Miracle-dz/UniFAR-FTUs huggingface dataset:
The pressure of water waves upon a fixed obstacle

The diffraction of plane water waves by a stationary obstacle with vertical sides is examined, in particular the variation of amplitude along the sides and the average steady pressure due to the wave motion. Results similar to those in other diffraction problems are obtained for an infinite plane and for cylinders of circular or parabolic section, and approximations are made for sections of ship form. The examination was made in view of possible applications in the problem of a ship advancing through a train of waves, and the results are discussed in relation to the average additional resistance in such circumstances. It appears that the mean pressure obtained on diffraction theory from the second order terms can only account, in general, for a small proportion of the observed effect; the motions of the ship, and in particular its oscillations, are essential factors in the problem.
Development of Singularities of Solutions of Nonlinear Hyperbolic Partial Differential Equations

In a recent paper Zabusky has given an accurate estimate of the time interval in which solutions of the nonlinear string equation ytt = c2(1 + eyx)yxx exist. A previous numerical study of solutions of this equation disclosed an anomaly in the partition of energy among the various modes; Zabusky's estimate shows that at the time when the anomaly was observed the solution does not exist. The proof of Zabusky uses the hodograph method; in this note we give a much simpler derivation of the same result based on an estimate given some years ago by the author.
BIO-INDICATION PERSPECTIVITY OF BETULA PENDULA ROTH. UNDER CONDITIONS OF ENVIRONMENTAL OIL POLLUTION

The biological and ecological features of Betula pendula Roth. and its bioindicative perspectivity under the conditions of environmental oil pollution are analyzed. A wide areas of Betula pendula distribution are found both in the conditions of urbanized and natural territories of Ukraine, and in the natural zones of the Northern Hemisphere. The morphological, physiological and biochemical vitality indicators of the species under the oil pollution conditions are studied. The stability of the species linear foliar parameters and leaf area in the experimental area conditions are established, with the exception of the leaf asymmetry parameter, which doubles comparing to the background value. Peculiarities of Betula pendula assimilation organs necrotization under the influence of oil pollution are studied. It is found that the spotted type of necrosis is the dominant in the conditions of the background area, while "fish skeleton" and interveinal necrosis types are dominant in the oil deposit conditions. In the conditions of the background area, the necrotic lesions affect the smaller half of Betula pendula leaves, and at the same time, the largest number of leaves is affected by the second degree of necrosis. Under conditions of oil pollution 65% of the species leaf plates are affected by the fifth necrosis degree. This indicates that pollutants reach the plants above-ground organs through the root system. Under the influence of oil pollution there is a decrease in the buffer resistance of the species leaves’ cells internal environment relatively to the background values. Betula pendula reacts sensitively to an additional stress factor – acid rain, and restores buffer resistance by only 38.2%, while in the conditions of the background area this value is 55.8%. The most sensitive morphological bioindicative markers of Betula pendula vitality in the conditions of the environmental oil pollution are the fluctuating asymmetry coefficient and the leaves necrotic lesion degree. The parameter of buffer stability is characterized by the highest informativeness among other physiological and biochemical indicators. According to a complex of morphological, physiological and biochemical indicators, Betula pendula shows an average sensitivity to environmental oil pollution and can be used as a biological indicator of the ecological state of man-made territories.
Adaptive Neural Network for Image Edge Detection

This paper presents a neural network adaptive image edge detection method, and from neural network theory, this paper gives the formula of adaptive neural network algorithm; quantitative given the momentum factor and error, momentum factor and error on the weight vector of norm of the gradient of the quantitative relationship; and gives the algorithm flow diagram. Through experiment we get the conclusion: by using this adaptive neural network for image edge detection is feasible, and it has good generalization ability.
PHARMACOLOGICAL POTENTIAL OF POLYHERBAL FORMULATION KABASURA KUDINEER: A REVIEW

Diabetes is a long-term medical condition in which the body has difficulty converting food into energy. High blood glucose levels are produced by poor glucose management, which alters the overall metabolic process of the body. The accumulation of abnormal (excess) blood glucose and the inability to metabolize plasma glucose causes lethal damage to the body's major organs or organ systems such as heart disease, nephropathy, retinal degeneration, sensory loss, and even premature death. Despite the administration of long-term medications, available diabetes mellitus therapy is unable to cure and unable to prevent patients from key organ damage. Diabetics have a significant financial burden, in addition to drug-drug interactions. A tremendous amount of data and proof has been amassed, proving medicinal plants' enormous potential. Plants are a naturally occurring source of therapeutic strategy for mankind. In accordance with the literature review, people who have metabolic syndrome are more likely to contract COVID-19. To overcome pandemics, we need efficient and effective herbal management. Kabasurakudineer is a polyherbal formulation that is gaining acceptance in the medical world. It may have a synergistic impact that is advantageous in the therapy of metabolic illnesses such as diabetes. Type 2 diabetes is expected to affect 640 million people by 2040, with 70% of those affected being hypertensive and predisposed to obesity and dyslipidemia.
Data Augmentation, Multimodality, Subject and Activity Specificity Improve Wearable Electrocardiogram Denoising with Autoencoders

: Event detection based on biosignals continuously acquired by wearable devices has become an emergent topic. Particularly, real-time event detection with the electrocardiogram (ECG) has been explored to monitor heart conditions and epileptic seizures in the ambulatory. However, ECG acquired in the ambulatory is much more prone to noise and artifacts, due to the dynamic nature of these environments. Therefore, real-time and robust ECG denoising methods are crucial if event detection is meant to succeed. Denoising autoencoders (DAEs) are studied as robust and fast methods to attenuate ECG noise and artifacts. ECG data augmentation techniques are shown to effectively improve the performance of such a deep learning method. Activity and subject speciﬁc models are shown to output better ECG denoised estimates, than non-speciﬁc ones. And using accelerometry (ACC) as noise reference exempliﬁes how biosignal multimodality improves ECG attenuation of muscle and motion artifacts. Therefore, this work establishes effective design techniques to be considered when engineering ECG deep learning models.
Delayed generalized predictive control

This paper presents an alternative approach to the traditional generalized predictive control (GPC) presented by Clarke et al. (1987). The specific aim is to provide a new controller with control increment u(t) depending on the past values of the output y(t-k), y(t-k-l), . at the present time t in order to represent the computational delays and/or compensate for system structural characteristics. A comparison is made between the GPC and the modified algorithm. The modified algorithm shows better performance than the traditional GPC.
Accuracy and robustness of learning and inference in Bayesian networks

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Interaction-based Implicit Calibration of Eye-Tracking in an Aircraft Cockpit

: We present a method to calibrate an eye-tracking system based on cockpit interactions of a pilot. Many studies show the feasibility of implicit calibration with specific interactions such as mouse clicks or smooth pursuit eye movements. In real-world applications, different types of interactions often co-exist in the “natural” operation of a system. Therefore, we developed a method that combines different types of interaction to enable implicit calibration in operational work environments. Based on a preselection of calibration candidates, we use an algorithm to select suitable samples and targets to perform implicit calibration. We evaluated our approach in an aircraft cockpit simulator with seven pilot candidates. Our approach reached a median accuracy between 2° to 4° on different cockpit displays dependent on the number of interactions. Differences between participants indicated that the correlation between gaze and interaction position is influenced by individual factors such as experience.
High topographical accuracy by optical noise reduction in digital holographic microscopy

In this work we present a different method to reduce shot noise in phase imaging from digital holograms. An averaging process of phase images reconstructed with different reconstruction algorithms of the complex amplitude of a phase object in digital holographic microscopy. We obtain an improved phase image reaching a 29% of shot noise reduction. We use a single object complex amplitude that is needed to perform our proposal. Also show the corresponding simulations and experimental results. As phase sample test we used a micro-thin film step surface made at home of 100 nm high of TiO2 on a glass substrate of 4.7 mm thickness, our system was calibrated and traceable to an Atomic Force Microscope results.
GaAs/AlGaAs light emitting diode with 2D photonic structure in the surface

In this paper, effect of two-dimensional photonic pattern on the properties of the GaAs/AlGaAs based light emitting diode (LED) is demonstrated. The interference lithography was employed to surface patterning of the GaAs/AlGaAs based LED. The active region of the LED includes a GaAs/AlGaAs triple quantum well emitting at 850 nm. Interference lithography was used for preparation of two-dimensional pattern in the upper diode layer. The prepared LED with two-dimensional patterned photonic crystal structure was then investigated by electrical and optical measurements. Prepared photonic crystal LED shows enhanced light extraction efficiency due to the more effective extraction of guiding modes, what was documented from finite difference time domain simulations as well as from L(I) measurements.
Plasma channels for multi-GeV laser-plasma accelerators using discharges in structured gas cells

Summary form only given. Laser-plasma accelerators use intense laser pulses to produce relativistic plasma density structures that can trap and accelerate electron bunches. The energy gain can be strongly enhanced by extending the acceleration length, where the laser stays focused to high intensity, to a distance much longer than the Rayleigh length. This can be achieved by propagating the laser in a preformed and fully-ionized cylindrical plasma with a parabolic density profile acting as a "graded-index optical plasma fiber". In this work we present a method to produce plasma channels adequate for compact multi-GeV electron acceleration. It uses a high-voltage and high-current discharge between two hollow conical electrodes, through a structured gas cell. This fast discharge produces a plasma line that evolves to a plasma channel by free radial expansion of the plasma inside the gas cell. We also present new experimental results demonstrating a significant improvement of the plasma smoothness and reproducibility over the previous work, by using a low-current simmer discharge to partially pre- ionize the interaction zone. The plasma channels are 2 cm long but this length may be extended to about 10 cm by increasing the length of the plasma cell. The plasma can be made of low Z gases (H2 or He) so full ionization can be achieved to avoid ionization induced defocusing of the intense laser pulses. Plasma channels with axial electron densities in the range 1 - 5times1017 cm-3 can be produced making this a potential target for multi-GeV accelerators with current technology high-power lasers.
A Semantic Frame Graph for Information Extraction

: The following paper describes a graphical representation of a short text based on the semantic frames (the Semantic Frame Graph) using Semantic Role Labeling (SRL). It can be a foundation of alternative approach for open information extraction (OIE). The approach postprocesses the output of pretrained SRL classifier and it does not use complex rules, training sets nor significant corpus to decompose sentences. Proposed decomposition and representation reduces number of paths between entities dropping ones that are linguistically unmotivated, generates sequences of frames as paths which can be controlled using dialog coherence approach which further increases plausibility of semantic relationship between entities.
An Assessment of SAPHIR Calibration Using Quality Tropical Soundings

AbstractThe Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie (SAPHIR) instrument on board the Megha-Tropiques (MT) platform is a cross-track, multichannel microwave humidity sounder with six channels near the 183.31-GHz water vapor absorption line, a maximum scan angle of 42.96° (resulting in a maximum incidence angle of 50.7°), a 1700-km-wide swath, and a footprint resolution of 10 km at nadir. SAPHIR L1A2 brightness temperature (BT) observations have been compared to BTs simulated by the radiative transfer model (RTM) Radiative Transfer for the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (RTTOV-10), using in situ measurements from radiosondes as input. Selected radiosonde humidity observations from the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year (CINDY)–Dynamics of the Madden–Julian Oscillation (DYNAMO) campaign (September 2011–March 2012) were spatiotemporally collocated with MT overpasses. Although several.
Phase Function Norm Estimates for Stability of Systems with Monotone Nonlinearities

A system involving a linear element $G(s)$ and odd monotone nonlinearity $N( \cdot )$ is considered. It is assumed that this system is stable for all $N(r) = lr$, $0 \leqq l \leqq k$. General $N( \cdot )$ satisfying $0 \leqq {{(N(r) - N(s))} / {(r - s)}} \leqq k - \varepsilon $ (for distinct r, s and sufficiently small $\varepsilon > 0$) are considered.Letting $\Phi (\omega )$ denote the phase function $\arg \{ G(i\omega ) + {1 / k}\} $ and $\Phi '(\omega )$ its derivative, stability theorems are proven which involve certain norms of $\Phi (\omega )$ and its derivative $\Phi '(\omega )$. Finally, a recipe is given for generating new stability results with predetermined characteristics.
Statistics of Cloud Optical Properties from Airborne Lidar Measurements

AbstractAccurate knowledge of cloud optical properties, such as extinction-to-backscatter ratio and depolarization ratio, can have a significant impact on the quality of cloud extinction retrievals from lidar systems because parameterizations of these variables are often used in nonideal conditions to determine cloud phase and optical depth. Statistics and trends of these optical parameters are analyzed for 4 yr (2003–07) of cloud physics lidar data during five projects that occurred in varying geographic locations and meteorological seasons. Extinction-to-backscatter ratios (also called lidar ratios) are derived at 532 nm by calculating the transmission loss through the cloud layer and then applying it to the attenuated backscatter profile in the layer, while volume depolarization ratios are computed using the ratio of the parallel and perpendicular polarized 1064-nm channels. The majority of the cloud layers yields a lidar ratio between 10 and 40 sr, with the lidar ratio frequency distribution centered .
GAN Inversion with Editable StyleMap

: Recently, the ﬁeld of GAN Inversion, which estimates the latent code of a GAN to reproduce the desired image, has attracted much attention. Once a latent variable that reproduces the input image is obtained, the image can be edited by manipulating the latent code. However, it is known that there is a trade-off between reconstruction quality, which is the difference between the input image and the reproduced image, and ed-itability, which is the plausibility of the edited image. In our study, we attempted to improve reconstruction quality by extending latent code that represents the properties of the entire image in the spatial direction. Next, since such an expansion signiﬁcantly impairs the editing quality, we performed a GAN Inversion that realizes both reconstruction quality and editability by imposing an additional regularization. As a result, the proposed method yielded a better trade-off between the reconstruction quality and the editability against the baseline from both quantitative and qualitative perspectives, and is comparable to state-of-the-art(SOTA) methods that adjust the weights of the generators.
3D Human Body Reconstruction from Head-Mounted Omnidirectional Camera and Light Sources

: In this paper, we propose a method for reconstructing a whole 3D shape of the human body from a single image taken by a head-mounted omnidirectional camera. In the image of a head-mounted camera, many parts of the human body are self-occluded, and it is very difficult to reconstruct the 3D shape of the human body including the invisible parts. The proposed method focuses on the shadows of the human body generated by the light sources in the scene and uses it to perform highly accurate 3D reconstruction of the whole human body including the hidden parts.
Methods to Estimate Respiratory Rate Using the Photoplethysmography Signal

: Academia and industry have devoted significant effort to the research and development of smart wearable devices applied to health monitoring. The photoplethysmography (PPG) sensor is widely used for monitoring biosignals, such as heart and respiratory rate (RR), which are influenced by the cardiovascular system. This work focuses on analyzing methods for RR estimation regarding the effect of breathing on the PPG signal variation. This work describes, implements, and analyzes four methods for estimating RR. These methods are based on capturing RR using Fast Fourier Transform, median, and extracting physiological characteristics induced by respiration in the PPG signal. The most efficient method merges three RR calculations analyzed on the same signal, achieving nearly 93% of efficacy in the best scenario. The method efficacies were calculated using PPG signals from the BIDMC and CapnoBase databases collected from patients during hospital care. The analysis allows for understanding and mitigating the RR estimation challenges and evaluating the most efficacy method for a wearable device monitoring scenario.
Using Infrastructure-Based Agents to Enhance Forensic Logging of Third-Party Applications

: Logs are the primary data source forensic analysts use to diagnose and investigate attacks on deployed applications. Since the default logs may not include all application events required during an investigation, application-specific forensic logging agents are used to forensically enhance third-party applications post-deployment and ensure that any critical events are logged. However, developing such application-specific agents is impractical as this relies on application-specific knowledge requiring significant code comprehension efforts. Furthermore, the resulting forensic logging agents are likely to break compatibility between application versions and across applications; thus, requiring the time-consuming process of agent re-development much more frequently. We propose a more practical approach to developing forensic logging agents that leverages commonly-used underlying infrastructure, which is more stable across application versions and common across different applications. We evaluate our approach in the context of enhanced logging of Android messaging apps. Our results show that this approach can be used to develop logging agents that work across multiple apps while preserving the accuracy of the logs generated, thus mitigating the challenges associated with forensically enhancing third-party applications.
Distributionally Robust Optimization of Adaptive Cruise Control Under Uncertainty

: Due to the recent advances in intelligent and connected vehicles, Adaptive Cruise Control (ACC) has become a key functionality of advanced driver-assistant systems (ADAS) to enhance comfort and safety. The evaluation of ACC’s efﬁciency and safety is also crucial for the industry to prove the reliability of its products. In our paper, we propose a distributional robust optimization-based ACC reference generation model to produce the optimal commands facing the uncertainty of sensors. By taking into account the uncertainty set with knowledge of the ﬁrst and second moments, the original optimization problem with chance constraints can be simpliﬁed and solved more efﬁciently. Numerical experiments in a driving simulator illustrate that the robustness of the results is largely increased by minimizing the risks of violation of safety constraints.
Rethinking Image-based Table Recognition Using Weakly Supervised Methods

Most of the previous methods for table recognition rely on training datasets containing many richly annotated table images. Detailed table image annotation, e.g., cell or text bounding box annotation, however, is costly and often subjective. In this paper, we propose a weakly supervised model named WSTabNet for table recognition that relies only on HTML (or LaTeX) code-level annotations of table images. The proposed model consists of three main parts: an encoder for feature extraction, a structure decoder for generating table structure, and a cell decoder for predicting the content of each cell in the table. Our system is trained end-to-end by stochastic gradient descent algorithms, requiring only table images and their ground-truth HTML (or LaTeX) representations. To facilitate table recognition with deep learning, we create and release WikiTableSet, the largest publicly available image-based table recognition dataset built from Wikipedia. WikiTableSet contains nearly 4 million English table images, 590K Japanese table images, and 640k French table images with corresponding HTML representation and cell bounding boxes. The extensive experiments on WikiTableSet and two large-scale datasets: FinTabNet and PubTabNet demonstrate that the proposed weakly supervised model achieves better, or similar accuracies compared to the state-of-the-art models on all benchmark datasets.