Miracle-dz/UniFAR-FTUs huggingface dataset:
A Modified Q-LDPC Decoding for Reducing Oscillation

Based on the oscillation phenomenon in the decoding of Q-LDPC(q-ary low-density parity-check) codes,a modified decoding algorithm was proposed.In each iteration,for oscillating variable nodes,the previous posteriori probabilities are added to the current posteriori probabilities to reduce effects of oscillating in decoding.The computer simulation shows that the modified decoding algorithm can reduce bit error rate and block error rate for short and middle length QLDPC codes with low complexity.
Transformer-Based Neural Augmentation of Robot Simulation Representations

Simulation representations of robots have advanced in recent years. Yet, there remain significant sim-to-real gaps because of modeling assumptions and hard-to-model behaviors such as friction. In this letter, we propose to augment common simulation representations with a transformer-inspired architecture, by training a network to predict the true state of robot building blocks given their simulation state. Because we augment building blocks, rather than the full simulation state, we make our approach modular which improves generalizability and robustness. We use our neural network to augment the state of robot actuators, and also of rigid body states. Our actuator augmentation generalizes well across robots, and our rigid body augmentation results in improvements even under high uncertainty in model parameters.
An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer

Lung and colon cancers are among the leading causes of human mortality and morbidity. Early diagnostic work up of these diseases include radiography, ultrasound, magnetic resonance imaging, and computed tomography. Certain blood tumor markers for carcinoma lung and colon also aid in the diagnosis. Despite the lab and diagnostic imaging, histopathology remains the gold standard, which provides cell-level images of tissue under examination. To read these images, a histopathologist spends a large amount of time. Furthermore, using conventional diagnostic methods involve high-end equipment as well. This leads to limited number of patients getting final diagnosis and early treatment. In addition, there are chances of inter-observer errors. In recent years, deep learning has shown promising results in the medical field. This has helped in early diagnosis and treatment according to severity of disease. With the help of EffcientNetV2 models that have been cross-validated and tested fivefold, we propose an automated method for detecting lung (lung adenocarcinoma, lung benign, and lung squamous cell carcinoma) and colon (colon adenocarcinoma and colon benign) cancer subtypes from LC25000 histopathology images. A state-of-the-art deep learning architecture based on the principles of compound scaling and progressive learning, EffcientNetV2 large, medium, and small models. An accuracy of 99.97%, AUC of 99.99%, F1-score of 99.97%, balanced accuracy of 99.97%, and Matthew’s correlation coefficient of 99.96% were obtained on the test set using the EffcientNetV2-L model for the 5-class classification of lung and colon cancers, outperforming the existing methods. Using gradCAM, we created visual saliency maps to precisely locate the vital regions in the histopathology images from the test set where the models put more attention during cancer subtype predictions. This visual saliency maps may potentially assist pathologists to design better treatment strategies. Therefore, it is possible to use the proposed pipeline in clinical settings for fully automated lung and colon cancer detection from histopathology images with explainability.
Inquiry in Conversation: Towards a Modelling in Inquisitive Pragmatics

Conversation is one of the main contexts in which we are conducting inquiries. Yet, little attention has been paid so far in pragmatics or epistemology to the process of inquiry in conversation. In this paper, we propose to trigger such an investigation through the development of a formal modelling based on inquisitive pragmatics — a framework offering a semantic representation of questions and answers, along with an analysis of the pragmatic principles that govern questioning and answering moves in conversations geared towards information exchange. Our starting observation is that an interrogative inquiry in a conversation takes the form of a finite sequence of questioning and answering steps, and appears thereby as an inherently temporal process . The central notion of interrogative protocol introduced in this paper precisely aims to capture the temporal dimension of inquiry. Interrogative protocols are defined as branching-time tree structures encoding all the possible sequences of questioning and answering steps — i.e., all the possible inquiry paths — that can subsequently occur in a starting conversational situation in accordance with the principles of inquisitive pragmatics. They provide us with a formal environment to define and investigate the epistemological notions of interrogative inquiry and interrogative consequence in conversational contexts. One of the main interests of the resulting framework is to enable a formal investigation of central epistemological issues relative to interrogative inquiry under the form of computational problems. We frame three key inquiry problems along this line, and we then propose an algorithmic procedure for solving them in the restricted case where the inquirer has at her disposal a finite number of questions in her inquiry. We conclude by relating our approach to Wiśniewski’s Erotetic Search Scenarios and Hintikka’s Interrogative Model of Inquiry.
A Survey on LoRaWAN for Smart Medical and Industries

As IoT emerges, LoRaWAN is a low-power wide area network (LPWAN) technology and is a huge boon for smart indoor industries and medical monitoring. In light of its broad coverage, minimal complication, along with low energy utilization, It is recommended as a potential wireless connectedness method for enabling the Internet of Things (IoT). In this work, a survey has been made to examine its performance in the smart industries and hospitals. In addition, the hospital can improve its existing care procedures by leveraging data, operating in an environment to build around connected assets and people. With these devices, clinicians can locate patients more quickly, reduce costs, and save lives in emergency situations. Manufacturing plants and mobile industries can use a LoRaWAN network for data relay that can be analyzed and optimized for the business.
Investigation of Frequency Controlled Electric Drive with Control Signal Limitation by the Frequency Controller

The paper is concerned with the automatic electric drive characteristics taking into account its coordinate limitations, in particular, frequency converter input voltage. A model developed in MATLAB environment in SIMULINK application was used to carry out the research work. The model includes a frequency converter, an asynchronous motor and a speed control system with vector control where the variables are represented both in fixed and rotating coordinate systems. To analyse the electric drives with vector control, the method for structural modeling was used. The model takes into account the voltage limitation at the current controller output in the speed control system because it, in fact, limits the amplitudes of input three-phase voltages in the frequency converter. Automatic electric drive dynamics is studied in the acceleration and load surge modes. When the changes in the electric drive resistance moment are significant, the increase in the voltages at the current controller output is possible, which exceeds the voltage capacity for the frequency converter input. In this case, the speed control system opens and causes electric drive malfunction. In order to adjust the drive operation in this mode, it was proposed to decrease the voltage set point at the input of the flux linkage controller. The simulation results confirm the efficiency of this solution.
The penultimate form of approximation to normal extremes

Let Yn denote the largest of n independent N(0,1) random variables. It is shown that the error in approximating the distribution of Yn by the type III extreme value distribution exp {– (–Ax + B) k }, k > 0, is uniformly of order (log n)–2 if and only if the constants A, B and k satisfy certain conditions. In particular, this holds for the penultimate form of Fisher and Tippett (1928). Furthermore, two sufficient conditions are given so that these results can be extended to a stationary Gaussian sequence.
Novel immune cell infiltration-related biomarkers in atherosclerosis diagnosis

Background Immune cell infiltration (ICI) has a close relationship with the progression of atherosclerosis (AS). Therefore, the current study was aimed to explore the role of genes related to ICI and to investigate potential mechanisms in AS. Methods Single-sample gene set enrichment analysis (ssGSEA) was applied to explore immune infiltration in AS and controls. Genes related to immune infitration were mined by weighted gene co-expression network analysis (WGCNA). The function of those genes were analyzed by enrichment analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The interactions among those genes were visualized in the protein-protein interaction (PPI) network, followed by identification of hub genes through Cytoscape software. A receiver operating characteristic (ROC) plot was generated to assess the performance of hub genes in AS diagnosis. The expressions of hub genes were measured by reverse transcription quantitative real-time PCR (RT-qPCR) in human leukemia monocyticcell line (THP-1) derived foam cells and macrophages, which mimic AS and control, respectively. Results We observed that the proportions of 27 immune cells were significantly elevated in AS. Subsequent integrative analyses of differential expression and WGCNA identified 99 immune cell-related differentially expressed genes (DEGs) between AS and control. Those DEGs were associated with tryptophan metabolism and extracellular matrix (ECM)-related functions. Moreover, by constructing the PPI network, we found 11 hub immune cell-related genes in AS. The expression pattern and receiver ROC analyses in two independent datasets showed that calsequestrin 2 (CASQ2), nexilin F-Actin binding protein (NEXN), matrix metallopeptidase 12 (MMP12), C-X-C motif chemokine ligand 10 (CXCL10), phospholamban (PLN), heme oxygenase 1 (HMOX1), ryanodine receptor 2 (RYR2), chitinase 3 like 1 (CHI3L1), matrix metallopeptidase 9 (MMP9), actin alpha cardiac muscle 1 (ACTC1) had good performance in distinguishing AS from control samples. Furthermore, those biomarkers were shown to be correlated with angiogenesis and immune checkpoints. In addition, we found 239 miRNAs and 47 transcription factor s (TFs), which may target those biomarkers and regulate their expressions. Finally, we found that RT-qPCR results were consistent with sequencing results.
Secondary Motion of Non-Spherical Particles in Gas Solid Flows

Objective of this study is to investigate the effect of secondary motion of particles in multiphase gas-solid flows parametrically and test the relative impacts of particle shape and orientation information on particle distribution. For that purpose, predictive accuracies of simplified drag coefficient models are assessed for the conditions relevant to a wood recovery plant operating at dilute flow regime. After demonstrating the strong impact of the shape and orientation information on the force balance for single particles, we compared the steady state Eulerian-Lagrangian simulation results for particle volume fractions, residence times and particle diameter distributions within the chamber for different (i) superficial gas velocities (5 m/s, 7.5 m/s), (ii) orientation tendencies and (iii) particle shapes. Transient simulations are performed until the system reaches steady state conditions by monitoring the mass flow rates of the particulate phases leaving the chamber. The secondary motion of non-spherical particles is represented by stochastic sampling from the available experimental data. Analysis of the force balance on single particles revealed log-scale variations if the orientation of the particles with respect to flow fluctuates. Variations in the single particle force balances are found to be still visible in the CFD analysis, where the secondary motion of particles drastically changed the particle distribution in the chamber. The native non-spherical model which only accounts for the shape correction was found to over-predict the entrainment, leading to a significantly different particle volume fraction and diameter distributions. Spherical particle assumption also caused significant errors in the particle distribution, which increases as aspect ratio of the cylindrical particle diverges from one. Results show that particle orientation statistics are extremely important to capture the particle mixing and segregation patterns at dilute regime, which cannot be captured with such simplifying assumptions.
YOLOv7 and DeepSORT for Intelligent Quail Behavioral Activities Monitoring

The use of modern technology, such as Artificial Intelligence (AI), for livestock farming applications continue to grow. Computer Vision (CV) enables remote, real-time, and noninvasive observation and monitoring of animals by employing detection and tracking algorithms. The integration of Deep Learning (DL) enhances the ability of computers to analyze, interpret and infer the information received. In this study, an Intelligent quail activity monitoring system (I-QAMS) is introduced. It provides detection and monitoring of quail feeding pattern and locomotion activities reared in a cage. In addition, it estimates the activeness based on the level of agility of the poultry animal. These parameters can be used as indicators to assess animal health. YOLOv7, a DL-based single stage detector, is employed for detection and classification of behavioral activities. The DeepSORT algorithm, an algorithm utilizing Kalman filter, Hungarian Algorithm, and Convolutional Neural Network (CCN), is employed for tracking. To evaluate the bird’s movement, the displacement of the centroid is determined using Euclidean distance. A 500-frame video sequence, approximately 20 seconds in length, is utilized for the agility assessment. The YOLOv7 achieved a mAP of 85.28 in training, and an accuracy of 90.49 on test scenes. The results show the potential of CV- and DL-based detectors and trackers such as YOLO and DeepSORT for poultry behavior monitoring and estimation of the animal’s welfare.
A Fast Loss Model for Cascode GaN-FETs and Real-Time Degradation-Sensitive Control of Solid-State Transformers

This paper proposes a novel, degradation-sensitive, adaptive SST controller for cascode GaN-FETs. Unlike in traditional transformers, a semiconductor switch’s degradation and failure can compromise its robustness and integrity. It is vital to continuously monitor a switch’s health condition to adapt it to mission-critical applications. The current state-of-the-art degradation monitoring methods for power electronics systems are computationally intensive, have limited capacity to accurately identify the severity of degradation, and can be challenging to implement in real time. These methods primarily focus on conducting accelerated life testing (ALT) of individual switches and are not typically implemented for online monitoring. The proposed controller uses accelerated life testing (ALT)-based switch degradation mapping for degradation severity assessment. This controller intelligently derates the SST to (1) ensure robust operation over the SST’s lifetime and (2) achieve the optimal degradation-sensitive function. Additionally, a fast behavioral switch loss model for cascode GaN-FETs is used. This proposed fast model estimates the loss accurately without proprietary switch parasitic information. Finally, the proposed method is experimentally validated using a 5 kW cascode GaN-FET-based SST platform.
Modular Technology of Definite Multiple Integrals Calculation: Analytical Analysis and Experimental Verification of Efficiency

Analyzed is a new approach to the use of enumeration procedures for calculation of definite multiple integrals. It is shown that the effectiveness of this approach depends on the following factors: on requirements for the accuracy of calculations, on complexity of the integrand and on the number of variables of the problem to be solved. Presented are the conditions that allow us to adapt the modular enumeration algorithms in relation to the numerical parameters, characterizing these factors. The aim of adaptation is to minimize the search time. Analytical analysis of the comparative efficiency of brute force and modular enumeration algorithms for calculating multiple definite integrals leads to the following results: 1. The optimal number of modules is really equal to two. This result coincides with the optimal search mode by modular enumeration of globally optimal solutions to discrete programming problems. 2. The gain in time η in the time of calculating a definite integral by modular technology compared with the time of calculating the same definite integral by brute force exceeds value of the number of variables n. 3. The greater is the number of variables n and the greater is the complexity of the integrand, the greater is the gain in computation time. Experimental verification of the analytical results obtained confirms their validity.
Using the Indian MST Radar Noise-Level Observations of Stratosphere-Troposphere Replace were made over the tropical station Gadanki

The mesosphere stratosphere-troposphere radar at Gadanki was used to record the climates of stratosphere disturbance into the tropospheric through the Indian Summer Monsoon. The existence of an enhanced signal-to-noise ratio of 3 dB and measurement powers complete supernatural size of greater than 0.2 ms-1 in the locality of the troposphere through the Indian Summer Monsoon is the most important and novel observation. From April to June, downdrafts (updrafts) predominate near the tropopause (July to March). The ten years of ozone dimensions from the five years of in-situ ozonesonde explanation also support the outcome of stratosphere air intrusion. A thorough analysis reveals that the tropical easterly jet's turbulence near the tropopause, horizontal advection, and troposphere height changeability is the leading candidate for ozone ship from the stratospheric to the tropospheric. In contrast, during the ISM, the tropical troposphere heat has an important impact on the carrying of water steam from the tropopause to the stratopause. The idea of the present revision is to use Indian MST radar to qualitatively construct the climate of these replace processes over Gadanki.
Modelling Rarefied Hypersonic Reactive Flows Using the Direct Simulation Monte Carlo Method

This paper presents the implementation, validation and application of TCE (total collision energy) model for simulating hypersonic reactive flows in a parallel direct simulation Monte Carlo code, named PDSC++, using an unstructured grid. A series of benchmarking test cases, which include reproduction of theoretical rate constants in a single cell, 2D hypersonic flow past a cylinder and 2D-axisymmetric hypersonic flow past a sphere, were performed to validate the implementation. Finally, detailed aerothermodynamics of the flown reentry Apollo 6 Command Module at 105 km is simulated to demonstrate the powerful capability of the PDSC++ in treating realistic hypersonic reactive flow at high altitude. AMS subject classifications: 76P05
Chemical information extraction from scanning electron microscopy images on the basis of image recognition

Traditional resist materials have faced challenges as the extreme ultraviolet (EUV) light source with a wavelength of 13.5 nm brought the evolution of lithography to the semiconductor industry. A significant issue in the development of resist materials or the discovery of new type resists is that numerous parameters involved in the resist pattern printing process cause the generation of defects. Meanwhile, the inherent chemical variation in resist materials and processes causes the stochastic defects. In addition, the stochastic defects caused by the inherent chemical variation in resist materials and processes become increasingly significant as feature scales continue to shrink. Consequently, the number of pattern data with failures is much greater than those without defects. However, by utilizing the information contained in pattern failures, chemical parameters can be adjusted to improve resist resolution. In this study, a new method is proposed for evaluating resist patterns with defects by fitting the experimental scanning electronic microscopy (SEM) images of line-and-space patterns with defects to simulated images.
Joint service placement and user assignment model in multi‐access edge computing networks against base‐station failure

Multi‐access edge computing (MEC) enables users to exploit the resources of cloud computing at a base station (BS) in proximity to the users where an MEC server is hosted. While we have advantage of being able to communicate with low latency and small network load in MEC networks, the resources in BSs are limited. One challenge is where to provide users with services from to make efficient use of resources. Furthermore, to enhance the reliability of MEC system, the case that a BS fails needs to be considered. This paper proposes a service placement and user assignment model with preventive start‐time optimization against a single BS failure in MEC networks. The proposed model preventively determines the service placement and user assignment in each BS failure pattern to minimize the worst‐case penalty which is the largest penalty among all failure patterns. We formulate the proposed model as an integer linear programming problem. We prove that the considered problem is NP‐hard. When the problem size becomes large, it may not be solved in a practical computation time. To solve larger size problems, we introduce two algorithms: one is the greedy algorithm with allocation upgrade and the other is with allocation upgrade and preemption. The results show that the introduced algorithms obtain solutions with smaller worst‐case penalty than the benchmark in a practical time.