eShard’s Post

Attention please! You can win a free ticket for the PyData Eindhoven conference (11th July 2024) if you join our OpenVINO PyData Eindhoven contest! What you need to do is follow these instructions: 1. Clone openvino_notebooks repo (https://lnkd.in/d6hpPiGw) 2. Run Latent Consistency Model with Optimum Intel notebook (https://lnkd.in/dzBuC2vK) 3. Generate an image depicting your dream (all crazy ideas are welcome, but please SFW 😉) 4. Post it on your LinkedIn and add the following tags #openvino #openvinopydatachallenge 5. 25th June 12 pm CET: Top 3 posts with the most reactions (like, celebrate, etc.) + 2 which appeal most to the jury win the ticket You can join this challenge if you're a resident of Europe. For full rules please visit: https://lnkd.in/df6zjMZ9 Dmitriy Pastushenkov Aziza Yacoub Arnaud Bastide Rob de Wit 🐘 John Sandall Jose Navarro Valerio Maggio, Ph.D. Łukasz Goncerzewicz Daniel Ringler #iamintel

PlanV has made a great enhancement to Verilator! PlanV has added basic randomization to Verilator! In Yilou's latest blog article, he describes its importance and how he added basic randomization to SystemVerilog aggregate data types. Check it out! https://lnkd.in/gGifsZZn

🚀 Exciting Update for the UVM and Verilator Community! 🚀 We've tackled a significant pain point: Verilator now fully supports basic randomization for aggregate data types! 🎉 Curious about how we made this happen? Check out PlanV latest blog to learn more. Constrained randomization support is on the way, bringing us closer to comprehensive randomization in Verilator. #UVM #Verilator #Randomization

Chat with your tabular data in buff.ly/3Uj4DXM https://buff.ly/4ehC44u

Screenshot from FireRescueTV.

Days 34 of the #100dayschallenge Days 28 of the #gfg160 challenge Today's Problem : Number of occurrence Approach : To count occurrences of a target in a sorted array, use binary search to find its first and last occurrences. The difference (last - first + 1) gives the count. If the target is not found, return 0. This approach ensures O(log⁡n)O(\log n)O(logn) time complexity, leveraging the array's sorted property. GeeksforGeeks #gfg160 #geekstreak2024 #datastruccture #problemsolving

🚀 Day 12 of the #gfg160 Challenge! 🎉#GeekStreak2024 Today, I tackled the "Max Circular Subarray Sum" problem on GeeksforGeeks! Here's a brief breakdown of my approach: 1. Initialization: a. maxSum: This variable keeps track of the maximum sum of any subarray found so far. It’s initialized to arr[0] as the first element is the start of the first subarray. b. minSum: This variable keeps track of the minimum sum of any subarray found so far. It’s initialized to arr[0] for the same reason as maxSum. c. total: This variable stores the total sum of the entire array, which will be used in the circular subarray calculation later. 2. Find the maximum sum of a non-circular subarray: We use Kadane's Algorithm to find the maximum sum subarray. This is achieved by iterating over the array, updating currMax at each step to be either the current element or the sum of the current element and currMax. maxSum is updated after each iteration. 3. Find the minimum sum of a non-circular subarray: Using a similar approach to Kadane’s algorithm, we calculate the minimum sum subarray (minSum). We update currMin at each step to be the smaller of either the current element or the sum of the current element and currMin. 4. Handle the circular nature: a. The key observation is that the maximum circular subarray sum can be calculated by subtracting the minimum subarray sum from the total sum of the array: maxCircularSum = total - minSum. b. Special Case: If the minimum subarray sum is equal to the total sum of the array (e.g., when all elements are negative), the circular subarray sum isn't valid, so we return the non-circular maxSum instead. 5. Final result: The final result is the maximum of the non-circular maximum subarray sum (maxSum) and the circular subarray sum (maxCircularSum), ensuring that we get the highest possible sum. Time complexity: O(n), since we calculate both minimum and maximum subarray sums and the total sum, in a single pass. Space complexity: O(1), as the algorithm uses a constant amount of extra space. 💡The algorithm efficiently handles the "Maximum Circular Subarray Sum" problem by dynamically maintaining both the maximum and minimum subarray sums. This approach is crucial because negative numbers can cause the minimum subarray sum to flip and become the maximum when combined with a negative value. By maintaining both values, the algorithm achieves optimal time complexity of O(n) and constant space complexity, making it both time-efficient and space-efficient. Looking forward to solving more challenges tomorrow! 💪 #gfg160 #geekstreak2024 #gfg #geeksforgeeks #codinglife #learndsa #dsa #problemsolving #consistency #daysofcode #learntocode #Array #GFGStudentChapter #GFGTMSL #GeeksForGeeksTechnoMain #GFGTechnoMainSaltLake

Our X-Labs team recently observed increased activity from a new infostealer called VIPKeyLogger. Prashant Kumar analyzes it to show how this keylogger variant works: https://bit.ly/3VCqLgw

Our X-Labs team recently observed increased activity from a new infostealer called VIPKeyLogger. Prashant Kumar analyzes it to show how this keylogger variant works: https://bit.ly/3Bvnrgu

Our X-Labs team recently observed increased activity from a new infostealer called VIPKeyLogger. Prashant Kumar analyzes it to show how this keylogger variant works: https://bit.ly/41OKAFz