Telecom companies last year spent nearly $295 billion in capital expenditures and over $1 trillion in operating expenditures. These large expenses are due in part to laborious manual processes that telcos face when operating networks that require continuous optimizations. For example, telcos must constantly tune network parameters for tasks — such as transferring calls from one network to another or distributing network traffic across multiple servers — based on the time of day, user behavior, mobility and traffic type. These factors directly affect network performance, user experience and energy consumption. To automate these optimization processes and save costs for telcos across the globe, NVIDIA today unveiled at GTC Paris its first AI Blueprint for telco network configuration. At the blueprint’s core are customized large language models trained specifically on telco network data — as well as the full technical and operational architecture for turning the LLMs into an autonomous, goal-driven AI agent for telcos. Automate Network Configuration With the AI Blueprint NVIDIA AI Blueprints — available on build.nvidia.com — are customizable AI workflow examples. They include reference code, documentation and deployment tools that show enterprise developers how to deliver business value with NVIDIA NIM microservices. The AI Blueprint for telco network configuration — built with BubbleRAN 5G solutions and datasets — enables developers, network engineers and telecom providers to automatically optimize the configuration of network parameters using agentic AI. This can streamline operations, reduce costs and significantly improve service quality by embedding continuous learning and adaptability directly into network infrastructures. Traditionally, network configurations required manual intervention or followed rigid rules to adapt to dynamic network conditions. These approaches limited adaptability and increased operational complexities, costs and inefficiencies. The new blueprint helps shift telco operations from relying on static, rules-based systems to operations based on dynamic, AI-driven automation. It enables developers to build advanced, telco-specific AI agents that make real-time, intelligent decisions and autonomously balance trade-offs — such as network speed versus interference, or energy savings versus utilization — without human input. Powered and Deployed by Industry Leaders Trained on 5G data generated by BubbleRAN, and deployed on the BubbleRAN 5G O-RAN platform, the blueprint provides telcos with insight on how to set various parameters to reach performance goals, like achieving a certain bitrate while choosing an acceptable signal-to-noise ratio — a measure that impacts voice quality and thus user experience. With the new AI Blueprint, network engineers can confidently set initial parameter values and update them as demanded by continuous network changes. Norway-based Telenor Group, which serves over 200 million customers globally, is the first telco to integrate the AI Blueprint for telco network configuration as part of its initiative to deploy intelligent, autonomous networks that meet the performance and agility demands of 5G and beyond. “The blueprint is helping us address configuration challenges and enhance quality of service during network installation,” said Knut Fjellheim, chief technology innovation officer at Telenor Maritime. “Implementing it is part of our push toward network automation and follows the successful deployment of agentic AI for real-time network slicing in a private 5G maritime use case.” Industry Partners Deploy Other NVIDIA-Powered Autonomous Network Technologies The AI Blueprint for telco network configuration is just one of many announcements at NVIDIA GTC Paris showcasing how the telecom industry is using agentic AI to make autonomous networks a reality. Beyond the blueprint, leading telecom companies and solutions providers are tapping into NVIDIA accelerated computing, software and microservices to provide breakthrough innovations poised to vastly improve networks and communications services — accelerating the progress to autonomous networks and improving customer experiences. NTT DATA is powering its agentic platform for telcos with NVIDIA accelerated compute and the NVIDIA AI Enterprise software platform. Its first agentic use case is focused on network alarms management, where NVIDIA NIM microservices help automate and power observability, troubleshooting, anomaly detection and resolution with closed loop ticketing. Tata Consultancy Services is delivering agentic AI solutions for telcos built on NVIDIA DGX Cloud and using NVIDIA AI Enterprise to develop, fine-tune and integrate large telco models into AI agent workflows. These range from billing and revenue assurance, autonomous network management to hybrid edge-cloud distributed inference. For example, the company’s anomaly management agentic AI model includes real-time detection and resolution of network anomalies and service performance optimization. This increases business agility and improves operational efficiencies by up to 40% by eliminating human intensive toils, overheads and cross-departmental silos. Prodapt has introduced an autonomous operations workflow for networks, powered by NVIDIA AI Enterprise, that offers agentic AI capabilities to support autonomous telecom networks. AI agents can autonomously monitor networks, detect anomalies in real time, initiate diagnostics, analyze root causes of issues using historical data and correlation techniques, automatically execute corrective actions, and generate, enrich and assign incident tickets through integrated ticketing systems. Accenture announced its new portfolio of agentic AI solutions for telecommunications through its AI Refinery platform, built on NVIDIA AI Enterprise software and accelerated computing. The first available solution, the NOC Agentic App, boosts network operations center tasks by using a generative AI-driven, nonlinear agentic framework to automate processes such as incident and fault management, root cause analysis and configuration planning. Using the Llama 3.1 70B NVIDIA NIM microservice and the AI Refinery Distiller Framework, the NOC Agentic App orchestrates networks of intelligent agents for faster, more efficient decision-making. Infosys is announcing its agentic autonomous operations platform, called Infosys Smart Network Assurance (ISNA), designed to accelerate telecom operators’ journeys toward fully autonomous network operations. ISNA helps address long-standing operational challenges for telcos — such as limited automation and high average time to repair — with an integrated, AI-driven platform that reduces operational costs by up to 40% and shortens fault resolution times by up to 30%. NVIDIA NIM and NeMo microservices enhance the platform’s reasoning and hallucination-detection capabilities, reduce latency and increase accuracy. Get started with the new blueprint today. Learn more about the latest AI advancements for telecom and other industries at NVIDIA GTC Paris, running through Thursday, June 12, at VivaTech, including a keynote from NVIDIA founder and CEO Jensen Huang and a special address from Ronnie Vasishta, senior vice president of telecom at NVIDIA. Plus, hear from industry leaders in a panel session with Orange, Swisscom, Telenor and NVIDIA.

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Posted on : June 13, 2025 By Tech World Times Development and Testing  Rate this post Sorting helps organize data in a specific order. It is used in search, reports, and efficient storage. Different sorting algorithms offer different performance. In this article, we will explain the Time Complexity of Sorting Algorithms in simple words. We will cover Python, Java, and C++ examples. 1. What Is Time Complexity? Time complexity tells how fast an algorithm runs. It measures the number of steps as input grows. It is written in Big-O notation. For example, O(n²) means steps grow with the square of inputs. 2. Types of Time Complexity Here are common types: O(1): Constant time O(n): Linear time O(n log n): Log-linear time O(n²): Quadratic time We will now apply these to sorting. 3. Bubble Sort Bubble Sort compares two numbers and swaps them if needed. It repeats until the list is sorted. Time Complexity: Best Case: O(n) (if already sorted) Average Case: O(n²) Worst Case: O(n²) Python Example: pythonCopyEditdef bubble_sort(arr): n = len(arr) for i in range(n): for j in range(n - i - 1): if arr[j] > arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] Java Example: javaCopyEditvoid bubbleSort(int arr[]) { int n = arr.length; for (int i = 0; i < n-1; i++) for (int j = 0; j < n-i-1; j++) if (arr[j] > arr[j+1]) { int temp = arr[j]; arr[j] = arr[j+1]; arr[j+1] = temp; } } C++ Example: cppCopyEditvoid bubbleSort(int arr[], int n) { for (int i = 0; i < n-1; i++) for (int j = 0; j < n-i-1; j++) if (arr[j] > arr[j+1]) swap(arr[j], arr[j+1]); } 4. Selection Sort This sort picks the smallest number and places it at the front. Time Complexity: Best Case: O(n²) Average Case: O(n²) Worst Case: O(n²) Python Example: pythonCopyEditdef selection_sort(arr): for i in range(len(arr)): min_idx = i for j in range(i+1, len(arr)): if arr[j] < arr[min_idx]: min_idx = j arr[i], arr[min_idx] = arr[min_idx], arr[i] 5. Insertion Sort This algorithm builds the final list one item at a time. Time Complexity: Best Case: O(n) Average Case: O(n²) Worst Case: O(n²) Java Example: javaCopyEditvoid insertionSort(int arr[]) { for (int i = 1; i < arr.length; i++) { int key = arr[i]; int j = i - 1; while (j >= 0 && arr[j] > key) { arr[j + 1] = arr[j]; j = j - 1; } arr[j + 1] = key; } } 6. Merge Sort Merge Sort splits the array into halves and merges them back in order. Time Complexity of Sorting Algorithms like Merge Sort is usually better. Best Case: O(n log n) Average Case: O(n log n) Worst Case: O(n log n) Python Example: pythonCopyEditdef merge_sort(arr): if len(arr) > 1: mid = len(arr) // 2 left = arr[:mid] right = arr[mid:] merge_sort(left) merge_sort(right) i = j = k = 0 while i < len(left) and j < len(right): if left[i] < right[j]: arr[k] = left[i] i += 1 else: arr[k] = right[j] j += 1 k += 1 arr[k:] = left[i:] + right[j:] 7. Quick Sort Quick Sort picks a pivot and places smaller numbers before it. Time Complexity: Best Case: O(n log n) Average Case: O(n log n) Worst Case: O(n²) C++ Example: cppCopyEditint partition(int arr[], int low, int high) { int pivot = arr[high]; int i = low - 1; for (int j = low; j < high; j++) { if (arr[j] < pivot) { i++; swap(arr[i], arr[j]); } } swap(arr[i+1], arr[high]); return i + 1; } void quickSort(int arr[], int low, int high) { if (low < high) { int pi = partition(arr, low, high); quickSort(arr, low, pi - 1); quickSort(arr, pi + 1, high); } } 8. Built-in Sort Methods Languages have built-in sort functions. These are well-optimized. Python: sorted() or list.sort() uses TimSort Time Complexity: O(n log n) Java: Arrays.sort() uses Dual-Pivot QuickSort Time Complexity: O(n log n) C++: std::sort() uses IntroSort Time Complexity: O(n log n) These are better for most real-world tasks. 9. Time Complexity Comparison Table AlgorithmBestAverageWorstStableBubble SortO(n)O(n²)O(n²)YesSelection SortO(n²)O(n²)O(n²)NoInsertion SortO(n)O(n²)O(n²)YesMerge SortO(n log n)O(n log n)O(n log n)YesQuick SortO(n log n)O(n log n)O(n²)NoTimSort (Python)O(n)O(n log n)O(n log n)YesIntroSort (C++)O(n log n)O(n log n)O(n log n)No 10. How to Choose the Right Algorithm? Use Merge Sort for large stable data. Use Quick Sort for faster average speed. Use Insertion Sort for small or nearly sorted lists. Use built-in sort functions unless you need control. Conclusion The Time Complexity of Sorting Algorithms helps us pick the right tool. Bubble, Selection, and Insertion Sort are simple but slow. Merge and Quick Sort are faster and used often. Built-in functions are highly optimized. Python, Java, and C++ each have their strengths. Understand your problem and input size. Then pick the sorting method. This ensures better speed and performance in your code. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

In-app bidding has automated most waterfall optimization, yet developers still manage multiple hybrid waterfalls, each with dozens of manual instances. Naturally, this can be timely and overwhelming to maintain, keeping you from optimizing to perfection and focusing on other opportunities to boost revenue.Rather than analyzing each individual network and checking if instances are available at each price point, breaking down your waterfall into different CPM ranges allows you to visualize the waterfall and easily identify the gaps.Here are some tips on how to use CPM buckets to better optimize your waterfall’s performance.What are CPM buckets?CPM buckets show you exactly how much revenue and how many impressions you’re getting from each CPM price range, giving you a more granular idea of how different networks are competing in the waterfall. CPM buckets are a feature of real time pivot reports, available on ironSource LevelPlay.Identifying and closing the gapsTypically in a waterfall, you can only see each ad network’s average CPM. But this keeps you from seeing ad network distribution across all price points and understanding exactly where ad networks are bidding. Bottom line - you don’t know where in the waterfall you should add a new instance.By separating CPM into buckets, (for example, seeing all the ad networks generating a CPM of $10-$20) you understand exactly which networks are driving impressions and revenue and which CPMs aren’t being filledNow how do you do it? As a LevelPlay client, simply use ironSource’s real time pivot reports - choose the CPM bucket filter option and sort by “average bid price.” From here, you’ll see how your revenue spreads out among CPM ranges and you’ll start to notice gaps in your bar graph. Every gap in revenue - where revenue is much lower than the neighboring CPM group - indicates an opportunity to optimize your monetization strategy. The buckets can range from small increments like $1 to larger increments like $10, so it’s important to compare CPM buckets of the same incremental value.Pro tip: To best set up your waterfall, create one tab with the general waterfall (filter app, OS, Ad unit, geo/geos from a specific group) and make sure to look at Revenue and eCPM in the “measures” dropdown. In the “show” section, choose CPM buckets and sort by average bid price. From here, you can mark down any gaps.But where do these gaps come from? Gaps in revenue are often due to friction in the waterfall, like not enough instances, instances that aren’t working, or a waterfall setup mistake. But gaps can also be adjusted and fixed.Once you’ve found a gap, you can look at the CPM buckets around it to better understand the context. Let’s say you see a strong instance generating significant revenue in the CPM bucket right below it, in the $70-80 group. This instance from this specific ad network has a lot of potential, so it’s worth trying to push it to a higher CPM bucket.In fact, when you look at higher CPM buckets, you don’t see this ad network anywhere else in the waterfall - what a missed opportunity! Try adding another instance of this network higher up in the waterfall. If you’re profiting well with a $70-80 CPM, imagine how much more revenue you could bring at a $150 CPM.Pro tip: Focusing on higher areas in the waterfall makes a larger financial impact, leading to bigger increases in ARPDAU.Let’s say you decide to add 5 instances of that network to higher CPM buckets. You can use LevelPlay’s quick A/B test to understand if this adjustment boosts your revenue - not just for this gap, but for any and all that you find. Simply compare your existing waterfall against the new waterfall with these 5 higher instances - then implement the one that drives the highest instances.Božo Janković, Head of Ad Monetization at GameBiz Consulting, uses CPM buckets "to understand at which CPMs the bidding networks are filling. From there, I can pinpoint exactly where in the waterfall to add more traditional instances - which creates more competition, especially for the bidding networks, and creates an opportunity for revenue growth."Finding new insightsYou can dig even deeper into your data by filtering by ad source. Before CPM buckets, you were limited to seeing an average eCPM for each bidding network. Maybe you knew that one ad source had an average CPM of $50, but the distribution of impression across the waterfall was a black box. Now, we know exactly which CPMs the bidders are filling. “I find ironSource CPM buckets feature very insightful and and use it daily. It’s an easy way to identify opportunities to optimize the waterfall and earn even more revenue." -Božo Janković, Head of Ad Monetization at GameBiz ConsultingUnderstanding your CPM distribution empowers you to not only identify your revenue sources, but also to promote revenue growth. Armed with the knowledge of which buckets some of their stronger bidding networking are performing in, some publishers actively add instances from traditional networks above those ranges. This creates better competition and also helps drive up the bids from the biddersThere’s no need for deep analysis - once you see the gaps, you can quickly understand who’s performing in the lower and higher buckets, and see exactly what’s missing. This way, you won’t miss out on any lost revenue.Learn more about CPM buckets, available exclusively to ironSource LevelPlay here.

One of the four pillars of good conversation is levity. You needn’t be a comedian, you can but have some funTetra Images, LLC/Alamy Conversation lies at the heart of our relationships – yet many of us find it surprisingly hard to talk to others. We may feel anxious at the thought of making small talk with strangers and struggle to connect with the people who are closest to us. If that sounds familiar, Alison Wood Brooks hopes to help. She is a professor at Harvard Business School, where she teaches an oversubscribed course called “TALK: How to talk gooder in business and life”, and the author of a new book, Talk: The science of conversation and the art of being ourselves. Both offer four key principles for more meaningful exchanges. Conversations are inherently unpredictable, says Wood Brooks, but they follow certain rules – and knowing their architecture makes us more comfortable with what is outside of our control. New Scientist asked her about the best ways to apply this research to our own chats. David Robson: Talking about talking feels quite meta. Do you ever find yourself critiquing your own performance? Alison Wood Brooks: There are so many levels of “meta-ness”. I have often felt like I’m floating over the room, watching conversations unfold, even as I’m involved in them myself. I teach a course at Harvard, and [my students] all get to experience this feeling as well. There can be an uncomfortable period of hypervigilance, but I hope that dissipates over time as they develop better habits. There is a famous quote from Charlie Parker, who was a jazz saxophonist. He said something like, “Practise, practise, practise, and then when you get on stage, let it all go and just wail.” I think that’s my approach to conversation. Even when you’re hyper-aware of conversation dynamics, you have to remember the true delight of being with another human mind, and never lose the magic of being together. Think ahead, but once you’re talking, let it all go and just wail. Reading your book, I learned that a good way to enliven a conversation is to ask someone why they are passionate about what they do. So, where does your passion for conversation come from? I have two answers to this question. One is professional. Early in my professorship at Harvard, I had been studying emotions by exploring how people talk about their feelings and the balance between what we feel inside and how we express that to others. And I realised I just had this deep, profound interest in figuring out how people talk to each other about everything, not just their feelings. We now have scientific tools that allow us to capture conversations and analyse them at large scale. Natural language processing, machine learning, the advent of AI – all this allows us to take huge swathes of transcript data and process it much more efficiently. Receive a weekly dose of discovery in your inbox. Sign up to newsletter The personal answer is that I’m an identical twin, and I spent my whole life, from the moment I opened my newborn eyes, existing next to a person who’s an exact copy of myself. It was like observing myself at very close range, interacting with the world, interacting with other people. I could see when she said and did things well, and I could try to do that myself. And I saw when her jokes failed, or she stumbled over her words – I tried to avoid those mistakes. It was a very fortunate form of feedback that not a lot of people get. And then, as a twin, you’ve got this person sharing a bedroom, sharing all your clothes, going to all the same parties and playing on the same sports teams, so we were just constantly in conversation with each other. You reached this level of shared reality that is so incredible, and I’ve spent the rest of my life trying to help other people get there in their relationships, too. “TALK” cleverly captures your framework for better conversations: topics, asking, levity and kindness. Let’s start at the beginning. How should we decide what to talk about? My first piece of advice is to prepare. Some people do this naturally. They already think about the things that they should talk about with somebody before they see them. They should lean into this habit. Some of my students, however, think it’s crazy. They think preparation will make the conversation seem rigid and forced and overly scripted. But just because you’ve thought ahead about what you might talk about doesn’t mean you have to talk about those things once the conversation is underway. It does mean, however, that you always have an idea waiting for you when you’re not sure what to talk about next. Having just one topic in your back pocket can help you in those anxiety-ridden moments. It makes things more fluent, which is important for establishing a connection. Choosing a topic is not only important at the start of a conversation. We’re constantly making decisions about whether we should stay on one subject, drift to something else or totally shift gears and go somewhere wildly different. Sometimes the topic of conversation is obvious. Even then, knowing when to switch to a new one can be trickyMartin Parr/Magnum Photos What’s your advice when making these decisions? There are three very clear signs that suggest that it’s time to switch topics. The first is longer mutual pauses. The second is more uncomfortable laughter, which we use to fill the space that we would usually fill excitedly with good content. And the third sign is redundancy. Once you start repeating things that have already been said on the topic, it’s a sign that you should move to something else. After an average conversation, most people feel like they’ve covered the right number of topics. But if you ask people after conversations that didn’t go well, they’ll more often say that they didn’t talk about enough things, rather than that they talked about too many things. This suggests that a common mistake is lingering too long on a topic after you’ve squeezed all the juice out of it. The second element of TALK is asking questions. I think a lot of us have heard the advice to ask more questions, yet many people don’t apply it. Why do you think that is? Many years of research have shown that the human mind is remarkably egocentric. Often, we are so focused on our own perspective that we forget to even ask someone else to share what’s in their mind. Another reason is fear. You’re interested in the other person, and you know you should ask them questions, but you’re afraid of being too intrusive, or that you will reveal your own incompetence, because you feel you should know the answer already. What kinds of questions should we be asking – and avoiding? In the book, I talk about the power of follow-up questions that build on anything that your partner has just said. It shows that you heard them, that you care and that you want to know more. Even one follow-up question can springboard us away from shallow talk into something deeper and more meaningful. There are, however, some bad patterns of question asking, such as “boomerasking”. Michael Yeomans [at Imperial College London] and I have a recent paper about this, and oh my gosh, it’s been such fun to study. It’s a play on the word boomerang: it comes back to the person who threw it. If I ask you what you had for breakfast, and you tell me you had Special K and banana, and then I say, “Well, let me tell you about my breakfast, because, boy, was it delicious” – that’s boomerasking. Sometimes it’s a thinly veiled way of bragging or complaining, but sometimes I think people are genuinely interested to hear from their partner, but then the partner’s answer reminds them so much of their own life that they can’t help but start sharing their perspective. In our research, we have found that this makes your partner feel like you weren’t interested in their perspective, so it seems very insincere. Sharing your own perspective is important. It’s okay at some point to bring the conversation back to yourself. But don’t do it so soon that it makes your partner feel like you didn’t hear their answer or care about it. Research by Alison Wood Brooks includes a recent study on “boomerasking”, a pitfall you should avoid to make conversations flowJanelle Bruno What are the benefits of levity? When we think of conversations that haven’t gone well, we often think of moments of hostility, anger or disagreement, but a quiet killer of conversation is boredom. Levity is the antidote. These small moments of sparkle or fizz can pull us back in and make us feel engaged with each other again. Our research has shown that we give status and respect to people who make us feel good, so much so that in a group of people, a person who can land even one appropriate joke is more likely to be voted as the leader. And the joke doesn’t even need to be very funny! It’s the fact that they were confident enough to try it and competent enough to read the room. Do you have any practical steps that people can apply to generate levity, even if they’re not a natural comedian? Levity is not just about being funny. In fact, aiming to be a comedian is not the right goal. When we watch stand-up on Netflix, comedians have rehearsed those jokes and honed them and practised them for a long time, and they’re delivering them in a monologue to an audience. It’s a completely different task from a live conversation. In real dialogue, what everybody is looking for is to feel engaged, and that doesn’t require particularly funny jokes or elaborate stories. When you see opportunities to make it fun or lighten the mood, that’s what you need to grab. It can come through a change to a new, fresh topic, or calling back to things that you talked about earlier in the conversation or earlier in your relationship. These callbacks – which sometimes do refer to something funny – are such a nice way of showing that you’ve listened and remembered. A levity move could also involve giving sincere compliments to other people. When you think nice things, when you admire someone, make sure you say it out loud. This brings us to the last element of TALK: kindness. Why do we so often fail to be as kind as we would like? Wobbles in kindness often come back to our egocentrism. Research shows that we underestimate how much other people’s perspectives differ from our own, and we forget that we have the tools to ask other people directly in conversation for their perspective. Being a kinder conversationalist is about trying to focus on your partner’s perspective and then figuring what they need and helping them to get it. Finally, what is your number one tip for readers to have a better conversation the next time they speak to someone? Every conversation is surprisingly tricky and complex. When things don’t go perfectly, give yourself and others more grace. There will be trips and stumbles and then a little grace can go very, very far. Topics:

Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a missed turn. But for an autonomous vehicle or a delivery robot, that level of imprecision is the difference between a successful mission and a costly failure. These machines require pinpoint accuracy to operate safely and efficiently. Addressing this critical challenge, researchers from the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland have introduced a groundbreaking new method for visual localization during CVPR 2025 Their new paper, “FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching,” presents a novel AI model that significantly enhances the ability of a ground-level system, like an autonomous car, to determine its exact position and orientation using only a camera and a corresponding aerial (or satellite) image. The new approach has demonstrated a remarkable 28% reduction in mean localization error compared to the previous state-of-the-art on a challenging public dataset. Key Takeaways: Superior Accuracy: The FG2 model reduces the average localization error by a significant 28% on the VIGOR cross-area test set, a challenging benchmark for this task. Human-like Intuition: Instead of relying on abstract descriptors, the model mimics human reasoning by matching fine-grained, semantically consistent features—like curbs, crosswalks, and buildings—between a ground-level photo and an aerial map. Enhanced Interpretability: The method allows researchers to “see” what the AI is “thinking” by visualizing exactly which features in the ground and aerial images are being matched, a major step forward from previous “black box” models. Weakly Supervised Learning: Remarkably, the model learns these complex and consistent feature matches without any direct labels for correspondences. It achieves this using only the final camera pose as a supervisory signal. Challenge: Seeing the World from Two Different Angles The core problem of cross-view localization is the dramatic difference in perspective between a street-level camera and an overhead satellite view. A building facade seen from the ground looks completely different from its rooftop signature in an aerial image. Existing methods have struggled with this. Some create a general “descriptor” for the entire scene, but this is an abstract approach that doesn’t mirror how humans naturally localize themselves by spotting specific landmarks. Other methods transform the ground image into a Bird’s-Eye-View (BEV) but are often limited to the ground plane, ignoring crucial vertical structures like buildings. FG2: Matching Fine-Grained Features The EPFL team’s FG2 method introduces a more intuitive and effective process. It aligns two sets of points: one generated from the ground-level image and another sampled from the aerial map. Here’s a breakdown of their innovative pipeline: Mapping to 3D: The process begins by taking the features from the ground-level image and lifting them into a 3D point cloud centered around the camera. This creates a 3D representation of the immediate environment. Smart Pooling to BEV: This is where the magic happens. Instead of simply flattening the 3D data, the model learns to intelligently select the most important features along the vertical (height) dimension for each point. It essentially asks, “For this spot on the map, is the ground-level road marking more important, or is the edge of that building’s roof the better landmark?” This selection process is crucial, as it allows the model to correctly associate features like building facades with their corresponding rooftops in the aerial view. Feature Matching and Pose Estimation: Once both the ground and aerial views are represented as 2D point planes with rich feature descriptors, the model computes the similarity between them. It then samples a sparse set of the most confident matches and uses a classic geometric algorithm called Procrustes alignment to calculate the precise 3-DoF (x, y, and yaw) pose. Unprecedented Performance and Interpretability The results speak for themselves. On the challenging VIGOR dataset, which includes images from different cities in its cross-area test, FG2 reduced the mean localization error by 28% compared to the previous best method. It also demonstrated superior generalization capabilities on the KITTI dataset, a staple in autonomous driving research. Perhaps more importantly, the FG2 model offers a new level of transparency. By visualizing the matched points, the researchers showed that the model learns semantically consistent correspondences without being explicitly told to. For example, the system correctly matches zebra crossings, road markings, and even building facades in the ground view to their corresponding locations on the aerial map. This interpretability is extremenly valuable for building trust in safety-critical autonomous systems. “A Clearer Path” for Autonomous Navigation The FG2 method represents a significant leap forward in fine-grained visual localization. By developing a model that intelligently selects and matches features in a way that mirrors human intuition, the EPFL researchers have not only shattered previous accuracy records but also made the decision-making process of the AI more interpretable. This work paves the way for more robust and reliable navigation systems for autonomous vehicles, drones, and robots, bringing us one step closer to a future where machines can confidently navigate our world, even when GPS fails them. Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Jean-marc MommessinJean-marc is a successful AI business executive .He leads and accelerates growth for AI powered solutions and started a computer vision company in 2006. He is a recognized speaker at AI conferences and has an MBA from Stanford.Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/AI-Generated Ad Created with Google’s Veo3 Airs During NBA Finals, Slashing Production Costs by 95%Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Highlighted at CVPR 2025: Google DeepMind’s ‘Motion Prompting’ Paper Unlocks Granular Video ControlJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Snowflake Charts New AI Territory: Cortex AISQL & Snowflake Intelligence Poised to Reshape Data AnalyticsJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Exclusive Talk: Joey Conway of NVIDIA on Llama Nemotron Ultra and Open Source Models

The recent Summer Game Fest 2025 has been quite successful. According to Variety, the live stream saw a massive growth year-over-year in terms of live viewership, coming in at an increase of 89 percent since the 2024 edition. The event saw a number of new games announced, as well as trailers for previously-announced games that will be coming soon. Among the headliners for the event were titles like Capcom’s Resident Evil Requiem, as well as gameplay for IO Interactive’s 007: First Light. “In total, the peak concurrent audience for SGF reached more than 3 million live simultaneous viewers across Twitch and YouTube, with significant year over year growth on both platforms in terms of average viewership, watch time and co-streams,” announced Summer Game Fest in a press release over the weekend. At the time of publishing Summer Game Fest 2025 had managed to get 8.5 million views on just one of the places where it was hosted – the official The Game Awards channel. While it is worth noting that this number takes both the live stream audience as well as those who watched the event afterwards into account, the number would quite likely be higher when taking other hosts, and even platforms like Twitch into account. Reports, have indicated that of the 8.5 million viewers, 1.5 million could be attributed to those watching during the live stream globally. Twitch, on the other hand, saw a growth of 38 percent in terms of live viewership among the over 8,900 channels that were co-streaming the event. This came in to around 1.4 million concurrent live viewers worldwide. Summer Games Fest 2025 was accompanied by a host of other events happening over the same weekend. This included events focused on PC Gaming, as well as Microsoft’s own Xbox Games Showcase, and even the indie game-focused Future Games Show 2025. Coinciding with the events was Valve kicking of its latest edition of Steam Next Fest, which featured a host of different game demos that players could try out. A lot of these games were unveiled or otherwise got new trailers during the live events last weekend. However, it is worth noting that today is the final day of this iteration of Steam Next Fest, which means that a lot of the demos will be going away.