.001 and p value = 0.03) of the CZT-SPECT were greater than those obtained by C-SPECT in a statistically significant way. ML approach showed that although the prognostic value of stress MPI by C-SPECT and CZT-SPECT is comparable, CZT-SPECT seems to have higher accuracy and recall.
Multiplexed Weak Waist-Enlarged Fiber Taper Curvature Sensor and Its Rapid Inline Fabrication

This study proposes a multiplexed weak waist-enlarged fiber taper (WWFT) curvature sensor and its rapid fabrication method. Compared with other types of fiber taper, the proposed WWFT has no difference in appearance with the single mode fiber and has ultralow insertion loss. The fabrication of WWFT also does not need the repeated cleaving and splicing process, and thereby could be rapidly embedded into the inline sensing fiber without splicing point, which greatly enhances the sensor solidity. Owing to the ultralow insertion loss (as low as 0.15 dB), the WWFT-based interferometer is further used for multiplexed curvature sensing. The results show that the different curvatures can be individually detected by the multiplexed interferometers. Furthermore, it also shows that diverse responses for the curvature changes exist in two orthogonal directions, and the corresponding sensitivities are determined to be 79.1°/m−1 and –48.0°/m−1 respectively. This feature can be potentially applied for vector curvature sensing.
A Review: Application and Implementation of Optic Fibre Sensors for Gas Detection

At the present time, there are major concerns regarding global warming and the possible catastrophic influence of greenhouse gases on climate change has spurred the research community to investigate and develop new gas-sensing methods and devices for remote and continuous sensing. Furthermore, there are a myriad of workplaces, such as petrochemical and pharmacological industries, where reliable remote gas tests are needed so that operatives have a safe working environment. The authors have concentrated their efforts on optical fibre sensing of gases, as we became aware of their increasing range of applications. Optical fibre gas sensors are capable of remote sensing, working in various environments, and have the potential to outperform conventional metal oxide semiconductor (MOS) gas sensors. Researchers are studying a number of configurations and mechanisms to detect specific gases and ways to enhance their performances. Evidence is growing that optical fibre gas sensors are superior in a number of ways, and are likely to replace MOS gas sensors in some application areas. All sensors use a transducer to produce chemical selectivity by means of an overlay coating material that yields a binding reaction. A number of different structural designs have been, and are, under investigation. Examples include tilted Bragg gratings and long period gratings embedded in optical fibres, as well as surface plasmon resonance and intra-cavity absorption. The authors believe that a review of optical fibre gas sensing is now timely and appropriate, as it will assist current researchers and encourage research into new photonic methods and techniques.
Pepper Mottle Virus and Its Host Interactions: Current State of Knowledge

Pepper mottle virus (PepMoV) is a destructive pathogen that infects various solanaceous plants, including pepper, bell pepper, potato, and tomato. In this review, we summarize what is known about the molecular characteristics of PepMoV and its interactions with host plants. Comparisons of symptom variations caused by PepMoV isolates in plant hosts indicates a possible relationship between symptom development and genetic variation. Researchers have investigated the PepMoV–plant pathosystem to identify effective and durable genes that confer resistance to the pathogen. As a result, several recessive pvr or dominant Pvr resistance genes that confer resistance to PepMoV in pepper have been characterized. On the other hand, the molecular mechanisms underlying the interaction between these resistance genes and PepMoV-encoded genes remain largely unknown. Our understanding of the molecular interactions between PepMoV and host plants should be increased by reverse genetic approaches and comprehensive transcriptomic analyses of both the virus and the host genes.
Effect of Dipeptidyl Peptidase-4 (DPP-4) Inhibition on Biomarkers of Kidney Injury and Vascular Calcification in Diabetic Kidney Disease: A Randomized Controlled Trial

Introduction Dipeptidyl peptidase-4 (DPP-4) inhibitors improve glycemic control and have pleiotropic effects on kidney injury, albuminuria, and vascular inflammation, especially in animal models. We evaluated the effects of a potent DPP4 inhibitor (gemigliptin) on these processes among patients with diabetic kidney disease (DKD). Methods This study employed a multicenter, prospective, randomized, placebo-controlled design. A total of 201 participants were enrolled and randomly assigned to one of two groups, one received treatment with 50 mg gemigliptin daily along with standard care for diabetes mellitus for 6 months. The changes in the coronary calcium score (CAC score), cardio-ankle vascular index (CAVI), estimated glomerular filtration rate (eGFR), vascular calcification level, and tubular renal injury marker expression were evaluated at baseline and 6 months. Results In total, 182 patients completed the study. Significant reductions in hemoglobin A1C levels were observed in both groups. The changes in the CAC score, CAVI, eGFR, and level of proteinuria over the 6 months of the study did not significantly differ between the gemigliptin and control groups. However, biomarkers of vascular calcification, including serum bone alkaline phosphatase and kidney injury, including urine neutrophil gelatinase-associated lipocalin (NGAL)/Cr and urine liver fatty acid-binding protein (L-FABP)/Cr, were improved significantly in the gemigliptin treatment group compared with the control group. No serious adverse events were observed during the study. Conclusion Our study showed that gemigliptin significantly improved the expression of renal tubular injury biomarkers and vascular calcification levels among patients with DKD; however, gemigliptin did not affect renal function or coronary calcification compared with those observed in the control. A larger study with a longer follow-up is essential to verify these beneficial effects. Clinical Trials. This trial is registered with ClinicalTrials.Gov Identifier NCT04705506.
Phages from Genus Bruynoghevirus and Phage Therapy: Pseudomonas Phage Delta Case

The applicability and safety of bacteriophage Delta as a potential anti-Pseudomonas aeruginosa agent belonging to genus Bruynoghevirus (family Podoviridae) was characterised. Phage Delta belongs to the species Pseudomonas virus PaP3, which has been described as a temperate, with cos sites at the end of the genome. The phage Delta possesses a genome of 45,970 bp that encodes tRNA for proline (Pro), aspartic acid (Asp) and asparagine (Asn) and does not encode any known protein involved in lysogeny formation or persistence. Analysis showed that phage Delta has 182 bp direct terminal repeats at the end of genome and lysogeny was confirmed, neither upon infection at low nor at high multiplicity of infection (MOI). The turbid plaques that appear on certain host lawns can result from bacteriophage insensitive mutants that occur with higher frequency (10−4). In silico analysis showed that the genome of Delta phage does not encode any known bacterial toxin or virulence factor, determinants of antibiotic resistance and known human allergens. Based on the broad host range and high lytic activity against planktonic and biofilm cells, phage Delta represents a promising candidate for phage therapy.
A Blockchain-Based Distributed Paradigm to Secure Localization Services

In recent decades, modern societies are experiencing an increasing adoption of interconnected smart devices. This revolution involves not only canonical devices such as smartphones and tablets, but also simple objects like light bulbs. Named the Internet of Things (IoT), this ever-growing scenario offers enormous opportunities in many areas of modern society, especially if joined by other emerging technologies such as, for example, the blockchain. Indeed, the latter allows users to certify transactions publicly, without relying on central authorities or intermediaries. This work aims to exploit the scenario above by proposing a novel blockchain-based distributed paradigm to secure localization services, here named the Internet of Entities (IoE). It represents a mechanism for the reliable localization of people and things, and it exploits the increasing number of existing wireless devices and blockchain-based distributed ledger technologies. Moreover, unlike most of the canonical localization approaches, it is strongly oriented towards the protection of the users’ privacy. Finally, its implementation requires minimal efforts since it employs the existing infrastructures and devices, thus giving life to a new and wide data environment, exploitable in many domains, such as e-health, smart cities, and smart mobility.
Dengue Virus Infection: A Tale of Viral Exploitations and Host Responses

Dengue is a mosquito-borne viral disease (arboviral) caused by the Dengue virus. It is one of the prominent public health problems in tropical and subtropical regions with no effective vaccines. Every year around 400 million people get infected by the Dengue virus, with a mortality rate of about 20% among the patients with severe dengue. The Dengue virus belongs to the Flaviviridae family, and it is an enveloped virus with positive-sense single-stranded RNA as the genetic material. Studies of the infection cycle of this virus revealed potential host targets important for the virus replication cycle. Here in this review article, we will be discussing different stages of the Dengue virus infection cycle inside mammalian host cells and how host proteins are exploited by the virus in the course of infection as well as how the host counteracts the virus by eliciting different antiviral responses.
Identification of Mycobacterium tuberculosis Antigens with Vaccine Potential Using a Machine Learning-Based Reverse Vaccinology Approach

Tuberculosis (TB) is the leading cause of death of any single infectious agent, having led to 1.4 million deaths in 2019 alone. Moreover, an estimated one-quarter of the global population is latently infected with Mycobacterium tuberculosis (MTB), presenting a huge pool of potential future disease. Nonetheless, the only currently licensed TB vaccine fails to prevent the activation of latent TB infections (LTBI). These facts together illustrate the desperate need for a more effective TB vaccine strategy that can prevent both primary infection and the activation of LTBI. In this study, we employed a machine learning-based reverse vaccinology approach to predict the likelihood that each protein within the proteome of MTB laboratory reference strain H37Rv would be a protective antigen (PAg). The proteins predicted most likely to be a PAg were assessed for their belonging to a protein family of previously established PAgs, the relevance of their biological processes to MTB virulence and latency, and finally the immunogenic potential that they may provide in terms of the number of promiscuous epitopes within each. This study led to the identification of 16 proteins with the greatest vaccine potential for further in vitro and in vivo studies. It also demonstrates the value of computational methods in vaccine development.