Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone

The prediction of the intensity, location and time of the landfall of a tropical cyclone well advance in time and with high accuracy can reduce human and material loss immensely. In this article, we develop a Long Short-Term memory based Recurrent Neural network model to predict intensity (in terms of maximum sustained surface wind speed), location (latitude and longitude), and time (in hours after the observation period) of the landfall of a tropical cyclone which originates in the North Indian ocean. The model takes as input the best track data of cyclone consisting of its location, pressure, sea surface temperature, and intensity for certain hours (from 12 to 36 hours) anytime during the course of the cyclone as a time series and then provide predictions with high accuracy. For example, using 24 hours data of a cyclone anytime during its course, the model provides state-of-the-art results by predicting landfall intensity, time, latitude, and longitude with a mean absolute error of 4.24 knots, 4.5 hours, 0.24 degree, and 0.37 degree respectively, which resulted in a distance error of 51.7 kilometers from the landfall location. We further check the efficacy of the model on three recent devastating cyclones Bulbul, Fani, and Gaja, and achieved better results than the test dataset.
Online Policies for Real-Time Control Using MRAC-RL

In this paper, we propose the Model Reference Adaptive Control & Reinforcement Learning (MRAC-RL) approach to developing online policies for systems in which modeling errors occur in real-time. Although reinforcement learning (RL) algorithms have been successfully used to develop control policies for dynamical systems, discrepancies between simulated dynamics and the true target dynamics can cause trained policies to fail to generalize and adapt appropriately when deployed in the real-world. The MRAC-RL framework generates online policies by utilizing an inner-loop adaptive controller together with a simulation-trained outer-loop RL policy. This structure allows MRAC-RL to adapt and operate effectively in a target environment, even when parametric uncertainties exists. We propose a set of novel MRAC algorithms, apply them to a class of nonlinear systems, derive the associated control laws, provide stability guarantees for the resulting closed-loop system, and show that the adaptive tracking objective is achieved. Using a simulation study of an automated quadrotor landing task, we demonstrate that the MRAC-RL approach improves upon state-of-the-art RL algorithms and techniques through the generation of online policies.
Polyp Segmentation in Colonoscopy Images using U-Net-MobileNetV2.

Colorectal cancer from the appearance of polyps that can be benign or malignant is one of the most fatal diseases in the world. To find these polyps in patients, colonoscopy is performed, which is a very efficient technique in this case. Clinically, detecting and segmenting these polyps in order to determine their presence or not is a very difficult process that requires a lot of time and experience from professionals, depending directly on these factors. Therefore, it becomes increasingly important to have an automatic, effective and reliable method of detecting and segmenting these polyps, making diagnoses faster and more accurate. In order to assist in the development of a method, we proposed the U-Net-MobileNetV2 model, which is the combination of two neural networks, where one acts as an encoder for the other and is responsible for learning image resources. Our experiments generated satisfactory results, demonstrating a good performance and good segmentation. U-Net-MobileNetV2 achieved a Dice Coefficient of 89.71% and an IoU of 81.64% for the Kvasir-SEG dataset, where both are higher than the results obtained by other state-of-the-art models.
Deep Compression for PyTorch Model Deployment on Microcontrollers

Neural network deployment on low-cost embedded systems, hence on microcontrollers (MCUs), has recently been attracting more attention than ever. Since MCUs have limited memory capacity as well as limited compute-speed, it is critical that we employ model compression, which reduces both memory and compute-speed requirements. In this paper, we add model compression, specifically Deep Compression, and further optimize Unlu's earlier work on arXiv, which efficiently deploys PyTorch models on MCUs. First, we prune the weights in convolutional and fully connected layers. Secondly, the remaining weights and activations are quantized to 8-bit integers from 32-bit floating-point. Finally, forward pass functions are compressed using special data structures for sparse matrices, which store only nonzero weights (without impacting performance and accuracy). In the case of the LeNet-5 model, the memory footprint was reduced by 12.45x, and the inference speed was boosted by 2.57x.
Stationary massive disks around black holes: Realistic equation of state and bifurcation

We study stationary and axially symmetric black hole-disk systems, assuming a combination of the DD2 and Timmes-Swesty equations of state and a three-parameter family of rotation laws. There exist two branches of solutions that are shown to bifurcate, for a suitable specific entropy and a parameter in the rotation law. Low entropy nuclear matter allows for the existence of moderately massive Keplerian disks.
Laboratory and field evaluation of five soil water sensors

Accurate in situ determination of soil water content is important in many fields of agricultural, environmental, hydrological, and engineering sciences. As numerous soil water content sensors are available on the market today, the knowledge of their performance will aid users in the selection of appropriate sensors. The objectives of this study were to evaluate five soil water sensors in the laboratory and to determine if laboratory calibration is appropriate for the field. In this study, the performances of five sensors, including the Profile Probe™ (PP), ThetaProbe™ , Watermark™, Aqua-Tel™, and Aquaterr™ were compared in the laboratory. The PP and ThetaProbe™ were more accurate than the other soil water sensors, reproducing soil water content using factory recommended parameters. However, when PP was installed on a loamy sand in the field, the same soil that was used for the laboratory evaluation, it overestimated field soil water, especially at depth. Another laboratory experiment showed that soil wate.
eWake: A Novel Architecture for Semi-Active Wake-Up Radios Attaining Ultra-High Sensitivity at Extremely-Low Consumption

In this work we propose a new scheme for semi-passive Wake-Up Receiver circuits that exhibits remarkable sensitivity beyond -70 dBm, while state-of-the-art receivers illustrate sensitivity of up to -55 dBm. The receiver employs the typical principle of an envelope detector that harvests RF energy from its antenna, while it employs a nano-power operation amplifier to intensify the obtained signal prior to the final decoding that is realized with the aid of a comparator circuit. It operates at the 868 MHz ISM band using OOK signals propagated through LoRa transceivers, while also supporting addressing capabilities in order to awake only the specified network's nodes. The power expenditure of the developed receiver is as low as 580 nA, remaining at the same power consumption levels as the state-of-the-art implementations.
A Proximal Quasi-Newton Trust-Region Method for Nonsmooth Regularized Optimization

We develop a trust-region method for minimizing the sum of a smooth term $f$ and a nonsmooth term $h$), both of which can be nonconvex. Each iteration of our method minimizes a possibly nonconvex model of $f + h$ in a trust region. The model coincides with $f + h$ in value and subdifferential at the center. We establish global convergence to a first-order stationary point when $f$ satisfies a smoothness condition that holds, in particular, when it has Lipschitz-continuous gradient, and $h$ is proper and lower semi-continuous. The model of $h$ is required to be proper, lower-semi-continuous and prox-bounded. Under these weak assumptions, we establish a worst-case $O(1/\epsilon^2)$ iteration complexity bound that matches the best known complexity bound of standard trust-region methods for smooth optimization. We detail a special instance, named TR-PG, in which we use a limited-memory quasi-Newton model of $f$ and compute a step with the proximal gradient method, resulting in a practical proximal quasi-Newton method. We establish similar convergence properties and complexity bound for a quadratic regularization variant, named R2, and provide an interpretation as a proximal gradient method with adaptive step size for nonconvex problems. R2 may also be used to compute steps inside the trust-region method, resulting in an implementation named TR-R2. We describe our Julia implementations and report numerical results on inverse problems from sparse optimization and signal processing. Both TR-PG and TR-R2 exhibit promising performance and compare favorably with two linesearch proximal quasi-Newton methods based on convex models.
Large-dimensional random matrix theory and its applications in deep learning and wireless communications

Large-dimensional random matrix theory, RMT for short, which originates from the research field of quantum physics, has shown tremendous capability in providing deep insights into large dimensional systems. With the fact that we have entered an unprecedented era full of massive amounts of data and large complex systems, RMT is expected to play more important roles in the analysis and design of modern systems. In this paper, we review the key results of RMT and its applications in two emerging fields: wireless communications and deep learning. In wireless communications, we show that RMT can be exploited to design the spectrum sensing algorithms for cognitive radio systems and to perform the design and asymptotic analysis for large communication systems. In deep learning, RMT can be utilized to analyze the Hessian, input-output Jacobian and data covariance matrix of the deep neural networks, thereby to understand and improve the convergence and the learning speed of the neural networks. Finally, we highlight some challenges and opportunities in applying RMT to the practical large dimensional systems.
Deep Regression on Manifolds: A 3D Rotation Case Study

Many machine learning problems involve regressing variables on a non-Euclidean manifold–e.g. a discrete probability distribution, or the 6D pose of an object. One way to tackle these problems through gradient-based learning is to use a differentiable function that maps arbitrary inputs of a Euclidean space onto the manifold. In this paper, we establish a set of desirable properties for such mapping, and in particular highlight the importance of pre-images connectivity/convexity. We illustrate these properties with a case study regarding 3D rotations. Through theoretical considerations and methodological experiments on a variety of tasks, we review various differentiable mappings on the 3D rotation space, and conjecture about the importance of their local linearity. We show that a mapping based on Procrustes orthonormalization generally performs best among the mappings considered, but that a rotation vector representation might also be suitable when restricted to small angles.
A Comparison of Different Electrodes Solutions on Demineralization of Permeate Whey

Whey can be used as a raw material and provide new potential for the industry because of the properties of its components. Electrodialysis has been used to demineralize solutions, thus separating nutrients. In the present paper, experiments are presented for the demineralization of whey permeate through electrodialysis to obtain a stream of purified lactose. Compartments consisting of various electrode solutions were tested sodium chloride, calcium chloride, potassium chloride, and potassium sulfate with electrical conductivities of 6 mS · cm−1. The most efficient solution was potassium sulfate, which promoted the purification of lactose solutions while showing good prospects for future manufacturing conditions.
The Source of Leaking Ionizing Photons from Haro11: Clues from HST/COS Spectroscopy of Knots A, B, and C

Understanding the escape of ionizing (Lyman continuum) photons from galaxies is vital for determining how galaxies contributed to reionization in the early universe. While directly detecting the Lyman continuum from high-redshift galaxies is impossible due to the intergalactic medium, low-redshift galaxies in principle offer this possibility but require observations from space. The first local galaxy for which Lyman continuum escape was found is Haro 11, a luminous blue compact galaxy at z = 0.02, where observations with the FUSE satellite revealed an escape fraction of 3.3%. However, the FUSE aperture covers the entire galaxy, and it is not clear from where the Lyman continuum is leaking out. Here we utilize Hubble Space Telescope/Cosmic Origins Spectrograph spectroscopy in the wavelength range 1100–1700 Å of the three knots (A, B, and C) of Haro 11 to study the presence of Lyα emission and the properties of intervening gas. We find that all knots have bright Lyα emission. UV absorption lines, originating in the neutral interstellar medium, as well as lines probing the ionized medium, are seen extending to blueshifted velocities of 500 km s−1 in all three knots, demonstrating the presence of an outflowing multiphase medium. We find that knots A and B have large covering fractions of neutral gas, making LyC escape along these sightlines improbable, while knot C has a much lower covering fraction (≲50%). Knot C also has the the highest Lyα escape fraction, and we conclude that it is the most likely source of the escaping Lyman continuum detected in Haro 11.
Analysis of mequindox and its two metabolites in swine liver by UPLC-MS/MS

A new ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method was developed for the determination of mequindox (MEQ) and its two metabolites (1-desoxymequindox and 1, 4-bisdesoxymequindox, 1-DMEQ and BDMEQ) in swine liver. This method was validated on the basis of Commission Decision 2002/657/EC. Target analytes were extracted from swine liver with acid acetonitrile, purified by an Oasis MAX cartridge, and separated by UPLC. The chromatography total time was less than 8 min with gradient elution of 0.1% formic acid in methanol. Data acquisition was carried out by electrospray ionization tandem mass spectrometry (ESI/MS/MS) operated in a multiple reaction monitoring mode. At fortified levels from 2 to 100 μg kg−1 in swine liver, recoveries of target analytes ranged from 80% to 85% with the intra-day coefficient of variation (CV) being ≤ 14.48% and inter-day CV ≤ 14.53%, respectively. The limit of detection (LOD) ranged from 0.58 to 1.02 μg kg−1 and the limit of quantification (LOQ) range was 1.93 to 3.40 μg kg−1 for each analyte. The result indicated that this method was specific, sensitive, and suitable for the quantification and conformation of MEQ and its two metabolites in swine liver.
Photochemistry of 2-(3-benzoylphenyl)propionic acid(ketoprofen) Part 1A picosecond and nanosecond time resolved study inaqueous solution

The photochemistry of ketoprofen (KPF), 2-(3-benzoylphenyl)propionic acid, has been studied in aqueous solutions by time resolved (picosecond and nanosecond) spectroscopic techniques. Excited-state calculations were performed. The overall reactivity of the molecule was rationalized within a scheme in which the lowest triplet state can undergo intramolecular electron transfer and photoionization. The main pathway for the formation of the decarboxylated photoproducts was shown to involve intramolecular electron transfer and to pass through triplet biradicals.
Finite-rate chemistry effects in turbulent hypersonic boundary layers: A direct numerical simulation study

The accurate prediction of hypersonic flows is a key challenge for the design of reentry bodies or hypersonic aircraft. Knowledge concerning the wall-bounded turbulence is especially needed. The present work is a fundamental study of a turbulent spatially developing flat-plate boundary layer subjected to high-enthalpy effects. The influence of air chemical reactions, typical of high-speed regimes, is investigated by means of direct numerical simulations. First- and second-order statistics are accurately examined, as well as spectral content and classical correlations.
Higher dimensional generalization of the Benjamin‐Ono equation: 2D case

We consider a higher‐dimensional version of the Benjamin‐Ono (HBO) equation in the 2D setting: ut−R1Δu+12(u2)x=0,(x,y)∈R2$u_t- \mathcal {R}_1 \Delta u + \frac{1}{2}(u^2)_x=0, (x,y) \in \mathbb {R}^2$ , which is L2$L^2$ ‐critical, and investigate properties of solutions both analytically and numerically. For a generalized equation (fractional 2D gKdV) after deriving the Pohozaev identities, we obtain nonexistence conditions for solitary wave solutions, then prove uniform bounds in the energy space or conditional global existence, and investigate the radiation region, a specific wedge in the negative x$x$ ‐direction. We then introduce our numerical approach in a general context, and apply it to obtain the ground state solution in the 2D critical HBO equation, then show that its mass is a threshold for global versus finite time existing solutions, which is typical in the focusing (mass‐)critical dispersive equations. We also observe that globally existing solutions tend to disperse completely into the radiation in this nonlocal equation. The blow‐up solutions travel in the positive x$x$ ‐direction with the rescaled ground state profile while also radiating dispersive oscillations into the radiative wedge. We conclude with examples of different interactions of two solitary wave solutions, including weak and strong interactions.
Sharp Thresholds for a SIR Model on One-Dimensional Small-World Networks

We study epidemic spreading according to a Susceptible-Infectious-Recovered (for short, SIR) network model known as the Reed-Frost model, and we establish sharp thresholds for two generative models of one-dimensional small-world graphs, in which graphs are obtained by adding random edges to a cycle. In 3-regular graphs obtained as the union of a cycle and a random perfect matching, we show that there is a sharp threshold at . 5 for the contagion probability along edges. In graphs obtained as the union of a cycle and of a Gn,c/n Erdős-Rényi random graph with edge probability c/n, we show that there is a sharp threshold pc for the contagion probability: the value of pc turns out to be √ 2− 1 ≈ . 41 for the sparse case c = 1 yielding an expected node degree similar to the random 3-regular graphs above. In both models, below the threshold we prove that the infection only affects O(log n) nodes, and that above the threshold it affects Ω(n) nodes. These are the first fully rigorous results establishing a phase transition for SIR models (and equivalent percolation problems) in small-world graphs. Although one-dimensional small-world graphs are an idealized and unrealistic network model, a number of realistic qualitative phenomena emerge from our analysis, including the spread of the disease through a sequence of local outbreaks, the danger posed by random connections, and the effect of super-spreader events.
Model-Based Safe Policy Search from Signal Temporal Logic Specifications Using Recurrent Neural Networks

—We propose a policy search approach to learn controllers from speciﬁcations given as Signal Temporal Logic (STL) formulae. The system model, which is unknown but assumed to be an afﬁne control system, is learned together with the control policy. The model is implemented as two feedforward neural networks (FNNs) - one for the drift, and one for the control directions. To capture the history dependency of STL speciﬁcations, we use a recurrent neural network (RNN) to implement the control policy. In contrast to prevalent model-free methods, the learning approach proposed here takes advantage of the learned model and is more efﬁcient. We use control barrier functions (CBFs) with the learned model to improve the safety of the system. We validate our algorithm via simulations and experiments. The results show that our approach can satisfy the given speciﬁcation within very few system runs, and can be used for on-line control.
LASER: Learning a Latent Action Space for Efficient Reinforcement Learning

The process of learning a manipulation task depends strongly on the action space used for exploration: posed in the incorrect action space, solving a task with reinforcement learning can be drastically inefficient. Additionally, similar tasks or instances of the same task family impose latent manifold constraints on the most effective action space: the task family can be best solved with actions in a manifold of the entire action space of the robot. Combining these insights we present LASER, a method to learn latent action spaces for efficient reinforcement learning. LASER factorizes the learning problem into two sub-problems, namely action space learning and policy learning in the new action space. It leverages data from similar manipulation task instances, either from an offline expert or online during policy learning, and learns from these trajectories a mapping from the original to a latent action space. LASER is trained as a variational encoder-decoder model to map raw actions into a disentangled latent action space while maintaining action reconstruction and latent space dynamic consistency. We evaluate LASER on two contact-rich robotic tasks in simulation, and analyze the benefit of policy learning in the generated latent action space. We show improved sample efficiency compared to the original action space from better alignment of the action space to the task space, as we observe with visualizations of the learned action space manifold. Additional details: pair.toronto. edu/laser
Optimal Stochastic Nonconvex Optimization with Bandit Feedback

In this paper, we analyze the continuous armed bandit problems for nonconvex cost functions under certain smoothness and sublevel set assumptions. We first derive an upper bound on the expected cumulative regret of a simple bin splitting method. We then propose an adaptive bin splitting method, which can significantly improve the performance. Furthermore, a minimax lower bound is derived, which shows that our new adaptive method achieves locally minimax optimal expected cumulative regret.
Periodic points of polynomials over finite fields

Fix an odd prime $p$. If $r$ is a positive integer and $f$ a polynomial with coefficients in $\mathbb{F}_{p^r}$, let $P_{p,r}(f)$ be the proportion of $\mathbb{P}^1(\mathbb{F}_{p^r})$ that is periodic with respect to $f$. We show that as $r$ increases, the expected value of $P_{p,r}(f)$, as $f$ ranges over quadratic polynomials, is less than $22/(\log{\log{p^r}})$. This result follows from a uniformity theorem on specializations of dynamical systems of rational functions over residually finite Dedekind domains. The specialization theorem generalizes previous work by Juul et al. that holds for rings of integers of number fields. Moreover, under stronger hypotheses, we effectivize this uniformity theorem by using the machinery of heights over general global fields; this version of the theorem generalizes previous work of Juul on polynomial dynamical systems over rings of integers of number fields. From these theorems we derive effective bounds on image sizes and periodic point proportions of families of rational functions over finite fields.