Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning Framework to Simulate Short-Term Learning Effects of Human Psychophysical Experiments Assessing the Quality of Artificial Vision

The quality of the artificial vision produced by visual prostheses has traditionally been evaluated by human psychophysical tests using images expressed with an array of phosphenes. However, such experiments involving human subjects are considerably time-consuming and labor-intensive. One potential solution may be implementing an efficient approach to assist or replace psychophysical experiments showing a short-term learning effect in human subjects. The present work developed a reinforcement learning (RL)-based feedback framework which built artificial agents to emulate the behavioral changes in the learning process of human subjects over the experimental trials. In our framework, we first trained an agent which can gradually learn to identify 720 faces presented in low-resolution phosphene images with feedback rewards received from the agreement in the perception of nine training human subjects. Then, in the automating stage of the framework, we created nine RL agents. By testing those agents, we found the RL agents mimicked the learning effects of nine test human subjects better than nine instances of a supervised learning (SL) model. Given the similar outcomes with human tests and the time efficiency of RL, our framework may expedite the development of visual prosthetic systems by at least partially replacing laborious human psychophysical experiments.
Self-learning for Annotating Website Privacy Policies at Scale

With the increasing importance of user data privacy, it is crucial for individuals to understand how companies handle their information. While considerable research has been conducted on automatically identifying privacy-related information in policies, the lack of high-quality annotated training data in this domain remains a significant challenge. Manual annotation of privacy policies is a demanding and time-consuming task that requires domain knowledge. To address this issue, we propose a semi-supervised method, specifically an iterative self-learning approach, to augment the limited training dataset and improve classification performance. Our approach leverages two state-of-the-art models, BERT and XLNet, and involves automatic labelling of data and model retraining with pseudo-labels. We evaluated our approach on the OPP-115 corpora and observed a 10% improvement in the macro F-1 score for BERT, demonstrating the effectiveness of self-learning. This is the first attempt to automatically annotate privacy policies using a self-learning method without requiring additional annotations, offering a promising solution to the challenge of training data scarcity in this domain.
Onset of transition in the flow of polymer solutions through microtubes

Experiments are performed to characterize the onset of laminar–turbulent transition in the flow of high-molecular-weight polymer solutions in rigid microtubes of diameters in the range $390~\unicode[STIX]{x03BC}\text{m}{-}470~\unicode[STIX]{x03BC}\text{m}$ using the micro-PIV technique. By considering flow in tubes of such small diameters, the present study probes higher values of elasticity numbers ( $E\equiv \unicode[STIX]{x1D706}\unicode[STIX]{x1D708}/R^{2}$ ) compared to existing studies, where $\unicode[STIX]{x1D706}$ is the longest relaxation time of the polymer solution, $R$ is the tube radius and $\unicode[STIX]{x1D708}$ is the kinematic viscosity of the polymer solution. For the Newtonian case, our experiments indicate that the natural transition (without the aid of any forcing mechanism) occurs at Reynolds number ( $Re$ ) $2000\pm 100$ . As the concentration of polymer is increased, initially there is a delay in the onset of the transition and the transition Reynolds number increases to $2500$ . Further increase in concentration of the polymer results in a decrease in the Reynolds number for transition. At sufficiently high concentrations, the added polymer tends to destabilize the flow and the transition is observed to happen at $Re$ as low as $800$ . It is also observed that the addition of polymers, regardless of their concentration, reduces the magnitude of the velocity fluctuations after transition. Dye-stream visualization is further used to corroborate the onset of transition in the flow of polymer solutions. The present work thus shows that addition of polymer, at sufficiently high concentrations, destabilizes the flow when compared to that of a Newtonian fluid, thereby providing additional evidence for ‘early transition’ or ‘elasto-inertial turbulence’ in the flow of polymer solutions. The data for the transition Reynolds number $Re_{t}$ from our experiments (for tubes of different diameters, and for two different polymers at varying concentrations) collapse well according to the scaling relation $Re_{t}\propto 1/\sqrt{E(1-\unicode[STIX]{x1D6FD})}$ , where $\unicode[STIX]{x1D6FD}$ is the ratio of solvent viscosity to the viscosity of the polymer solution.
Embracing the dropouts in single-cell RNA-seq dynamics modelling

Single-cell RNA sequencing (scRNA-seq) is one of the most exciting technological breakthroughs that many believe would revolutionise medical research. At the same time, this new technology has created some urgent data-mining challenges. Unlike sequencing data obtained from the old bulk sequencing technology, the scRNA-seq data are more noisy and biased. One particular data challenge is dropouts, where a low amount of mRNA leads to zero detection. Recently, an appealing idea has been to embrace dropouts and use potential dropout patterns for analysis. It has led to surprising results. In this paper, we take this idea further and focus on the problem of recovering gene dynamics from single-cell data. We show that dropouts can mislead the most commonly used model to produce the wrong dynamics. We propose a solution with two components, a nonlinear neural model based on neural ODEs and a hurdle distribution adaptable to potential signals in dropout patterns. We provide empirical evidence that demonstrates the advantages of our proposed model over a state-of-the-art method for scRNA-seq analysis.
Comparison of discrete and continuum Liouville first passage percolation

Discrete and continuum Liouville first passage percolation (DLFPP, LFPP) are two approximations of the conjectural $\gamma$-Liouville quantum gravity (LQG) metric, obtained by exponentiating the discrete Gaussian free field (GFF) and the circle average regularization of the continuum GFF respectively. We show that these two models can be coupled so that with high probability distances in these models agree up to $o(1)$ errors in the exponent, and thus have the same distance exponent. Ding and Gwynne (2018) give a formula for the continuum LFPP distance exponent in terms of the $\gamma$-LQG dimension exponent $d_\gamma$. Using results of Ding and Li (2018) on the level set percolation of the discrete GFF, we bound the DLFPP distance exponent and hence obtain a new lower bound $d_\gamma \geq 2 + \frac{\gamma^2}2$. This improves on previous lower bounds for $d_\gamma$ for the regime $\gamma \in (\gamma_0, 0.576)$, for some small nonexplicit $\gamma_0 > 0$.
Fixed Point Laplacian Mapping: A Geometrically Correct Manifold Learning Algorithm

Dimensionality reduction (DR) and manifold learning (ManL) have been applied extensively in many machine learning tasks, including computer vision, image analysis and pattern recognition just to name a few. However, the geometrical correctness of DR and ManL models learning results is largely neglected. In this work, we investigate this important aspect of some widely used DR and ManL methods through the lens of the chart map function, which is the essential part of the definition of a manifold. It turns out that the mapping functions induced by these methods do not have the injectivity (i.e. one-to-one mapping) between ambient space and latent space. This poses the distinguishability problem for down-stream tasks. Without injectivity, two distinct points on a manifold may be projected to the same point in the latent space learnt by DR or ManL methods, and hence the later process cannot separate them apart, resulting in inevitable errors. To address this problem, we provide a provably correct algorithm called fixed points Laplacian mapping (FPLM), which has the geometric guarantee to find a representation of a manifold with injectivity by using a simplical complex generated on manifold. We further discuss the property of our proposed method via various aspects, including its link to the popular graph neural networks (GNNs) and deep neural networks (DNNs). Its geometric correctness is demonstrated by extensive experimental results and theoretical proofs. Moreover, experiments also show our method is capable of evaluating the quality of simplex decomposition of the manifold and detecting manifold intrinsic dimensions for real-world datasets.
Performance Comparison of 15-Level Multilevel Inverter Topologies

Multilevel inverters (MLI) have gained a significant emphasis in the area of renewable energy applications in recent years. The popularity of MLIs is due to their ability to efficiently convert the sustainable sources of energy, such as wind and solar power, into usable electrical energy. One key advantage of using reduced multilevel inverter configurations is that it can help to reduce the system overall cost by decreasing number of components required. The reduction of components in the inverter circuit results in simplification of design and lower expenditure. This paper proposes a fifteen-level modified multilevel inverter with cascaded h-bridge with a minimized switch count. The simulation results of the proposed inverter demonstrate that it is able to effectively attenuate harmonics and with reduced number of components required while still providing good performance. The suggested inverter design is also evaluated on its harmonic reduction characteristics, providing a cleaner output waveform and less distortion.
Testing for differential abundance in compositional counts data, with application to microbiome studies

In order to identify which taxa differ in the microbiome community across groups, the relative frequencies of the taxa are measured for each unit in the group by sequencing PCR amplicons. Statistical inference in this setting is challenging due to the high number of taxa compared to sampled units, low prevalence of some taxa, and strong correlations between the different taxa. Moreover, the total number of sequenced reads per sample is limited by the sequencing procedure. Thus, the data is compositional: a change of a taxon's abundance in the community induces a change in sequenced counts across all taxa. The data is sparse, with zero counts present either due to biological variance or limited sequencing depth, i.e. a technical zero. For low abundance taxa, the chance for technical zeros, is non-negligible and varies between sample groups. Compositional counts data poses a problem for standard normalization techniques since technical zeros cannot be normalized in a way that ensures equality of taxon distributions across sample groups. This problem is aggravated in settings where the condition studied severely affects the microbial load of the host. We introduce a novel approach for differential abundance testing of compositional data, with a non-neglible amount of "zeros". Our approach uses a set of reference taxa, which are non-differentially abundant. We suggest a data-adaptive approach for identifying a set of reference taxa from the data. We demonstrate that existing methods for differential abundance testing, including methods designed to address compositionality, do not provide control over the rate of false positive discoveries when the change in microbial load is vast. We demonstrate that methods using microbial load measurements do not provide valid inference, since the microbial load measured cannot adjust for technical zeros.
RD-Gen: Random DAG Generator Considering Multi-rate Applications for Reproducible Scheduling Evaluation

Real-time systems have various requirements such as the deadline and resource constraints. In addition, real-time systems are becoming larger and more complex, and studies on performance analysis and efficient scheduling algorithms are becoming increasingly important. Directed acyclic graph (DAG) models, which can express task dependencies and parallelism, are used for such studies. Random DAG sets are used to demonstrate the effectiveness and objectivity of methods proposed for real-time systems. However, there is no random DAG generation tool available that can generate a DAG set that considers the latest multi-rate applications. Therefore, researchers need to generate random DAG sets on their own, leading to additional effort and reduced reliability and reproducibility. To solve this problem, we propose a random DAG generator considering multi-rate applications for reproducible scheduling evaluation (RD-Gen). RD-Gen also enables batch generation of random DAG sets with different parameters. Case studies are used to demonstrate that RD-Gen can manage various problem settings and DAG study requirements.
SegQNAS: Quantum-inspired Neural Architecture Search applied to Medical Image Semantic Segmentation

Semantic segmentation can be applied to a wide range of applications. One of the most interesting is medical image analysis. Applying semantic segmentation techniques in that field has great potential to assist physicians in diagnosing and analysing medical scans for the patient's benefit. Traditionally, several techniques have been applied in order to perform semantic segmentation. However, with the development of Deep Learning methods, there was a paradigm shift. Deep learning techniques are able to achieve great results, comparable to human-level performance. In order to achieve state-of-the-art results, researchers have to lean on the task of designing novel deep neural network architectures. That process is very time-consuming and heavily relies on experience and expert knowledge. Neural architecture search is the process of automatising the search for new deep neural network architectures. Quantum-Inspired Neural Architecture Search is a neural architecture search algorithm that leverages the benefits of quantum-inspired computing to search for neural network architectures efficiently. In this work, we adapt this to search for semantic segmentation neural networks and apply it to medical image analysis. The Spleen and Prostate datasets from the Medical Segmentation Decathlon challenge were used. Results show that our work was able to find better-performing semantic segmentation architectures for both datasets: $0.9583\pm 0.006$ in comparison to ResU-Net $0.9525\pm 0.008$ for the spleen dataset, and $0.6887\pm 0.067$ in comparison to $0.6529\pm 0.070$ for the prostate dataset.
MobileWeatherNet for LiDAR-Only Weather Estimation

An autonomous vehicle relying on LiDAR data should be able to assess its limitations in real time without depending on external information or additional sensors. The point cloud generated by the sensor is subjected to significant degradation under adverse weather conditions (rain, fog, and snow), which limits the vehicle's visibility and performance. With this in mind, we show that point cloud data contains sufficient information to estimate the weather accurately and present Mo-bileWeatherNet, a LiDAR-only convolutional neural network that uses the bird's-eye view 2D projection to extract point clouds' weather condition and improves state-of-the-art performance by 15% in terms of the balanced accuracy while reducing inference time by 63%. Moreover, this paper demonstrates that among common architectures, the use of the bird's eye view significantly enhances their performance without an increase in complexity. To the extent of our knowledge, this is the first approach that uses deep learning for weather estimation using point cloud data in the form of a bird's-eye-view projection.
Wildland fuel type mapping in Attica using Sentinel-2 time-series

Wildland fuels are a key component in wildfire behavior modelling and wildfire prevention actions and their accurate mapping is becoming more critical, on account of the increased pressure emanating from human activities and the effects of climate change. The work presented in this manuscript aims at mapping fuels at a regional scale using a newly proposed fuel type classification system. The region of Attica in Greece was selected as the study site with an area of approximately 2,900 km2. Two fuel type maps were generated for the years 2020 and 2022, each based on a timeseries of six Sentinel-2 images with minimum cloud cover, selected in two-month intervals. The methodology proposed separates the study area into 3 land cover types, namely agricultural, urban and vegetation, and performs a classification of the area into the respective fuel types employing machine learning algorithms along with information derived from official thematic maps. The final fuel map is generated with the integration of vegetation height and density information derived from official products. To assess the accuracy of the produced map for the year 2020 the Land Use and Coverage Area frame Survey (LUCAS 2018) dataset was employed. The results display a promising accuracy of 72%, considering the first level of the hierarchical classification scheme. Our work demonstrates that accurate and adaptable maps of wildland fuels can be generated for Mediterranean complex ecosystems using a novel classification approach. Updated fuel type maps will be generated in the future, with enhanced information regarding height information in grasslands and shrublands and understory structure by means of expert knowledge or field observations.
Efficient and Reconfigurable Directional Beam Steering in Phased Arrays using AI and Edge Computing

This paper presents an Artificial Intelligence (AI) based method for controlling the radiation pattern of phased arrays. The method employs a feed-forward Artificial Neural Network (ANN) that is trained to steer the beam towards a desired direction by changing the radiation pattern. The ANN acts as a function that takes the pointing direction as input and returns the corresponding phase shift matrix as output for each radiating element. To ensure efficiency in terms of computational complexity and time response, specific layers are extracted from the level curve of the array factor at -3dB before training the neural network by assigning the connection weights. This approach achieves a balanced trade-off between the number of phase-shifting processes and the spatial resolution, which is crucial in contexts such as IoT and 5G. The proposed AI-based method has been successfully tested and verified using an AI-oriented Microcontroller for Edge Computing applications, where a specific neural network is implemented and used to compute phase matrices of a 4x4 phased array for a number of pointing directions obtained through data fusion of IMU data.
Steady-State Thermal Analysis on a Real-Time Plasma Impedance Tuner for High-Power RF Matching Applications

With fifth generation (5G) wireless spectrum reallocation presenting an increasingly congested spectral environment, radar systems need to utilize the advantages of real-time high-power impedance tuners to reconfigure in real time for maximization of output power and detection range. The ability to analyze a low-loss plasma switched-network impedance tuner, previously demonstrated for high-power capability, is further developed for analysis of thermal failure. The goal is both to understand the thermal properties and limitations of the radio-frequency (RF) board of the tuner under impedance tuning conditions and to assess the thermal power handling capabilities of the tuner structure. Based on thermal failure, an approach for assessing power-handling capability is demonstrated using CST's 3-D Multiphysics solver for Electromagnetic (EM)-Thermal Coupled simulations. The EM simulator analyzes the current density across the structure and translates this current density into temperature through the electrothermal model. The thermal analysis shows at least 51.1 W of power handling over all tuner states and frequencies, given failure by solder reflow at 91.5°C. The results of this paper provide a framework for analysis of tuner thermal failure and assessment of power handling.
Leakage mechanisms of double-perovskite Bi2FeMnO6 epitaxial thin films

Multiferroic Bi2FeMnO6 epitaxial thin films were deposited on conductive SrRuO3/SrTiO3 (0 0 1) substrate by pulse laser deposition. The Au/Bi2FeMnO6/SrRuO3 capacitor structure was used to investigate the electrical property. Bi2FeMnO6 thin film has a good ferroelectric retention and the downward polarization state. For Bi2FeMnO6 film, different leakage mechanisms play a predominant role under different electric field regions. Ohmic conduction works under 80 kV cm−1. From 80 to 1350 kV cm−1, the leakage mechanism depends on the electric field polarity and Poole–Frenkel emission is the dominant mechanism at negative bias. Schottky emission dominates the leakage current above 1350 kV cm−1. The extracted zero-field ionization energy for Poole–Frenkel emission is ~0.4 eV. A schematic energy band alignment of Au/Bi2FeMnO6/SrRuO3 capacitor was constructed to describe the contribution of Schottky emission.
Satellite-Based Characterization of Convective Growth and Glaciation and Its Relationship to Precipitation Formation over Central Europe

AbstractThe growth phase of convective storms and their transition to maturity is investigated based on more than 100 cases selected from the years 2012–14 over central Europe. Dynamical growth properties as well as cloud-top glaciation and microphysical characteristics are derived from the SEVIRI imaging radiometer aboard the geostationary Meteosat satellites. In addition, onset and intensity of surface precipitation are related to growth and glaciation processes using observations from the radar network of the German Weather Service. The majority of analyzed cases shows a distinct maximum in cloud-top cooling rate, which is used here for temporal synchronization. Cloud growth spans a period of approximately half an hour. Glaciation rate indicators suggest that freezing 15 min prior to the maximum cooling plays an important role in invigorating convective updrafts through the release of latent heat. Smaller ice particles are found for larger cloud-top cooling, which provides observational evidence that i.
to Export from Neighbors

This paper studies how learning from neighboring (cid:133)rms a⁄ects new exporters(cid:146)performance. We develop a statistical decision model in which a (cid:133)rm updates its prior belief about demand in a foreign market based on several factors, including the number of neighbors currently selling there, the level and heterogeneity of their export sales, and the (cid:133)rm(cid:146)s own prior knowledge about the market. A positive signal about demand inferred from neighbors(cid:146)export performance raises the (cid:133)rm(cid:146)s probability of entry and initial sales in the market but, conditional on survival, lowers its post-entry growth. These learning e⁄ects are stronger when there are more neighbors to learn from or when the (cid:133)rm is less familiar with the market. We (cid:133)nd supporting evidence for the main predictions of the model from transaction-level data for all Chinese exporters from 2000 to 2006. Our (cid:133)ndings are robust to controlling for (cid:133)rms(cid:146)supply shocks, countries(cid:146)demand shocks, and city-country (cid:133)xed e⁄ects.