Miracle-dz/UniFAR-FTUs huggingface dataset:
KPmixer-a ConvMixer-based Network for Finger Knuckle Print Recognition

Biometric technology is increasingly popular and has many practical applications in our lives, such as fingerprint recognition, face recognition, iris recognition, etc. In biometrics based recognition technologies, finger knuckle print recognition (FKP) has received a lot of research attention recently. This method has many advantages compared to the others such as fingerprint recognition and iris recognition. Motivated by the advantages of FKR and advances of deep learning, this paper proposes a finger knuckle print recognition model, namely KPmixer. In particular, we modify the Convmixer model using variable-size kernels to reduce the number of parameters of the model and help the model mix spatial information at various distances. At the same time, we recommend the SE+ module to increase the accuracy of FKP recognition. Moreover, we propose to use a set of effective data augmentation methods for FKP recognition. The performance of the proposed model is compared with modern CNN models such as Convmixer, Resnet18, MobileNet, and DenseNet, showing an outstanding result in terms of accuracy.
Efficient Parallel Cache-Oblivious Sorting Algorithms

We consider the sequential cache-oblivious and parallel cache-oblivious models of computation. First, we study Sample Partition Merge Sort (SPMS) of Cole and Ramachandran, the best-known parallel cache-oblivious deterministic sorting on these memory models. Then we present a simpler version of SPMS, which achieves a constant factor improvement in parallel time complexity over SPMS. We also present the first 1/O efficient algorithm for Bundle Sort, a stable counting sort, on these memory models. To sort an ordered list of $n$ items with $k (k < < n)$ distinct items, our sequential cache-oblivious Bundle Sort takes $O(\frac{n}{B}\log_{M}k)$ cache misses, while our parallel cache-oblivious Bundle Sort takes $O (>\log n$ log log $n$) parallel steps and $O(\frac{n}{B}\log_{M}k)$ cache misses. Here, $M$ is the cache memory size and $B$ is the size of each block in the cache memory.
Joint Policy Optimization for Task-Oriented Grasping with a Humanoid Gripper

Reinforcement learning (RL) algorithms signifi-cantly impact robotics by overcoming the limitations of conventional robot control and enabling new-generation collaborative and bio-inspired robots to explore and learn from experience. As a result, we can approach fundamental challenges in robotics such as object grasping and manipulation in a new way and embed their coherent task-oriented relation as an optimization problem. We have trained two RL policies in a self-supervised manner, where the first agent learns to optimally grasp an object based on the success rate of the second agent, which executes the following manipulation task with the previously grasped object. The agents learned to control and manipulate a robotic arm with a humanoid gripper to grasp a hammer and hit a nail in a simulation environment. As a result, the trained agents successfully grasp the hammer in a task-oriented manner and hit the nail in more than 99 % of all given situations. In addition, splitting the tasks resulted in significant training steps reduction even for more than 50 %.
Effective tip radius in electrostatic force microscopy

A method to determine the effective electrostatic tip radius of arbitrarily shaped conducting tips in atomic force microscopy is presented. The method is based on the finding that for conductive samples, the electrostatic force can be separated into two contributions: one from a constant background that depends only on the macroscopic shape of the tip (cone or pyramid and cantilever), and another that depends only on the radius of curvature of the tip apex. Based on a simple theoretical expression derived from the generalized image charge method, we show that the tip radius can be directly determined from experimental force-distance characteristics. For irregular tip shapes, we show that the measured tip radius is the average of two principal curvatures, in agreement with tip shape images obtained by scanning electron microscopy.
Mapping Threats in Smart Grid System Using the MITRE ATT&CK ICS Framework

The smart grid system is an integration between power distribution systems with communication networks. A smart grid offers various benefits, but at the same time inherits various vulnerabilities from the implemented information and communication technology (ICT). Many devices in smart grid systems implement the TCP/IP stack to exchange data, which can lead to significant new cyber attack vectors, such as malware, Denial-of-service (DoS), man-in-the-middle (MITM), and replay attacks, as well as various other cybersecurity threats. One approach to deal with these security issues proactively is through threat modeling. We can utilize some tools to gather the threat data targeting the smart grid, such as using honeypots, then analyze the collected threat data to obtain the threat model in order to study the attackers' behavior. In this paper, we collected threat data targeting the smart grid system by deploying GridPot honeypot and analyzed the collected threat data by mapping them to the MITRE ATT&CK for Industrial Control System (ICS) framework. This experiment shows that the threats targeting the smart grid systems are real, and could harm any smart grid system in the world.
Demodulation pixels in CCD and CMOS technologies for time-of-flight ranging

A new generation of smart pixels, so-called demodulation or lock-in pixels is introduced in this paper. These devices are capable of measuring phase, amplitude and offset of modulated light up to some tens of MHz, making them ideally suited to be used as receivers in 3D time-of-flight (TOF) distance measurement systems. Different architectures of such devices are presented and their specific advantages and disadvantages are discussed. Furthermore, a simple model is introduced giving the shot noise limited range resolution of a range camera working with these demodulation pixels. Finally, a complete TOF range camera based on an array of one of the new lock-in pixels will be described. This TOF- camera uses only standard components and does not need any mechanically scanning parts. With this camera non- cooperative targets can be measured with a few centimeters resolution over a distance of up to 20 meters.
Atomic Dynamics From Coupled Quantized Electronic Oscillators

The quantum mechanical concept of an “atom in a molecule” is synthetically emulated and analyzed in the form of a nonlinear electronic circuit. The circuit is comprised of coupled quantized oscillators. Quantized oscillators are novel and not generally known. Such systems are shown to be consistent with quantum chemical dynamics. Such a network of quantized oscillators achieves equilibriums that are consistent with naturally occurring molecules. These capabilities offer an alternative to von Neumann digital based computations, whereby solutions to complex systems are determined as equilibriums, as is the case of nature. Historically, quantized solutions have been associated with solutions to Shrödinger’s Equation. Shrödinger Equation based observations are typically rendered, if possible, by numerical calculations. By comparison, the approach presented in this work relies on synthesizing quantized electronic devices, which are novel, in order to achieve dynamical interactions consistent with naturally occurring ones. Detailed dynamics based on actual devices are provided. The theoretical underpinnings are described. This topic has broad implications for a variety of concepts associated with quantum mechanics, including computations and entanglement, as well as various oscillator circuits of fundamental importance.
The Radio Structure of Supernova Remnant 0540-693

We present an Australia Telescope Compact Array multifrequency study of the supernova remnant 0540-693 which is the youngest known remnant, after SN 1987A, in the Large Magellanic Cloud. The radio observations reveal that it consists of a central synchrotron nebula of diameter 1.4 pc which is powered by the 50 ms pulsar PSR B0540-69, and a nearly circular low-surface brightness annulus of diameter 17.5 pc. The central nebula has a flatter spectrum than the annular emission and is the radio counterpart of the synchrotron nebula previously detected at optical and X-ray wavelengths. Its properties are very similar to those of the Crab Nebula
THE QUANTITATIVE DISTRIBUTION OF HECKE EIGENVALUES

Abstract In this paper, we prove that the Sato–Tate conjecture for primitive Maass forms holds on average. We also investigate the rate of convergence in the Sato–Tate conjecture and establish some estimates of the discrepancy with respect to the Sato–Tate measure on the average of primitive Maass forms.
Dielectric dispersion of ferroelectric ceramics and single crystals at microwave frequencies

The dielectric constant of ferroelectric ceramics shows strong Debye-like relaxation at GHz frequencies. This relaxation is caused by sound generation inside the crystallites. The ferroelastic domain walls which vibrate in the electric ac field are very effective shear wave transducers. The shear wave emission has a maximum when the wavelength of the sound wave is comparable to the width of the domains. Above this frequency the domain walls can no longer vibrate due to inertia and do not contribute to the dielectric constant. The observed dependence of the microwave dispersion on temperature, doping and grain size of the ferroelectric ceramic is in good agreement with the proposed mechanism of the emission of acoustic shear waves.
Prevalence, bacteriology and antimicrobial susceptibility of bacteria associated with cattle and buffaloes mastitis in Khyber Pakhtunkhwa during 2020-21

Mastitis, "inflammation of the mammary gland", is economically devastating disease of dairy industry and also a significant disease of public health importance. The bearing of various bacteria in milk renders it objectionable for human consumption, which also might be a source of spreading of tuberculosis, staphylococcal infection and toxins, streptococcal sore throat, gastroenteritis and brucellosis. Additionally, diseases like bovine mastitis and reproductive infections in dairy animals are also important reasons for the usage of antimicrobial agents for frequent and prolonged durations, thus it contributes considerably to the dissemination of multi-drug resistant (MDR) bacteria. Therefore, this study was carried out to investigate the prevalence of bacterial pathogens associated with cattle and buffaloes mastitis and their antimicrobial susceptibilities. Milk samples (n = 1166) from cattle (n = 870) and buffaloes (n = 296) were processed during 2020-21. Besides, 821 mastitic samples were further processed for bacteriology and antimicrobial susceptibility test by disc diffusion method. The results showed a prevalence of 17.9% clinical mastitis in cattle and 10.8% in buffaloes. Subclinical mastitis was 69% in cattle and 79.4% in buffaloes. The most prevalent bacteria associated with mastitis were Escherichia coli (34%) and Staphylococci spp. (26%). Some other bacteria were Streptococci spp. (8%), Salmonella spp. (5%), Klebsiella spp. (5%), Pasturella spp. (4%) and Bacillus spp. (0.1%). The bacterial isolates were mostly resistant to streptomycin (97%), sulphamethoxazole (94%), penicillin (91%), and oxytetracycline (91%). However, high susceptibilities were noted in case of agents like gentamycin (87%), enrofloxacin (86%), and florfenicol (69%). This study may suggest high prevalence of mastitis in cattle and buffaloes caused by various bacteria, which were found highly resistant to various antimicrobial agents
Satellite image classification using HOG and DAISY Feature Descriptors

Advancements in remote sensing techniques provide crucial information for a variety of applications, including landscape changes, land cover categorization, enhanced weather forecasting, and climate observation. With the recent breakthroughs that have made satellites both more powerful and cheaper to deploy, the volume of high-resolution satellite images collected has increased exponentially. Manual classification techniques of these high-resolution satellite images are too inaccurate and inefficient to handle the challenge. Hence, automation of satellite image classification has become a crucial part of the field of remote sensing. The major purpose of this work is to evaluate the ability of a feature extraction model to identify satellite pictures utilizing different approaches. This study presents actual data demonstrating that the Bag of Feature (BoF) approach outperforms conventional feature extraction algorithms for multilabel scene classification. We employ local feature descriptors such as DAISY features for local feature extraction and classification, and global feature descriptors such as the histogram of oriented gradients (HOG) for global feature extraction and classification, using the Bag of Features technique. The bag of feature encoding is augmented by the Mini-Batch K-Means method to decrease the complications of the feature encoding technique. A method for pooling is applied to combine DAISY and HOG data relevant to each image. Finally, we perform a 10-fold cross-validation experiment with a support vector machine (SVM) classifier and classify our results on a dataset of 21 scene categories. The hybrid model attained an accuracy of 81.76% and the KNN model attained an accuracy of 67.14%. The study demonstrates how this categorization method may be used to identify changes in land use and land cover, as well as aid in the improvement of satellite classification.
Error Rate Analysis of NOMA inspired DF Relay System with Imperfect SIC in IoT Network

In IoT network the usage of short packets poses a challenge to reliable and secure transmissions due to low complexity limitations and large connection demand. To improve the spectrum usage and system performance, this research study investigates the cooperative NOMA (C-NOMA) downlink short packet transmission scheme in IoT network, where the direct link is available to both the IoT users. This model includes a source, strong user (SU), weak user (WU) and a decode-and-forward (DF) relay. Based on NOMA transmission scheme, packets from source to SU is transmitted via direct link and WU via cooperative relay link where both perfect and imperfect successive interference cancellation (SIC) scenarios are considered. Using linear estimates on block error rate (BLER), this research study theoretically compute the average BLER of both the IoT users. Moreover, the asymptotic average BLER analysis is also presented at high signal-to-noise ratios. The derived analytical BLER collides with the simulated result, as proved by numerical analysis. Furthermore, in high SNR zone when the average BLER does not approaches to unity, power allocation factors become insignificant for both the IoT users.
Simulation-based test methods with an automotive camera-in-the-loop for automated driving algorithms

Testing automated driving systems is challenging due to the numerous and unexpected scenarios these can en-counter in road traffic. In order to reduce the effort and costs of real test drives, different simulation-based test methods for environment sensors have been developed. The purpose is to enable the hardware integration of sensors in the early development phase of environment perception systems for au-tomated driving applications. Thus, the test results are also subject to the uncertainties and inaccuracies of the real sensors rather than the ideal sensor models. In this work, two different methods for injecting synthetic image data into camera sensors are investigated: Over-the-Air (OTA) and Direct Data Injection (DDI). The goal is to verify to what extent the method for data injection degrades the image quality and, thus, influences the performance of the YOLOv4 algorithm for object detection. It is shown that both methods have their advantages and limitations: DDI achieves better image quality with real and synthetic data after image injection; OTA could present itself as a cost-effective solution for specific test scenarios.
The Evolution of Dispersion Spectra and the Evaluation of Model Differences in an Ensemble Estimation of Error Statistics for a Limited-Area Analysis

Abstract An ensemble of limited-area forecasts has been obtained by integrating the Aire Limitee Adaptation Dynamique Developpement International (ALADIN) limited-area model, in cold-starting mode, from an ensemble of Action de Recherche Petite Echelle Grande Echelle (ARPEGE) global analyses and forecasts. This permits error covariances of the ALADIN 6-h forecast and of the ARPEGE analysis to be estimated. These two fields may be combined in a future ALADIN analysis. The evolution of dispersion spectra is first studied in a perfect model framework. The ARPEGE analysis reduces the large-scale dispersion of the ARPEGE background by extracting some information from observations. Then, the digital filter initialization reduces the small-scale dispersion by removing the noise caused by interpolation of the ARPEGE analysis onto the ALADIN grid. Finally, the ALADIN 6-h forecast strongly increases the small-scale dispersion, in accordance with its ability to represent small-scale processes. Some model error contr.
Engagement With Social Media Posts in Experimental and Naturalistic Settings: How Do Message Incongruence and Incivility Influence Commenting?

Research on factors that encourage people to speak out online uses either experimental or observational data, and it is unclear whether patterns detected in one setting apply to the other. This project examines whether message incongruence and incivility influence the likelihood of commenting on social media posts about politics in both controlled and naturalistic settings. An online experiment on American adults using a mock Facebook page (Study 1, N = 424) showed that incivility decreased commenting when the original comment was pro-attitudinal but had no effects when it was counter-attitudinal and that incivility directly depressed commenting (i.e., unmediated via anger), but increased it indirectly through anger. An observational study on Spanish Twitter users (Study 2, N tweets = 4,153) demonstrated that in a naturalistic setting, there were more pro- than counter-attitudinal comments in response to the original tweet, and incivility was not associated with the desire to speak out. The implications are discussed.
What can AI do for me? Evaluating Machine Learning Interpretations in Cooperative Play

Machine learning is an important tool for decision making, but its ethical and responsible application requires rigorous vetting of its interpretability and utility: an understudied problem, particularly for natural language processing models. We propose an evaluation of interpretation on a real task with real human users, where the effectiveness of interpretation is measured by how much it improves human performance. We design a grounded, realistic human-computer cooperative setting using a question answering task, Quizbowl. We recruit both trivia experts and novices to play this game with computer as their teammate, who communicates its prediction via three different interpretations. We also provide design guidance for natural language processing human-in-the-loop settings.
An introduction to wavelets and other filtering methods in finance and economics

Much of finance theory has been dominated in the past thirty years by the success of simple stochastic (continuous-time diffusion) models in giving clean and tractable solutions to the problems of optimal investment and option pricing. In particular, the work of Merton, Black and Scholes has had enormous appeal for mathematicians and physicists with training in stochastic processes and differential equations.
Development of E-Modules with a Guided Inquiry Approach on Ecology and Environmental Change Materials

: The purpose of this study was to produce an E-Module on ecology and environmental change using a guided inquiry approach that is valid, practical, and effective. This type of research is development research using the Plomp model. The development phase of this model begins with the Preliminary research phase, the Development or prototyping phase, and the Assessment phase. The instruments used in this study were validity assessment sheets, practicality assessment sheets
Insulin sensitivity, β cell function, and adverse pregnancy outcomes in women with gestational diabetes

Abstract Background: The potential impact of β cell function and insulin sensitivity on adverse pregnancy outcomes in women with gestational diabetes mellitus (GDM) remains uncertain. We aimed to investigate the association between β cell dysfunction, insulin resistance, and the composite adverse pregnancy outcomes. Methods: This observational study included 482 women diagnosed with GDM during pregnancy. Quantitative metrics on β cell function and insulin sensitivity during pregnancy were calculated using traditional equations. The association of β cell dysfunction and insulin resistance with the risk of the composite adverse pregnancy outcomes was investigated using multivariable-adjusted logistic regression models. Results: Multivariable-adjusted odds ratios (ORs) of adverse pregnancy outcomes across quartiles of homeostatic model assessment for insulin resistance (HOMA-IR) were 1.00, 0.95, 1.34, and 2.25, respectively (P for trend = 0.011). When HOMA-IR was considered as a continuous variable, the multivariable-adjusted OR of adverse pregnancy outcomes was 1.34 (95% confidence interval 1.16–1.56) for each 1-unit increase in HOMA-IR. Multivariable-adjusted ORs of adverse pregnancy outcomes across quartiles of homeostatic model assessment for β cell function (HOMA-β) were 1.00, 0.51, 0.60, and 0.53, respectively (P for trend = 0.068). When HOMA-β was considered as a continuous variable, the multivariable-adjusted OR of adverse pregnancy outcomes was 0.57 (95% CI 0.24–0.90) for each 1-unit increase in HOMA-β. However, other quantitative metrics were not associated with the composite adverse pregnancy outcomes. Conclusions: We demonstrated a significant association of β cell function and insulin sensitivity with the risk of adverse pregnancy outcomes. We have provided additional evidence on the early identification of adverse pregnancy outcomes besides the glycemic values.