Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of Bacterial Diversity of Propolis Microbiota

Propolis is a natural resinous mixture produced by the excretions of honeybees. PCR amplification of the 16S rRNA gene region was achieved using DNA of pre‐enriched propolis samples collected from Apis mellifera production hives (n=37) in Eastern Türkiye (Bingöl and its regions). Next‐generation sequencing and metabarcoding techniques were used to identify bacterial communities in propolis samples. Firmicutes dominated the phylum structure, with Proteobacteria, Actinobacteria, Tenericutes, and Spirochaetes following. The top three bacterial families were Bacillaceae, Enterobacteriaceae, and Enterococcaceae. Bacillus (dominantly B. badius and B. thermolactis at the species level) was recognized at the genus level, followed by Enterococcus and Clostridium sensu stricto. Our study comprehensively identified the bacterial diversity of propolis samples. Further investigations targeting to enlighten the microbiota of propolis and its potential application fields are required to gain better insight into ecological, nutritional, and medicinal perspectives.
Couette flow for a gas with a discrete velocity distribution

We consider a kinetic theory model of a gas, whose molecular velocities are restricted to a set of fourteen given vectors. For this model we study the Couette flow problem, the boundary conditions on the walls being the conditions of pure diffuse reflexion. The kinetic equations can be integrated by quadrature under the assumption that the walls have opposite velocities and equal temperatures. The presence on the walls of tangential velocities leads to the consequence that the velocity slip coefficient does not in general vanish when the Knudsen number goes to zero. Considering the same problem again after the suppression of tangential velocities, we obtain formulae for the velocity and temperature slip coefficients which generalize results of Broadwell (1964b), and which agree qualitatively with experiments.
Differential modularity of the mammalian Engrailed 1 enhancer network directs sweat gland development

Enhancers are context-specific regulators of expression that drive biological complexity and variation through the redeployment of conserved genes. An example of this is the enhancer-mediated control of Engrailed 1 (EN1), a pleiotropic gene whose expression is required for the formation of mammalian eccrine sweat glands. We previously identified the En1 candidate enhancer (ECE) 18 cis-regulatory element that has been highly and repeatedly derived on the human lineage to potentiate ectodermal EN1 and induce our species’ uniquely high eccrine gland density. Intriguingly, ECE18 quantitative activity is negligible outside of primates and ECE18 is not required for En1 regulation and eccrine gland formation in mice, raising the possibility that distinct enhancers have evolved to modulate the same trait. Here we report the identification of the ECE20 enhancer and show it has conserved functionality in mouse and human developing skin ectoderm. Unlike ECE18, knock-out of ECE20 in mice reduces ectodermal En1 and eccrine gland number. Notably, we find ECE20, but not ECE18, is also required for En1 expression in the embryonic mouse brain, demonstrating that ECE20 is a pleiotropic En1 enhancer. Finally, that ECE18 deletion does not potentiate the eccrine phenotype of ECE20 knock-out mice supports the secondary incorporation of ECE18 into the regulation of this trait in primates. Our findings reveal that the mammalian En1 regulatory machinery diversified to incorporate both shared and lineage-restricted enhancers to regulate the same phenotype, and also have implications for understanding the forces that shape the robustness and evolvability of developmental traits.
Predictive power of LDA to discriminate abnormal wine fermentations

Wine fermentation is a critical step of winemaking. Unfavorable conditions can seriously affect the quality of the final product; however, it is difficult to anticipate these abnormal behaviors. In this study, the predictive power of stepwise linear discriminant analysis (SLDA) was evaluated to discriminate the behavior of wine fermentation. Information on different chemical concentrations from 18 industrial wine fermentations of Cabernet Sauvignon was used in this study. The statistical procedure consisted of curve fitting with exponential curve, and Stepwise LDA applied to the parameters of the curve. This methodology was applied to different times between the beginning and the end of fermentation (72, 95, 100, 150, 200 and 400 h). The results revealed that between seven and eight, of the 28 variables studied, minimized the Standard Error of Cross‐Validation (SECV) for the different times. In almost all times studied, correlation coefficient of alcoholic degree, initial concentration of glucose, initial density and correlation coefficient of tartaric acid were the variables more discriminant, and they indicated some differences between a normal and an abnormal fermentation, which need to be corroborated with more information. In this work, before 95 h, it was not possible to minimize the prediction error and find the most discriminant variables. Copyright © 2011 John Wiley & Sons, Ltd.
Identifying Cloud-Uncontaminated AIRS Spectra from Cloudy FOV Based on Cloud-Top Pressure and Weighting Functions

Abstract An effort is made to increase the number of Advanced Infrared Sounder (AIRS) cloud-uncontaminated infrared data for regional mesoscale data assimilation and short-term quantitative precipitation forecast (QPF) applications. The cloud-top pressure from Moderate Resolution Imaging Spectroradiometer (MODIS) is utilized in combination with weighting functions (WFs) to develop a channel-based cloudy-data-removal algorithm. This algorithm identifies “clear channels” for which the brightness temperature (BT) values are not cloud contaminated. A channel-dependent cutoff pressure (COP) level is first determined based on the structure of the WF of each channel. It is usually below the maximum WF level. If the cloud top (as identified by a MODIS cloud mask) is above (below) the COP level of a channel, this channel is then deemed cloudy (clear) and removed (retained). Using this algorithm, a sizable increase of cloud-uncontaminated AIRS data can be obtained. There are more usable domain points for those chan.
Parallelizing Contextual Bandits

Standard approaches to decision-making under uncertainty focus on sequential exploration of the space of decisions. However, \textit{simultaneously} proposing a batch of decisions, which leverages available resources for parallel experimentation, has the potential to rapidly accelerate exploration. We present a family of (parallel) contextual bandit algorithms applicable to problems with bounded eluder dimension whose regret is nearly identical to their perfectly sequential counterparts -- given access to the same total number of oracle queries -- up to a lower-order ``burn-in"term. We further show these algorithms can be specialized to the class of linear reward functions where we introduce and analyze several new linear bandit algorithms which explicitly introduce diversity into their action selection. Finally, we also present an empirical evaluation of these parallel algorithms in several domains, including materials discovery and biological sequence design problems, to demonstrate the utility of parallelized bandits in practical settings.
Stock Trading Optimization through Model-based Reinforcement Learning with Normalizing Flows (preprint)

With the fast development of quantitative portfolio optimization in financial engineering, lots of promising algorithmic trading strategies have shown competitive advantages in recent years. However, the environment from real financial markets is complex and hard to be fully simulated, considering non-stationarity of the stock data, unpredictable hidden causal factors and so on. Fortunately, difference of stock prices is often stationary series, and the internal relationship between difference of stocks can be linked to the decision-making process, then the portfolio should be able to achieve better performance. In this paper, we demonstrate normalizing flows is adopted to simulated high-dimensional joint probability of the complex trading environment, and develop a novel model based reinforcement learning framework to better understand the intrinsic mechanisms of quantitative online trading. Second, we experiment various stocks from three different financial markets (Dow, NASDAQ and S&P 500) and show that among these three financial markets, Dow gets the best performance results on various evaluation metrics under our back-testing system. Especially, our proposed method even resists big drop (less maximum drawdown) during COVID-19 pandemic period when the financial market got unpredictable crisis. All these results are comparatively better than modeling the state transition dynamics with independent Gaussian Processes. Third, we utilize a causal analysis method to study the causal relationship among different stocks of the environment. Further, by visualizing high dimensional state transition data comparisons from real and virtual buffer with t-SNE, we uncover some effective patterns of bet
Modeling the 2022 τ-Herculid Outburst

The τ-Herculids (IAU shower number 61 TAH) is a minor meteor shower associated with comet 73P/Schwassmann–Wachmann 3, a Jupiter-family comet that disintegrated into several fragments in 1995. As a consequence of the nucleus breakup, possible increased meteor rates were predicted for 2022. On May 30–31, observation networks around the world reported two distinct peaks of TAH activity, around solar longitudes 69. °02 and 69. °42. This work examines the encounter conditions of the Earth with meteoroids ejected from 73P during the splitting event and on previous perihelion passages. Numerical simulations suggest that the main peak observed in 2022 was caused by meteoroids ejected from the splitting nucleus with four times the typical cometary gas expansion speed. High-resolution measurements performed with the Canadian Automated Meteor Observatory indicate that these meteoroids are fragile, with estimated bulk densities of 250 kg m−3. In contrast with the main peak, the first TAH activity peak in 2022 is best modeled with trails ejected prior to 1960. We find that ordinary cometary activity could have produced other TAH apparitions observed in the past, including in 1930 and 2017. The extension of our model to future years predicts significant returns of the shower in 2033 and 2049.
Arena-Web - A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches

In recent years, mobile robot navigation approaches have become increasingly important due to various application areas ranging from healthcare to warehouse logistics. In particular, Deep Reinforcement Learning approaches have gained popularity for robot navigation but are not easily accessible to non-experts and complex to develop. In recent years, efforts have been made to make these sophisticated approaches accessible to a wider audience. In this paper, we present Arena-Web, a web-based development and evaluation suite for developing, training, and testing DRL-based navigation planners for various robotic platforms and scenarios. The interface is designed to be intuitive and engaging to appeal to non-experts and make the technology accessible to a wider audience. With Arena-Web and its interface, training and developing Deep Reinforcement Learning agents is simplified and made easy without a single line of code. The web-app is free to use and openly available under the link stated in the supplementary materials.
Probing single electron scattering through a non-Fermi-liquid charge-Kondo device

Among the exotic and yet unobserved features of multi-channel Kondo impurity models is their sub-unitary single electron scattering. In the two-channel Kondo model, for example, an incoming electron is fully scattered into a many-body excitation such that the single particle Green function vanishes. Here we propose to directly observe these features in a charge-Kondo device encapsulated in a Mach-Zehnder interferometer - within a device already studied in Ref. [1]. We provide detailed predictions for the visibility and phase of the Aharonov-Bohm oscillations depending on the number of coupled channels and the asymmetry of their couplings.
DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection

Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, DeepAstroUDA, as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation (DA) and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities (three-class and ten-class problems), with anomaly detection: (1) datasets created after different numbers of observing years from a single survey (Legacy Survey of Space and Time mock data of one and ten years of observations); (2) data from different surveys (Sloan Digital Sky Survey (SDSS) and DECaLS); and (3) data from observing fields with different depths within one survey (wide field and Stripe 82 deep field of SDSS). For the first time, we demonstrate the successful use of DA between very discrepant observational datasets. DeepAstroUDA is capable of bridging the gap between two astronomical surveys, increasing classification accuracy in both domains (up to 40% on the unlabeled data), and making model performance consistent across datasets. Furthermore, our method also performs well as an anomaly detection algorithm and successfully clusters unknown class samples even in the unlabeled target dataset.
Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative Multi-Agent Reinforcement Learning

Value decomposition methods have gained popularity in the field of cooperative multi-agent reinforcement learning. However, almost all existing methods follow the principle of Individual Global Max (IGM) or its variants, which limits their problem-solving capabilities. To address this, we propose a dual self-awareness value decomposition framework, inspired by the notion of dual self-awareness in psychology, that entirely rejects the IGM premise. Each agent consists of an ego policy for action selection and an alter ego value function to solve the credit assignment problem. The value function factorization can ignore the IGM assumption by utilizing an explicit search procedure. On the basis of the above, we also suggest a novel anti-ego exploration mechanism to avoid the algorithm becoming stuck in a local optimum. As the first fully IGM-free value decomposition method, our proposed framework achieves desirable performance in various cooperative tasks.
Classification of transformations of equivalent kernels of some determinantal point processes

In this paper, we prove that, under the condition that a kernel K of a determinantal point process (abbr., DPP) is non-zero with some particular non-zero associated 2x2 determinants, any equivalent kernel Q of K (i.e., some other kernel of the DPP) can be transformed into K by only two possible transformations: conjugation and/or transposition transformations. This substantially builds on an answer from Stevens [Random Matrices: Theory and Applications, 10(03):2150027, 2021] to a conjecture of Bufetov to now include equivalent kernels that are non-symmetric.
Reach-avoid Verification using Lyapunov Densities

Reach-avoid analysis combines the construction of safety and specific progress guarantees, and is able to formalize many important engineering problems. In this paper we study the reach-avoid verification problem of systems modelled by ordinary differential equations using Lyapunov densities. Firstly, the weak reach-avoid verification is considered. Given an initial set, a safe set and a target set, the weak reach-avoid verification is to verify whether the reach-avoid property (i.e., the system will enter the target set eventually while staying inside the safe set before the first target hitting time) holds for almost all states in the initial set. We propose two novel sufficient conditions using Lyapunov densities for the weak reach-avoid verification. These two sufficient conditions are shown to be weaker than existing ones, providing more possibilities of verifying weak reach-avoid properties successfully. Then, we generalize these conditions to the verification of reach-avoid properties for all states in the initial set. Finally, an example demonstrates theoretical developments of proposed conditions.
CosPGD: a unified white-box adversarial attack for pixel-wise prediction tasks

While neural networks allow highly accurate predictions in many tasks, their lack of robustness towards even slight input perturbations hampers their deployment in many real-world applications. Recent research towards evaluating the robustness of neural networks such as the seminal projected gradient descent(PGD) attack and subsequent works have drawn significant attention, as they provide an effective insight into the quality of representations learned by the network. However, these methods predominantly focus on image classification tasks, while only a few approaches specifically address the analysis of pixel-wise prediction tasks such as semantic segmentation, optical flow, disparity estimation, and others, respectively. Thus, there is a lack of a unified adversarial robustness benchmarking tool(algorithm) that is applicable to all such pixel-wise prediction tasks. In this work, we close this gap and propose CosPGD, a novel white-box adversarial attack that allows optimizing dedicated attacks for any pixel-wise prediction task in a unified setting. It leverages the cosine similarity between the distributions over the predictions and ground truth (or target) to extend directly from classification tasks to regression settings. We outperform the SotA on semantic segmentation attacks in our experiments on PASCAL VOC2012 and CityScapes. Further, we set a new benchmark for adversarial attacks on optical flow, and image restoration displaying the ability to extend to any pixel-wise prediction task.
A Game-Theoretic Approach to Solving the Roman Domination Problem

The Roamn domination problem is one important combinatorial optimization problem that is derived from an old story of defending the Roman Empire and now regains new significance in cyber space security, considering backups in the face of a dynamic network security requirement. In this paper, firstly, we propose a Roman domination game (RDG) and prove that every Nash equilibrium (NE) of the game corresponds to a strong minimal Roman dominating function (S-RDF), as well as a Pareto-optimal solution. Secondly, we show that RDG is an exact potential game, which guarantees the existence of an NE. Thirdly, we design a game-based synchronous algorithm (GSA), which can be implemented distributively and converge to an NE in $O(n)$ rounds, where $n$ is the number of vertices. In GSA, all players make decisions depending on the local information. Furthermore, we enhance GSA to be enhanced GSA (EGSA), which converges to a better NE in $O(n^2)$ rounds. Finally, we present numerical simulations to demonstrate that EGSA can obtain a better approximate solution in promising computation time compared with state-of-the-art algorithms.
Laplacian ICP for Progressive Registration of 3D Human Head Meshes

We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP). Since it uses the Laplace-Beltrami operator for deformation regularisation, we view the overall process as Laplacian ICP (L-ICP). This exploits a ‘small deformation per iteration’ assumption and is progressively coarse-to-fine, employing an increasingly flexible deformation model, an increasing number of correspondence sets, and increasingly sophisticated correspondence estimation. Correspondence matching is only permitted within predefined vertex subsets derived from domain-specific feature extractors. Additionally, we present a new benchmark and a pair of evaluation metrics for 3D non-rigid registration, based on annotation transfer. We use this to evaluate our framework on a publicly-available dataset of 3D human head scans (Headspace). The method is robust and only requires a small fraction of the computation time compared to the most popular classical approach, yet has comparable registration performance.
AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms

Mobile applications are widely used for online services sharing a large amount of personal data online. One-time authentication techniques such as passwords and physiological biometrics (e.g., fingerprint, face, and iris) have their own advantages but also disadvantages since they can be stolen or emulated, and do not prevent access to the underlying device, once it is unlocked. To address these challenges, complementary authentication systems based on behavioural biometrics have emerged. The goal is to continuously profile users based on their interaction with the mobile device. However, existing behavioural authentication schemes are not (i) user-agnostic meaning that they cannot dynamically handle changes in the user-base without model re-training, or (ii) do not scale well to authenticate millions of users. In this paper, we present AuthentiSense, a user-agnostic, scalable, and efficient behavioural biometrics authentication system that enables continuous authentication and utilizes only motion patterns (i.e., accelerometer, gyroscope and magnetometer data) while users interact with mobile apps. Our approach requires neither manually engineered features nor a significant amount of data for model training. We leverage a few-shot learning technique, called Siamese network, to authenticate users at a large scale. We perform a systematic measurement study and report the impact of the parameters such as interaction time needed for authentication and n-shot verification (comparison with enrollment samples) at the recognition stage. Remarkably, AuthentiSense achieves high accuracy of up to 97% in terms of F1-score even when evaluated in a few-shot fashion that requires only a few behaviour samples per user (3 shots). Our approach accurately authenticates users only after 1 second of user interaction. For AuthentiSense, we report a FAR and FRR of 0.023 and 0.057, respectively.
Quantized-Constraint Concatenation and the Covering Radius of Constrained Systems

We introduce a novel framework for implementing error-correction in constrained systems. The main idea of our scheme, called Quantized-Constraint Concatenation (QCC), is to employ a process of embedding the codewords of an error-correcting code in a constrained system as a (noisy, irreversible) quantization process. This is in contrast to traditional methods, such as concatenation and reverse concatenation, where the encoding into the constrained system is reversible. The possible number of channel errors QCC is capable of correcting is linear in the block length n, improving upon the $O\left( {\sqrt n } \right)$ possible with the state-of-the-art known schemes. For a given constrained system, the performance of QCC depends on a new fundamental parameter of the constrained system – its covering radius.Motivated by QCC, we study the covering radius of constrained systems in both combinatorial and probabilistic settings. We reveal an intriguing characterization of the covering radius of a constrained system using ergodic theory.