Miracle-dz/UniFAR-FTUs huggingface dataset:
PXC2, a polarized receptor kinase, functions to repress ground tissue cell divisions and restrict stele size

Coordination of cell division and differentiation is critical for tissue patterning during organ development. Directional signaling and cell polarity have important roles in coordination of these processes. For instance, the Arabidopsis receptor-like kinase INFLORESCENCE AND ROOT APICES KINASE (IRK) functions to restrict stele area and repress longitudinal anticlinal divisions (LADs) in the endodermis where it is polarly localized. IRK is closely related to PXY/TDR CORRELATED 2 (PXC2); here, we show that PXC2 exhibits similar polarized accumulation suggesting they have related functions. pxc2 roots have increased stele area and irk pxc2 double mutant roots show increases in stele area and endodermal LADs beyond either single mutant, indicating that polarly localized IRK and PXC2 function redundantly to repress endodermal LADs and stele area. The double mutant also exhibits agravitropic root growth and abnormal cotyledon vein patterning. Manipulation of PIN1 trafficking and (re)localization mechanisms indicate that PXC2 and/or IRK suggest a link to modulation of polar auxin transport. We conclude that IRK and PXC2 have partially redundant functions in the root, but may have independent functions that are tissue-specific. We propose that repression of endodermal LADs and stele area through a PXC2/IRK-mediated, directional signaling pathway is required for normal ground tissue cell divisions, tissue patterning, and growth in Arabidopsis roots.
Comparison of Human Experts and AI in Predicting Autism from Facial Behavior

Advances in computational behavior analysis via artificial intelligence (AI) promise to improve mental healthcare services by providing clinicians with tools to assist diagnosis or measurement of treatment outcomes. This potential has spurred an increasing number of studies in which automated pipelines predict diagnoses of mental health conditions. However, a fundamental question remains unanswered: How do the predictions of the AI algorithms correspond and compare with the predictions of humans? This is a critical question if AI technology is to be used as an assistive tool, because the utility of an AI algorithm would be negligible if it provides little information beyond what clinicians can readily infer. In this paper, we compare the performance of 19 human raters (8 autism experts and 11 non-experts) and that of an AI algorithm in terms of predicting autism diagnosis from short (3-minute) videos of N = 42 participants in a naturalistic conversation. Results show that the AI algorithm achieves an average accuracy of 80.5%, which is comparable to that of clinicians with expertise in autism (83.1%) and clinical research staff without specialized expertise (78.3%). Critically, diagnoses that were inaccurately predicted by most humans (experts and non-experts, alike) were typically correctly predicted by AI. Our results highlight the potential of AI as an assistive tool that can augment clinician diagnostic decision-making.
Cryptography for Bitcoin and friends

Numerous cryptographic extensions to Bitcoin have been proposed since Satoshi Nakamoto introduced the revolutionary design in 2008. However, only few proposals have been adopted in Bitcoin and other prevalent cryptocurrencies, whose resistance to fundamental changes has proven to growwith their success. In this dissertation, we introduce four cryptographic techniques that advance the functionality and privacy provided by Bitcoin and similar cryptocurrencies without requiring fundamental changes in their design: First, we realize smart contracts that disincentivize parties in distributed systems from making contradicting statements by penalizing such behavior by the loss of funds in a cryptocurrency. Second, we propose CoinShuffle++, a coin mixing protocol which improves the anonymity of cryptocurrency users by combining their transactions and thereby making it harder for observers to trace those transactions. The core of CoinShuffle++ is DiceMix, a novel and efficient protocol for broadcasting messages anonymously without the help of any trusted third-party anonymity proxies and in the presence of malicious participants. Third, we combine coin mixing with the existing idea to hide payment values in homomorphic commitments to obtain the ValueShuffle protocol, which enables us to overcome major obstacles to the practical deployment of coin mixing protocols. Fourth, we show how to prepare the aforementioned homomorphic commitments for a safe transition to post-quantum cryptography.
Comparative Study of Dynamic TDD with Full-Duplex in Cell-Free Massive MIMO Systems

In this paper, we present a comparative study of the performance of a dynamic time division duplex (DTDD) enabled cell free massive multiple-input multiple-output (CF-mMIMO) with a full-duplex (FD)CF-mMIMO system. Both DTDD and FD enable a CF-system to serve uplink (UL) and downlink (DL) user equipments (UEs) simultaneously over the same time-frequency resources. However, interference from other access points (APs) affects the performance of both of these duplexing strategies. Additionally, the intra-AP self-interference suppression level has a significant impact on the sum UL-DL spectral efficiency (SE) of an FD CF-system. On the other hand, in DTDD, concurrent UL/DL reception/transmission is facilitated by judiciously apportioning the UL and DL time slots across half-duplex APs based on the localized UL/DL traffic load. Due to this, DTDD obviates the need for intra-AP interference cancelation. Our numerical experiments reveal that the achievable sum SE with DTDD can match, and even outperform, an FD system with similar antenna density. Thus, DTDD is a potential duplexing scheme that can be incorporated in the next generation wireless systems to serve concurrent UL-DL traffic load without the need for implementing complex hardware and algorithms for self-interference cancelation as in FD systems.
Solitary Wave Diffraction around Large Cylinder

The diffraction of a solitary wave around a vertical surface-piercing circular cylinder is described to a first approximation, and the resulting forces on the cylinder are calculated. This represents a limiting case of a cnoidal wave diffraction solution given previously, and in order to treat this limit the incident wave is represented in terms of a Fourier integral rather than a Fourier series. The maximum force coefficient and the dimensionless runup depend on a single parameter which replaces the usual diffraction parameter. A criterion for diffraction to be significant for shallow water waves is established and depends upon both the diffraction parameter as well as the additional parmater used here.
Lift-Enhancing Vortex Flows Generated by Plunging Rectangular Wings with Small Amplitude

Experiments in a water tunnel have been carried out on low-aspect-ratio rectangular wings undergoing a small-amplitude harmonic plunge motion at Reynolds numbers 10,000 and 20,000. A series of measurement techniques have been used, including force measurements, hot film, particle image velocimetry, and volumetric velocimetry measurements, to study the lift enhancement as a function of forcing frequency. Multiple peaks in the time-averaged lift have been observed, occurring at frequencies in the order of natural vortex-shedding frequencies of the stationary wings. It is postulated that interaction between the leading-edge and trailing-edge vortices contributes to the selection of the optimal frequencies for the time-averaged lift. At a specific Strouhal number, an adverse interaction between the vortices results in a vortex dipole that directs flow upstream. A comparison between a NACA 0012 and flat-plate profile provides further insight into the advantages and disadvantages of using a thinner profile in l.
Joint Hypothesis Specification for Unit Root Tests with a Structural Break

Several tests based on a t-ratio have been proposed in the literature to decide the order of integration of a time series allowing for a structural break. However, another approach based on testing a joint hypothesis of unit root and the irrelevance of some nuisance parameters is also feasible. This paper proposes new unit root tests consistent with the presence of a structural break applying this second perspective. Our approach deals both with the case where the break is not allowed under the null hypothesis, and where it is allowed. Simulations investigate the performance of this proposal compared to the existing tests and show important gains in terms of power. Copyright Royal Economic Society 2006
A Review on Multi-Focus Image Fusion Techniques in Surveillance Applications for Image Quality Enhancement

Image fusion is process of aggregating two or more than two degraded images into single image whose overall image quality is better than the previous input images. Multi-focus image fusion (MFIF) is one of the kinds of image fusion. MFIF is process of aggregating two or more than two multi-focused images (left/right/top/bottom focused) into single fused image whose overall image quality is better than input images. Out of various applications, surveillance is the major application of MFIF. Weak images may be enhanced by image fusion technique. This paper discusses various multi-focus image fusion techniques. The main discussed techniques are based SWT, DWT, PCA, CNN, Method noise etc. These techniques work under the environment of visual sensor network (VSN), where all the surveillance cameras are attached to single central computer system. This system performs the operation of MFIF. This paper also comparatively discusses the techniques based on various parameters like objective, methodology, merits, and demerits.
ON THE REALIZATION OF WIRTINGER PRESENTATIONS AS KNOT GROUPS

The weight test of Gersten and Pride is modified, to test whether a given abstract Wirtinger presentation occurs as the geometric Wirtinger presentation of a knot. A more refined version of this test then shows that the groups presented by certain abstract Wirtinger presentations are hyperbolic and so do not occur as knot groups. We give an infinite class of examples of such groups with symmetric Alexander polynomial.
Demand Response of HVAC Systems Using Data-Driven Approaches and Modelling Procedure

Demand Response (DR) from Heating, Ventilation, and Air-Conditioning (HVAC) systems is quantified by studying performance of energy buildings. Physical models, hybrid methods and data-driven approaches are used to predict the performance of building energy. Physical models require numerical equations that account for specific physical attributes and characteristics of building envelope materials. While the physical models are advantageous in describing heat transfer mechanisms, they are time-consuming, require expertise, are difficult to make proper assumptions and may not adapt to environmental or socio-economic variabilities. Hybrid models have similar drawbacks as physical models and require expertise and improper assumptions. But, data-driven approaches build models based on statistical data and overcome the shortcomings of model-based and hybrid approaches. Due to these advantages, data-driven approaches have gained popularity in recent years. In this context, this paper attempts to summarise and develop an overarching view of data-driven approach for building DR. Moreover, this review highlights the comparison of model-based and data-driven approaches for building DR and highlights the key benefits of the data-driven approach for building DR in power systems.
Characteristics of corona and silent discharges as influenced by geometry of the discharge reactor

This paper is aimed at investigating how the characteristics of corona and silent discharges including ozone generation are influenced by geometry of the discharge reactor. The corona discharges have been generated in coaxial wire–cylinder and wire–duct reactors stressed by dc and ac voltages. The silent discharges have been generated in the same reactors in addition to tubular reactors under ac voltage after pasting a dielectric barrier on the ground electrode of the reactors. The reactors were fed by dry air flowing at atmospheric pressure and temperature. The pulse characteristics of silent discharges are compared with those of ac corona discharges. The current–voltage and the ozone generation characteristics of silent and ac discharges are recorded. These characteristics depend significantly on the geometry of the reactor irrespective of the discharge type.
Dysfluency Classification in Speech Using a Biological Sound Perception Model

Dysfluency classification for stuttered speech has been tackled from different perspectives over the years, with research being more and more focused on deep learning. Here, we use a specific biological model of sound texture perception to extract a subband representation of speech and statistical features. A statistical analysis was also performed to identify relevant features. Afterwards, dysfluency classification was performed using a Random Forest Classifier to perform multi-label classification on the FluencyBank dataset and Support Vector Machine on the UCLASS dataset. This method performs as well or better than current state of the art deep learning algorithm, suggesting that approaching speech classification problems from a more biological point of view is a promising direction.
Stationary Wavelet Transform Based Detection of Aortic Stenosis Using Seismocardiogram Signal

Aortic stenosis (AS) is one of the most common and severe valvular heart diseases, which can cause heart failure. Early detection and treatment are the most effective ways to prevent AS. This study proposes a stationary wavelet transform (SWT) based machine learning framework to detect AS using seismocardiogram (SCG) signal. First, the SCG signal is preprocessed and segmented into cardiac cycles. Then, SWT is deployed to decompose each cardiac cycle into subbands. Further, each subband is used to extract the novel statistical features, which include log-energy entropy, relative wavelet energy, multi-scale kurtosis, and median absolute deviation. Finally, these features are fed into the random forest classifier for automated detection of AS. The effectiveness of the proposed method is evaluated using data from two publicly available databases. Our method achieves an overall accuracy of 99.4%, sensitivity of 98.9%, and specificity of 99.9%. The proposed method provides comparable performance with the current state-of-the-art techniques. The impressive results of the proposed framework make it useful to detect AS in primary healthcare units.
Future Distribution Power Flow Scenario Generation Method Using Generative Adversarial Network Considering Correlation Between DERs

New distribution grid planning method considering distributed energy resource (DER) effects has been developed by some distribution system operators. The future power flows in the distribution system are assumed by overlaying the hourly profiles of the traditional area load and DERs. Although the future DER profiles are made from historical data, the data of some DERs are quite small because they are not metered individually besides the low penetration. As a result, the coincidence factor (CF) of the future profiles becomes too high because the profiles strongly re-flect the specific data. In our previous work, we developed a method to generate DER profiles by generative adversarial network and showed that the forecasted profiles had proper CF. In this paper, we expand our work and propose a model that can maintain correlation between daily profiles of traditional area load and DERs in generating various future power flows in the distribution system.
Small Signal Stability Analysis of Dispatchable Virtual Oscillator Controlled Inverters with Adaptive Virtual Inertia Control

In this study, the small signal stability analysis of a system including adaptive modified dispatchable virtual oscillator control (dVOC)-based inverter connecting to a synchronous machine(SM) is conducted. Virtual oscillator-based controllers, such as dVOC are promising candidates for controlling the grid-forming inverters as they provide faster dynamic response, more accurate synchronization and power sharing among different units. While the conventional dVOC is not able to provide inertial response, modified dVOC overcomes this deficiency. To leverage the advantages of modified dVOC in various cases, the adaptive modified dVOC is used, which its parameter design is critical. Additionally, the fast dynamic of this controller interacts the line as well as SM dynamics. To avoid these issues, in this study, a detailed stability analysis based on the eigenvalue analysis is conducted and the allowable boundary for controller coefficients are determined. Results highlight proper design of adaptive modified dVOC improves the system dynamic and eliminates the stability issues within the interaction of SM and inverter.
Intercomparison of SCIAMACHY and SIM vis-IR irradiance over several solar rotational timescales

The two satellite spectrometers SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) aboard ENVISAT (Environmental Satellite), and SIM (Spectral Irradiance Monitor) aboard SORCE (Solar Radiation and Climate Experiment) observe since 2002 and 2003, respectively, daily solar spectral irradiance (SSI) not only in UV but extending to the visible and near- infrared (vis-NIR) regions. In this work, we intercompare (1) spectra and (2) timeseries of SSI measurements from SCIAMACHY and SIM. In (1) same-day (April 21, 2004) SSI measurements from these two instruments are compared to reference spectra from ground (new Kurucz), high-altitude (Hall and Anderson, Neckel and Labs, and Wehrli composite), and space (SOLSPEC/ATLAS 3, and SUSIM/UARS). In (2) timeseries of measurements (July 3 to August 21, 2004) covering several solar rotations in 2004 are compared to VIRGO sunphotometers (SPM) aboard SOHO. In general, SCIAMACHY and SIM are in agreement to within 4% over the common spectral domain and with respect to the other reference data. Apart from SSI and its variability, we integrate SSI over selected wavelength intervals and compare qualitatively to total solar irradiance (TSI) variability from PMOD/WRC and TIM/SORCE. Timeseries of integrated SSI in the vis (400–700 nm), NIR (700–1600 nm), and UV-vis-NIR (240–1600 nm) bands are compared. The overall rise and fall of integrated SCIAMACHY and SIM irradiances over several solar rotations are in good agreement and agree in most cases qualitatively with TSI variations in the visible and near IR. The application of White Light Source (WLS) corrections brings SCIAMACHY irradiances in closer agreement with SIM. Since WLS is also degrading with time, the WLS lamp ratios cannot be used for SSI degradation corrections after 2004.
OPTIMIZATION OF BIODIESEL PRODUCTION FROM PRUNUS SCOPARIA USING ARTIFICIAL BEE COLONY ALGORITHM

Renewable energy sources are developed worldwide, owing to high oil prices and in order to limit greenhouse gas emissions. The objective of this research was to study the feasibility of biodiesel production from mountain almond (Prunus Scoparia) oil using ultrasonic system and optimization of the process using Artificial Bees Colony (ABC) Algorithm. The results showed that by increasing the molar ratio, the conversion percentage increased and after reaching a certain ratio, further increase in the molar ratio caused decrease in the conversion percentage. Increasing in the ultrasound amplitude resulted in an increase in the conversion percentage which tends to ascend; Furthermore, results of optimization showed that the amount of molar ratio, amplitude, pulse and reaction time were 5.6, 0.90, 0.33 and 5 min, respectively. For independent variables, the values of yield and energy consumption were obtained which were equal to 96.1% and 9912 J, respectively. This finding proves that ABC algorithm can estimate the optimum point in biodiesel production with high accuracy. Renewable energy sources are developed worldwide, owing to high oil prices and in order to limit greenhouse gas emissions. The objective of this research was to study the feasibility of biodiesel production from Mountain almond (Prunus scoparia) oil using ultrasonic system and optimization of the process using Artificial Bees Colony Algorithm. The results showed that with increasing the molar ratio, the conversion percentage increased and after reaching a certain ratio, further increase in the molar ratio caused decrease in the conversion percentage. The increase in the ultrasound amplitude resulted in an increase in the conversion percentage which tends to ascend. Furthermore, results of optimization showed that the amount of molar ratio, amplitude, pulse and reaction time were 5.6, 0.90 0.33 and 5 min, respectively. For independent variables, the values of yield and energy consumption were obtained which were equal to 96.1% and 9912 J, respectively. This finding proves that ABC algorithm can estimate the optimum point in biodiesel production with high accuracy.
Pre-main-sequence stars in the stellar association N 11 in the Large Magellanic Cloud: clustering properties

Abstract Magellanic Clouds are of extreme importance to study the star-formation process in low-metallicity environments. Here, we discuss the clustering properties of the pre-main-sequence candidates and young embedded stellar objects in N 11, located in the Large Magellanic Cloud. Deep archival HST/ACS photometry is used to derive color–magnitude diagrams of the associations in N 11 and of the foreground field population. These data are complemented by archival infrared Spitzer data which allow detection of young embedded stellar objects. The spatial distribution of the pre-main-sequence candidates and young embedded stellar objects is discussed. The degree of clustering is derived using the minimal-spanning-tree method. No significant difference is found in clustering degree of young blue main-sequence stars and faint pre-main-sequence candidates, suggesting that they might be part of the same formation process.
Classifying Fault Category and Severity of UAV Flight Controllers’ Reported Issues

Unmanned Ariel Vehicles (UAVs) have gained significant importance in diverse sectors. Thus, a profound safety risk analysis/assessment to prevent any possible damage to themselves, the environment, and humans is fundamental for building and utilizing UAVs. To achieve that, two fundamental challenges should be addressed: i) identification of types and frequency of the issues and ii) assessment of their impact. In this paper, we aim to address the first challenge by automatizing the process of data field analysis. To do so, we first performed some statistical analysis of the reported issues of UAV systems (in Github) and manually extracted detailed data from the reports to better understand the type and nature of the issues. Then, to automatize the analysis, we used natural language processing algorithm to extract the keywords from the reports, and then applied four machine learning algorithms to build classifier models to classify the reports according to the fault category and severity level. The good performance results obtained suggest that these analyzes can be performed to further understand the UAV system issues, and help in the risk assessment procedure to identify the hazard and define the frequency and severity of the risk. Moreover, the results of this work can help a big community of developers and researchers in the precise and fast analysis of bug reports and safety risk assessment of any software system.