Bringing Woodwalkers to life was no small feat, and PXO rose to the challenge as the lead VFX house, working closely with Blue Eyes Productions. Under the guidance of PXO VFX Supervisor Max Riess, the team meticulously crafted photorealistic animal transformations that seamlessly blend live-action and CGI.The film follows Carag, a shapeshifting puma, and his adventures at Clearwater High, a secret school for Woodwalkers. Creating lifelike digital creatures required a deep understanding of animal behavior, ensuring that each transformation felt natural and emotionally resonant.From concept development to final execution, PXO employed cutting-edge techniques to capture dynamic movements and detailed textures, making every shift between human and animal both believable and visually stunning. Woodwalkers pushes the boundaries of creature VFX, setting a new standard for shapeshifter storytelling in cinema.The post Turning Actors into Animals Woodwalkers VFX Breakdown appeared first on Vfxexpress.

Deep learning models, having revolutionized areas of computer vision and natural language processing, become less efficient as they increase in complexity and are bound more by memory bandwidth than pure processing power. The latest GPUs struggle with tremendous bandwidth limitations as they are constantly needed to move data between varying levels of memory. This process slows computations and increases energy consumption. The challenge is to develop methods that minimize unnecessary data transfers while maximizing computational throughput improving training and inference efficiency.A primary issue in deep learning computation is optimizing data movement within GPU architectures. Although GPUs provide immense processing power, their performance is often restricted by the bandwidth required for memory transfers rather than their computing capabilities. Current deep learning frameworks struggle to address this inefficiency, leading to slow model execution and high energy costs. FlashAttention has previously demonstrated performance improvements by reducing redundant data movement, but existing methods rely heavily on manual optimizations. The absence of an automated, structured approach to GPU workload optimization remains a major hurdle in the field.Existing techniques such as FlashAttention, grouped query attention, KV-caching, and quantization aim to reduce memory transfer costs while maintaining computational efficiency. FlashAttention, for example, reduces overhead by performing key operations within local memory, eliminating unnecessary intermediate data transfers. However, these methods have traditionally required manual optimization tailored to specific hardware. Some automated approaches, such as Triton, exist but have not yet reached the performance levels of manually tuned solutions. The demand for a systematic and structured approach to developing memory-efficient deep learning algorithms remains unfulfilled.University College London & MIT researchers introduced a diagrammatic approach to optimize deep learning computations. Their method extends Neural Circuit Diagrams to account for GPU resource usage and hierarchical memory distribution. By visualizing computational steps, this technique enables systematic derivation of GPU-aware optimizations. The researchers propose a framework that simplifies algorithmic design by providing a structured methodology for performance modeling. The approach focuses on minimizing data movement, optimizing execution strategies, and leveraging hardware features to achieve high computational efficiency.The proposed methodology employs a hierarchical diagramming system that models data transfers across different levels of GPU memory. This framework enables researchers to break down complex algorithms into structured visual representations, allowing for the identification of redundant data movements. Researchers can derive streaming and tiling strategies that maximize throughput by relabeling and restructuring computations. The diagrammatic approach also considers quantization and multi-level memory structures, making it applicable across different GPU architectures. The framework provides a scientific foundation for GPU optimization beyond ad-hoc performance tuning.The research demonstrates that the diagrammatic approach significantly enhances performance by reducing memory transfer inefficiencies. FlashAttention-3, optimized using this method, achieves a 75% improvement in forward speed on newer hardware. The study shows that utilizing structured diagrams for GPU-aware optimizations enables high efficiency, with empirical results confirming the approachs effectiveness. Specifically, FP16 FlashAttention-3 reaches 75% utilization of its maximum theoretical performance, while FP8 achieves 60%. These findings highlight the potential of diagram-based optimization for improving memory efficiency and computational throughput in deep learning.This study introduces a structured framework for deep learning optimization, focusing on minimizing memory transfer overhead while enhancing computational performance. The proposed method provides a systematic alternative to traditional manual optimization approaches. Researchers can better understand hardware constraints and develop efficient algorithms by leveraging diagrammatic modeling. The findings suggest that structured GPU optimization can significantly improve deep learning efficiency, paving the way for more scalable and high-performance AI models in real-world applications.Check outthe Paper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our80k+ ML SubReddit. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces a Parameter-Efficient Fine-Tuning Framework: LoRA, QLoRA, and Test-Time Scaling for Optimized LLM PerformanceNikhilhttps://www.marktechpost.com/author/nikhil0980/How to Use Jupyter Notebooks for Interactive Coding and Data AnalysisNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from Aalto University Introduces VQ-VFM-OCL: A Quantization-Based Vision Foundation Model for Object-Centric LearningNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Identifies Function Vector Heads as Key Drivers of In-Context Learning in Large Language Models Parlant: Build Reliable AI Customer Facing Agents with LLMs (Promoted)

Author(s): Kaitai Dong Originally published on Towards AI. Figure 1: Gaussian mixture model illustration [Image by AI]IntroductionIn a time where deep learning (DL) and transformers steal the spotlight, its easy to forget about classic algorithms like K-means, DBSCAN, and GMM. But heres a hot take: for anyone tackling real-world clustering and anomaly detection challenges, these statistical workhorses remain indispensable tools with surprising staying power.Consider the everyday clustering puzzles: customer segmentation, social network analysis, or image segmentation. K-means has been used to solve these problems for decades with its simple centroid-based approach. When data forms irregular shapes, DBSCAN steps in with its density-based algorithm to identify non-convex clusters that leave K-means bewildered.But real-world data rarely forms neat, separated bubbles. Enter Gaussian Mixture Model and its variants! GMMs acknowledge the fundamental uncertainty in cluster assignments. By modeling the probability density of normal behavior, they can identify observations that dont fit the expected pattern without requiring labeled examples. So before chasing the latest neural architecture for your clustering or segmentation task, consider the statistical classics such as GMMs.Many people can confidently talk about how K-means works but I bet my good dollars that not many have that confidence when it comes to GMMs. This article will discuss the math behind GMM and its variants in an understandable way (I will try my best!), and showcase why it deserves more attention for your next clustering tasks.Remember this. Classics never make a comeback. They wait for that perfect moment to take the spotlight from overdone, tired trends.What is a Gaussian Mixture Model?A Gaussian mixture is a function that is composed of several Gaussian distributions, each identified by k {1,, K}, where K is the number of clusters of our dataset, which you must know in advance. Each Gaussian distribution k in the mixture contain the following parameters:A mean that defines its center.A covariance matrix that describes its shape and orientation. This would be equivalent to the dimensions of an ellipsoid in a multivariate scenario.A mixing coefficient that defines the weight of the Gaussian function, where 0 and the sum of k adds up to 1.Mathematically, it can be written in:where p(x) represents the probability density at point x, and N(x|, ) is the multivariate Gaussian density with mean and covariance matrix .Equations all look scary. But lets take a step back and look at this multivariate Gaussian density function N(x|, ) and the dimension of each parameter in it. Assume the dataset include N = 500 three-dimensional data points (D=3), then the dataset x is essentially a 500 3 matrix, is a is a 3 3 matrix. The output of the Gaussian distribution function will be a 500 1 vector.When working with a GMM, we face a circular problem:To know which Gaussian cluster each data point belongs to, we need to know the parameters of each Gaussian (means, covariances, weights).But to estimate these parameters correctly, we need to know which data points belong to each Gaussian.To break this cycle, here enters the Expectation-Maximization (EM) algorithm, where it makes educated guesses and then refines them iteratively.Parameter estimation with EM algorithmEM algorithm helps determine the optimal values for the parameters of a GMM through the following steps:Step 1: Initialize Start with random guesses for parameters (, , ) of each Gaussian cluster.Step 2: Expectation (E-step) Calculate how much each point belongs to each Gaussian cluster and then compute a set of responsibilities for each data point, which represents the probabilities that the data point comes from each cluster.Step 3: Maximization (M-step) Update each Gaussian cluster using all the instances in the dataset, with each instance weighted by the estimated probability (a.k.a. responsibilities) that it belongs to that cluster. Specifically, new means are the weighted average of all data points, where weights are the responsibilities. New covariances are the weighted spread around each new mean. Finally, new mixing weights are the fraction of the total responsibilities each component receives. Note that each clusters update will mostly be impacted by the instances it is most responsible for.Step 4: Repeat Go back to Step 2 with these updated parameters and continue until the changes become minimal (convergence).Often, people get confused with the M-step as a lot of terms are thrown in. I will use the previous example (500 3-D data points with 3 Gaussian clusters) to break it down into more concrete terms.For updating the means, were doing a weighted average where each point contributes according to its responsibility value to the corresponding Gaussian cluster. Mathematically, for kth Gaussian cluster,new means = (sum of [responsibility_ik point_i]) / (sum of all responsibilities for cluster k)For updating the covariances, we use a similar weighted approach. For each point, calculate how far it is from the new mean, and then multiply this deviation by its transpose to get a matrix. Subsequently, weight this matrix by the points responsibility and sum these weighted matrices across all points. Finally, divide it by the total responsibility for that cluster.For updating the mixing weights, we simply sum up all the responsibilities for cluster k and then divide by the total number of data points.Lets say for Gaussian cluster 2:The sum of responsibilities is 200 (out of 500 points)The weighted sum of points is (400, 600, 800)The weighted sum of squared deviations gives a certain covariance matrixThen:New mean for cluster 2 = (400, 600, 800)/200 = (2, 3, 4)New mixing weight = 200/500 = 0.4New covariance = (weighted sum of deviations)/200Hopefully it makes a lot more sense now!!Clustering with GMMNow that I have an estimate of the location, size, shape, orientation, and relative weights of each Gaussian cluster, GMM can easily assign data point to the most likely cluster (hard clustering) or estimate the probability that it belongs to a particular cluster (soft clustering).The implementation of GMM in Python is quite simple and straightforward, thanks to the good old scikit-learn library. Here I provide a sample code for a clustering task using built-in GMM using randomly generated data points with 3 clusters, shown in Figure 2.Figure 2: Data points for clustering [Image by author]The Python code is given below:from sklearn.mixture import GaussianMixturegm = GaussianMixture(n_components=3, n_init=10, random_state=42)gm.fit(X)# To see the parameters the GM has estimated# weights, means, and covariancesgm.weights_gm.means_gm.covariances_# To make a prediction for the data point# hard clusteringgm.predict(X)# soft clusteringgm.predict_proba(X).round(2)Figure 3 illustrates the cluster locations, decision boundaries and the density contours of the GMM (it estimates the density of the model at any given location).Figure 3: Cluster locations, decision boundaries, and density contours of a trained GMM [Image by author]It looks like the GMM has clearly found a great solution! But it is worth noting that real-life data is not always so Gaussian and low-dimensional. EM can struggle to converge to the optimal solution when the problem is of high dimensions and high number of clusters. To tackle this issue, you can limit the number of parameters GMM has to learn. One way to do this is to limit the range of shapes and orientations that the clusters can have. This is achieved by imposing constraints on the covariance matrices, which can be done by setting the covariance_type hyperparameter.gm_full = GaussianMixture(n_components=3, n_init=10, covariance_type="full", # default value random_state=42)"full" (default): No constraint, all clusters can take on any ellipsoidal shape of any size [1]."spherical": All clusters must be spherical, but they can have different diameters (i.e., different variances)."diag": Clusters can take on any ellipsoidal shape of any size, but the ellipsoid's axes must be parallel to the axes (i.e., the covariance matrices must be diagonal)."tied": All clusters must have the same shape, which can be any ellipsoid (i.e., they all share the same covariance matrix).To show the difference with the default setting, Figure 4 illustrates the solutions found by the EM algorithm when covariance_type is set to "tied".Figure 4: Clustering result of the same task using GMM with tied clusters [Image by author]It is also important to discuss the computational complexity of training a GMM. It largely depends on the number of data points m, the number of dimensions n, the number of clusters k, and the constraints on the covariance matrices (4 types mentioned above). If covariance_type is "spherical" or "diag", the complexity is O(kmn), assuming the data has a clustering structure. If covariance_type is "tied" or "full", the complexity then becomes O(kmn + kn), this will not scale well [1].Finding the right number of clustersThe given example is quite simple partially due to the fact that the number of clusters is already known when I generated the dataset. But when you do not have this information prior to the training, certain metrics are required to help determine the optimal number of clusters.For GMM, you can try to find the model that minimizes a theoretical information criterion, such as the Bayesian information criterion (BIC) and the Akaike information criterion (AIC), defined in equations below.BIC = log(m)*p 2*log(L)AIC = 2*p 2*log(L)Where m is the number of data points, p is the number of parameters learned by the model, and L is the maximized value of the likelihood function of the model. The computation of these values are simple with Python.gm.bic(X)gm.aic(X)BIC and AIC penalize models that have more parameters to learn (e.g., more clusters) and reward the models that fit the data well. The lower the value, the better model fits the data. In practice, you can set a range of numbers of clusters k and plot the BIC or AIC against different k and find the one with the lowest BIC or AIC [2].Variants of GMMI find some variants of GMM quite useful and handy and often can make further improvements on its classic form.Bayesian GMM: It is capable of giving weights equal to or close to zero to unnecessary clusters. In practice, you can set the number of clusters n_components to a value that you have good reasons to believe is greater than the actual optimal number of clusters, and then it will automatically handle the learning for you.Robust GMM: It addresses GMMs over-sensitivity to outliers issue by modifying the objective function. Instead of maximizing the standard log-likelihood, it uses robust estimators to put less weight on points that are far from cluster centers. It provides a more stable outcome.Online/Incremental GMM: It deals with computational and memory limitations of standard GMMs. Parameters are updated after seeing each new data point or small batch, rather than requiring the full dataset. It also includes a forgetting mechanism that allows the model to forget older data and adapt more to non-stationary distributions.GMM vs K-meansSince real-life data is often complex, GMM generally outperforms K-means in clustering and segmentation tasks. I typically run K-means first as a baseline and then try GMM or its variants to see if additional complexity provides any meaningful improvements. But lets compare them side by side and see the main differences between these two classic algorithms.Figure 5: Comparison between K-means and GMM over different aspects [Image by author]Bonus point of GMM Handy anomaly detection tool!Using a GMM for anomaly detection task is simple: any instance located in a low-density region can be considered as an anomaly. However, the trick is you must define what density threshold you want to use. As an example, I will use GMM to identify abnormal network traffic patterns.The features included in this task are as follows:Packet size (bytes)Inter-arrival time (ms)Connection duration (s)Protocol-specific valueEntropy of packet payloadTCP window sizeThe raw dataset looks like this:Figure 6: The headers and value formats of the network traffic dataset [Image by author]The code snippet will show how to preprocess the data, train and evaluate the model, and also compare with other common anomaly detection methods.import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom sklearn.mixture import GaussianMixturefrom sklearn.preprocessing import StandardScalerfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import precision_recall_curve, average_precision_scorefrom sklearn.metrics import f1_score# raw_df has been shown previouslydf = raw_df.copy()X = df.drop(columns=['is_anomaly'])y = df['is_anomaly']# Split the dataX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.3, random_state=42, stratify=y)# Scale the featuresscaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)X_test_scaled = scaler.transform(X_test)# We'll create models using only normal traffic data for training# This is a common approach for anomaly detectionX_train_normal = X_train_scaled[y_train == 0]# Try different numbers of components to find the best fitn_components_range = range(1, 10)bic_scores = []aic_scores = []for n_components in n_components_range: gmm = GaussianMixture(n_components=n_components, covariance_type='full', random_state=42) gmm.fit(X_train_normal) bic_scores.append(gmm.bic(X_train_normal)) aic_scores.append(gmm.aic(X_train_normal))# Choose the optimal number of components based on BICoptimal_components = n_components_range[np.argmin(bic_scores)]print(f"Optimal number of components based on BIC: {optimal_components}")# Train the final modelgmm = GaussianMixture(n_components=optimal_components, covariance_type='full', random_state=42)gmm.fit(X_train_normal)# Calculate negative log probability (higher means more anomalous)# gmm_train_scores is very important to determine the threshold percentile in the evaluationgmm_train_scores = -gmm.score_samples(X_train_scaled)gmm_test_scores = -gmm.score_samples(X_test_scaled)def evaluate_model(y_true, anomaly_scores, threshold_percentile=3): """ Evaluate model performance with various metrics Parameters: y_true: True labels (0 for normal, 1 for anomaly) anomaly_scores: Scores where higher means more anomalous threshold_percentile: Percentile for threshold selection Returns: Dictionary of performance metrics """ # Use a percentile threshold from the training scores threshold = np.percentile(anomaly_scores, 100 - threshold_percentile) # Predict anomalies y_pred = (anomaly_scores > threshold).astype(int) # calculate evaluation metrics f1 = f1_score(y_true, y_pred) precision, recall, _ = precision_recall_curve(y_true, anomaly_scores) avg_precision = average_precision_score(y_true, anomaly_scores) return { 'f1_score': f1, 'avg_precision': avg_precision, 'precision_curve': precision, 'recall_curve': recall, 'threshold': threshold, 'y_pred': y_pred }# Calculate metricsgmm_results = evaluate_model(y_test, gmm_test_scores)Lets throw in a few common anomaly detection methods, i.e., Isolation Forest, One-Class SVM, and Local Outlier Factor (LOF), and check their performance. Since irregular traffic pattern is a rare case, so I will use PR-AUC as the evaluation metric for models effectiveness. The result is given in Figure 7, where the closer the result to 1 the more accurate the model is.Figure 7: Comparative analysis for network traffic detection task using PR-AUC metric to evaluate GMM, Isolation Forest, One-class SVM, and LOF. [Image by author]The result shows GMM is pretty strong in identifying irregular network traffic and outperforms other common methods! GMM can be a good start for anomaly detection tasks especially if normal behaviors include multiple distinct patterns or if you need probability anomaly scores.Real-life cases are usually more complex than the steps I have shown, but hopefully this blog provides a good foundation for you to understand how GMM works and how you can implement it for your clustering or anomaly detection tasks.References[1] Aurelien Geron. Hands-on machine learning with scikit-learn, keras & tensorflow. OReilly, 2023[2] Bayesian information criterion, Wikipedia. https://en.wikipedia.org/wiki/Bayesian_information_criterionJoin thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AI

You can trust VideoGamer. Our team of gaming experts spend hours testing and reviewing the latest games, to ensure you're reading the most comprehensive guide possible. Rest assured, all imagery and advice is unique and original. Check out how we test and review games hereMarvel Rivals has finally launched the highly anticipated Galactas Cosmic Adventure event, which introduces players to the chaotic and exhilarating Clone Rumble mode. In the game mode, teams vote for one hero, leaving players with only two options for heated 6v6 clashes on the Intergalactic Empire of Wakanda: Birnin TChalla map.The mode eliminates standard hero limitations, allowing for bizarre scenarios such as 12 Groots transforming the battlefield into a forest or a dozen Mister Fantastics colliding in stretchy mayhem. Along with the mode, the event features a board game-style adventure in which players roll dice with Galactas Power Cosmic tokens collected during missions to unlock gifts such as the free Black Widow Mrs. Barnes cowboy skin, sprays, and Units.Clone Rumble is scheduled to take place over three weekends: March 7-10, 14-17, and 21-24. However, the secret reward, which is expected to be unlocked around March 31, is sparking intrigue. Players are buzzing with thoughts, with many speculating that it could indicate the release of a new map.Marvel Rivals player theory suggests new map coming at the end of Clone Rumble eventMarvel Rivals players are now taking to social media to speculate about the mystery reward thats positioned at the center of the Galactas Cosmic Adventure event map. While the event itself grants players multiple units, cosmetic items and a free skin, the mystery reward hasnt been leaked nor teased as of yet.Cosmic Adventure event board with different maps. Image by Reddit/BakeMate.In the Reddit post shared by BakeMate, they circled each different map from the existing roll that is featured on the event screen and the one at the center. In the game there are six different areas, namely: Empire of Eternal Night, Hydra Charteris Base, Intergalactic Empire of Wakanda, Klyntar, Tokyo 2099, and Yggsgard.In the post they claim, new map revealed in 23 days 10 hours theory? Agreeing to the theory, several players shared a similar sentiment where one user wrote, Each part of the map is segregated by the floating stones, whilst leaving the middle part almost empty. Theres a piece of floating stone that says itll be revealed in 23 days and 10 hours. My theory is that we are gonna get a new map, probably Krakoa?According to the leaks, Krakoa is one of the highly speculated maps that is often teased in the files revealed by data miners and is said to be released in Marvel Rivals Season 2, which will also bring the iconic Hellfire Gala theme to the game.Marvel RivalsPlatform(s):macOS, PC, PlayStation 5, Xbox Series S, Xbox Series XGenre(s):Fighting, ShooterRelated TopicsMarvel Rivals Subscribe to our newsletters!By subscribing, you agree to our Privacy Policy and may receive occasional deal communications; you can unsubscribe anytime.Share

LAS VEGAS, NEVADA - MARCH 08: (R-L) Magomed Ankalaev of Russia punches Alex Pereira of Brazil in the ... [+] UFC light heavyweight championship fight during the UFC 313 event at T-Mobile Arena on March 08, 2025 in Las Vegas, Nevada. (Photo by Jeff Bottari/Zuffa LLC)Zuffa LLCThe UFC has a new light heavyweight champion, and it happened in a pretty lackluster main event. Magomed Ankalaev did what he needed to do to dethrone Alex Pereira via unanimous decision (49-46, 48-47 2).Pereira was not robbed. Ankalaev was able to neutralize Pereiras offense, and he used his ability to hold Poatan against the cage to earn the unanimous decision victory.Pereira took the first round with steady leg kicks and Octagon control. Ankalaev easily claimed the second frame as he turned up the pressure and landed a big punch at the end of the round that wobbled Pereira.I thought Pereira rebounded well in the third round and won an uneventful frame, but thats where the judges saw things a bit differently.All three judges gave Ankalaev the third round.Ankalaev went to a wrestle-heavy approach, holding Pereira against the cage for most of the round. Ankalaev didnt do much damage, but he definitely controlled the five minutes.In the last round, Pereira did the better work, but one judge inconsequentially gave it to Ankalaev.The MMA world reacted to the title change, recognizing that the result was not necessarily best for business or exciting, but the decision wasnt horrible. I scored the fight 48-47 for Pereira, but I cannot scream bloody murder over Ankalaev winning.Here is some of the reaction to the result.Well see whats next for Pereira and who Ankalaev defends the title against first.UFC 313: Justin Gaethje Delivers in His Comeback FightJustin Gaethje is an unbelievable human being. Gaethje ran his record against Rafael Fiziev to 2-0 with a gutsy performance in the UFC 313 co-main event. After losing the first round, Gaethje bounced back with two big rounds to pull out the unanimous decision win.Gaethje landed this huge uppercut to drop Fiziev in the second round, and it changed the entire fight.After the fight, the praise and appreciation for Gaethje poured in on social media.The win puts Gaethje back on the right track following the devastating KO loss to Max Holloway at UFC 300. It is possible Gaethje could challenge for the lightweight title in his next fight.Fiziev deserves respect for his performance. He took the fight on 12 days' notice and performed admirably. He seemed to run out of gas late, and that might have been because he didnt have a complete camp.In any case, he should not lose ground in the next UFC rankings. These two rightfully secured the Fight of the Night performance bonus.Take a look at the results and highlights of every finish below.UFC 313 Results and Bonus WinnersMain CardLight Heavyweight Championship Bout: Magomed Ankalaev def. Alex Pereira via unanimous decision (49-46, 48-47x2)Lightweight Bout: Justin Gaethje def. Rafael Fiziev via unanimous decision (29-28x3)Lightweight Bout: Ignacio Bahamondes def. Jalin Turner via first-round submission (arm triangle)Bahamondes is becoming one of the more well-rounded lightweights in the world. This sparkling submission earned him one of the performance bonuses.Womens Strawweight Bout: Amanda Lemos def. Iasmin Lucindo via unanimous decision (29-28x3)Lightweight Bout: Mauricio Ruffy def. King Green via first-round KORuffys spinning wheel kick is one of the most amazing KOs in UFC history and he is a legit star. He earned a performance bonus for this explosive finish.Preliminary CardFlyweight Bout: Joshua Van def. Rei Tsuruya via unanimous decision (30-27x3)Middleweight Bout: Brunno Ferreira def. Armen Petrosyan via second-round submission (arm bar)Welterweight Bout: Carlos Leal def. Alex Morono via first-round TKOEarly Preliminary CardFeatherweight Bout: Mairon Santos def. Francis Marshall via split decision (29-28x2, 27-30)Middleweight Bout: Ozzy Diaz def. Djorden Santos via unanimous decision (29-28x3)

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While there is an abundance of melee weapons in Red Dead Redemption 2, there are seven variations of the default knife that players can find through specific encounters and locations. Knives are the perfect tool for skinning animals, killing enemies during stealth, and genuinely just looking like a cool outlaw with the right tools for the job.

The Legend of Zelda: Tears of the Kingdom takes after its predecessor, Breath of the Wild, by featuring a vast and open Hyrule populated with all sorts of characters. While there's already plenty to do in the game just from completing the main story, there are also countless side quests and objectives that players may not even encounter during their playthrough.