Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable and Efficient URLLC Resource Allocation Based on Offline Graph Reinforcement Learning

Deep reinforcement learning (RL) has emerged as a transformative technology for addressing intricate radio resource allocation problems in both 5G and 6G networks. Nevertheless, during the training phases of RL-based resource allocation algorithms, online RL methods introduce potential risks as they necessitate continuous interaction with the environment. Motivated by the fact that the historical data of the resource allocation process in the base station (BS) reveals the operation rules of the mobile network dynamics, this paper proposes a novel data-driven framework for resource allocation optimization, termed Offline Graph Reinforcement Learning (OGRL). Different from existing intelligent resource allocation schemes, which acquire policies by interacting either with wireless network simulators or real-world networks, the proposed OGRL framework exclusively leverages historical data from BS to train a high-performance resource allocation policy. Furthermore, by harnessing the capabilities of the graph neural networks (GNNs) at processing intricate data structures, OGRL can adeptly manage dynamic network environments characterized by continuously changing active users. Extensive experimental results substantiate that, following training with historical data, the proposed OGRL attains Quality of Service (QoS) and packet loss rates comparable to those of the best online algorithms. Furthermore, OGRL exhibits excellent scalability and generalization capabilities.
5-Lipoxygenase and 5-Lipoxygenase-Activating Protein Gene Polymorphisms, Dietary Linoleic Acid, and Risk for Breast Cancer

The n-6 polyunsaturated fatty acid 5-lipoxygenase pathway has been shown to play a role in the carcinogenesis of breast cancer. We conducted a population-based case-control study among Latina, African-American, and White women from the San Francisco Bay area to examine the association of the 5-lipoxygenase gene (ALOX5) and 5-lipoxygenase-activating protein gene (ALOX5AP) with breast cancer risk. Three ALOX5AP polymorphisms [poly(A) microsatellite, −4900 A>G (rs4076128), and −3472 A>G (rs4073259)] and three ALOX5 polymorphisms [Sp1-binding site (-GGGCGG-) variable number of tandem repeat polymorphism, −1279 G>T (rs6593482), and 760 G>A (rs2228065)] were genotyped in 802 cases and 888 controls. We did not find significant main effects of ALOX5 and ALOX5AP genotypes on breast cancer risk that were consistent across race or ethnicity; however, there was a significant interaction between the ALOX5AP −4900 A>G polymorphism and dietary linoleic acid intake (P = 0.03). Among women consuming a diet high in linoleic acid (top quartile of intake, >17.4 g/d), carrying the AA genotype was associated with higher breast cancer risk (age- and race-adjusted odds ratio, 1.8; 95% confidence interval, 1.2-2.9) compared with carrying genotypes AG or GG. Among women consuming ≤17.4 g/d of linoleic acid, ALOX5AP −4900 genotype was not associated with breast cancer risk (age- and race-adjusted odds ratio, 0.9; 95% confidence interval, 0.7-1.2). These results support a role for n-6 polyunsaturated fatty acids in breast carcinogenesis and suggest that epidemiologic studies on dietary fat and breast cancer should take into account genetic predisposition related to n-6 polyunsaturated fatty acid metabolism. (Cancer Epidemiol Biomarkers Prev 2008;17(10):2748–54)
Interactive Trajectory Planning of Autonomous Vehicle Based on State Lattice and Potential Field

In order to solve the problem of trajectory planning in complex traffic scenarios, such as falling into local optimization and generating trajectory machinery, it will lead to low acceptance degree of users. We propose an interactive trajectory planning framework based on the combination of state lattice and potential field. The lattice search method is used to discretization the driving environment and reduce the solution space. At the same time, by constructing an interactive road environment potential field, considering changes in traffic participants' intentions, the trajectory is updated in a timely manner, and the trajectory is optimized based on the scene. This article conducts real-world experiments in various challenging and complex scenarios, and the experimental results verify that the trajectory planner can generate safe, comfortable, and flexible trajectories with high timeliness.
Online speech source separation based on maximum likelihood of local Gaussian modeling

We propose an online speech source separation method which can separate sources under underdetemined conditions. The proposed method is based on local Gaussian modeling (LGM). At first, we derive an extended approach of conventional offline speech source separation methods based on LGM, which can separate speech sources in an online manner. The likelihood function of the online LGM based approach (OLGM) is approximately maximized by incremental EM based approach. Additionally, we propose an initialization method of OLGM based on a least squares approach to improve convergence time. Experimental results show that the proposed method can separate sources effectively even when the number of iterations is small.
Stochastic Powered Descent Guidance with Atmospheric Drag and Control Chance Constraint

This paper presents a convex programming approach for random disturbed fuel-optimal powered descent landing in the presence of aerodynamic drag, which is modeled as a chance-constrained covariance control problem. The main challenges for the problem include the nonlinear stochastic dynamics and the nonconvex chance constraints. For stochastic dynamics, successive linearization is applied to eliminate the influence of nonlinearities, and the propagation of the first two moments of stochasticity is obtained by linear covariance analysis. For the nonconvex chance constraints, a conservative approximation is applied to convert chance constraints into deterministic form, and the lossless convexification approach for the deterministic system is employed to convexify the constraints. An iterative covariance steering algorithm is summarized to handle the stochastic fuel-optimal trajectory. In numerical simulation, a Mars' powered descent guidance is exhibited as an example, which shows that the proposed optimal closed loop guidance can achieve the desired landing site with predefined accuracy and satisfy the related control constraint.
LookTwice: An Online Assembly Action Recognition Method Based on Deep Learning

In automotive production, ensuring the safe assem-bly of engines is critical. Currently, in industrial production environments, deep learning techniques are mostly used for surface defect detection, which can only play a post-control role and cannot detect problems during the production process. We propose a deep learning-based process detection method. We constructed a spatiotemporal action detection model that can detect whether the workers have properly installed the engine according to the procedural standard. This model aims to reduce the problems of misalignment and missing parts caused by relying on manual engine assembly, and to reduce non-standard assembly behavior of products, thereby further reducing manufacturing costs and improving the yield rate. Based on a limited training dataset, this project successfully applied the model to the recognition of engine assembly actions at the automotive engine workstation. Under the current environment, the model exhibits good generalization ability and can achieve low-latency online detection at 15fps on a single Quadro GPI00 graphics card.
Low Latency Self-Interference Suppression in In-Band Full-Duplex Relay

This paper reports on work in progress on a self-interference (SI) suppression method for in-band full-duplex (IBFD) relaying. We propose to implement the two-stage SI suppression method where feedforward linear finite impulse response (FIR) filters are designed in the digital domain considering the processing delay and error vector magnitude (EVM). While the analog SI cancellation (ASIC) filter coefficient is determined based on the least square (LS) estimator, the digital SI cancellation (DSIC) filter is designed by optimizing the estimation error as well as the filter length. Simulation results show that the proposed method can significantly suppress SI while reducing the processing delay at the relay.
Tsunami Hazard Assessment of the Black Sea Coast in the Regions of the Planned Coming Out of the Russia–Turkey Gas Pipelines

The results of numerical simulation of tsunami waves in the Black Sea basin are represented for assessing possible tsunami hazards for the coastal zone in the points where the planned underwater parts of the Russia–Turkey gas pipelines (“Turkish Stream” and “Blue Stream – 2”) enter the sea and come out to the coast. Numerical simulation of the tsunami source generation and tsunami wave propagation in the Black Sea is carried out in two scenarios for 7 seismic sources. To assess potential tsunami hazard, each of the performed calculations implies modeling of the seismic sources in the basin regions (with due regard to the character of their geodynamic faults and structures) where the pipelines can possibly come out to the coast. Synthetic tide gauges along the coastline are used to analyze the results of the calculations. The simulation is done within the framework of the earthquake key-board model which takes into account the zones of active faults. For each model, the characteristics of the wave fields and the direction of the most intense propagation of the wave fronts are obtained and analyzed at the selected time points. The tsunami wave maximum heights are estimated for the places where the gas pipelines come out to the coast and enter the sea both on the Russian and Turkish coasts. It is shown that at moderate earthquakes, hazard of tsunami wave impact upon laying the gas pipelines along the considered directions is insignificant. At the same time, at stronger earthquakes, the danger of the coastal infrastructure destruction is real. Thus, the drawn conclusion implies strong necessity in the detailed tsunami zonation of the coast and marking out the local sections where the planned gas pipelines enter the sea and come out to the coast.
High Speed Digitizer for a Contactless Potentiometric Displacement Sensor

This work presents a high-speed digitizer circuit for interfacing a contactless potentiometric displacement sensor (CPOT) which is simple to realize. The circuit comprises a dual-slope integrator and a comparator that work together to produce a digital time sequence output signal. The feedback of the integrator consists of a T-Network which is realized using three branches of the CPOT. The small value air capacitor of the wiper $(5~pF$) of CPOT is used for integration operation, so it requires a small measurement time $(0.70~{ms})$. The other two branches ($R_{2}$ and $R_{3}$) of the CPOT are switched between the ground and virtual ground of the integrator during the charging and discharging of the capacitor respectively. Avoiding the use of switched capacitor technique in the circuit leads to a reduced number of switches, eliminating the need for a clock signal, and simplifies the circuit significantly. The CPOT used for simulation is $100~\mathrm{k}\Omega$ which is varied in a step change of $5~\mathrm{k}\Omega$. The circuit exhibits an average error of ± 0.64%.
Modularity of the rodent mandible: Integrating bones, muscles, and teeth

Summary Several models explain how a complex integrated system like the rodent mandible can arise from multiple developmental modules. The models propose various integrating mechanisms, including epigenetic effects of muscles on bones. We test five for their ability to predict correlations found in the individual (symmetric) and fluctuating asymmetric (FA) components of shape variation. We also use exploratory methods to discern patterns unanticipated by any model. Two models fit observed correlation matrices from both components: (1) parts originating in same mesenchymal condensation are integrated, (2) parts developmentally dependent on the same muscle form an integrated complex as do those dependent on teeth. Another fits the correlations observed in FA: each muscle insertion site is an integrated unit. However, no model fits well, and none predicts the complex structure found in the exploratory analyses, best described as a reticulated network. Furthermore, no model predicts the correlation between proximal parts of the condyloid and coronoid, which can exceed the correlations between proximal and distal parts of the same process. Additionally, no model predicts the correlation between molar alveolus and ramus and/or angular process, one of the highest correlations found in the FA component. That correlation contradicts the basic premise of all five developmental models, yet it should be anticipated from the epigenetic effects of mastication, possibly the primary morphogenetic process integrating the jaw coupling forces generated by muscle contraction with those experienced at teeth.
A Lightweight YOLO V5 Method for Detecting Thermal Spot Defects in PV Panels in Infrared Images

As an important component of photovoltaic power generation, PV panels play a crucial role in the photovoltaic power generation industry. In order to overcome the current problem of low speed and accuracy in detecting hot spot faults of PV panels in photovoltaic power plants, this paper proposes a lightweight YOLO V5 model to realize the detection of hot spot defects of PV panels in infrared images. First, data enhancement is performed on the existing dataset so as to increase the number of samples i n the database and enhance the generalization ability of the model. Second, a lightweight network structure Shufflenet v2 is introduced to replace the YOLO V5 backbone network to enhance the speed of detection. Finally, the attention mechanism is combined with the neck network to enhance the extraction of hot spot features. It can effectively balance the network's speed and accuracy. The experimental results show that the improved network achieves recognition accuracy of 93.5% for hot spots, which is 4.4% higher compared to the original network.
Identification of Skin Conditions with Convolutional Neural Networks: A Deep Learning Approach

This paper presents a robust approach for detecting skin diseases using advanced image detection methods supported by Convolutional Neural Network (CNN) models. Traditionally, skin diseases are diagnosed by experienced dermatologists. However, due to a scarcity of these specialists, there is a pressing need for alternative, reliable solutions. Our methodology capitalizes on image detection techniques to accurately and efficiently identify potential skin diseases from photographs. The cornerstone of our approach is the CNN model, which is renowned for its efficacy in processing visual data. The system we developed demonstrates a high level of accuracy, achieving a validation accuracy of at least 80 % in the identification of specific skin diseases. This level of precision is particularly noteworthy, considering the inherent challenges in diagnosing skin conditions through images. Despite this success, our research also acknowledges the difficulties in gathering a vast, diverse dataset of skin disease images from the internet. Such datasets are crucial for training and refining our model to ensure its effectiveness across various skin types and conditions. Furthermore, this paper investigates the factors that might lead to inaccuracies in predictions. It addresses common issues such as variations in image quality, lighting conditions, and the presence of similar-looking skin conditions, which can all potentially confound the model's decision-making process. By understanding and mitigating these factors, we aim to enhance the model's reliability and accuracy, moving closer to bridging the gap between automated detection and expert human diagnosis. This research not only contributes to the field of medical image analysis but also offers a promising tool for assisting dermatologists and improving patient outcomes in skin disease diagnosis.
Language Homogeneity in the Japanese Wikipedia

Wikipedia is a potentially very useful source of information, but intuitively it is difficult to have confidence in the quality of an encyclopedia that anyone can modify. One aspect of correctness is writing style, which we examine in a computer based study of the full Japanese Wikipedia. This is possible because Japanese is a language with clearly distinct writing styles using e.g., different verb forms. We find that the writing style of the Japanese Wikipedia is largely consistent with the style guidelines for the project. Exceptions appear to occur primarily in articles with a small number of changes and editors.
Air Change Rates and Interzonal Flows in Residences, and the Need for Multi-Zone Models for Exposure and Health Analyses

Air change rates (ACRs) and interzonal flows are key determinants of indoor air quality (IAQ) and building energy use. This paper characterizes ACRs and interzonal flows in 126 houses, and evaluates effects of these parameters on IAQ. ACRs measured using weeklong tracer measurements in several seasons averaged 0.73 ± 0.76 h−1 (median = 0.57 h−1, n = 263) in the general living area, and much higher, 1.66 ± 1.50 h−1 (median = 1.23 h−1, n = 253) in bedrooms. Living area ACRs were highest in winter and lowest in spring; bedroom ACRs were highest in summer and lowest in spring. Bedrooms received an average of 55 ± 18% of air from elsewhere in the house; the living area received only 26 ± 20% from the bedroom. Interzonal flows did not depend on season, indoor smoking or the presence of air conditioners. A two-zone IAQ model calibrated for the field study showed large differences in pollutant levels between the living area and bedroom, and the key parameters affecting IAQ were emission rates, emission source locations, air filter use, ACRs, interzonal flows, outdoor concentrations, and PM penetration factors. The single-zone models that are commonly used for residences have substantial limitations and may inadequately represent pollutant concentrations and exposures in bedrooms and potentially other environments other where people spend a substantial fraction of time.
Unlinkability of Biometric Templates Using Hierarchical Deterministic Wallets

Biometric authentication is widely used but raises privacy and security concerns due to the vulnerability of biometric templates stored in centralized databases. To protect biometric templates, the ISO/IEC 24745 standard established two requirements: irreversibility and unlinkability. While most existing approaches compromise unlinkability, we propose a novel approach to achieving both requirements using HD wallets to change the password hash stored in the database periodically. This approach ensures that compromised biometric templates cannot be used for future authentication, protecting user privacy and security. Our approach offers a high level of security and privacy while maintaining usability for users. By periodically changing the password hash, we ensure that even if an attacker gains access to a user's biometric template, it cannot be used for future authentication, reducing the risk of unauthorized access to sensitive information.
An Expert System for Diagnosis of Cancer Diseases

Traditional ways of diagnosing patients can take a lot of time and are sometimes undependable thus computerized technologies could be employed for more accurate and faster analysis. The third most diagnosed cancer in the world is a cancer called colorectal cancer (CRC). CRC results from unrestrained cell growth in sections of the large intestine, particularly the colon or rectum. Early diagnosis of CRC is important for the survival of the patient since it can result from changes in lifestyle and increase in age of the patient. The MATLAB Image Processing toolbox together with the QUPATH software was utilized for image preprocessing, segmentation and feature extraction. Image were extracted for fine tuning the Visual Geometry Group-16 (VGG16) Convolutional Neural Network (CNN), which was pretrained on the ImageNet database thus enabling it to learn domain specific features necessary to classify the whole slide images. The concluding evaluation notes that the ground truth annotations are not ideal and thus the ability of deep learning to overcome issues in the quality of the data is verified. While due to the nature of the domain, deep learning techniques may never be suited to replace the expertise of practicing pathologists, they promise to aid in tasks which can be tedious, painstaking and subject to a degree of error and subjectivity. "
An Improved Method to Measure, Characterize, and Model Microstrip Antennas in the W Band

The rapid evolution of wireless technology demands an ever-growing number of connected devices, and hence, a greater availability of antennas for a gamut of applications in different frequency ranges [1]–[3]. The size, materials, and geometry of antennas, however, depend on the application as well as on important considerations, including resonant frequency, bandwidth, gain, efficiency, shape of the radiation pattern, input impedance, and many more [4]. Hence, a universal measuring technique for antennas cannot be derived, and in general, each case requires that it be treated individually. In addition, as frequency of measurement increases, more effects must be taken into account when interpreting data; one of relevance is that introduced by the surface roughness at the interface between the printed circuit board (PCB) substrate and the metal foil that serves as a conductor [5]–[6]. In fact, the surface roughness of the copper foil is necessary to achieve good adherence to the dielectric substrate, and thus, it is voluntarily included in the manufacturing process. Unfortunately, the variation of the metal surface from the ideal smooth case increases the resistance of the foil at microwave frequencies. In consequence, it negatively impacts the electrical performance of structures and should be considered when assessing the response of antennas on PCB.
Bayesian Computation in Recurrent Neural Circuits

A large number of human psychophysical results have been successfully explained in recent years using Bayesian models. However, the neural implementation of such models remains largely unclear. In this article, we show that a network architecture commonly used to model the cerebral cortex can implement Bayesian inference for an arbitrary hidden Markov model. We illustrate the approach using an orientation discrimination task and a visual motion detection task. In the case of orientation discrimination, we show that the model network can infer the posterior distribution over orientations and correctly estimate stimulus orientation in the presence of significant noise. In the case of motion detection, we show that the resulting model network exhibits direction selectivity and correctly computes the posterior probabilities over motion direction and position. When used to solve the well-known random dots motion discrimination task, the model generates responses that mimic the activities of evidence-accumulating neurons in cortical areas LIP and FEF. The framework we introduce posits a new interpretation of cortical activities in terms of log posterior probabilities of stimuli occurring in the natural world.
A Multiple Kernel Minimum Entropy Kalman Filter

In this paper, a multiple kernel minimum error entropy Kalman filter is proposed to estimate the filter state under abnormal measurement pollution. Firstly, an augmented Kalman filter model is constructed, and the minimum mean square error criterion under the traditional Kalman filter is replaced by the traditional minimum error entropy criterion. Then the quadratic rational kernel function and Gaussian kernel function are combined to construct the cost function, and the state results are obtained by fixed point iteration. Finally, the effectiveness of the filter is proved by a master-slave inertial navigation transfer alignment experiment.