Maximum Probability Research Articles

Nazofarenks karsinomu olan hastalarda metastatik lenf nodlarının saptanmasında manyetik rezonans görüntüleme doku analizi

Abstract Aims: To investigate the role of magnetic resonance imaging (MRI) texture analysis (TA) in the detection of metastatic lymph nodes in patients with nasopharyngeal carcinoma (NPC). Material and methods: Between January 2020 and October 2021, 15 NPC patients with 32 metastatic lymph nodes and 30 healthy subjects with benign lymph nodes were included in the study. The texture features compared between metastatic and benign lymph nodes. The independent predictor parameters of metastatic lymph nodes were determined using multivariate regression analysis. Receiver operator characteristics (ROC) analysis was used to evaluate the diagnostic performance of the regression models. Results: The first order texture features did not differ significantly between groups (p>0.05). Except for correlation in metastatic lymph nodes, all gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) features were significantly different (p<0.05). The GLCM features of joint entropy, joint energy, and maximum probability; and the GLRLM features of gray level non uniformity and low gray level run emphasis were independent predictors of metastatic lymph nodes. The area under the curve (AUC) values for the GLCM regression model and GLRLM regression model were 0.975 and 0.928, respectively. Conclusion: MRI texture analysis may be useful to detect metastatic lymph nodes in patients with NPC by providing quantitative information on tissue heterogeneity and cellular composition.

A Study on Dam Break Simulation considering Continuous Rainfall Events

In recent years, flood damage resulting from continuous rainfall events has been on the rise. However, existing Emergency Action Plans (EAPs) addressing reservoir or dam failure only consider Probable Maximum Precipitation (PMP), which represents a single rainfall event. This study aims to address this limitation by simulating a dam break scenario caused by continuous rainfall events. The Chungju dam watershed was selected as the study area and a mega rainfall event scenario, involving continuous 500-year frequency rainfall intensity events, was defined. Next, the flood discharge resulting from the mega rainfall event scenario was estimated using the Consecutive Storm Event Based (ConSEB) model. The Simplified Dam Break (SMPDBK) model was employed to simulate the dam break. The flood discharge estimated for this scenario amounted to 31,080.34 cms, which is about 1.2 times larger than the Probable Maximum Flood (PMF). The dam break water level simulated by the SMPDBK model was 150 m and the flood damage area was estimated to be 307.45 km2. Based on the results of the study, we highlighted the need to consider continuous rainfall events within EAPs.

Estimation of Probable Maximum Flood by Flood Frequency Analysis at Tiuni Barrage Site on River Tons

Estimation of Probable Maximum Flood by Flood Frequency Analysis at Tiuni Barrage Site on River Tons

Qualitative reachability for open interval Markov chains.

Interval Markov chains extend classical Markov chains with the possibility to describe transition probabilities using intervals, rather than exact values. While the standard formulation of interval Markov chains features closed intervals, previous work has considered open interval Markov chains, in which the intervals can also be open or half-open. In this article we focus on qualitative reachability problems for open interval Markov chains, which consider whether the optimal (maximum or minimum) probability with which a certain set of states can be reached is equal to 0 or 1. We present polynomial-time algorithms for these problems for both of the standard semantics of interval Markov chains. Our methods do not rely on the closure of open intervals, in contrast to previous approaches for open interval Markov chains, and can address situations in which probability 0 or 1 can be attained not exactly but arbitrarily closely.

Refined evaluation methods for preventive maintenance of project-level asphalt pavement based on confusion-regression model

Accurate evaluation of the performance of airport pavement can help managers make scientific and systematic preventive maintenance (PM) decisions, ensuring pavement safety and extending service life. To this end, firstly, this paper sorted out the pavement performance evaluation index system, based on which a multi-dimensional evaluation system for PM was proposed, including control indexes, macroscopic indexes, and microscopic indexes. Then, the pavement decay model based on the pavement condition index (PCI) was developed, and the regression model between PCI and International roughness index (IRI) and friction coefficient (μ) was established. Finally, by converting the PM decisions for project-level pavement into a binary classification problem in machine learning, the optimal maintenance thresholds and intervals for PM of each index were determined using receiver operating characteristic (ROC) curve and Kolmogorov-Smirnov (K-S) curve. The optimal threshold represents the threshold when the index can separate the binary classification problem (PM decisions) with maximum probability. Based on confusion-regression model, the pavement performance can be evaluated more comprehensively, and the timing of PM can be accurately determined. The model helps airport managers develop accurate PM plans to address future maintenance needs.

SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural Machine Translation

Sub-word segmentation is an essential pre-processing step for Neural Machine Translation (NMT). Existing work has shown that neural sub-word segmenters are better than Byte-Pair Encoding (BPE), however, they are inefficient, as they require parallel corpora, days to train, and hours to decode. This article introduces SelfSeg, a self-supervised neural sub-word segmentation method that is much faster to train/decode and requires only monolingual dictionaries instead of parallel corpora. SelfSeg takes as input a word in the form of a partially masked character sequence, optimizes the word generation probability, and generates the segmentation with the maximum posterior probability, which is calculated using a dynamic programming algorithm. The training time of SelfSeg depends on word frequencies, and we explore several word frequency normalization strategies to accelerate the training phase. Additionally, we propose a regularization mechanism that allows the segmenter to generate various segmentations for one word. To show the effectiveness of our approach, we conduct MT experiments in low-, middle-, and high-resource scenarios, where we compare the performance of using different segmentation methods. The experimental results demonstrate that, on the low-resource ALT dataset, our method achieves more than 1.2 BLEU score improvement compared with BPE and SentencePiece, and a 1.1 score improvement over Dynamic Programming Encoding (DPE) and Vocabulary Learning via Optimal Transport (VOLT), on average. The regularization method achieves approximately a 4.3 BLEU score improvement over BPE and a 1.2 BLEU score improvement over BPE-dropout, the regularized version of BPE. We also observed significant improvements on IWSLT15 Vi→En, WMT16 Ro→En, and WMT15 Fi→En datasets and competitive results on the WMT14 De→En and WMT14 Fr→En datasets. Furthermore, our method is 17.8× faster during training and up to 36.8× faster during decoding in a high-resource scenario compared to DPE. We provide extensive analysis, including why monolingual word-level data is enough to train SelfSeg.

Estimating Typhoon-Induced Maximum Flood for Spillway Safety Assessment—Case Studies in Taiwan

Dam safety assessment is usually conducted regularly to investigate the risks associated with the dam and propose remedies to ensure effective reservoir operations. One crucial aspect of the evaluation involves performing hydrological analyses to determine if the existing spillway can successfully deliver the probable maximum flood (PMF) downstream. This study applied storm transposition and typhoon rainstorm methods for PMP estimations. The resulting PMP values were then used as input for runoff models to generate flood hydrographs for PMF determination. A modification for the storm transposition method was proposed to determine the barrier height considering the moisture inflow direction. In estimating the orographic rainfall in the typhoon rainstorm model, an effective terrain slope was suggested according to different windward directions based on DEM analysis. Shihmen Reservoir and Feitsui Reservoir, located in northern Taiwan, were used as examples to conduct the PMP and PMF analysis. The obtained PMPs were further compared with the results generated by Hershfield’s method to assess the reasonability of the estimation. The results show that the maximum deviation of the 24-h PMP values estimated by the three methods is within 30% in the Shihmen watershed and 16% in the Feitsui watershed. The PMF estimations in the two reservoir watersheds are larger than the design discharges of the existing spillways. Hence, extending the capacity of the existing spillways or constructing upstream bypasses to avoid catastrophic flooding downstream is required.

Unifying Token- and Span-level Supervisions for Few-shot Sequence Labeling

Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or span-level labeling models based on metric learning. However, these methods are only trained at a single granularity (i.e., either token-level or span-level) and have some weaknesses of the corresponding granularity. In this article, we first unify token- and span-level supervisions and propose a Consistent Dual Adaptive Prototypical (CDAP) network for few-shot sequence labeling. CDAP contains the token- and span-level networks, jointly trained at different granularities. To align the outputs of two networks, we further propose a consistent loss to enable them to learn from each other. During the inference phase, we propose a consistent greedy inference algorithm that first adjusts the predicted probability and then greedily selects non-overlapping spans with maximum probability. Extensive experiments show that our model achieves new state-of-the-art results on three benchmark datasets. All the code and data of this work will be released at https://github.com/zifengcheng/CDAP .

Raw EMG classification using extreme value machine

Electromyogram (EMG) signal is considered as an easy-to-capture (i.e. skin-mounted) and promissing biometric for the control of prosthetic hands. Despite the plethora number of researches on EMG-based control of prosthetics, two major challenges have not sufficiently been addressed , i.e. realtime classification, and robustness against noise. Our contribution in this paper is two fold. First, we have proposed a deep neural network (DNN) model with customized architecture to learn features directly from raw EMG data. In this model, a self-improvement module enhances the accuracy and robustness against artifact. Second, we utilize extreme value machine (EVM) for classification of the learnt feature. EVM models the statistical distribution of the latent space and classifies finger movements based on the maximum cumulative distribution probability to the fitted model. Our experimental results, using public domain EMG data, are very promising. They indicate the average accuracy of 98.5% for 750 msec window for 10-class classification. This result is superior or competitive compared with other online classifications reported in the literature on the same dataset. Additionally, our results illustrate that self-improvement feature learner (SIFL) is much more resistant against the noise than the models that employ engineered features. For example, in presence of severe white noise, the accuracy of our methodology degrades 10−15% compared to 30−50% of others.

Numerical simulation analysis of ecological monitoring of small reservoir dam based on maximum entropy algorithm

Abstract Displacement monitoring method of reservoir dam is a key research topic at present. In order to better display the overall efficiency of horizontal displacement and vertical displacement monitoring, a numerical simulation analysis method of ecological monitoring of small reservoir dam based on the maximum entropy algorithm is proposed. The virtual value is calculated by the maximum entropy algorithm, and the probability distribution function of random variables is obtained. The comprehensive prediction model of ecological monitoring results is constructed by the probability distribution function, and the daily monitoring values of ecological history of small reservoir dams are obtained. The maximum entropy probability density function is used to calculate the initial moment of small reservoir displacement samples, calculate the abnormal probability of the dam, get the maximum entropy probability density, realize the unbiased distribution of simulation values, and complete the dam deformation monitoring of small reservoirs. The simulation experiment is verified by numerical simulation. The results show that this method can effectively monitor the horizontal and vertical displacement of the dam; monitor the water-level hydrograph of pressure pipes at each measuring point; and obtain the changes of ecological runoff, temperature difference, and sediment discharge around the dam of small reservoirs in real time, which provides data guarantee for improving the ecological added value of small reservoirs.

Bell-state measurement exceeding 50% success probability with linear optics.

Bell-state projections serve as a fundamental basis for most quantum communication and computing protocols today. However, with current Bell-state measurement schemes based on linear optics, only two of four Bell states can be identified, which means that the maximum success probability of this vital step cannot exceed 50%. Here, we experimentally demonstrate a scheme that amends the original measurement with additional modes in the form of ancillary photons, which leads to a more complex measurement pattern, and ultimately a higher success probability of 62.5%. Experimentally, we achieve a success probability of (57.9 ± 1.4)%, a substantial improvement over the conventional scheme. With the possibility of extending the protocol to a larger number of ancillary photons, our work paves the way toward more efficient realizations of quantum technologies based on Bell-state measurements.

Experimental study on chloride ion diffusion behavior and microstructure in concrete under alternating ambient humidity conditions

The migration of chloride in concrete in atmospheric zones is mainly influenced by climatic conditions, e.g., various dry-wet cycles in marine tidal zones, and ambient humidity is one of the most important influences. Ambient humidity also determines the moisture content in concrete, which is necessary for the diffusion or transport of chloride in concrete and can enhance the migration of chloride in micropores, thus affecting the distribution of chloride in concrete. In this study, the effects of chloride diffusion and its mechanism, and the evolution of moisture in concrete under alternating drying-wetting cycles, were investigated using an environmental moisture simulation chamber. Chloride diffusion tests were also performed to evaluate the effects of different dry-wet ratios and numbers of drying-wetting cycles on the chloride diffusion depth, chloride content and saturation in concrete. The microstructure of concrete was studied and analyzed using XRD, SEM and MIP technology. The findings showed that the saturation of concrete decreased rapidly with increasing number of wet-dry cycles and finally stabilized. Under different alternating moisture conditions, the saturation of concrete was greater, especially when the wetting time was longer. Additionally, in areas near the surface of concrete (depths of 2 mm-6 mm), the chloride content was proportional to the number of wet-dry cycles. In the interior of concrete (depth > 6 mm), when the number of drying-wetting cycles was larger, the chloride concentration was smaller. However, as the number of wet-dry cycles increased, this led to the decalcification of C-S-H gels, which resulted in the reduction in the number of C-S-H reticular gels with good crystallinity. A pore analysis showed that the cumulative pore size and the maximum probability pore size of concrete gradually increased with increasing number of wet-dry cycles, the deterioration of pore structure was more obvious, and the compactness of concrete gradually decreased, which accelerated the migration of chloride in the matrix. Overall, this study can provide reliable and valuable test data for the development and design of high-performance concrete for applications in atmospheric marine tidal zones.

Geomorphic factors influencing the spatial distribution of eroded Chernozems in automated digital soil erosion mapping

Among the factors influencing the intensity of soil erosion by water, the relief sets the basic conditions for the occurrence of erosion-sedimentation processes: the geometry of the contact surface with water runoff and its primary spatial characteristics. We developed a model that describes the categories of the intensity of soil erosion by water only by geomorphic parameters using a DEM. The study was conducted in arable soils in the southeastern part of the Central Russian Upland, namely, typical Chernozems (TCh) in the forest-steppe zone and ordinary Chernozems (OCh) of the northern steppe of the temperate climate zone. Based on 1146 ground soil-erosion survey points, the relationship between the category of soil erosion intensity and terrain parameters (slope steepness, slope length and exposure, topographic position index, and slope profile curvature, etc.) was analyzed. The results of the revealed dependencies for the studied soils made it possible to develop prognostic models using ordinal logistic regression with an assessment of their accuracy. It was found that the model for OCh soils shows in all parameters a stronger relationship between geomorphic factors and the category of soil erosion intensity than the model for TCh soils. The regression model for OCh exceeds the model for TCh by 12 % in overall accuracy. For both soils, eroded areas are determined with much less accuracy (50–60 %) than non-eroded areas (80–95 %). Based on the modeling results, maps of soil erosion by water were constructed, where belonging to the category of soil erosion intensity was determined by the maximum probability. It is also shown that soil erosion intensity modeling based only on a set of geomorphic predictors is not inferior in accuracy to the conventional visual-expert cartographic method and can be a more objective and efficient alternative in automated digital soil erosion mapping.

Negation of a probability distribution: An information theoretic analysis

Yager (2014) proposed a transformation for negating the happening of an uncertain event which opposes the occurrence of any uncertain event by redistributing its probability equally among the other outcomes. Yager’s negation applied repeatedly on any probability distribution converges to the uniform distribution (all events having identical probability of occurrence). In most cases, uniform distribution is the maximum entropy probability distribution (MEPD) as it has maximum uncertainty inherent in it. However, if some information is available regarding the occurrence of events associated with a random experiment, then MEPD may or may not be a uniform distribution. In this work, we have first explored some new properties of Yager’s negation and then investigated Yager’s negation when we have some additional information about the probability distribution other than the natural constraints. The MEPD in the presence of this additional information is determined using the negation of probabilities. It is shown that the existence of MEPD (and as a result the maximum entropy) largely depends on the parameters of the additional constraints. Some numerical examples have been considered for comparing the MEPD with and without additional constraints.

Performance analysis and optimization of multiple IIoT devices radio frequency energy harvesting NOMA mobile edge computing networks

In this day and age, the Industrial Internet of Things (IIoT) has been considered to revolutionize industrial manufacturing by capturing and accessing massive data sources with incredible speed and efficiency than before. Combined with it, Mobile Edge Computing (MEC) is a comprehensive Digital Transformation tendency to solve the problems Cloud computing faces. However, the fundamental challenges of energy and latency make deploying IIoT MEC networks difficult. Accordingly, this paper considers the efficient design of time allocation for successful computation probability (SCP) maximization for multiple energy-constrained mobile devices (MD) and multiple antennas access point (AP) in uplink radio frequency energy harvesting (RF EH) non-orthogonal multiple access (NOMA) IIoT network. Specifically, multiple MDs need to receive the energy and compute support of a MEC server placed in a multiple antenna wireless AP to complete the task immediately. Accordingly, a four-phase communication protocol is proposed to ensure system performance. The system follows the cluster head (CH) scheme based on the channel state information (CSI) to harvest RF energy from the AP. To ensure the highest system performance, we propose two algorithms for determining the optimal EH time for two CHs: SCPM-GSS and SCPM-GA. In addition, we derive the closed-form expressions for the SCP of the system and each CH. Monte Carlo simulations are used to verify the results of the analysis. The numerical results demonstrate the effects of crucial system parameters of our proposed NOMA scheme with those of conventional orthogonal multiple access (OMA) schemes. Furthermore, the proposed optimization algorithms allow the system to avoid outages like the random parameters setting approach and improve the SCP by 3 to 30% compared to the fixed parameters set when the transmit power is low and medium.

Flexible structure learning under uncertainty.

Experience is known to facilitate our ability to interpret sequences of events and make predictions about the future by extracting temporal regularities in our environments. Here, we ask whether uncertainty in dynamic environments affects our ability to learn predictive structures. We exposed participants to sequences of symbols determined by first-order Markov models and asked them to indicate which symbol they expected to follow each sequence. We introduced uncertainty in this prediction task by manipulating the: (a) probability of symbol co-occurrence, (b) stimulus presentation rate. Further, we manipulated feedback, as it is known to play a key role in resolving uncertainty. Our results demonstrate that increasing the similarity in the probabilities of symbol co-occurrence impaired performance on the prediction task. In contrast, increasing uncertainty in stimulus presentation rate by introducing temporal jitter resulted in participants adopting a strategy closer to probability maximization than matching and improving in the prediction tasks. Next, we show that feedback plays a key role in learning predictive statistics. Trial-by-trial feedback yielded stronger improvement than block feedback or no feedback; that is, participants adopted a strategy closer to probability maximization and showed stronger improvement when trained with trial-by-trial feedback. Further, correlating individual strategy with learning performance showed better performance in structure learning for observers who adopted a strategy closer to maximization. Our results indicate that executive cognitive functions (i.e., selective attention) may account for this individual variability in strategy and structure learning ability. Taken together, our results provide evidence for flexible structure learning; individuals adapt their decision strategy closer to probability maximization, reducing uncertainty in temporal sequences and improving their ability to learn predictive statistics in variable environments.

Application of scaled boundary finite element method in failure probability estimation of square nanographene sheet

In recent years, so much attention has been drawn to the field of nano computational material science. Due to the extensive applications of nanographene sheets in various industries such as aerospace industry, fracture analysis of square nanographene sheets is of crucial importance. Stochastic analysis is widely applied in fracture mechanics because it helps the results to be more realistic. In the present work, probabilistic fracture analysis of nanographene sheets is done using scaled boundary finite element (SBFEM) that is a novel semi-analytical method. In order to apply the stochastic method, we need to select certain statistical parameters, the ones considered here include crack length and failure stress. The results using the stochastic response method are compared with those of the Monte Carlo, which shows good agreement. Average, variance, third and fourth moments of stress and the fracture probability are calculated for all the bonds in two states of crack angle 0°, 90°. The results indicate that the maximum crack-tip fracture probability for central crack θ = 90° is 95%, while for θ = 0° equals 58%. For central crack θ = 90°, the failure probability in crack tip bonds, bonds adjacent to the crack edge and other bonds equal 95%, 88% and 65%, respectively. For nanographene sheet having the central crack θ = 90°, probabilistic density function is Gaussian, which reaches the highest point at the displacement being 0.990 pm. The third moment of the stress turned out to be zero, which shows that the input uncertain variables are all normal. The results obtained from computing the failure probability of all the bonds indicates that the highest failure probability is in the tip and edges of the crack. The stochastic fracture sensitivity versus crack length is calculated which shows that the maximum sensitivity in central crack happens when θ = 90° and crack length ranges between 0.3 and 0.4.

Intelligent Resource Allocation for Edge-Cloud Collaborative Networks: A Hybrid DDPG-D3QN Approach

Mobile edge computing (MEC) has recently emerged as an effective paradigm for computation-intensive and delay-critical applications supported by Internet of Things (IoT) devices. However, the computational resources at MEC servers are normally much smaller compared with the remote cloud server (CS). To handle the ever-increasing IoT devices and applications, the collaborative edge and cloud computing should be jointly exploited. Further, with finite block length (FBL) utilized in URLLC-supported networks, the coding error probability cannot be ignored. On these grounds, this paper investigates the dynamic offloading of FBL packets in an edge-cloud collaborative MEC system consisting of multi-mobile IoT devices (MIDs) with energy harvesting (EH), multi-edge servers, and one CS in a dynamic environment. The optimization problem is formulated to minimize the average long-term service cost defined as the weighted sum of MID energy consumption and service delay (including the uploading transmission delay, the handover cost, and the execution delay for the partial offloaded part, the local execution delay for the local processing part), with the constraints of the available resource, the energy causality, the allowable service delay, and the maximum decoding error probability. To address the problem involving both discrete and continuous variables, we propose a multi-device hybrid decision-based deep reinforcement learning (DRL) solution, named DDPG-D3QN algorithm, where the deep deterministic policy gradient (DDPG) and dueling double deep Q networks (D3QN) are invoked to tackle continuous and discrete action domains, respectively. Specifically, we improve the actor-critic structure of DDPG by combining D3QN. It utilizes the actor part of DDPG to search for the optimal offloading rate and power control of local execution. Meanwhile, it combines the critic part of DDPG with D3QN to select the optimal server for offloading. Simulation results demonstrate the proposed DDPG-D3QN algorithm has better stability and faster convergence while achieving higher rewards than the existing DRL-based methods. Furthermore, the edge-cloud collaboration has been proven to attain improved performance when compared with other offloading schemes with no collaboration between edge and cloud.

Attentive continuous generative self-training for unsupervised domain adaptive medical image translation.

Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a labeled source domain to unlabeled and heterogeneous target domains. While self-training-based UDA has shown considerable promise on discriminative tasks, including classification and segmentation, through reliable pseudo-label filtering based on the maximum softmax probability, there is a paucity of prior work on self-training-based UDA for generative tasks, including image modality translation. To fill this gap, in this work, we seek to develop a generative self-training (GST) framework for domain adaptive image translation with continuous value prediction and regression objectives. Specifically, we quantify both aleatoric and epistemic uncertainties within our GST using variational Bayes learning to measure the reliability of synthesized data. We also introduce a self-attention scheme that de-emphasizes the background region to prevent it from dominating the training process. The adaptation is then carried out by an alternating optimization scheme with target domain supervision that focuses attention on the regions with reliable pseudo-labels. We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation. Extensive validations with unpaired target domain data showed that our GST yielded superior synthesis performance in comparison to adversarial training UDA methods.

On the joint survival probability of two collaborating firms

AbstractWe consider the problem of controlling the drift and diffusion rate of the endowment processes of two firms such that the joint survival probability is maximized. We assume that the endowment processes are continuous diffusions, driven by independent Brownian motions, and that the aggregate endowment is a Brownian motion with constant drift and diffusion rate. Our results reveal that the maximal joint survival probability depends only on the aggregate risk-adjusted return and on the maximal risk-adjusted return that can be implemented in each firm. Here the risk-adjusted return is understood as the drift rate divided by the squared diffusion rate.