The experimental arrangement in MMD is a prime factor that defines the integrity of the dataset. The dataset is obtained in lab environment with a PI sensitive coil made up of muti-stranded wire with coil diameter of 170mm. It is mounted on a transparent acrylic sheet with a miniaturized Tx/Rx (also mounted) at a distance of 100mm. The electromagnetic field (EMF) simulation of search-head in close proximity of mine is shown in Fig.7. The received signal is digitized, and synchronized data is obtained for both the transmitted positive and negative pulses. The dataset is then populated with this synchronized pulse data. The pulse repetition frequency, including both pulses, is 880Hz.The number of pulses M (refer to Eq.(1)) obtained per class is 1330, representing concatenated positive and negative pulses. It is done to simplify the model, with a total number of concatenated samples being N=244, consisting of 122 samples from each received pulse, respectively. It is approximately 3s of pulsed data per class.

Shows Electromagnetic field simulation of search head in (a) and search head in proximity of mine in (b).

The samples/targets used to represent the nine classes (previously discussed) include minrl/brick (mineralized soil), sand (non-mineralized soil), APM (standard 0.2 gm) and vertical paper pins (0.2 gm). Mineralization is an indication of magnetic permeability (or susceptibility) of the surface soils that have been exposed to high temperatures and heavy rainfall or water for extended periods of time, often exhibit high mineralization due to the presence of residual iron components. For an in-depth exploration of the magnetic susceptibility across a wide range of soil types, you can find comprehensive information in reference18. The choice of using brick, a clay-based material, as a representative sample for mineralized soil is grounded in its unique composition. It contains minerals like iron oxide, such as magnetite or hematite, and exhibits relatively low electrical conductivity19. These distinctive characteristics significantly enhance its detectable response when subjected to a MMD. In fact, this response is typically more robust than that of conventional mineralized soil (from which it originates) or even APM. For the sake of simplicity and consistency, we will refer to this material as "minrl" throughout this paper.

All of the targets mentioned pose their own challenges, but they are placed in close proximity to the MMD, within a distance of no more than 20mm parallel to the surface of the coil. The targets are positioned at the center of the coil. The received signals from different target samples of a positive and a negative transmitted pulses can be observed in Figs. 8 and 9 respectively. The figures display a magnified section of the received signal, focusing on the initial samples that are more strongly influenced by the secondary magnetic field compared to later samples. It can also be seen that signals vary in opposite directions as per polarity of the transmitted pulses.

Received signals of a positive transmitted pulse picked up at the sensor coil from the secondary magnetic field produced by the eddy currents induced within the targets. The x-axis shows few numbers of samples (initial part of the signal) per pulse and y-axis shows amplitude of the signal in volts. Signals from nine targets air, APM, pins, minrl, minrl+APM, minrl+pins, sand, sand+APM and sand+pins have been shown.

Received signals of a negative transmitted pulse picked up at the sensor coil from the secondary magnetic field produced by the eddy currents induced within the targets. The x-axis shows few numbers of samples (initial part of the signal) per pulse and y-axis shows amplitude of the signal in volts. Signals from nine targets air, APM, pins, minrl, minrl+APM, minrl+pins, sand, sand+APM and sand+pins have been shown.

The overall dataset comprises a total of 11,970 pulses, representing nine different classes. The dataset is sufficiently diverse, as illustrated in Fig.10 by examining inter-class distances. For this analysis, two distances are employed: Euclidean distance, which measures point-to-point distance, and Bhattacharyya distance, a metric indicating dissimilarity between two probability distributions. Two cases will be briefly discussed here: one involving the Euclidean distance between air and pins, where the maximum distance is observed as depicted in Fig.10, which is also evident in the received signal shown in Figs. 8 and 9. The second case pertains to the Bhattacharyya distance between air and sand, illustrating minimal dissimilarity. The impact of this dissimilarity will become evident in the overall results. To prepare this dataset for modelling, these pulses are randomly shuffled and subsequently split into two separate sets: a training dataset containing 10,773 pulses and a validation dataset comprising 1197 pulses.

Shows inter-class similarity through Euclidean and Bhattacharyya distances.

During the model training phase, input data is structured as a matrix with dimensions [10,773244], and the output, following a supervised learning approach, is provided as a one-hot encoded labeled matrix with dimensions [10,7739]. The accuracy of the trained model on the provided data is tracked across multiple epochs, including both training and validation accuracy. In the context of this training process, one epoch signifies a complete iteration over the entire training dataset of size [10,773244], with all training samples processed by the model. Figure11 depicts the trend, showing that as the training process repeats over multiple epochs, the model steadily enhances its performance and optimizes its parameters. After 4000 epochs, the trained accuracy reaches approximately 98%, while the validation accuracy hovers above 93%. It also shows that the DL-MMD model has more or less converged at 4000epochs, by achieving the optimum training performance. Likewise, its evident that the models error loss diminishes with the progression of epochs, as illustrated in Fig.12.

Shows the accuracy and validation accuracy of novel DL-MMD model versus epochs. For comparison, the validation accuracy of KNN and SVM classifier are also shown for k=8 and C=100 respectively.

Shows the loss and validation loss of novel DL-MMD model versus epochs.

Figure11, also shows that the presented model performs substantially better compared to support vector machine (SVM) and K-Nearest Neighbors (KNN) classifiers. The main working principle of SVM is to separate several classes in the training set with a surface that maximizes the margin (decision boundary) between them. It uses Structural Risk Minimization principle (SRM) that allows the minimization of a bound on the generalization error20. SVM model used in this research achieved a training accuracy of 93.6% and a validation accuracy of 86.5%, which is far lower than the performance achieved by the presented model. The parameter for kernel function used is the most popular i.e. radial basis function (RBF) and the value of regularization parameter c optimally selected is 100. The regularization parameter controls the trade-off between classifying the training data correctly and the smoothness of the decision boundary. Figure13 shows the influence of the regularization parameter c, on the performance of the classifier. The gamma is automatically calculated based on the inverse of the number of features, which ensures that each feature contributes equally to the decision boundary. The hyperparameter optimization is achieved through a manual grid search method. The code iterates through a predefined list of C values [0.1, 1, 10, 100, 1000, 10000], and for each value of C, it trains a Support Vector Machine (SVM) classifier with a radial basis function (RBF) kernel and evaluates its performance on the training and test sets. The accuracy and C values are then plotted to visually check the best performance. It can be seen that the generalization error increases when the value of C is greater than 100, the SVM starts to overfit the training data and thus resulting in decrease in validation accuracy.

Shows the accuracy of SVM classifier versus regularization parameter C.

While K-Nearest Neighbors (KNN) model with 8 neighbors (k) achieved a training accuracy of 92.6% and a validation accuracy of 90.7% (see Fig.11), which is lower than the performance achieved by the presented model. To enable comparative analysis, it is essential to showcase the performance of this non-parametric machine learning algorithm. In this context, the algorithm predicts the value of a new data point by considering the majority vote or average of its k nearest neighbors within the feature space21. Figure14 illustrates the influence of the hyperparameter k, the number of neighbors, on the performance of the algorithm. The graph demonstrates that the validation accuracy reaches a maximum of 90.7% when 8 neighbors are considered.

Shows the accuracy of KNN classifier versus number of neighbors k.

To further analyze the DL-MMD model versus the experimental data, one more graph has been plotted shown in Fig.15. This graph illustrates the comparative performance of the presented model using a different data split ratio (7030), with 70% for training and 30% for validation. The graph shows a slightly degraded performance when compared to the split ratio (9010) of 90% for training and 10% for validation. However, it still shows validation accuracy of above 88% at 4000 epochs. This degradation is attributed to epistemic uncertainty (model uncertainty) due to slightly less effective learning on a reduced training data and as the training data increases, this uncertainty also reduces.

Shows the accuracy and validation accuracy of novel DL-MMD model versus epochs at two different data split ratios i.e. of 9010 and 7030.

The performance of the model can also be inferred from the confusion matrix shown in Fig.16. It provides a tabular representation of the predicted and actual class labels, giving a very important analysis of the models in terms of true positives, true negatives, false positives, and false negatives. For an application perspective of an MMD, safety of the user is of utmost importance for which false negative matters a lot since mine as target must not be missed.. The overall prediction accuracy is above 93.5%, however, for cases of air and sand it is approximately 85 and 86.5% respectively, inferred from the confusion matrix. These two classification cases of relatively less prediction accuracy can be neglected since sand being wrongly classified as air only and vice-versa. These two classes (air & sand) do not trigger any detection alarm by an MMD, thus misclassification of them will not impact efficiency of DL-MMD classifier. It also highlights the fact that sand (of river) has minimal mineralized content and is generally designated as non-mineralised soil. It is therefore difficult to separate the boundary between these two classes in presence of noise and interference.

Confusion matrix of the proposed DL-MMD classification on 9 classes.

In addition to this, two further cases need to be examined: one involves mineralized soil (minrl) being wrongly classified as APM, and the other involves APM in sand (sand+APM) being wrongly classified as minrl. The first case is of false positive, it will generate a false alarm and will waste time of the user by requiring unnecessary further investigation. The second case is of more importance i.e. of false negative where an APM is detected but wrongly classified by a DL-MMD and will be discussed in next section. Apart from them, there are minor cases e.g. an APM misclassified as APM in sand (sand+APM), it will not have any impact since target of concern (APM) will remain the same but now being shown buried in sand. The occurrence of all these misclassification cases (apart from the air/sand case & vice-versa) is less than 5% approximately.

These results have been obtained by a substantial dataset based on actual data acquired in two sets of 665 (pulses per class) each obtained at two different times through the experimental setup explained previously and then combined together. Comprehensive simulations have been carried out in the Tensor Flow environment for evaluation of the proposed method. In addition to this, the algorithm has been extensively tested with an increased number of layers and channels, resulting in overfitting. Furthermore, the proposed model has been tested with different optimizers, such as Adagrad, Adamax, and Adam. The comparative analysis of Adam and Adamax can be seen in Fig.17. Both show equivalent performance after 2000epochs.

Shows the accuracy and validation accuracy of novel DL-MMD model versus epochs using two different optimizers Adamax and Adam.

In addition to the aforementioned analysis, the dataset underwent evaluation using other prevalent classification algorithms22, which utilize the principle of ensemble learning. However, upon comparison, the proposed deep learning architecture exhibited superior performance, achieving an accuracy exceeding 90%. The confusion matrices of these classification algorithms, AdaBoost and Bagged tree, are depicted in Figs. 18, 19, and 20, with the dataset partitioned into an 80/20 ratio, resulting in accuracies of 75.4%, 80%, and 83.3%, respectively. AdaBoost was employed without PCA, utilizing the maximum number of splits and learners set to 30, with a learning rate of 0.1. For Bagged tree, only Model 2 underwent preprocessing with PCA with a variance of 95%. They both utilized the same number of learners as AdaBoost and a maximum split of 11,969.

Confusion matrix model 1 AdaBoost.

Confusion matrix model 2 Bagged Tree.

Confusion matrix model 3 Bagged Tree.

It is pertinent to mention that there is always redundant information within the received signal that creates background bias, especially in sensitive areas with low metal content. Information regarding the detection of APM mines buried at different depths is available (in the parameter decay rate), but it is not utilized. Therefore, for an APM buried at a different depth (relative to the search head) to the one it is trained on, there is a chance that it can be misclassified. The information exists, but it needs to be pre-processed before feeding the signal to the model. One approach could be to use focused AI models, similar to those shown in Ref23, that inject synthetic bias into the signal to generalize the model in our case at different depths. Another approach can be to localize the area with different decay rates, similar to the one shown in Ref24 for 2D image application. One of the future work will be to utilize this information and integrate it into the DL_MMD architecture.

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Dataset description

The dataset we used for this study is accessible through this link: https://www.cs.uoi.gr/~marina/sipakmed.html. It contains five different cell types, as detailed in24. In our research, we've transformed this dataset into a two-class system with two categories: normal and abnormal. Specifically, the normal category includes superficial intermediate cells and parabasal cells, while the aberrant category covers koilocytotic, dyskeratotic, and metaplastic cell types25. Within the normal category, we've further divided cells into two subcategories: superficial intermediate cells and parabasal cells. The essential dataset characteristics are summarized in Table 2. The SIPaKMeD dataset comprises a total of 4068 images, with 3254 allocated for training (making up 80% of the total), and 813 set aside for testing (accounting for 20% of the total). This dataset consists of two distinct classes: normal photos, totalling 1618, and aberrant images, amounting to 2450. Figure2 provides visual examples of photographs from these two different categories. The existing literature extensively covers different screening methods for cervical cancer, such as Pap smear, colposcopy, and HPV testing, emphasizing the importance of early detection. However, a significant gap exists in automated screening systems using pap smear images. Traditional methods rely on expert interpretation, but integrating deep learning (DL) and machine learning (ML) offers potential for intelligent automation. Despite this potential, few studies focus on developing and evaluating such systems specifically for cervical cancer prediction using pap smear images. This research addresses this gap by proposing a methodology that utilizes pre-trained deep neural network models for feature extraction and applies various ML algorithms for prediction. The study aims to contribute to advancing automated screening systems for cervical cancer, aiming to improve early detection and patient outcomes.

Proposed model cervical cancer classification.

The schematic representation of our proposed system can be observed in Fig.2. To facilitate the classification task for cervical cancer, we employ the SIPaKMeD dataset, which comprises images of pap smears. This dataset is categorized into two groups: abnormal and normal, with a distribution of 60% for training and 40% for testing. To extract relevant feature sets from well-established CNN architectures such as Alexnet, Resnet-101, Resnet-152, and InceptionV3, we initiate feature extraction from these pretrained CNN models. This step allows us to gather valuable information from the final layer activation values. For the task of classifying images into normal and abnormal categories, we leverage a variety of machine learning techniques, including Simple Logistic, Decision Tree, Random Forest, Naive Bayes, and Principal Component Analysis. Our approach is designed as a hybrid strategy, merging both DL and ML methodologies. The utilization of DL enables our model to capture intricate and complex features inherent in the data, while ML provides the necessary flexibility to handle diverse scenarios. By harnessing the last layer of pretrained models for feature extraction, we enable different machine learning algorithms to classify data based on these extracted attributes. This combination of DL and ML enhances our system's ability to effectively categorize cervical cancer cases.

The pre-trained model has undergone training on a larger dataset, acquiring specific weights and biases that encapsulate the dataset's distinctive characteristics. This model has been effectively employed for making predictions based on data. The transferability of learned features to other datasets is possible because certain fundamental abstract properties remain consistent across various types of images. By utilizing pre-trained models, significant time and effort savings are achieved, as a substantial portion of the feature extraction process has already been completed. Noteworthy examples of pre-trained models include Resnet152, ResNet101, Inceptionv3, and Alexnet, which are summarized in Table 3 for reference.

The image classification framework based on ResNet-101 consists of two main parts: feature extraction and feature classification. In Fig.3, you can see how the feature extractor is built, comprising five main convolution modules with a total of one hundred convolution layers, an average pooling layer, and a fully connected layer26. Once the features are extracted, they are used to train a classifier with a Softmax structure. Table 4 lists the convolution layers and their configurations in the ResNet-101 backbone. Using shortcut connections to increase data dimensions, the ResNet-101 model significantly improves performance by increasing convolutional depth. These shortcut connections also address the problem of network depth causing degradation by enabling identity mapping. For most binary classification tasks, the loss function is applied using the logical cross-entropy function, as shown in Eq.(1).

$$k_{({h_l},;{q_l})}^b = - {f_l}log left( {q_l} right) - left( {1 - {f_l}} right)log left( {1 - {q_l}} right)$$

(1)

where the ground truth value, (% {f_l}), and the predicted value, (% {q_l}), are respectively indicated as the lth training dataset's ground truth and predicted values. The value of the loss, ({k}_{({h_{l}}, ; {q_{l}})}^{b}), is then backpropagated through the CNN model. At the same time, the CNN model parameters (weights and biases) are gradually optimised during each epoch. This process continues until the loss is minimised and the CNN model converges to a solution.

The ResNet architecture is efficient, promoting the training of very deep neural networks (DNN) and enhancing accuracy. It addresses the challenge of accuracy degradation associated with increasing network depth. When depth is increased, accuracy often drops, which is a drawback. However, deeper networks can improve accuracy by avoiding the saturation of shallow networks, where errors remain minimal27. The key idea here is that information from one layer should easily flow to the next with the help of identity mapping. ResNet tackles the degradation problem, along with the gradient vanishing issue, using residual blocks. These blocks handle the remaining computation while considering the input and output of the block. Figure4, illustrates architecture of ResNet152. Table 5, illustrates the configuration of ResNet152.

This advanced model has undergone training by one of the industry's most renowned hardware specialists, leveraging an impressive repertoire of over 20 million distinct parameters. The model's architecture is a harmonious blend of symmetrical and asymmetrical construction blocks, each meticulously crafted with its own unique set of convolutional, average, and maximum pooling layers, concatenation operations, and fully connected layers configurations. Furthermore, the model's design incorporates an activation layer that takes advantage of batch normalization, a widely adopted technique in the field. This technique helps stabilize and accelerate the training process, making the model more robust and efficient28. For the critical task of classification, the model employs the Softmax method, a popular and well-established approach in machine learning. Softmax is instrumental in producing probability distributions over multiple classes, which enables the model to make informed and precise predictions. To provide a visual understanding of the Inception-V3 model's intricate design, Fig.5 serves as a diagrammatic representation, offering insights into the model's underlying architecture and the various components that make it a powerhouse in the realm of machine learning and artificial intelligence.

InceptionV3 architecture.

The field of machine learning, particularly in the domain of image processing, has witnessed a profound impact thanks to the advent of Alexnet. As suggested in Ref.29, this influential model boasts a preconfigured Convolutional Neural Network (CNN) with a total of eight distinct layers29. Its remarkable performance in the 2012 ImageNet Large Scale Visual Recognition Challenge (LSVRC-2012) competition marked a watershed moment, as it clinched victory with a substantial lead over its competitors. The architectural blueprint of Alexnet bears some resemblance to Yann Lecun's pioneering LeNet, highlighting its historical lineage and the evolutionary progress of convolutional neural networks.

Figure6 provides an insightful visual representation of the holistic design of the Alexnet system. In the journey of data processing within Alexnet, input data traverse through an intricate sequence, comprising five convolution layers and three max-pooling layers, as vividly illustrated in Fig.5. These layers play a pivotal role in feature extraction and hierarchical representation, which are vital aspects of image analysis and understanding. The culmination of AlexNet's network journey is marked by the application of the SoftMax activation function in the final layer, enabling it to produce probabilistic class predictions. Along the way, the Rectified Linear Unit (ReLU) activation function is systematically employed across all the network's convolution layers, providing a nonlinear transformation that enhances the network's capacity to learn and extract features effectively. This combination of architectural elements and activation functions has played a significant role in solidifying AlexNet's position as a groundbreaking model in the domain of image processing and machine learning.

Logistic regression serves as a powerful method for modelling the probability of a discrete outcome based on input variables, making the choice of input variables a pivotal aspect of this modelling process. The most common application of logistic regression involves modelling a binary outcome, which pertains to scenarios where the result can exclusively assume one of two possible values, such as true or false, yes or no, and the like. However, in situations where there are more than two discrete potential outcomes, multinomial logistic regression proves invaluable in capturing the complexity of the scenario. Logistic regression finds its primary utility in the realm of classification problems30. It becomes particularly valuable when the task at hand involves determining which category a new sample best aligns with. This becomes especially pertinent when dealing with substantial datasets, where the need to classify or categorize data efficiently and accurately is paramount. One noteworthy domain where logistic regression finds widespread application is in cybersecurity, where classification challenges are ubiquitous. A pertinent example is the detection of cyberattacks. Here, logistic regression plays a crucial role in identifying and categorizing potential threats, contributing significantly to bolstering the security of digital systems and networks.

In the realm of supervised learning algorithms, decision trees emerge as a highly versatile and powerful tool for both classification and regression tasks. They operate by constructing a tree-like structure, wherein internal nodes serve as decision points, branches represent the outcomes of attribute tests, and terminal nodes store class labels. The construction of a decision tree is an iterative process, continually dividing the training data into subsets based on attribute values until certain stopping conditions, such as reaching the maximum tree depth or the minimum sample size required for further division, are met. To guide this division process, the Decision Tree algorithm relies on metrics like entropy or Gini impurity, which gauge the level of impurity or unpredictability within the data subsets31. These metrics inform the algorithms choice of the most suitable attribute for data splitting during training, aiming to maximize information gain or minimize impurity. In essence, the central nodes of a decision tree represent the features, the branches encapsulate the decision rules, and the leaf nodes encapsulate the algorithms outcomes. This design accommodates both classification and regression challenges, making decision trees a flexible tool in supervised machine learning. One notable advantage of decision trees is their effectiveness in handling a wide range of problems. Moreover, their ability to be leveraged in ensembles, such as the Random Forest algorithm, enables the simultaneous training on multiple subsets of data, elevating their efficacy and robustness in real-world applications.

A Random Forest is a powerful machine learning tool that handles both regression and classification tasks effectively. It works by combining the predictions of multiple decision trees to solve complex problems. Here's how it works: The Random Forest algorithm builds a forest of decision trees using a technique called bagging. Bagging improves the precision and reliability of machine learning ensembles32. The algorithm then makes predictions by averaging the results from these trees, determining the final outcome. What makes the Random Forest special is its scalability. Unlike single decision trees, it can adapt to complex data and improves its accuracy as you add more trees to the forest. The Random Forest also helps prevent overfitting, making it a valuable tool for real-world applications with noisy and complex datasets. Moreover, it reduces the need for extensive fine-tuning, making it an appealing choice for practitioners seeking effective and dependable machine learning models.

Nave Bayes theorem forms the fundamental principle underlying the Naive Bayes algorithm. In this method, a key assumption is that there's no interdependence among the feature pairs, resulting in two pivotal presumptions: feature independence and attribute equality. Naive Bayes classifiers are versatile, existing in three primary variants: Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes33. The choice of variant depends on the nature of the data being analyzed. For binary data, Bernoulli Nave Bayes is employed, while count data finds its match in Multinomial Nave Bayes, and continuous data is aptly handled by Gaussian Nave Bayes. Equation(2) serves as a proof of Bayes theorem, underpinning the mathematical foundations of this approach.

$$Zleft( {b|a} right) = frac{Zleft( b right)Zleft( b right)}{{Zleft( a right)}}$$

(2)

Principal Component Analysis (PCA) serves as a powerful technique designed to mitigate the impact of correlations among variables through an orthogonal transformation. PCA finds widespread use in both exploratory data analysis and machine learning for predictive modelling. In addition, PCA stands out as an unsupervised learning algorithm that offers a valuable approach for delving into the intricate relationships between variables. This method, also referred to as generic factor analysis, enables the discovery of the optimal line of fit through regression analysis34. What sets PCA apart is its ability to reduce the dimensionality of a dataset without prior knowledge of the target variables while preserving the most critical patterns and interdependencies among the variables. By doing so, PCA simplifies complex data, making it more amenable for various tasks, such as regression and classification. The result is a more streamlined subset of variables that encapsulates the essential essence of the data.

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Deep neural networks have been demonstrated to predict accurate molecular structures from descriptors ({{{mathcal{D}}}}) for many molecular systems1,5,6,9,10,11,12. Here, DiG aims to take one step further to predict not only the most probable structure but also diverse structures with probabilities under the equilibrium distribution. To tackle this challenge, inspired by the heatingannealing paradigm, we break down the difficulty of this problem into a series of simpler problems. The heatingannealing paradigm can be viewed as a pair of reciprocal stochastic processes on the structure space that simulate the transformation between the system-specific equilibrium distribution and a system-independent simple distribution psimple. Following this idea, we use an explicit diffusion process (forward process; Fig. 1b, orange arrows) that gradually transforms the target distribution of the molecule ({q}_{{{{mathcal{D}}}},0}), as the initial distribution, towards psimple through a time period . The corresponding reverse diffusion process then transforms psimple back to the target distribution ({q}_{{{{mathcal{D}}}},0}). This is the generation process of DiG (Fig. 1b, blue arrows). The reverse process is performed by incorporating updates predicted by deep neural networks from the given ({{{mathcal{D}}}}), which are trained to match the forward process. The descriptor ({{{mathcal{D}}}}) is processed into node representations ({{{mathcal{V}}}}) describing the feature of each system-specific individual element and a pair representation ({{{mathcal{P}}}}) describing inter-node features. The ({{{{mathcal{V}}}},{{{mathcal{P}}}}}) representation is the direct input from the descriptor part to the Graphormer model10, together with the geometric structure input R to produce a physically finer structure (Supplementary Information sections B.1 and B.3). Specifically, we choose ({p}_{{{mbox{simple}}}}:= {{{mathcal{N}}}}({{{bf{0}}}},{{{bf{I}}}})) as the standard Gaussian distribution in the state space, and the forward diffusion process as the Langevin diffusion process targeting this psimple (OrnsteinUhlenbeck process)40,41,42. A time dilation scheme t (ref. 43) is introduced for approximate convergence to psimple after a finite time . The result is written as the following stochastic differential equation (SDE):

$${{{rm{d}}}}{{{{bf{R}}}}}_{t}=-frac{{beta }_{t}}{2}{{{{bf{R}}}}}_{t},{{{rm{d}}}}t+sqrt{{beta }_{t}},{{{rm{d}}}}{{{{bf{B}}}}}_{t}$$

(1)

where Bt is the standard Brownian motion (a.k.a. Wiener process). Choosing this forward process leads to a psimple that is more concentrated than a heated distribution, hence it is easier to draw high-density samples, and the form of the process enables efficient training and sampling.

Following stochastic process theory (see, for example, ref. 44), the reverse process is also a stochastic process, written as the following SDE:

$${{{rm{d}}}}{{{{bf{R}}}}}_{bar{t}}=frac{{beta }_{bar{t}}}{2}{{{{bf{R}}}}}_{bar{t}},{{{rm{d}}}}bar{t}+{beta }_{bar{t}}nabla log {q}_{{{{mathcal{D}}}},bar{t}}({{{{bf{R}}}}}_{bar{t}}),{{{rm{d}}}}bar{t}+sqrt{{beta }_{bar{t}}},{{{rm{d}}}}{{{{bf{B}}}}}_{bar{t}}$$

(2)

where (bar{t}:= tau -t) is the reversed time, ({q}_{{{{mathcal{D}}}},bar{t}}:= {q}_{{{{mathcal{D}}}},t = tau -bar{t}}) is the forward process distribution at the corresponding time and ({{{{bf{B}}}}}_{bar{t}}) is the Brownian motion in reversed time. Note that the forward and corresponding reverse processes, equations (1) and (2), are inspired from but not exactly the heating and annealing processes. In particular, there is no concept of temperature in the two processes. The temperature T mentioned in the PIDP loss below is the temperature of the real target system but is not related to the diffusion processes.

From equation (2), the only obstacle that impedes the simulation of the reverse process for recovering ({q}_{{{{mathcal{D}}}},0}) from psimple is the unknown (nabla log {q}_{{{{mathcal{D}}}},bar{t}}({{{{bf{R}}}}}_{bar{t}})). Deep neural networks are then used to construct a score model ({{{{bf{s}}}}}_{{{{mathcal{D}}}},t}^{theta }({{{bf{R}}}})), which is trained to predict the true score function (nabla log {q}_{{{{mathcal{D}}}},t}({{{bf{R}}}})) of each instantaneous distribution ({q}_{{{{mathcal{D}}}},t}) from the forward process. This formulation is called a diffusion-based generative model and has been demonstrated to be able to generate high-quality samples of images and other content27,28,45,46,47. As our score model is defined in molecular conformational space, we use our previously developed Graphormer model10 as the neural network architecture backbone of DiG, to leverage its capabilities in modelling molecular structures and to generalize to a range of molecular systems. Note that the score model aims to approximate a gradient, which is a set of vectors. As these are equivariant with respect to the input coordinates, we designed an equivariant vector output head for the Graphormer model (Supplementary Information section B.4).

With the ({{{{bf{s}}}}}_{{{{mathcal{D}}}},t}^{theta }({{{bf{R}}}})) model, drawing a sample R0 from the equilibrium distribution of a system ({{{mathcal{D}}}}) can be done by simulating the reverse process in equation (2) on N+1 steps that uniformly discretize [0,] with step size h=/N (Fig. 1b, blue arrows), thus

$$begin{array}{ll}&{{{{bf{R}}}}}_{N} sim {p}_{{{mbox{simple}}}},\ &{{{{bf{R}}}}}_{i-1}=frac{1}{sqrt{1-{beta }_{i}}}left({{{{bf{R}}}}}_{i}+{beta }_{i}{{{{bf{s}}}}}_{{{{mathcal{D}}}},i}^{theta }({{{{bf{R}}}}}_{i})right)+{{{mathcal{N}}}}({{{bf{0}}}},{beta }_{i}{{{bf{I}}}}),,i=N,cdots ,,1,end{array}$$

where the discrete step index i corresponds to time t=ih, and i:=ht=ih. Supplementary Information section A.1 provides the derivation. Note that the reverse process does not need to be ergodic. The way that DiG models the equilibrium distribution is to use the instantaneous distribution at the instant t=0 (or (bar{t}=tau)) on the reverse process, but not using a time average. As RN samples can be drawn independently, DiG can generate statistically independent R0 samples for the equilibrium distribution. In contrast to MD or MCMC simulations, the generation of DiG samples does not suffer from rare events that link different states and can thus be far more computationally efficient.

DiG can be trained by using conformation data sampled over a range of molecular systems. However, collecting sufficient experimental or simulation data to characterize the equilibrium distribution for various systems is extremely costly. To address this data scarcity issue, we propose a pre-training algorithm, called PIDP, which effectively optimizes DiG on an initial set of candidate structures that need not be sampled from the equilibrium distribution. The supervision comes from the energy function ({E}_{{{{mathcal{D}}}}}) of each system ({{{mathcal{D}}}}), which defines the equilibrium distribution ({q}_{{{{mathcal{D}}}},0}({{{bf{R}}}})propto exp (-frac{{E}_{{{{mathcal{D}}}}}({{{bf{R}}}})}{{k}_{{{{rm{B}}}}}T})) at the target temperature T.

The key idea is that the true score function (nabla log {q}_{{{{mathcal{D}}}},t}) from the forward process in equation (1) obeys a partial differential equation, known as the FokkerPlanck equation (see, for example, ref. 48). We then pre-train the score model ({{{{bf{s}}}}}_{{{{mathcal{D}}}},t}^{theta }) by minimizing the following loss function that enforces the equation to hold:

$$begin{array}{rc}&mathop{sum }limits_{i=1}^{N}frac{1}{M}mathop{sum }limits_{m=1}^{M}leftVert frac{{beta }_{i}}{2}left(nabla left({{{{bf{R}}}}}_{{{{mathcal{D}}}},i}^{(m)}cdot {{{{bf{s}}}}}_{{{{mathcal{D}}}},i}^{theta }({{{{bf{R}}}}}_{{{{mathcal{D}}}},i}^{(m)})right)right.+nabla leftVert {{{{bf{s}}}}}_{{{{mathcal{D}}}},i}^{theta }({{{{bf{R}}}}}_{{{{mathcal{D}}}},i}^{(m)})rightVert ^{2}+nabla left(nabla cdot {{{{bf{s}}}}}_{{{{mathcal{D}}}},i}^{theta }({{{{bf{R}}}}}_{{{{mathcal{D}}}},i}^{(m)})right)right)\ &left.-frac{partial }{partial t}{{{{bf{s}}}}}_{{{{mathcal{D}}}},i}^{theta }left({{{{bf{R}}}}}_{{{{mathcal{D}}}},i}^{(m)}right)rightVert^{2}+frac{{lambda }_{1}}{M}mathop{sum }limits_{m=1}^{M}leftVertfrac{1}{{k}_{{{{rm{B}}}}}T}nabla {E}_{{{{mathcal{D}}}}}left({{{{bf{R}}}}}_{{{{mathcal{D}}}},1}^{(m)}right)+{{{{bf{s}}}}}_{{{{mathcal{D}}}},1}^{theta }left({{{{bf{R}}}}}_{{{{mathcal{D}}}},1}^{(m)}right)rightVert^{2}end{array}$$

Here, the second term, weighted by 1, matches the score model at the final generation step to the score from the energy function, and the first term implicitly propagates the energy function supervision to intermediate time steps (Fig. 1b, upper row). The structures ({{{{{{bf{R}}}}}_{{{{mathcal{D}}}},i}^{(m)}}}_{m = 1}^{M}) are points on a grid spanning the structure space. Since these structures are only used to evaluate the loss function on discretized points, they do not have to obey the equilibrium distribution (as is required by structures in the training dataset), therefore the cost of preparing these structures can be much lower. As structure spaces of molecular systems are often very high dimensional (for example, thousands for proteins), a regular grid would have intractably many points. Fortunately, the space of actual interest is only a low-dimensional manifold of physically reasonable structures (structures with low energy) relevant to the problem. This allows us to effectively train the model only on these relevant structures as R0 samples. Ri samples are produced by passing R0 samples through the forward process. See Supplementary Information section C.1 for an example on acquiring relevant structures for protein systems.

We also leverage stochastic estimators, including Hutchinsons estimator49,50, to reduce the complexity in calculating derivatives of high order and for high-dimensional vector-valued functions. Note that, for each step i, the corresponding model ({{{{bf{s}}}}}_{{{{mathcal{D}}}},i}^{theta }) receives a training loss independent of other steps and can be directly back-propagated. In this way, the supervision on each step can improve the optimizing efficiency.

In addition to using the energy function for information on the probability distribution of the molecular system, DiG can also be trained with molecular structure samples that can be obtained from experiments, MD or other simulation methods. See Supplementary Information section C for data collection details. Even when the simulation data are limited, they still provide information about the regions of interest and about the local shape of the distribution in these regions; hence, they are helpful to improve a pre-trained DiG. To train DiG on data, the score model ({{{{bf{s}}}}}_{{{{mathcal{D}}}},i}^{theta }({{{{bf{R}}}}}_{i})) is matched to the corresponding score function (nabla log {q}_{{{{mathcal{D}}}},i}) demonstrated by data samples. This can be done by minimizing ({{mathbb{E}}}_{{q}_{{{{mathcal{D}}}},i}({{{{bf{R}}}}}_{i})}{parallel {{{{bf{s}}}}}_{{{{mathcal{D}}}},i}^{theta }({{{{bf{R}}}}}_{i})-nabla log {q}_{{{{mathcal{D}}}},i}({{{{bf{R}}}}}_{i})parallel }^{2}) for each diffusion time step i. Although a precise calculation of (nabla log {q}_{{{{mathcal{D}}}},i}) is impractical, the loss function can be equivalently reformulated into a denoising score-matching form51,52

$$frac{1}{N}mathop{sum }limits_{i=1}^{N}{{mathbb{E}}}_{{q}_{{{{mathcal{D}}}},0}({{{{bf{R}}}}}_{0})}{{mathbb{E}}}_{p({{{{mathbf{epsilon }}}}}_{i})}{parallel {sigma }_{i}{{{{bf{s}}}}}_{{{{mathcal{D}}}},i}^{theta }({alpha }_{i}{{{{bf{R}}}}}_{0}+{sigma }_{i}{{{{mathbf{epsilon }}}}}_{i})+{{{{mathbf{epsilon }}}}}_{i}parallel }^{2}$$

where ({alpha }_{i}:= mathop{prod }nolimits_{j = 1}^{i}sqrt{1-{beta }_{j}}), ({sigma }_{i}:= sqrt{1-{alpha }_{i}^{2}}) and p(i) is the standard Gaussian distribution. The expectation under ({q}_{{{{mathcal{D}}}},0}) can be estimated using the simulation dataset.

We remark that this score-predicting formulation is equivalent (Supplementary Information section A.1.2) to the noise-predicting formulation28 in the diffusion model literature. Note that this function allows direct loss estimation and back-propagation for each i in constant (with respect to i) cost, recovering the efficient step-specific supervision again (Fig. 1b, bottom).

The computation of many thermodynamic properties of a molecular system (for example, free energy or entropy) also requires the density function of the equilibrium distribution, which is another aspect of the distribution besides a sampling method. DiG allows for this by tracking the distribution change along the diffusion process45:

$$begin{array}{l}log {p}_{{{{mathcal{D}}}},0}^{theta }({{{{bf{R}}}}}_{0})=log {p}_{{{mbox{simple}}}}left({{{{bf{R}}}}}_{{{{mathcal{D}}}},tau }^{theta }({{{{bf{R}}}}}_{0})right)\qquadqquadqquad;;-displaystyleintnolimits_{0}^{tau }frac{{beta }_{t}}{2}nabla cdot {{{{bf{s}}}}}_{{{{mathcal{D}}}},t}^{theta }left({{{{bf{R}}}}}_{{{{mathcal{D}}}},t}^{theta }({{{{bf{R}}}}}_{0})right),{{{rm{d}}}}t-frac{D}{2}intnolimits_{0}^{tau }{beta }_{t},{{{rm{d}}}}tend{array}$$

where D is the dimension of the state space and ({{{{bf{R}}}}}_{{{{mathcal{D}}}},t}^{theta }({{{{bf{R}}}}}_{0})) is the solution to the ordinary differential equation (ODE)

$${{{rm{d}}}}{{{{bf{R}}}}}_{t}=-frac{{beta }_{t}}{2}left({{{{bf{R}}}}}_{t}+{{{{bf{s}}}}}_{{{{mathcal{D}}}},t}^{theta }({{{{bf{R}}}}}_{t})right),{{{rm{d}}}}t$$

(3)

with initial condition R0, which can be solved using standard ODE solvers or more efficient specific solvers (Supplementary Information section A.6).

There is a growing demand for the design of materials and molecules that possess desired properties, such as intrinsic electronic band gaps, elastic modulus and ionic conductivity, without going through a forward searching process. DiG provides a feature to enable such property-guided structure generation, by directly predicting the conditional structural distribution given a value c of a microscopic property.

To achieve this goal, regarding the data-generating process in equation (2), we only need to adapt the score function from (nabla log {q}_{{{{mathcal{D}}}},t}({{{bf{R}}}})) to ({nabla }_{{{{bf{R}}}}}log {q}_{{{{mathcal{D}}}},t}({{{bf{R}}}}| c)). Using Bayes rule, the latter can be reformulated as ({nabla }_{{{{bf{R}}}}}log {q}_{{{{mathcal{D}}}},t}({{{bf{R}}}}| c)=nabla log {q}_{{{{mathcal{D}}}},t}({{{bf{R}}}})+{nabla }_{{{{bf{R}}}}}log {q}_{{{{mathcal{D}}}}}(c| {{{bf{R}}}})), where the first term can be approximated by the learned (unconditioned) score model; that is, the new score model is

$${{{{bf{s}}}}}_{{{{mathcal{D}}}},i}^{theta }({{{{bf{R}}}}}_{i}| c)={{{{bf{s}}}}}_{{{{mathcal{D}}}},i}^{theta }({{{{bf{R}}}}}_{i})+{nabla }_{{{{{bf{R}}}}}_{i}}log {q}_{{{{mathcal{D}}}}}(c| {{{{bf{R}}}}}_{i})$$

Hence, only a ({q}_{{{{mathcal{D}}}}}(c| {{{bf{R}}}})) model is additionally needed45,46, which is a property predictor or classifier that is much easier to train than a generative model.

In a normal workflow for ML inverse design, a dataset must be generated to meet the conditional distribution, then an ML model will be trained on this dataset for structure distribution predictions. The ability to generate structures for conditional distribution without requiring a conditional dataset places DiG in an advantageous position when compared with normal workflows in terms of both efficiency and computational cost.

Given two states, DiG can approximate a reaction path that corresponds to reaction coordinates or collective variables, and find intermediate states along the path. This is achieved through the fact that the distribution transformation process described in equation (1) is equivalent to the process in equation (3) if ({{{{bf{s}}}}}_{{{{mathcal{D}}}},i}^{theta }) is well learned, which is deterministic and invertible, hence establishing a correspondence between the structure and latent space. We can then uniquely map the two given states in the structure space to the latent space, approximate the path in the latent space by linear interpolation and then map the path back to the structure space. Since the distribution in the latent space is Gaussian, which has a convex contour, the linearly interpolated path goes through high-probability or low-energy regions, so it gives an intuitive guess of the real reaction path.

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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Safer and more reliable autonomous systems, such as self-driving vehicles, may be possible thanks to a new understanding of deep learning, a type of artificial intelligence (AI) that mimics the way humans learn and process information.

The study, conducted at Bar-Ilan University and published in the Physica A journal, highlights the interplay between AI confidence levels and decision-making processes.

Understanding the confidence levels of AI systems allows us to develop applications that prioritize safety and reliability, explained Ella Koresh, an undergraduate student who contributed to the research.

For instance, in the context of autonomous vehicles, when confidence in identifying a road sign is exceptionally high, the system can autonomously make decisions. However, in scenarios where confidence levels are lower, the system prompts for human intervention, ensuring cautious and informed decision-making.

According to the researchers, deep learning architectures can achieve higher confidence levels for a substantial portion of inputs, while maintaining an overall average confidence.

Put more simply: deep learning AI can be more certain about a lot of things without sacrificing overall reliability.

The ability to bolster the confidence levels of AI systems establishes a new benchmark for AI performance and safety and could be applicable across a spectrum of fields, from AI-driven writing and image classification to pivotal decision-making processes in healthcare and autonomous vehicles.

In addition to Koresh, the study was authored by Yuval Meir, Ofek Tevet, Yarden Tzach and Prof. Ido Kanter from the department of physics at Bar-Ilan and the universitys brain research center.

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A new artificial intelligence approach developed by investigators in Cedars-Sinai's Los Angeles-based Smidt Heart Institute has been shown to detect abnormal heart rhythms associated with atrial fibrillation that might otherwise be unnoticed by physicians.

WHY IT MATTERS Researchers at Smidt Heart Institute say the findings point to the potential for artificial intelligence to be used more widely in cardiac care.

In a recent study, published in npj Digital Medicine, Cedars-Sinai clinicians show how the deep learning model was developed to analyze images from echocardiogram imaging, in which sound waves show the heart's rhythm.

Researchers trained a program to study more than 100,000 echocardiogram videos from patients with atrial fibrillation, they explain. The model distinguished between echocardiograms showing a heart in sinus rhythm normal heartbeats and those showing a heart in an irregular heart rhythm.

The program was able to predict which patients in sinus rhythm had experienced or would develop atrial fibrillation within 90 days, they said, noting that the AI model evaluating the images performed better than estimating risk based on known risk factors.

"We were able to show that a deep learning algorithm we developed could be applied to echocardiograms to identify patients with a hidden abnormal heart rhythm disorder called atrial fibrillation," explained Dr. Neal Yuan, a staff scientist with the Smidt Heart Institute.

"Atrial fibrillation can come and go," he added, "so it might not be present at a doctor's appointment. This AI algorithm identifies patients who might have atrial fibrillation even when it is not present during their echocardiogram study."

THE LARGER TREND The Smidt Heart Institute is the biggest cardiothoracic transplant center in California and the third-largest in the United States.

An estimated 12.1 million people in the United States will have atrial fibrillation in 2030, according to the CDC. During AFib, the heart's upper chambers sometimes beat in sync with the lower chamber and sometimes they do not making the arrhythmia often difficult for clinicians to detect. In some patients, the condition causes no symptoms at all.

Researchers say a machine learning model trained to analyze echo imaging could help clinicians detect early and subtle changes in the hearts of patients with undiagnosed arrhythmias.

Indeed, AI has long shown big promise for early detection of AFib, as evidenced by similar studies at health systems such as Geisinger and Mayo Clinic.

ON THE RECORD "We're encouraged that this technology might pick up a dangerous condition that the human eye would not while looking at echocardiograms," said Dr. David Ouyang, a cardiologist and AI researcher in the Smidt Heart Institute. "It might be used for patients at risk for atrial fibrillation or who are experiencing symptoms associated with the condition."

"The fact that this program predicted which patients had active or hidden atrial fibrillation could have immense clinical applications," added Dr. Christine M. Albert, chair of the Department of Cardiology at the Smidt Heart Institute. "Being able to identify patients with hidden atrial fibrillation could allow us to treat them before they experience a serious cardiovascular event."

Mike Miliard is executive editor of Healthcare IT News Email the writer: mike.miliard@himssmedia.com Healthcare IT News is a HIMSS publication.

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Despite Cardano (ADA) taking a cue from Bitcoin (BTC) and the rest of the crypto sector in recent sluggishness, a deep learning algorithm has predicted it still has enough room to recover, perhaps even hitting the price of $0.50 by September 1, 2023.

Indeed, NeuralProphets PyTorch-based prediction algorithm that relies on an open-source machine learning framework has projected that ADA would hit $0.51 in the next month, an increase of 73.4% from its current price, as per the most recent data seen by Finbold on August 4.

Although the above model, which covers the period between January 1 and December 31, 2023, is not an accurate indicator of future prices and should not be taken as such, its predictions have historically proven to be relatively correct.

At the same time, the advanced machine learning algorithms deployed by the cryptocurrency analytics and forecasting platform PricePredictions are more bearish, having set the price of Cardano on September 1, 2023, at $0.275974. according to the latest information.

As things stand, Cardano is currently changing hands at the price of $0.29429, which is an advance of 0.09% in the last 24 hours, a decline of 5.59% across the previous seven days, and a 2.75% gain over the past month, as the charts show.

Meanwhile, the Cardano blockchain development team has continued to make strides, including with the recent launch of Mithril, a stake-based signature protocol to improve the efficiency of the node sync, and its founder Charles Hoskinson debunking the ghost chain myth, all of which could contribute to ADAs price.

Disclaimer: The content on this site should not be considered investment advice. Investing is speculative. When investing, your capital is at risk.

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Deep learning (DL), a branch of artificial intelligence (AI), has made significant strides in the medical field. It utilizes artificial neural networks (ANN) to learn from large amounts of data and extract relevant information for various tasks. DL has found applications in imaging diagnosis, clinical and drug research, disease classification and prediction, personalized therapy design, and public health monitoring. The advantages of DL over traditional data analysis methods include improved performance and automation. It also provides evidence-based clinical decision support tools to healthcare professionals.

However, DL presents challenges and limitations. One challenge is the need for quality and representative data. ANNs can fail to generalize when trained on data that does not accurately reflect the problem being addressed. In the medical field, privacy laws like the General Data Protection Regulation (GDPR) restrict the use of clinical data without patient consent. Even with consent, data must be anonymized and ethical approval obtained before use.

Federated learning (FL) offers a solution to these challenges. FL is a privacy-preserving and GDPR-compliant strategy for distributed machine learning. It allows a federation of clients to learn a model without exchanging data. This enables the utilization of vast and diverse medical data available from different sources, increasing the statistical power and generalizability of ML models while addressing privacy, security, and data governance concerns. FL has been successfully applied in various clinical fields, including imaging diagnosis, drug research, and genomics.

Although FL enables data sharing, the lack of explainability in ML models, like ANNs, is a limitation. Explainable AI (XAI) solutions provide tools to interpret and understand ML algorithms. Data type-specific solutions, such as Grad-CAM for image classification, and data type-independent solutions like LIME or NAMs, can be used to enhance interpretability.

Making ML models interpretable is a step towards Trustworthy AI, which ensures reliability and ethicality. XAI helps build robust and ethically sound AI systems.

The CADUCEO project, focused on digestive system diseases, proposes a federated platform that employs FL algorithms. This platform allows medical centers to share knowledge without compromising patient privacy. The project also introduces machine learning algorithms for automated image processing, data augmentation, and diagnosis support.

In conclusion, DL has the potential to improve medical operations in terms of efficiency and treatment quality. With FL and XAI, the challenges associated with data sharing and model interpretability can be addressed, leading to advancements in medical AI applications.

Note: The rest of the article includes details on the materials and methods used, results, functionalities, use cases, and future work.

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Revolutionizing Telecommunications: The Impact of Deep Learning on Global Connectivity

The telecommunications industry is on the brink of a significant transformation, thanks to the advent of deep learning technologies. Deep learning, a subset of artificial intelligence (AI), is poised to revolutionize global connectivity, bringing about unprecedented changes in the way we communicate and interact with the world.

Deep learning algorithms, which mimic the human brains ability to learn from experience, are being harnessed to improve the efficiency, reliability, and security of telecommunications networks. These algorithms can analyze vast amounts of data, identify patterns, and make predictions, enabling telecom companies to optimize network performance, predict and prevent outages, and enhance customer experience.

One of the most significant impacts of deep learning on telecommunications is in the area of network optimization. Telecom networks generate massive amounts of data every second. Analyzing this data manually to optimize network performance is virtually impossible. However, deep learning algorithms can sift through this data, identify patterns, and make predictions about network performance. This allows telecom companies to proactively address issues, optimize bandwidth allocation, and ensure seamless connectivity for their customers.

Moreover, deep learning is playing a crucial role in enhancing the security of telecommunications networks. Cybersecurity threats are a significant concern for telecom companies, given the sensitive nature of the data they handle. Deep learning algorithms can analyze network traffic, identify unusual patterns, and flag potential security threats. This proactive approach to cybersecurity can help prevent data breaches and protect customer information.

In addition to network optimization and security, deep learning is also transforming customer experience in the telecom sector. Telecom companies are using deep learning algorithms to analyze customer behavior, predict their needs, and personalize their services. This not only enhances customer satisfaction but also helps telecom companies retain their customers and increase their market share.

Furthermore, deep learning is paving the way for the development of advanced telecommunications technologies. For instance, it is playing a crucial role in the development of 5G technology, which promises to revolutionize global connectivity with its high-speed, low-latency connectivity. Deep learning algorithms are being used to optimize the allocation of 5G spectrum, enhance network performance, and ensure seamless connectivity.

However, the integration of deep learning into telecommunications is not without its challenges. Telecom companies need to invest in advanced infrastructure and skilled personnel to harness the power of deep learning. They also need to address concerns related to data privacy and security, given the sensitive nature of the data they handle.

Despite these challenges, the potential benefits of integrating deep learning into telecommunications are immense. It promises to revolutionize global connectivity, enhance customer experience, and pave the way for the development of advanced telecommunications technologies. As such, telecom companies around the world are investing heavily in deep learning, heralding a new era in global connectivity.

In conclusion, deep learning is set to revolutionize the telecommunications industry. Its ability to analyze vast amounts of data, identify patterns, and make predictions can help telecom companies optimize network performance, enhance security, and improve customer experience. While there are challenges to overcome, the potential benefits of integrating deep learning into telecommunications are immense. As we move towards a more connected world, deep learning will play a crucial role in shaping the future of telecommunications.

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Deep learning is an advanced type of artificial intelligence that uses neural networks and complex algorithms to process big data and produce detailed and contextualized outputs, simulating the ways in which human brains process and share information.

This type of artificial intelligence is the foundation for a number of emerging technologies, but despite its many advantages, it also brings forth distinct disadvantages that users need to be aware of.

A quick summary: There are both pros and cons to the practice of deep learning. As far as pros go:users can benefit from a machine learning solution that is highly scalable, automated, hands-off, and capable of producing state-of-the-art AI models, such as large language models. However, the cons are also significant: Deep learning is expensive, consumes massive amounts of power, and creates both ethical and security concerns through its lack of transparency.

Deep learning is a type of artificial intelligence that consists of neural networks with multiple layers, algorithmic training that teaches these neural networks to mimic human brain activity, and training datasets that are massive and nuanced enough to address various AI use cases. Deep learning uses large language models.

Because of its complex neural network architecture, deep learning is a mature form of artificial intelligence that can handle higher-level computation tasks, such as natural language processing, fraud detection, autonomous vehicle driving, and image recognition. Deep learning is one of the core engines running at the heart of generative AI technology.

Examples of deep learning models and their neural networks include the following:

Also see:Generative AI Companies: Top 12 Leaders

Deep learning is a specialized type of machine learning. It has more power and can handle large amounts of different types of data, whereas a typical machine learning model operates on more general tasks and a smaller scale.

Deep learning is primarily used for more complex projects that require human-level reasoning, like designing an automated chatbot or generating synthetic data, for example.

Learn more: Machine Learning vs. Deep Learning

Neural networks constitute a key piece of deep learning model algorithms, creating the human-brain-like neuron pattern that supports deep model training and understanding. A single-layer neural network is whats used in most traditional AI/ML models, but with deep learning models, multiple neural networks are present. A model is not a deep learning model unless it has at least three neural networks, but many deep learning models have dozens of neural networks.

Also see:Best Artificial Intelligence Software 2023

Deep learning models are designed to handle various inputs and learn through different methods. Many businesses choose to use deep learning models because they can learn and act on tasks independent of hands-on human intervention and data labeling. Their varied learning capabilities also make them great AI models for scalable automation.

Although there are subsets and nuances to each of these learning types, deep learning models can learn through each of the following methods:

Generative AI models are the latest and greatest in the world of artificial intelligence, giving businesses and individuals alike the opportunity to generate original content at scale, usually from natural language inputs.

But these models can only produce logical responses to user queries because of the deep learning and neural network mechanisms that lie at their foundation, allowing them to generate reasonable and contextualized responses on a grand scale and about a variety of topics.

More on this topic: Top 9 Generative AI Applications and Tools

Unstructured datasets especially large unstructured datasets are difficult for most artificial intelligence models to interpret and apply to their training. That means that, in most cases, images, audio, and other types of unstructured data either need to go through extensive labeling and data preparation to be useful, or do not get used at all in training sets.

With deep learning neural networks, unstructured data can be understood and applied to model training without any additional preparation or restructuring. As deep learning models have continued to mature, a number of these solutions have become multimodal and can now accept both structured written content and unstructured image inputs from users.

The neural network design of deep learning models is significant because it gives them the ability to mirror even the most complex forms of human thought and decision-making.

With this design, deep learning models can understand the connections between and the relevance of different data patterns and relationships in their training datasets. This human-like understanding can be used for classification, summarization, quick search and retrieval, contextualized outputs, and more without requiring the model to receive guided training from a human.

Because deep learning models are meant to mimic the human brain and how it operates, these AI models are incredibly adaptable and great multitaskers. This means they can be trained to do more and different types of tasks over time, including complex computations that normal machine learning models cant do and parallel processing tasks.

Through strategies like transfer learning and fine-tuning, a foundational deep learning model can be continually trained and retrained to take on a variety of business and personal use cases and tasks.

Deep learning models require more computing power than traditional machine learning models, which can be incredibly costly and require more hardware and compute resources to operate. These computing power requirements not only limit accessibility but also have severe environmental consequences.

Take generative AI models, for example: Many of these deep learning models have not yet had their carbon footprint tested, but early research about this type of technology suggests that generative AI model emissions are more impactful than many roundtrip airplane fights. While not all deep learning models require the same amount of energy and resources that generative AI models do, they still need more than the average AI tool to perform their complex tasks.

Deep learning models are typically powered with graphics processing units (GPUs), specialized chips, and other infrastructure components that can be quite expensive, especially at the scale that more advanced deep learning models require.

Because of the quantity of hardware these models need to operate, theres been a GPU shortage for several years, though some experts believe this shortage is coming to an end. Additionally, only a handful of companies make this kind of infrastructure. Without the right quantity and types of infrastructure components, deep learning models cannot run.

Data scientists and AI specialists more than likely know whats in the training data for deep learning models. However, especially for models that learn through unsupervised learning, these experts may not fully understand the outputs that come out of these models or the processes deep learning models follow to get those results.

As a consequence, users of deep learning models have even less transparency and understanding of how these models work and deliver their responses, making it difficult for anyone to do true quality assurance.

Even though deep learning models can work with data in varying formats, both unstructured and structured, these models are only as good as the data and training they receive.

Training and datasets need to be unbiased, datasets need to be large and varied, and raw data cant contain errors. Any erroneous training data, regardless of how small the error, could be magnified and made worse as models are fine-tuned and scaled.

Deep learning models have introduced a number of security and ethical concerns into the AI world. They offer limited visibility into their training practices and data sources, which opens up the possibility of personal data and proprietary business data getting into training sets without permission.

Unauthorized users could get access to highly sensitive data, leading to cybersecurity issues and other ethical use concerns.

More on a similar topic: Generative AI Ethics: Concerns and Solutions

Deep learning is a powerful artificial intelligence tool that requires dedicated resources and raises some significant concerns. However, the pros outweigh the cons at this point, as deep learning gives businesses the technology backbone they need to develop and run breakthrough solutions for everything from new pharmaceuticals to smart city infrastructure.

The best path forward is not to get rid of or limit deep learnings capabilities but rather to develop policies and best practices for using this technology in a responsible way.

Read next: 100+ Top Artificial Intelligence (AI) Companies

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Exploring the Potential of AI EfficientNet: Breakthroughs in Deep Learning and Computer Vision

Artificial intelligence (AI) has come a long way in recent years, with advancements in deep learning and computer vision leading the charge. One of the most promising developments in this field is the AI EfficientNet, a family of advanced deep learning models that have the potential to revolutionize various industries and applications. In this article, we will explore the potential of AI EfficientNet and discuss some of the breakthroughs it has made in deep learning and computer vision.

Deep learning, a subset of machine learning, involves training artificial neural networks to recognize patterns and make decisions based on large amounts of data. One of the most significant challenges in deep learning is creating models that are both accurate and efficient. This is where AI EfficientNet comes in. Developed by researchers at Google AI, EfficientNet is a family of models that are designed to be both highly accurate and computationally efficient. This is achieved through a technique called compound scaling, which involves scaling the depth, width, and resolution of the neural network simultaneously.

The development of AI EfficientNet has led to several breakthroughs in deep learning and computer vision. One of the most notable achievements is the improvement in image classification accuracy. EfficientNet models have been able to achieve state-of-the-art accuracy on the ImageNet dataset, a widely used benchmark for image classification algorithms. This is particularly impressive considering that EfficientNet models are significantly smaller and faster than other leading models, making them more suitable for deployment on devices with limited computational resources, such as smartphones and IoT devices.

Another significant breakthrough made possible by AI EfficientNet is the improvement in object detection and segmentation. These tasks involve identifying and locating objects within an image and are crucial for applications such as autonomous vehicles, robotics, and surveillance systems. EfficientNet models have been combined with other deep learning techniques, such as the Focal Loss and the Feature Pyramid Network, to create state-of-the-art object detection and segmentation systems. These systems have achieved top performance on benchmark datasets such as COCO and PASCAL VOC, demonstrating the potential of AI EfficientNet in these critical applications.

The advancements made by AI EfficientNet in deep learning and computer vision have far-reaching implications for various industries and applications. In healthcare, for example, EfficientNet models can be used to improve the accuracy of medical image analysis, enabling faster and more accurate diagnosis of diseases. In agriculture, these models can be used to analyze satellite imagery and identify areas that require attention, such as regions affected by pests or diseases. In retail, AI EfficientNet can be used to improve the accuracy of visual search engines, making it easier for customers to find the products they are looking for.

Furthermore, the efficiency of AI EfficientNet models makes them ideal for deployment on edge devices, such as smartphones, drones, and IoT devices. This opens up new possibilities for real-time applications, such as facial recognition, object tracking, and augmented reality. By bringing advanced deep learning capabilities to these devices, AI EfficientNet has the potential to transform the way we interact with technology and the world around us.

In conclusion, AI EfficientNet represents a significant breakthrough in deep learning and computer vision, offering state-of-the-art accuracy and efficiency in a range of applications. From healthcare to agriculture, retail to edge devices, the potential of AI EfficientNet is vast and exciting. As researchers continue to refine and expand upon this technology, we can expect to see even more impressive advancements in the field of artificial intelligence, ultimately leading to a more connected, intelligent, and efficient world.

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Exploring the Synergy between AI Deep Learning and Quantum Computing: Unleashing New Possibilities

The intersection of artificial intelligence (AI) deep learning and quantum computing is creating a powerful partnership that promises to revolutionize the way we solve complex problems and transform industries. As we continue to explore the synergy between these two cutting-edge technologies, we are witnessing the emergence of new possibilities and applications that were once considered science fiction.

AI deep learning, a subset of machine learning, involves the use of artificial neural networks to enable machines to learn and make decisions without explicit programming. This technology has already made significant strides in areas such as image and speech recognition, natural language processing, and autonomous vehicles. However, the computational power required to process and analyze the vast amounts of data involved in deep learning is immense, and this is where quantum computing comes into play.

Quantum computing, which leverages the principles of quantum mechanics, has the potential to solve problems that are currently intractable for classical computers. Unlike classical computers that use bits to represent information as 0s and 1s, quantum computers use quantum bits, or qubits, which can represent both 0 and 1 simultaneously. This allows quantum computers to perform multiple calculations at once, exponentially increasing their processing power.

The convergence of AI deep learning and quantum computing is expected to unlock new possibilities in various fields. For instance, in drug discovery, quantum computing can be used to simulate and analyze complex molecular structures, while AI deep learning can help identify patterns and predict the effectiveness of potential treatments. This powerful combination could significantly accelerate the drug discovery process, ultimately leading to more effective treatments for a wide range of diseases.

In the field of finance, quantum computing can optimize trading strategies and risk management, while AI deep learning can analyze large datasets to predict market trends and identify investment opportunities. Together, these technologies could revolutionize the financial industry by providing more accurate predictions and enabling faster, more informed decision-making.

Moreover, the partnership between AI deep learning and quantum computing has the potential to enhance cybersecurity. Quantum computers can efficiently solve complex cryptographic problems, while AI deep learning can detect and respond to cyber threats in real-time. This combination could lead to the development of more secure communication systems and robust defense mechanisms against cyberattacks.

However, the integration of AI deep learning and quantum computing is not without its challenges. One of the main hurdles is the current lack of mature quantum hardware, as quantum computers are still in their infancy and not yet capable of outperforming classical computers in most tasks. Additionally, developing algorithms that can harness the full potential of quantum computing for AI deep learning is a complex task that requires a deep understanding of both fields.

Despite these challenges, researchers and tech giants such as Google, IBM, and Microsoft are investing heavily in the development of quantum computing and AI deep learning technologies. As these efforts continue, we can expect to see significant advancements in the coming years that will further strengthen the partnership between AI deep learning and quantum computing.

In conclusion, the intersection of AI deep learning and quantum computing holds immense promise for solving complex problems and transforming industries. By harnessing the power of these two cutting-edge technologies, we can unlock new possibilities and applications that will shape the future of technology and innovation. As we continue to explore the synergy between AI deep learning and quantum computing, we are poised to witness a technological revolution that will redefine the boundaries of what is possible.

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A research team from the Universidad Carlos III de Madrid (UC3M), together with University College London in the United Kingdom, has carried out a study to analyze how chronic pain affects each patient's body.Within this framework, a deep learning method has been developed to analyze the biometric data of people with chronic conditions.

The analysis is based on the hypothesis that people with chronic lower back pain have variations in their biometric data compared to healthy people.These variations are related to body movements or walking patterns and are believed to be due to an adaptive response to avoid further pain or injury.

However, research to date has found it difficult to accurately distinguish these biometric differences between people with and without pain.There have been several factors, such as the scarcity of data related to this issue, the particularities of each type of chronic pain and the inherent complexity in the measurement of biometric variables.

People with chronic pain often adapt their movements to protect themselves from further pain or injury.This adaptation makes it difficult for conventional biometric analysis methods to accurately capture physiological changes.Hence the need to develop this system, says Doctor Mohammad Mahdi Dehshibi, a postdoctoral researcher at the i_mBODY Laboratory in UC3M's Computer Science Department, who led this study.

The research carried out by UC3M has developed a new method that uses a type of deep learning called s-RNNs (sparsely connected recurrent neural networks) together with GRUs (closed recurrent units), which are a type of neural network unit that is used to model sequential data.With this development, the team has managed to capture changes in pain-related body behavior over time.Furthermore, it surpasses existing approaches to accurately classify pain levels and pain-related behavior.

The innovation of the proposed method has been to take advantage of an advanced deep learning architecture and add additional features to address the complexities of sequential data modelling.The ultimate goal is to achieve more robust and accurate results related to sequential data analysis.

One of the main research focuses in our lab is the integration of deep learning techniques to develop objective measures that improve our understanding of people's body perceptions through the analysis of body sensor data, without relying exclusively on direct questions to individuals, says Ana Tajadura Jimnez, a lecturer from UC3M's Computer Science Department and lead researcher of the BODYinTRANSIT project, who leads the i_mBODY Laboratory.

The new method developed by the UC3M research team has been tested with the EmoPain database, which contains data on pain levels and behaviors related to these levels.This study also highlights the need for a reference database dedicated to analyzing the relationship between chronic pain and biometrics.This database could be used to develop applications in areas such as security or healthcare, says Mohammad Mahdi.

These results of this research are used in the design of new medical therapies focused on the body and different clinical conditions.In healthcare, the method can be used to improve the measurement and treatment of chronic pain in people with conditions such as fibromyalgia, arthritis and neuropathic pain.It can help control pain-related behaviors and tailor treatments to improve patient outcomes.In addition, it can be beneficial for monitoring pain responses during post-surgical recovery, says Mohammad Mahdi.

In this regard, Ana Tajadura also highlights the relevance of this research for other medical processes: In addition to chronic pain, altered movement patterns and negative body perceptions have been observed, such as in eating disorders, chronic cardiovascular disease or depression, among others .It is extremely interesting to carry out studies using the above method in these populations in order to better understand medical conditions and their impact on movement.These studies could provide valuable information for the development of more effective screening tools and treatments, and improve the quality of life of people affected by these conditions.

In addition to health applications, the results of this project can be used for the design of sports, virtual reality, robotics or fashion and art applications, among others.

This research is carried out within the framework of the BODYinTRANSIT project, led by Ana Tajadura Jimnez and funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (GA 101002711).

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Researchers at the University of California, Los Angeles have conducted a study to test the cognitive abilities of deep learning models. Using the GPT-3 large language model, they found that it performed at or above human capabilities for resolving complex reasoning problems. Specifically, the researchers tested the model on analogical tasks, such as the Ravens Progressive Matrices, which require test takers to identify patterns.

The results showed that the AI performed at the higher end of humans scores and made a few of the same mistakes. The researchers also asked the AI to solve a set of SAT analogy questions involving word pairs, in which it performed slightly above the average human level. However, the AI struggled with analogy problems based on short stories.

The study suggested that the AI could be employing a mapping process similar to how humans approach these types of problems. The researchers speculated that the AI might have developed some alternate form of machine intelligence.

It is important to note that the AIs performance was based on its training data, which has not been publicly disclosed by OpenAI, the creator of GPT-3. Therefore, it is unclear whether the AI is genuinely reasoning or if it is simply relying on its training data to generate answers.

Overall, this study adds to the ongoing discussion about the cognitive abilities of AI systems. While the AI showed promise in certain areas, there are still limitations and questions about its true intelligence. Further research is needed to understand the capabilities and limitations of deep learning models.

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The Cognitive Abilities of Deep Learning Models - Fagen wasanni

School of Physics, Mathematics and Computing Department of Computer Science and Software Engineering

The University of Western Australia (UWA) is ranked among the top 100 universities in the world and a member of the prestigious Australian Group of Eight research intensive universities. With a strong research track record, vibrant campus and working environments, supported by the freedom to innovate and inspire, there is no better time to join Western Australias top university.

About the team

The Department of Computer Science and Software Engineering under the School of Physics, Mathematics and Computing is renowned for its award-winning researchers, teachers and facilities. The broad-based undergraduate and postgraduate programs are complemented by a wide range of research activities and the School is a leader in developing graduates with high level expertise in computer programming and the methods involved in performing complex computations and processing data. In the resource rich state of Western Australia the opportunities for partnership and collaborative research are extensive and the School has well established links with industry.

About the opportunity

As the appointee, you will primarily be involved in the development of state-of-the-art computer vision and deep learning algorithms, with a focus on object detection. The scope of this research has broad applicability, including but not limited to domains such as ecology, agriculture, augmented reality, and surveillance. As a key member of our multidisciplinary team, you will contribute to ground-breaking research, creating cutting-edge solutions that have real-world applications. This opportunity will provide you with a platform to leverage your skills and expertise to shape the future of these fields, and also a unique chance to collaborate with other brilliant minds.

About you

You will be an ambitious individual looking to push the boundaries of technology and make significant contributions to the field. This opportunity will provide you with a platform to leverage your skills and expertise to shape the future of these fields, and also a unique chance to collaborate with other brilliant minds.

To be considered for this role, you will demonstrate:

About your application

Full details of the position's responsibilities and the selection criteria are outlined in the position description: PD - Research Fellow - 51531.pdf

The content of your Resume and Cover Letter should demonstrate how you meet the selection criteria.

Closing date: 11:55 PM AWST on Sunday, 13 August 2023

To learn more about this opportunity, please contact Professor Mohammed Bennamoun at mohammed.bennamoun@uwa.edu.au) and Professor Farid Boussaid at farid.boussaid@uwa.edu.au

This position is only open to applicants with relevant rights to work in Australia.

Application Details: Please apply online via the Apply Now button.

Our commitment to inclusion and diversity

UWA is committed to a diverse workforce and an equitable and inclusive workplace. We celebrate difference and believe diversity is fundamental to achieving our goals as a globally recognised Top 100 educational and research institution. We are committed to creating a safe work environment for Aboriginal and Torres Strait Islander people, women, people from culturally and linguistically diverse backgrounds, the LGBTIQA+ community and people living with disability.

Should you have any queries relating to your application, please contact the individual named in the advertisement. Alternatively, contact the Talent team at talent-hr@uwa.edu.au with details of your query. To enable a quick response, please include the 6-digit job reference number and a member of the team will respond to your enquiry.

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Research Fellow: Computer Vision and Deep Learning job with ... - Times Higher Education