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Hi. Jason Pargin (@JohnDiesattheEn) joins Katy and Cody to discuss Ye's disturbing antisemitic comments, Tulsi Gabbard leaving the Democratic Party (say it ain't so!), and the media's insistence on pretending that drug dealers are sneaking rainbow fentanyl into trick-or-treat candy.Support us on our PATREON: http://patreon.com/somemorenewsCheck out our MERCH STORE: https://www.teepublic.com/stores/somemorenews?ref_id=9949SUBSCRIBE to SOME MORE NEWS: https://tinyurl.com/ybfx89rhPlease fill out our SURVEY: https://kastmedia.com/survey/Subscribe to the Even More News and SMN audio podcasts here -Apple Podcasts: https://podcasts.apple.com/us/podcast/some-more-news/id1364825229Spotify: https://open.spotify.com/show/6ebqegozpFt9hY2WJ7TDiA?si=5keGjCe5SxejFN1XkQlZ3w&dl_branch=1Stitcher: https://www.stitcher.com/show/even-more-news Follow us on social media: Twitter: https://twitter.com/SomeMoreNews Instagram: https://www.instagram.com/SomeMoreNews/ Facebook: https://www.facebook.com/SomeMoreNews/ TikTok: https://www.tiktok.com/@somemorenews Make sure your online activity and data is protected with the best VPN money can buy. Visit https://ExpressVPN.com/morenews right now and get three extra months free. Get a 4-week trial, free postage, and a digital scale at https://www.stamps.com/MORENEWS. Thanks to Stamps.com for sponsoring the show!Athletic Greens is going to give you an immune supporting FREE 1-year supply of Vitamin D AND 5 free travel packs with your first purchase if you visit https://athleticgreens.com/morenews today. Support the show!: http://patreon.com.com/somemorenewsSee omnystudio.com/listener for privacy information.

The talk will consist of two parts. In the first part, I will present prior work aimed at developing new algorithms for materials science applications. I developed a novel numerical method to optimize the shape of mechanical parts and maximize their strength. This optimization problem was extremely challenging as it involved changing the topology and therefore classical optimization methods do not apply. A similar idea can be applied to model shape memory alloys and plasticity in metals. In this project, the optimization led to the construction of a tree of laminated microstructures. Constraints from mechanical equilibrium were used to construct an adaptive tree and to determine the optimal number of levels. The second part involves more recent work. I will present the implementation of a new heat flux algorithm to understand failures in micro-devices in LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator); LAMMPS is a high performance code which was benchmarked on Blue Gene L with 90% parallel efficiency. Also I am currently extending the capabilities of ParaDiS (Parallel Dislocation Simulator), another high performance code which has run on 135k processors of Blue Gene L. ©2008 Microsoft Corporation. All rights reserved.

YouTube's algorithms are abhorrent in a new way, details on the next Playstation console, Google makes nice with developers, and three interesting sides to the TikTok story.Sponsors:EveryKey.com 20% off with promocode RIDE20 Mealime AppLinks: YouTube shows 9/11 link on live videos of unrelated Notre Dame fire (The Verge)EXCLUSIVE: WHAT TO EXPECT FROM SONY'S NEXT-GEN PLAYSTATION (Wired) 15 MONTHS OF FRESH HELL INSIDE FACEBOOK (Wired) Next major macOS version will include standalone Music, Podcasts, and TV apps, Books app gets major redesign (9to5Mac) Improving the update process with your feedback (Android Developer's Blog) AT&T sells Hulu stake, valuing the video streaming company at $15 billion (CNBC) Short video app TikTok extends reach in global markets, sees in-app purchases surge more than 200 per cent (South China Morning Post)TikTok downloads ordered to be blocked on iOS and Android in India over porn and other illegal content (TechCrunch) TikTok sensation Lil Nas X rewrites the rules of country music (Axios)

This study aims to assess the impact of skin tone biases in algorithms on technology use. Algorithmic bias, an algorithm that deviates from some standard in a manner that systematically dis/advantages a category of users or a category of data over another, in ways that are not designed as a function of that algorithm, is a frequent problem in society. Specifically, many image recognition algorithms historically and currently advantage users with lighter skin tones over users with darker skin tones, causes functional discrepancies in how the technology functions between use. Therefore, this study seeks to see if this bias impacts how people of varying skin tones perceive and use technology. It is important to note that the relationships under investigation here are not inherently driven by skin tone, group membership, or any other feature of the user (i.e., there is nothing inherent about skin tone that would affect technology use). Instead, the proposed relationships reflect the concern over significant social consequences when algorithms are affected by bias.

Algorithms, Languages, Automata, & Compilers: A Practical Approach is designed to cover the standard theory of computing topics through a strong emphasis on practical applications rather than theorems and proofs. Finite automata, Turing machines, models of computation, complexity, solvability, and other topics that form a foundation of modern programming are discussed -first with a gentle theoretical orientation, and then applied through programming code and practical examples. JFLAP projects and applications are integrated throughout the book, and C# is used for all code.

Do personalization algorithms serve up ever more extreme videos to users, resulting in affective and issue polarization? Based in part on vivid anecdotes, a media narrative has taken hold ascribing worrisome power to hidden algorithms. Academic researchers have struggled to add evidence to this debate due to both data limitations and serious research design challenges. We propose a novel experimental approach that seeks to resolve the impasse on this critical question by randomizing the recommendations themselves. Using a custom-built interface that serves sequences of videos from YouTube's API, we create our own variations on the existing recommendation algorithm by manipulating the ideological balance of recommended video content to randomly assigned groups of subjects. This approach enables an attractive combination of experimental control and external validity without the need for reverse-engineering YouTube's blackbox recommendations. Subjects in our study will choose to watch a sequence of short videos and follow recommendations while the interface tracks watch time and other measures of engagement. After the viewing session, participants will be surveyed on issue opinions, affective polarization, and perceptions of media hostility. This pre-analysis plan describes a three-wave study focusing on the issue of gun control.

Do personalization algorithms serve up ever more extreme videos to users, resulting in affective and issue polarization? Based in part on vivid anecdotes, a media narrative has taken hold ascribing worrisome power to hidden algorithms. Academic researchers have struggled to add evidence to this debate due to both data limitations and serious research design challenges. We propose a novel experimental approach that seeks to resolve the impasse on this critical question by randomizing the recommendations themselves. Using a custom-built interface that serves sequences of videos from YouTube's API, we create our own variations on the existing recommendation algorithm by manipulating the ideological balance of recommended video content to randomly assigned groups of subjects. This approach enables an attractive combination of experimental control and external validity without the need for reverse-engineering YouTube's blackbox recommendations. Subjects in our study will choose to watch a sequence of short videos and follow recommendations while the interface tracks watch time and other measures of engagement. After the viewing session, participants will be surveyed on issue opinions, affective polarization, and perceptions of media hostility. This pre-analysis plan describes a three-wave study focusing on the issue of gun control.

Do personalization algorithms serve up ever more extreme videos to users, resulting in affective and issue polarization? Based in part on vivid anecdotes, a media narrative has taken hold ascribing worrisome power to hidden algorithms. Academic researchers have struggled to add evidence to this debate due to both data limitations and serious research design challenges. We propose a novel experimental approach that seeks to resolve the impasse on this critical question by randomizing the recommendations themselves. Using a custom-built interface that serves sequences of videos from YouTube's API, we create our own variations on the existing recommendation algorithm by manipulating the ideological balance of recommended video content to randomly assigned groups of subjects. This approach enables an attractive combination of experimental control and external validity without the need for reverse-engineering YouTube's blackbox recommendations. Subjects in our study will choose to watch a sequence of short videos and follow recommendations while the interface tracks watch time and other measures of engagement. After the viewing session, participants will be surveyed on issue opinions, affective polarization, and perceptions of media hostility. This pre-analysis plan describes a three-wave study focusing on the issue of gun control.

In this dissertation, a series of studies are designed to investigate the brain connectivity patterns (both functional connectivity and effective connectivity), comparing subjects with depression history vs. control and subjects with OCD vs. control. Depression and OCD are examples of trans-diagnostic disorders, which may be more likely due to alteration of connectivity patterns (ex., hyper-connectivity in default mode network or other networks) rather than localized brain abnormality.

This study investigates consumers' search and purchase behavior on the Amazon.it marketplace, with a special focus on whether and how they rely on the "Buy-Box". Across treatments, we manipulate the amount of information disclosed to consumers about the functioning (and the hidden risks) of this peculiar platform feature, and observe their behavior on the online marketplace, holding fixed the current design of the platform. We further manipulate the characteristics of the (exogenously determined) products for which we ask participants to perform the e-shopping task.

We suggest that people develop an implicit bias (i.e., prejudice) against artificial intelligence (AI) systems, as a different and threatening “species” the behavior of which is unknown. We expected people to be guided by this implicit bias but try to override it. This leads to some willingness to rely on algorithmic advice (appreciation) which is reduced as a function of people’s implicit prejudice against the machine. Next, we provide an explanation as to why aversion is ephemeral. As people learn about the performance of an algorithm, they depend less on primal implicit biases when deciding whether to rely on the AI’s advice. Two studies (n1=675, n2=317) that use the Implicit Association Test consistently support this view. Two additional studies (n3=255, n4=332) rule out alternative explanations and provide stronger support for our assertions.

MEP Marietje Schaake argues that social media algorithms are extremely influential. An excerpt from the debate "Constitutional Free Speech Principles Can Save Social Media Principles From Themselves." For the Motion: David French − Senior Writer, National Review Corynne McSherry − Legal Director, Electronic Frontier Foundation Against the Motion: Nathaniel Persily − Professor, Stanford Law Marietje Schaake − Dutch Politician & Member, European Parliament =================================== Cast Your Vote: http://smarturl.it/FakeNewsFreeSpeech Subscribe: http://bit.ly/IQ2onYouTube Official site: iq2us.org IQ2US Twitter: http://bit.ly/IQ2Twitter IQ2US Facebook: http://bit.ly/IQ2onFacebook ===================================

Brian interviews two different guests.First is Hillary Boehm-Edwards. Hillary is a first-year commerce student at McMaster University. She talked about an article she wrote and published in the Toronto Star, an opinion editorial regarding the dangers of media algorithms and their ability to polarize opinions. Hillary explained how media algorithms influence the content users see on social media, especially within a political context and elaborates on how these algorithms contribute to misinformation and vaccine hesitancy? The article raises the question of who controls a user's opinions - the viewer or the algorithm. Could you discuss the balance of responsibility between the two?Then, Jenny Kim talks about the Kindfulness Workshop with the Urban Buddhist Monk's 6th Annual Kindful Canada Symposium 2023 event which happens this Saturday, Oct. 14th.

Brian interviews two different guests.First is Hillary Boehm-Edwards. Hillary is a first-year commerce student at McMaster University. She talked about an article she wrote and published in the Toronto Star, an opinion editorial regarding the dangers of media algorithms and their ability to polarize opinions. Hillary explained how media algorithms influence the content users see on social media, especially within a political context and elaborates on how these algorithms contribute to misinformation and vaccine hesitancy? The article raises the question of who controls a user's opinions - the viewer or the algorithm. Could you discuss the balance of responsibility between the two?Then, Jenny Kim talks about the Kindfulness Workshop with the Urban Buddhist Monk's 6th Annual Kindful Canada Symposium 2023 event which happens this Saturday, Oct. 14th.

Brian interviews two different guests.First is Hillary Boehm-Edwards. Hillary is a first-year commerce student at McMaster University. She talked about an article she wrote and published in the Toronto Star, an opinion editorial regarding the dangers of media algorithms and their ability to polarize opinions. Hillary explained how media algorithms influence the content users see on social media, especially within a political context and elaborates on how these algorithms contribute to misinformation and vaccine hesitancy? The article raises the question of who controls a user's opinions - the viewer or the algorithm. Could you discuss the balance of responsibility between the two?Then, Jenny Kim talks about the Kindfulness Workshop with the Urban Buddhist Monk's 6th Annual Kindful Canada Symposium 2023 event which happens this Saturday, Oct. 14th.

Brian interviews two different guests.First is Hillary Boehm-Edwards. Hillary is a first-year commerce student at McMaster University. She talked about an article she wrote and published in the Toronto Star, an opinion editorial regarding the dangers of media algorithms and their ability to polarize opinions. Hillary explained how media algorithms influence the content users see on social media, especially within a political context and elaborates on how these algorithms contribute to misinformation and vaccine hesitancy? The article raises the question of who controls a user's opinions - the viewer or the algorithm. Could you discuss the balance of responsibility between the two?Then, Jenny Kim talks about the Kindfulness Workshop with the Urban Buddhist Monk's 6th Annual Kindful Canada Symposium 2023 event which happens this Saturday, Oct. 14th.

A collection of algorithms and data structures Algorithms & data structures project Algorithms and data structures are fundamental to efficient code and good software design. Creating and designing excellent algorithms is required for being an exemplary programmer. This repository's goal is to demonstrate how to correctly implement common data structures and algorithms in the simplest and most elegant ways. Contributing This repository is contribution friendly :smiley:. If you'd like to add or improve an algorithm, your contribution is welcome! Please be sure to checkout the Wiki for instructions. Other programming languages? This repository provides algorithm implementations in Java, however there are other forks that provide implementations in other languages, most notably: C++/Python : https://github.com/akzare/Algorithms Rust : https://github.com/TianyiShi2001/Algorithms Running an algorithm implementation To compile and run any of the algorithms here, you need at least JDK version 8. Gradle can make things more convenient for you, but it is not required. Running with Gradle (recommended) This project supports the Gradle Wrapper . The Gradle wrapper automatically downloads Gradle at the first time it runs, so expect a delay when running the first command below. If you are on Windows, use gradlew.bat instead of ./gradlew below. Run a single algorithm like this: ./gradlew run -Palgorithm=. Alternatively, you can run a single algorithm specifying the full class name ```./gradlew run -Pmain= ``` For instance: ./gradlew run -Palgorithm=search.BinarySearch or ./gradlew run -Pmain=com.williamfiset.algorithms.search.BinarySearch Compiling and running with only a JDK Create a classes folder cd Algorithmsmkdir classes Compile the algorithm javac -sourcepath src/main/java -d classes src/main/java/ Run the algorithm java -cp classes Example $ javac -d classes -sourcepath src/main/java src/main/java/com/williamfiset/algorithms/search/BinarySearch.java$ java -cp classes com.williamfiset.algorithms.search.BinarySearch Data Structures :movie_camera: Balanced Trees AVL Tree (recursive) Red Black Tree (recursive) :movie_camera: Binary Search Tree Splay Tree :movie_camera: Dynamic Array Dynamic array (integer only, fast) Dynamic array (generic) :movie_camera: Fenwick Tree Fenwick Tree (range query, point updates) Fenwick Tree (range update, point query) Fibonacci Heap :movie_camera: Hashtable Hashtable (double hashing) Hashtable (linear probing) Hashtable (quadratic probing) Hashtable (separate chaining) :movie_camera: Linked List :movie camera: Priority Queue Min Binary Heap Min Indexed Binary Heap (sorted key-value pairs, similar to hash-table) Min D-Heap :moviecamera: Min Indexed D-Heap (sorted key-value pairs, similar to hash-table) :movie_camera: Queue Queue (integer only, fixed size, fast) Queue (linked list, generic) Segment Tree Segment tree (array based, compact) Segment tree (pointer implementation) :movie_camera: Sparse Table :movie_camera: Stack Stack (integer only, fixed size, fast) Stack (linked list, generic) Stack (array, generic) :movie_camera: Suffix Array Suffix Array (O(n²logn) construction) Suffix Array (O(nlog²(n)) construction) Suffix Array (O(nlog(n)) construction) Trie :movie_camera: Union Find Dynamic Programming Dynamic Programming Classics Coin change problem - O(nW) Edit distance - O(nm) :movie_camera: Knapsack 0/1 - O(nW) Knapsack unbounded (0/∞) - O(nW) Maximum contiguous subarray - O(n) Longest Common Subsequence (LCS) - O(nm) Longest Increasing Subsequence (LIS) - O(n 2 ) Longest Palindrome Subsequence (LPS) - O(n 2 ) :movie_camera: Traveling Salesman Problem (dynamic programming, iterative) - O(n 2 2 n ) Traveling Salesman Problem (dynamic programming, recursive) - O(n 2 2 n ) Minimum Weight Perfect Matching (iterative, complete graph) - O(n 2 2 n ) Dynamic Programming Problem Examples Adhoc :movie_camera: Magic Cows Tiling problems :movie_camera: Tiling Dominoes :movie_camera: Tiling Dominoes and Trominoes :movie_camera: Mountain Scenes Geometry Angle between 2D vectors - O(1) Angle between 3D vectors - O(1) Circle-circle intersection point(s) - O(1) Circle-line intersection point(s) - O(1) Circle-line segment intersection point(s) - O(1) Circle-point tangent line(s) - O(1) Closest pair of points (line sweeping algorithm) - O(nlog(n)) Collinear points test (are three 2D points on the same line) - O(1) Convex hull (Graham Scan algorithm) - O(nlog(n)) Convex hull (Monotone chain algorithm) - O(nlog(n)) Convex polygon area - O(n) Convex polygon cut - O(n) Convex polygon contains points - O(log(n)) Coplanar points test (are four 3D points on the same plane) - O(1) Line class (handy infinite line class) - O(1) Line-circle intersection point(s) - O(1) Line segment-circle intersection point(s) - O(1) Line segment to general form (ax + by = c) - O(1) Line segment-line segment intersection - O(1) Longitude-Latitude geographic distance - O(1) Point is inside triangle check - O(1) Point rotation about point - O(1) Triangle area algorithms - O(1) [UNTESTED] Circle-circle intersection area - O(1) [UNTESTED] Circular segment area - O(1) Graph theory Tree algorithms :movie_camera: Rooting an undirected tree - O(V+E) :movie_camera: Identifying isomorphic trees - O(?) :movie_camera: Tree center(s) - O(V+E) Tree diameter - O(V+E) :movie_camera: Lowest Common Ancestor (LCA, Euler tour) - O(1) queries, O(nlogn) preprocessing Network flow Bipartite graph verification (adjacency list) - O(V+E) :movie_camera: Max flow & Min cut (Ford-Fulkerson with DFS, adjacency list) - O(fE) Max flow & Min cut (Ford-Fulkerson with DFS, adjacency matrix) - O(fV 2 ) :movie_camera: Max flow & Min cut (Edmonds-Karp, adjacency list) - O(VE 2 ) :movie_camera: Max flow & Min cut (Capacity scaling, adjacency list) - O(E 2 log 2 (U)) :movie_camera: Max flow & Min cut (Dinic's, adjacency list) - O(EV 2 ) or O(E√V) for bipartite graphs Maximum Cardinality Bipartite Matching (augmenting path algorithm, adjacency list) - O(VE) Min Cost Max Flow (Bellman-Ford, adjacency list) - O(E 2 V 2 ) Min Cost Max Flow (Johnson's algorithm, adjacency list) - O(E 2 Vlog(V)) Main graph theory algorithms Articulation points/cut vertices (adjacency list) - O(V+E) Bellman-Ford (edge list, negative cycles, fast & optimized) - O(VE) :movie_camera: Bellman-Ford (adjacency list, negative cycles) - O(VE) Bellman-Ford (adjacency matrix, negative cycles) - O(V 3 ) :movie_camera: Breadth first search (adjacency list) - O(V+E) Breadth first search (adjacency list, fast queue) - O(V+E) Bridges/cut edges (adjacency list) - O(V+E) Find connected components (adjacency list, union find) - O(Elog(E)) Find connected components (adjacency list, DFS) - O(V+E) Depth first search (adjacency list, iterative) - O(V+E) Depth first search (adjacency list, iterative, fast stack) - O(V+E) :movie_camera: Depth first search (adjacency list, recursive) - O(V+E) :movie_camera: Dijkstra's shortest path (adjacency list, lazy implementation) - O(Elog(V)) :movie_camera: Dijkstra's shortest path (adjacency list, eager implementation + D-ary heap) - O(Elog E/V (V)) :movie_camera: Eulerian Path (directed edges) - O(E+V) :movie_camera: Floyd Warshall algorithm (adjacency matrix, negative cycle check) - O(V 3 ) Graph diameter (adjacency list) - O(VE) :movie_camera: Kahn's algorithm (topological sort, adjacency list) - O(E+V) Kruskal's min spanning tree algorithm (edge list, union find) - O(Elog(E)) :movie_camera: Kruskal's min spanning tree algorithm (edge list, union find, lazy sorting) - O(Elog(E)) :movie_camera: Prim's min spanning tree algorithm (lazy version, adjacency list) - O(Elog(E)) Prim's min spanning tree algorithm (lazy version, adjacency matrix) - O(V 2 ) :movie_camera: Prim's min spanning tree algorithm (eager version, adjacency list) - O(Elog(V)) Steiner tree (minimum spanning tree generalization) - O(V 3 + V 2 _ 2 T + V _ 3 T ) :movie_camera: Tarjan's strongly connected components algorithm (adjacency list) - O(V+E) :movie_camera: Topological sort (acyclic graph, adjacency list) - O(V+E) Topological sort (acyclic graph, adjacency matrix) - O(V 2 ) Traveling Salesman Problem (brute force) - O(n!) :movie_camera: Traveling Salesman Problem (dynamic programming, iterative) - O(n 2 2 n ) Traveling Salesman Problem (dynamic programming, recursive) - O(n 2 2 n ) Linear algebra Freivald's algorithm (matrix multiplication verification) - O(kn 2 ) Gaussian elimination (solve system of linear equations) - O(cr 2 ) Gaussian elimination (modular version, prime finite field) - O(cr 2 ) Linear recurrence solver (finds nth term in a recurrence relation) - O(m 3 log(n)) Matrix determinant (Laplace/cofactor expansion) - O((n+2)!) Matrix inverse - O(n 3 ) Matrix multiplication - O(n 3 ) Matrix power - O(n 3 log(p)) Square matrix rotation - O(n 2 ) Mathematics [UNTESTED] Chinese remainder theorem Prime number sieve (sieve of Eratosthenes) - O(nlog(log(n))) Prime number sieve (sieve of Eratosthenes, compressed) - O(nlog(log(n))) Totient function (phi function, relatively prime number count) - O(n 1/4 ) Totient function using sieve (phi function, relatively prime number count) - O(nlog(log(n))) Extended euclidean algorithm - ~O(log(a + b)) Greatest Common Divisor (GCD) - ~O(log(a + b)) Fast Fourier transform (quick polynomial multiplication) - O(nlog(n)) Fast Fourier transform (quick polynomial multiplication, complex numbers) - O(nlog(n)) Primality check - O(√n) Primality check (Rabin-Miller) - O(k) Least Common Multiple (LCM) - ~O(log(a + b)) Modular inverse - ~O(log(a + b)) Prime factorization (pollard rho) - O(n 1/4 ) Relatively prime check (coprimality check) - ~O(log(a + b)) Other Bit manipulations - O(1) List permutations - O(n!) :movie_camera: Power set (set of all subsets) - O(2 n ) Set combinations - O(n choose r) Set combinations with repetition - O((n+r-1) choose r) Sliding Window Minimum/Maximum - O(1) Square Root Decomposition - O(1) point updates, O(√n) range queries Unique set combinations - O(n choose r) Lazy Range Adder - O(1) range updates, O(n) to finalize all updates Search algorithms Binary search (real numbers) - O(log(n)) Interpolation search (discrete discrete) - O(n) or O(log(log(n))) with uniform input Ternary search (real numbers) - O(log(n)) Ternary search (discrete numbers) - O(log(n)) Sorting algorithms Bubble sort - O(n 2 ) Bucket sort - Θ(n + k) Counting sort - O(n + k) Heapsort - O(nlog(n)) Insertion sort - O(n 2 ) Mergesort - O(nlog(n)) Quicksort (in-place, Hoare partitioning) - Θ(nlog(n)) Quicksort3 (Dutch National Flag algorithm) - Θ(nlog(n)) Selection sort - O(n 2 ) Radix sort - O(n*w) String algorithms Booth's algorithm (finds lexicographically smallest string rotation) - O(n) Knuth-Morris-Pratt algorithm (finds pattern matches in text) - O(n+m) Longest Common Prefix (LCP) array - O(nlog(n)) bounded by SA construction, otherwise O(n) :movie_camera: Longest Common Substring (LCS) - O(nlog(n)) bounded by SA construction, otherwise O(n) :movie_camera: Longest Repeated Substring (LRS) - O(nlog(n)) Manacher's algorithm (finds all palindromes in text) - O(n) Rabin-Karp algorithm (finds pattern match positions in text) - O(n+m) Substring verification with suffix array - O(nlog(n)) SA construction and O(mlog(n)) per query License This repository is released under the MIT license . In short, this means you are free to use this software in any personal, open-source or commercial projects. Attribution is optional but appreciated. To restore the repository download the bundle wget https://archive.org/download/github.com-williamfiset-Algorithms_-_2021-01-31_00-59-15/williamfiset-Algorithms_-_2021-01-31_00-59-15.bundle and run: git clone williamfiset-Algorithms_-_2021-01-31_00-59-15.bundle Source: https://github.com/williamfiset/Algorithms Uploader: williamfiset Upload date: 2021-01-31

A collection of algorithms and data structures Algorithms & data structures project Algorithms and data structures are fundamental to efficient code and good software design. Creating and designing excellent algorithms is required for being an exemplary programmer. This repository's goal is to demonstrate how to correctly implement common data structures and algorithms in the simplest and most elegant ways. Contributing This repository is contribution friendly :smiley:. If you'd like to add or improve an algorithm, your contribution is welcome! Please be sure to checkout the Wiki for instructions. Other programming languages? This repository provides algorithm implementations in Java, however there are other forks that provide implementations in other languages, most notably: C++/Python : https://github.com/akzare/Algorithms Rust : https://github.com/TianyiShi2001/Algorithms Running an algorithm implementation To compile and run any of the algorithms here, you need at least JDK version 8. Gradle can make things more convenient for you, but it is not required. Running with Gradle (recommended) This project supports the Gradle Wrapper . The Gradle wrapper automatically downloads Gradle at the first time it runs, so expect a delay when running the first command below. If you are on Windows, use gradlew.bat instead of ./gradlew below. Run a single algorithm like this: ./gradlew run -Palgorithm=. Alternatively, you can run a single algorithm specifying the full class name ```./gradlew run -Pmain= ``` For instance: ./gradlew run -Palgorithm=search.BinarySearch or ./gradlew run -Pmain=com.williamfiset.algorithms.search.BinarySearch Compiling and running with only a JDK Create a classes folder cd Algorithmsmkdir classes Compile the algorithm javac -sourcepath src/main/java -d classes src/main/java/ Run the algorithm java -cp classes Example $ javac -d classes -sourcepath src/main/java src/main/java/com/williamfiset/algorithms/search/BinarySearch.java$ java -cp classes com.williamfiset.algorithms.search.BinarySearch Data Structures :movie_camera: Balanced Trees AVL Tree (recursive) Red Black Tree (recursive) :movie_camera: Binary Search Tree Splay Tree :movie_camera: Dynamic Array Dynamic array (integer only, fast) Dynamic array (generic) :movie_camera: Fenwick Tree Fenwick Tree (range query, point updates) Fenwick Tree (range update, point query) Fibonacci Heap :movie_camera: Hashtable Hashtable (double hashing) Hashtable (linear probing) Hashtable (quadratic probing) Hashtable (separate chaining) :movie_camera: Linked List :movie camera: Priority Queue Min Binary Heap Min Indexed Binary Heap (sorted key-value pairs, similar to hash-table) Min D-Heap :moviecamera: Min Indexed D-Heap (sorted key-value pairs, similar to hash-table) :movie_camera: Queue Queue (integer only, fixed size, fast) Queue (linked list, generic) Segment Tree Segment tree (array based, compact) Segment tree (pointer implementation) :movie_camera: Sparse Table :movie_camera: Stack Stack (integer only, fixed size, fast) Stack (linked list, generic) Stack (array, generic) :movie_camera: Suffix Array Suffix Array (O(n²logn) construction) Suffix Array (O(nlog²(n)) construction) Suffix Array (O(nlog(n)) construction) Trie :movie_camera: Union Find Dynamic Programming Dynamic Programming Classics Coin change problem - O(nW) Edit distance - O(nm) :movie_camera: Knapsack 0/1 - O(nW) Knapsack unbounded (0/∞) - O(nW) Maximum contiguous subarray - O(n) Longest Common Subsequence (LCS) - O(nm) Longest Increasing Subsequence (LIS) - O(n 2 ) Longest Palindrome Subsequence (LPS) - O(n 2 ) :movie_camera: Traveling Salesman Problem (dynamic programming, iterative) - O(n 2 2 n ) Traveling Salesman Problem (dynamic programming, recursive) - O(n 2 2 n ) Minimum Weight Perfect Matching (iterative, complete graph) - O(n 2 2 n ) Dynamic Programming Problem Examples Adhoc :movie_camera: Magic Cows Tiling problems :movie_camera: Tiling Dominoes :movie_camera: Tiling Dominoes and Trominoes :movie_camera: Mountain Scenes Geometry Angle between 2D vectors - O(1) Angle between 3D vectors - O(1) Circle-circle intersection point(s) - O(1) Circle-line intersection point(s) - O(1) Circle-line segment intersection point(s) - O(1) Circle-point tangent line(s) - O(1) Closest pair of points (line sweeping algorithm) - O(nlog(n)) Collinear points test (are three 2D points on the same line) - O(1) Convex hull (Graham Scan algorithm) - O(nlog(n)) Convex hull (Monotone chain algorithm) - O(nlog(n)) Convex polygon area - O(n) Convex polygon cut - O(n) Convex polygon contains points - O(log(n)) Coplanar points test (are four 3D points on the same plane) - O(1) Line class (handy infinite line class) - O(1) Line-circle intersection point(s) - O(1) Line segment-circle intersection point(s) - O(1) Line segment to general form (ax + by = c) - O(1) Line segment-line segment intersection - O(1) Longitude-Latitude geographic distance - O(1) Point is inside triangle check - O(1) Point rotation about point - O(1) Triangle area algorithms - O(1) [UNTESTED] Circle-circle intersection area - O(1) [UNTESTED] Circular segment area - O(1) Graph theory Tree algorithms :movie_camera: Rooting an undirected tree - O(V+E) :movie_camera: Identifying isomorphic trees - O(?) :movie_camera: Tree center(s) - O(V+E) Tree diameter - O(V+E) :movie_camera: Lowest Common Ancestor (LCA, Euler tour) - O(1) queries, O(nlogn) preprocessing Network flow Bipartite graph verification (adjacency list) - O(V+E) :movie_camera: Max flow & Min cut (Ford-Fulkerson with DFS, adjacency list) - O(fE) Max flow & Min cut (Ford-Fulkerson with DFS, adjacency matrix) - O(fV 2 ) :movie_camera: Max flow & Min cut (Edmonds-Karp, adjacency list) - O(VE 2 ) :movie_camera: Max flow & Min cut (Capacity scaling, adjacency list) - O(E 2 log 2 (U)) :movie_camera: Max flow & Min cut (Dinic's, adjacency list) - O(EV 2 ) or O(E√V) for bipartite graphs Maximum Cardinality Bipartite Matching (augmenting path algorithm, adjacency list) - O(VE) Min Cost Max Flow (Bellman-Ford, adjacency list) - O(E 2 V 2 ) Min Cost Max Flow (Johnson's algorithm, adjacency list) - O(E 2 Vlog(V)) Main graph theory algorithms Articulation points/cut vertices (adjacency list) - O(V+E) Bellman-Ford (edge list, negative cycles, fast & optimized) - O(VE) :movie_camera: Bellman-Ford (adjacency list, negative cycles) - O(VE) Bellman-Ford (adjacency matrix, negative cycles) - O(V 3 ) :movie_camera: Breadth first search (adjacency list) - O(V+E) Breadth first search (adjacency list, fast queue) - O(V+E) Bridges/cut edges (adjacency list) - O(V+E) Find connected components (adjacency list, union find) - O(Elog(E)) Find connected components (adjacency list, DFS) - O(V+E) Depth first search (adjacency list, iterative) - O(V+E) Depth first search (adjacency list, iterative, fast stack) - O(V+E) :movie_camera: Depth first search (adjacency list, recursive) - O(V+E) :movie_camera: Dijkstra's shortest path (adjacency list, lazy implementation) - O(Elog(V)) :movie_camera: Dijkstra's shortest path (adjacency list, eager implementation + D-ary heap) - O(Elog E/V (V)) :movie_camera: Eulerian Path (directed edges) - O(E+V) :movie_camera: Floyd Warshall algorithm (adjacency matrix, negative cycle check) - O(V 3 ) Graph diameter (adjacency list) - O(VE) :movie_camera: Kahn's algorithm (topological sort, adjacency list) - O(E+V) Kruskal's min spanning tree algorithm (edge list, union find) - O(Elog(E)) :movie_camera: Kruskal's min spanning tree algorithm (edge list, union find, lazy sorting) - O(Elog(E)) :movie_camera: Prim's min spanning tree algorithm (lazy version, adjacency list) - O(Elog(E)) Prim's min spanning tree algorithm (lazy version, adjacency matrix) - O(V 2 ) :movie_camera: Prim's min spanning tree algorithm (eager version, adjacency list) - O(Elog(V)) Steiner tree (minimum spanning tree generalization) - O(V 3 + V 2 _ 2 T + V _ 3 T ) :movie_camera: Tarjan's strongly connected components algorithm (adjacency list) - O(V+E) :movie_camera: Topological sort (acyclic graph, adjacency list) - O(V+E) Topological sort (acyclic graph, adjacency matrix) - O(V 2 ) Traveling Salesman Problem (brute force) - O(n!) :movie_camera: Traveling Salesman Problem (dynamic programming, iterative) - O(n 2 2 n ) Traveling Salesman Problem (dynamic programming, recursive) - O(n 2 2 n ) Linear algebra Freivald's algorithm (matrix multiplication verification) - O(kn 2 ) Gaussian elimination (solve system of linear equations) - O(cr 2 ) Gaussian elimination (modular version, prime finite field) - O(cr 2 ) Linear recurrence solver (finds nth term in a recurrence relation) - O(m 3 log(n)) Matrix determinant (Laplace/cofactor expansion) - O((n+2)!) Matrix inverse - O(n 3 ) Matrix multiplication - O(n 3 ) Matrix power - O(n 3 log(p)) Square matrix rotation - O(n 2 ) Mathematics [UNTESTED] Chinese remainder theorem Prime number sieve (sieve of Eratosthenes) - O(nlog(log(n))) Prime number sieve (sieve of Eratosthenes, compressed) - O(nlog(log(n))) Totient function (phi function, relatively prime number count) - O(n 1/4 ) Totient function using sieve (phi function, relatively prime number count) - O(nlog(log(n))) Extended euclidean algorithm - ~O(log(a + b)) Greatest Common Divisor (GCD) - ~O(log(a + b)) Fast Fourier transform (quick polynomial multiplication) - O(nlog(n)) Fast Fourier transform (quick polynomial multiplication, complex numbers) - O(nlog(n)) Primality check - O(√n) Primality check (Rabin-Miller) - O(k) Least Common Multiple (LCM) - ~O(log(a + b)) Modular inverse - ~O(log(a + b)) Prime factorization (pollard rho) - O(n 1/4 ) Relatively prime check (coprimality check) - ~O(log(a + b)) Other Bit manipulations - O(1) List permutations - O(n!) :movie_camera: Power set (set of all subsets) - O(2 n ) Set combinations - O(n choose r) Set combinations with repetition - O((n+r-1) choose r) Sliding Window Minimum/Maximum - O(1) Square Root Decomposition - O(1) point updates, O(√n) range queries Unique set combinations - O(n choose r) Lazy Range Adder - O(1) range updates, O(n) to finalize all updates Search algorithms Binary search (real numbers) - O(log(n)) Interpolation search (discrete discrete) - O(n) or O(log(log(n))) with uniform input Ternary search (real numbers) - O(log(n)) Ternary search (discrete numbers) - O(log(n)) Sorting algorithms Bubble sort - O(n 2 ) Bucket sort - Θ(n + k) Counting sort - O(n + k) Heapsort - O(nlog(n)) Insertion sort - O(n 2 ) Mergesort - O(nlog(n)) Quicksort (in-place, Hoare partitioning) - Θ(nlog(n)) Quicksort3 (Dutch National Flag algorithm) - Θ(nlog(n)) Selection sort - O(n 2 ) Radix sort - O(n*w) String algorithms Booth's algorithm (finds lexicographically smallest string rotation) - O(n) Knuth-Morris-Pratt algorithm (finds pattern matches in text) - O(n+m) Longest Common Prefix (LCP) array - O(nlog(n)) bounded by SA construction, otherwise O(n) :movie_camera: Longest Common Substring (LCS) - O(nlog(n)) bounded by SA construction, otherwise O(n) :movie_camera: Longest Repeated Substring (LRS) - O(nlog(n)) Manacher's algorithm (finds all palindromes in text) - O(n) Rabin-Karp algorithm (finds pattern match positions in text) - O(n+m) Substring verification with suffix array - O(nlog(n)) SA construction and O(mlog(n)) per query License This repository is released under the MIT license . In short, this means you are free to use this software in any personal, open-source or commercial projects. Attribution is optional but appreciated. To restore the repository download the bundle wget https://archive.org/download/github.com-williamfiset-Algorithms_-_2021-01-31_00-59-15/williamfiset-Algorithms_-_2021-01-31_00-59-15.bundle and run: git clone williamfiset-Algorithms_-_2021-01-31_00-59-15.bundle Source: https://github.com/williamfiset/Algorithms Uploader: williamfiset Upload date: 2021-01-31

A collection of algorithms and data structures Algorithms & data structures project Algorithms and data structures are fundamental to efficient code and good software design. Creating and designing excellent algorithms is required for being an exemplary programmer. This repository's goal is to demonstrate how to correctly implement common data structures and algorithms in the simplest and most elegant ways. Contributing This repository is contribution friendly :smiley:. If you'd like to add or improve an algorithm, your contribution is welcome! Please be sure to checkout the Wiki for instructions. Other programming languages? This repository provides algorithm implementations in Java, however there are other forks that provide implementations in other languages, most notably: C++/Python : https://github.com/akzare/Algorithms Rust : https://github.com/TianyiShi2001/Algorithms Running an algorithm implementation To compile and run any of the algorithms here, you need at least JDK version 8. Gradle can make things more convenient for you, but it is not required. Running with Gradle (recommended) This project supports the Gradle Wrapper . The Gradle wrapper automatically downloads Gradle at the first time it runs, so expect a delay when running the first command below. If you are on Windows, use gradlew.bat instead of ./gradlew below. Run a single algorithm like this: ./gradlew run -Palgorithm=. Alternatively, you can run a single algorithm specifying the full class name ```./gradlew run -Pmain= ``` For instance: ./gradlew run -Palgorithm=search.BinarySearch or ./gradlew run -Pmain=com.williamfiset.algorithms.search.BinarySearch Compiling and running with only a JDK Create a classes folder cd Algorithmsmkdir classes Compile the algorithm javac -sourcepath src/main/java -d classes src/main/java/ Run the algorithm java -cp classes Example $ javac -d classes -sourcepath src/main/java src/main/java/com/williamfiset/algorithms/search/BinarySearch.java$ java -cp classes com.williamfiset.algorithms.search.BinarySearch Data Structures :movie_camera: Balanced Trees AVL Tree (recursive) Red Black Tree (recursive) :movie_camera: Binary Search Tree Splay Tree :movie_camera: Dynamic Array Dynamic array (integer only, fast) Dynamic array (generic) :movie_camera: Fenwick Tree Fenwick Tree (range query, point updates) Fenwick Tree (range update, point query) Fibonacci Heap :movie_camera: Hashtable Hashtable (double hashing) Hashtable (linear probing) Hashtable (quadratic probing) Hashtable (separate chaining) :movie_camera: Linked List :movie camera: Priority Queue Min Binary Heap Min Indexed Binary Heap (sorted key-value pairs, similar to hash-table) Min D-Heap :moviecamera: Min Indexed D-Heap (sorted key-value pairs, similar to hash-table) :movie_camera: Queue Queue (integer only, fixed size, fast) Queue (linked list, generic) Segment Tree Segment tree (array based, compact) Segment tree (pointer implementation) :movie_camera: Sparse Table :movie_camera: Stack Stack (integer only, fixed size, fast) Stack (linked list, generic) Stack (array, generic) :movie_camera: Suffix Array Suffix Array (O(n²logn) construction) Suffix Array (O(nlog²(n)) construction) Suffix Array (O(nlog(n)) construction) Trie :movie_camera: Union Find Dynamic Programming Dynamic Programming Classics Coin change problem - O(nW) Edit distance - O(nm) :movie_camera: Knapsack 0/1 - O(nW) Knapsack unbounded (0/∞) - O(nW) Maximum contiguous subarray - O(n) Longest Common Subsequence (LCS) - O(nm) Longest Increasing Subsequence (LIS) - O(n 2 ) Longest Palindrome Subsequence (LPS) - O(n 2 ) :movie_camera: Traveling Salesman Problem (dynamic programming, iterative) - O(n 2 2 n ) Traveling Salesman Problem (dynamic programming, recursive) - O(n 2 2 n ) Minimum Weight Perfect Matching (iterative, complete graph) - O(n 2 2 n ) Dynamic Programming Problem Examples Adhoc :movie_camera: Magic Cows Tiling problems :movie_camera: Tiling Dominoes :movie_camera: Tiling Dominoes and Trominoes :movie_camera: Mountain Scenes Geometry Angle between 2D vectors - O(1) Angle between 3D vectors - O(1) Circle-circle intersection point(s) - O(1) Circle-line intersection point(s) - O(1) Circle-line segment intersection point(s) - O(1) Circle-point tangent line(s) - O(1) Closest pair of points (line sweeping algorithm) - O(nlog(n)) Collinear points test (are three 2D points on the same line) - O(1) Convex hull (Graham Scan algorithm) - O(nlog(n)) Convex hull (Monotone chain algorithm) - O(nlog(n)) Convex polygon area - O(n) Convex polygon cut - O(n) Convex polygon contains points - O(log(n)) Coplanar points test (are four 3D points on the same plane) - O(1) Line class (handy infinite line class) - O(1) Line-circle intersection point(s) - O(1) Line segment-circle intersection point(s) - O(1) Line segment to general form (ax + by = c) - O(1) Line segment-line segment intersection - O(1) Longitude-Latitude geographic distance - O(1) Point is inside triangle check - O(1) Point rotation about point - O(1) Triangle area algorithms - O(1) [UNTESTED] Circle-circle intersection area - O(1) [UNTESTED] Circular segment area - O(1) Graph theory Tree algorithms :movie_camera: Rooting an undirected tree - O(V+E) :movie_camera: Identifying isomorphic trees - O(?) :movie_camera: Tree center(s) - O(V+E) Tree diameter - O(V+E) :movie_camera: Lowest Common Ancestor (LCA, Euler tour) - O(1) queries, O(nlogn) preprocessing Network flow Bipartite graph verification (adjacency list) - O(V+E) :movie_camera: Max flow & Min cut (Ford-Fulkerson with DFS, adjacency list) - O(fE) Max flow & Min cut (Ford-Fulkerson with DFS, adjacency matrix) - O(fV 2 ) :movie_camera: Max flow & Min cut (Edmonds-Karp, adjacency list) - O(VE 2 ) :movie_camera: Max flow & Min cut (Capacity scaling, adjacency list) - O(E 2 log 2 (U)) :movie_camera: Max flow & Min cut (Dinic's, adjacency list) - O(EV 2 ) or O(E√V) for bipartite graphs Maximum Cardinality Bipartite Matching (augmenting path algorithm, adjacency list) - O(VE) Min Cost Max Flow (Bellman-Ford, adjacency list) - O(E 2 V 2 ) Min Cost Max Flow (Johnson's algorithm, adjacency list) - O(E 2 Vlog(V)) Main graph theory algorithms Articulation points/cut vertices (adjacency list) - O(V+E) Bellman-Ford (edge list, negative cycles, fast & optimized) - O(VE) :movie_camera: Bellman-Ford (adjacency list, negative cycles) - O(VE) Bellman-Ford (adjacency matrix, negative cycles) - O(V 3 ) :movie_camera: Breadth first search (adjacency list) - O(V+E) Breadth first search (adjacency list, fast queue) - O(V+E) Bridges/cut edges (adjacency list) - O(V+E) Find connected components (adjacency list, union find) - O(Elog(E)) Find connected components (adjacency list, DFS) - O(V+E) Depth first search (adjacency list, iterative) - O(V+E) Depth first search (adjacency list, iterative, fast stack) - O(V+E) :movie_camera: Depth first search (adjacency list, recursive) - O(V+E) :movie_camera: Dijkstra's shortest path (adjacency list, lazy implementation) - O(Elog(V)) :movie_camera: Dijkstra's shortest path (adjacency list, eager implementation + D-ary heap) - O(Elog E/V (V)) :movie_camera: Eulerian Path (directed edges) - O(E+V) :movie_camera: Floyd Warshall algorithm (adjacency matrix, negative cycle check) - O(V 3 ) Graph diameter (adjacency list) - O(VE) :movie_camera: Kahn's algorithm (topological sort, adjacency list) - O(E+V) Kruskal's min spanning tree algorithm (edge list, union find) - O(Elog(E)) :movie_camera: Kruskal's min spanning tree algorithm (edge list, union find, lazy sorting) - O(Elog(E)) :movie_camera: Prim's min spanning tree algorithm (lazy version, adjacency list) - O(Elog(E)) Prim's min spanning tree algorithm (lazy version, adjacency matrix) - O(V 2 ) :movie_camera: Prim's min spanning tree algorithm (eager version, adjacency list) - O(Elog(V)) Steiner tree (minimum spanning tree generalization) - O(V 3 + V 2 _ 2 T + V _ 3 T ) :movie_camera: Tarjan's strongly connected components algorithm (adjacency list) - O(V+E) :movie_camera: Topological sort (acyclic graph, adjacency list) - O(V+E) Topological sort (acyclic graph, adjacency matrix) - O(V 2 ) Traveling Salesman Problem (brute force) - O(n!) :movie_camera: Traveling Salesman Problem (dynamic programming, iterative) - O(n 2 2 n ) Traveling Salesman Problem (dynamic programming, recursive) - O(n 2 2 n ) Linear algebra Freivald's algorithm (matrix multiplication verification) - O(kn 2 ) Gaussian elimination (solve system of linear equations) - O(cr 2 ) Gaussian elimination (modular version, prime finite field) - O(cr 2 ) Linear recurrence solver (finds nth term in a recurrence relation) - O(m 3 log(n)) Matrix determinant (Laplace/cofactor expansion) - O((n+2)!) Matrix inverse - O(n 3 ) Matrix multiplication - O(n 3 ) Matrix power - O(n 3 log(p)) Square matrix rotation - O(n 2 ) Mathematics [UNTESTED] Chinese remainder theorem Prime number sieve (sieve of Eratosthenes) - O(nlog(log(n))) Prime number sieve (sieve of Eratosthenes, compressed) - O(nlog(log(n))) Totient function (phi function, relatively prime number count) - O(n 1/4 ) Totient function using sieve (phi function, relatively prime number count) - O(nlog(log(n))) Extended euclidean algorithm - ~O(log(a + b)) Greatest Common Divisor (GCD) - ~O(log(a + b)) Fast Fourier transform (quick polynomial multiplication) - O(nlog(n)) Fast Fourier transform (quick polynomial multiplication, complex numbers) - O(nlog(n)) Primality check - O(√n) Primality check (Rabin-Miller) - O(k) Least Common Multiple (LCM) - ~O(log(a + b)) Modular inverse - ~O(log(a + b)) Prime factorization (pollard rho) - O(n 1/4 ) Relatively prime check (coprimality check) - ~O(log(a + b)) Other Bit manipulations - O(1) List permutations - O(n!) :movie_camera: Power set (set of all subsets) - O(2 n ) Set combinations - O(n choose r) Set combinations with repetition - O((n+r-1) choose r) Sliding Window Minimum/Maximum - O(1) Square Root Decomposition - O(1) point updates, O(√n) range queries Unique set combinations - O(n choose r) Lazy Range Adder - O(1) range updates, O(n) to finalize all updates Search algorithms Binary search (real numbers) - O(log(n)) Interpolation search (discrete discrete) - O(n) or O(log(log(n))) with uniform input Ternary search (real numbers) - O(log(n)) Ternary search (discrete numbers) - O(log(n)) Sorting algorithms Bubble sort - O(n 2 ) Bucket sort - Θ(n + k) Counting sort - O(n + k) Heapsort - O(nlog(n)) Insertion sort - O(n 2 ) Mergesort - O(nlog(n)) Quicksort (in-place, Hoare partitioning) - Θ(nlog(n)) Quicksort3 (Dutch National Flag algorithm) - Θ(nlog(n)) Selection sort - O(n 2 ) Radix sort - O(n*w) String algorithms Booth's algorithm (finds lexicographically smallest string rotation) - O(n) Knuth-Morris-Pratt algorithm (finds pattern matches in text) - O(n+m) Longest Common Prefix (LCP) array - O(nlog(n)) bounded by SA construction, otherwise O(n) :movie_camera: Longest Common Substring (LCS) - O(nlog(n)) bounded by SA construction, otherwise O(n) :movie_camera: Longest Repeated Substring (LRS) - O(nlog(n)) Manacher's algorithm (finds all palindromes in text) - O(n) Rabin-Karp algorithm (finds pattern match positions in text) - O(n+m) Substring verification with suffix array - O(nlog(n)) SA construction and O(mlog(n)) per query License This repository is released under the MIT license . In short, this means you are free to use this software in any personal, open-source or commercial projects. Attribution is optional but appreciated. To restore the repository download the bundle wget https://archive.org/download/github.com-williamfiset-Algorithms_-_2021-01-31_00-59-15/williamfiset-Algorithms_-_2021-01-31_00-59-15.bundle and run: git clone williamfiset-Algorithms_-_2021-01-31_00-59-15.bundle Source: https://github.com/williamfiset/Algorithms Uploader: williamfiset Upload date: 2021-01-31

In this study we gathered extracted information from different databases and studied the performance of machine learning algorithms in detecting skull base tumors via CT & MRI

In this study we gathered extracted information from different databases and studied the performance of machine learning algorithms in detecting skull base tumors via CT & MRI

Acute coma, a life-threatening condition often encountered in emergency medical settings, represents a major challenge in clinical neurology due to its heterogeneous causes and time-sensitive nature. The condition results from various disruptions in neuronal functions, ranging from primary central nervous system (CNS) diseases to diffuse physiological dysfunctions. Despite the advances in neuroimaging, laboratory diagnostics, and clinical evaluations, early identification and effective management of acute coma remain difficult. Current data indicate that disorders of consciousness account for approximately 10% of neurological illnesses, with acute coma affecting around 5% of patients presenting with consciousness disturbances in the emergency department. The mortality rate in these cases is alarmingly high, reaching up to 30%, further underscoring the urgency of accurate diagnosis and intervention.

The aim of this scoping review is to gather together studies that seek to improve the accuracy of dispatch system to allocate appropriate resources to the injured patients, whether computer-aided dispatch, staff-lead phone call systems, non-computer aided (e.g. flip-card) systems. This includes the identification of patients at high or low risk of adverse outcomes either in the prehospital phase on after arrival at hospital. These studies will then be evaluated with in clinical prediction rule framework.

We investigate algorithms for reconstructing a convex body $K$ in $\mathbb {R}^n$ from noisy measurements of its support function or its brightness function in $k$ directions $u_1,...,u_k$. The key idea of these algorithms is to construct a convex polytope $P_k$ whose support function (or brightness function) best approximates the given measurements in the directions $u_1,...,u_k$ (in the least squares sense). The measurement errors are assumed to be stochastically independent and Gaussian. It is shown that this procedure is (strongly) consistent, meaning that, almost surely, $P_k$ tends to $K$ in the Hausdorff metric as $k\to\infty$. Here some mild assumptions on the sequence $(u_i)$ of directions are needed. Using results from the theory of empirical processes, estimates of rates of convergence are derived, which are first obtained in the $L_2$ metric and then transferred to the Hausdorff metric. Along the way, a new estimate is obtained for the metric entropy of the class of origin-symmetric zonoids contained in the unit ball. Similar results are obtained for the convergence of an algorithm that reconstructs an approximating measure to the directional measure of a stationary fiber process from noisy measurements of its rose of intersections in $k$ directions $u_1,...,u_k$. Here the Dudley and Prohorov metrics are used. The methods are linked to those employed for the support and brightness function algorithms via the fact that the rose of intersections is the support function of a projection body.

We consider the Rosenfeld-Groebner algorithm for computing a regular decomposition of a radical differential ideal generated by a set of ordinary differential polynomials in n indeterminates. For a set of ordinary differential polynomials F, let M(F) be the sum of maximal orders of differential indeterminates occurring in F. We propose a modification of the Rosenfeld-Groebner algorithm, in which for every intermediate polynomial system F, the bound M(F) is less than or equal to (n-1)!M(G), where G is the initial set of generators of the radical ideal. In particular, the resulting regular systems satisfy the bound. Since regular ideals can be decomposed into characterizable components algebraically, the bound also holds for the orders of derivatives occurring in a characteristic decomposition of a radical differential ideal. We also give an algorithm for converting a characteristic decomposition of a radical differential ideal from one ranking into another. This algorithm performs all differentiations in the beginning and then uses a purely algebraic decomposition algorithm.

The recent paper of M.Dolg is discussed and his critical remarks with respect to the Generalized Relativistic Effective Core Potential (GRECP) method are shown to be incorrect. Some main features of GRECP are discussed as compared with the ``energy-consistent/adjusted'' pseudopotential and with the conventional shape-consistent RECP.

The Stoermer-Verlet-leapfrog group of integrators commonly used in molecular dynamics simulations has long become a textbook subject and seems to have been studied exhaustively. There are, however, a few striking effects in performance of algorithms which are well-known but have not received adequate attention in the literature. A closer view of these unclear observations results in unexpected conclusions. It is shown here that contrary to the conventional point of view, the leapfrog scheme is distinguished in this group both in terms of the order of truncation errors and the conservation of the total energy. In this case the characteristic square growth of fluctuations of the total energy with the step size, commonly measured in numerical tests, results from additional interpolation errors with no relation to the accuracy of the computed trajectory. An alternative procedure is described for checking energy conservation of leapfrog-like algorithms which is free from interpolation errors. Preliminary tests on a representative model system suggest that standard step size values used at present are lower than necessary for accurate sampling.

The quantum Fourier transform (QFT) is a powerful tool in quantum computing. The main ingredients of QFT are formed by the Walsh-Hadamard transform H and phase shifts P(.), both of which are 2x2 unitary matrices as operators on the two-dimensional 1-qubit space. In this paper, we show that H and P(.) suffice to generate the unitary group U(2) and, consequently, through controlled-U operations and their concatenations, the entire unitary group U(2^n) on n-qubits can be generated. Since any quantum computing algorithm in an n-qubit quantum computer is based on operations by matrices in U(2^n), in this sense we have the universality of the QFT.

This work presents the first application of the method of Genetic Algorithms (GAs) to data analysis for the Laser Interferometer Space Antenna (LISA). In the low frequency regime of the LISA band there are expected to be tens of thousands galactic binary systems that will be emitting gravitational waves detectable by LISA. The challenge of parameter extraction of such a large number of sources in the LISA data stream requires a search method that can efficiently explore the large parameter spaces involved. As signals of many of these sources will overlap, a global search method is desired. GAs represent such a global search method for parameter extraction of multiple overlapping sources in the LISA data stream. We find that GAs are able to correctly extract source parameters for overlapping sources. Several optimizations of a basic GA are presented with results derived from applications of the GA searches to simulated LISA data.

We investigate the algorithms for dynamical overlap fermions aiming at improving the performance for large-scale simulations. We look for the best combination of Hybrid Monte Carlo options and iterative quark solvers with respect to the numerical costs. Our main target is a $N_f=2$ simulation with overlap fermion on a $16^3\times 32$ lattice at lattice spacing around 0.12 fm.

I present a summary of recent algorithmic developments for lattice field theories. In particular I give a pedagogical introduction to the new Multicanonical algorithm, and discuss the relation between the Hybrid Overrelaxation and Hybrid Monte Carlo algorithms. I also attempt to clarify the role of the dynamical critical exponent z and its connection with `computational cost.' [Includes four PostScript figures]

Unique games are constraint satisfaction problems that can be viewed as a generalization of MAX CUT to a larger domain: We are given a graph G = (V,E) on n vertices and a permutation P uv on the set of labels {1,...,k} for every edge (u, v). Our goal is to assign a label X u in {1,..., k} to each vertex u, so as to maximize the number of satisfied constraints P uv (X u ) = X v . This problem has recently attracted a lot of attention since hardness of approximation for many problems, such as Sparsest Cut and Vertex Cover, was proved assuming the Unique Games Conjecture. Roughly speaking, this conjecture says that even if almost all constraints in a unique game are satisfiable it is NP-hard to satisfy a small constant fraction of constraints.Unique games pose a great challenge for our existing techniques:Typically, semidefinite programming (SDP) relaxations are well suited for optimization problems involving boolean variables (e.g. MAX CUT). But little is known about how to analyze SDP solutions for problems with larger domains. We present three approximation algorithms for Unique Games that satisfy roughly k -epsilon/2 , 1 - O(sqrt{epsilon log k}) and 1 - epsilon * O(sqrt{log k log n}) fraction of all constraints if a (1-epsilon) fraction of all constraints is satisfiable. This talk is based on joint papers with Moses Charikar, Eden Chlamtac, and Konstantin Makarychev. ©2007 Microsoft Corporation. All rights reserved.

Even commercial router vendors have adopted randomized algorithms in a few cases because of their simplicity, speed, and memory-efficiency in a few cases. Further, because of the opportunity to see every member of population (i.e., every arriving packet) and preserve summary information about the entire population, such randomized algorithms can obtain an "edge" over standard algorithms that merely sample the population. I illustrate this thesis by three algorithms. First, I will describe a simple algorithm for finding sources that send a large proportion of traffic, and its application in a worm detection chip. Second, I will describe an algorithm that provides an inexpensive technique for measuring the average and variance of packet latencies and loss on a link. By contrast, the majority of routers have no support for fine-grained latency measurement; managers must instead rely on approximate methods such as sending probe packets or using "tomographic" techniques. If time permits, I will describe a third algorithm which allows scalable logging, say of millions of network addresses infected after an attack, using only a small amount of memory. In all three cases I will quantify the edge obtained over simple sampling. ©2010 Microsoft Corporation. All rights reserved.

The Microsoft Biology Initiative (MBI) is an effort in Microsoft Research to bring new technology and tools to the area of bioinformatics and biology. This initiative is comprised of two primary components, the Microsoft Biology Foundation (MBF) and the Microsoft Biology Tools (MBT). The Microsoft Biology Foundation (MBF) is a language-neutral bioinformatics toolkit built as an extension to the Microsoft .NET Framework, initially aimed at the area of Genomics research. Currently, it implements a range of parsers for common bioinformatics file formats; a range of algorithms for manipulating DNA, RNA, and protein sequences; and a set of connectors to biological web services such as NCBI BLAST. MBF is available under an open source license, and executables, source code, demo applications, and documentation are freely downloadable. The Microsoft Biology Tools (MBT) are a collection of tools targeted at helping the biology and bioinformatics researcher be more productive in making scientific discoveries. The tools provided here take advantage of the capabilities provided in the Microsoft Biology Foundation, and are good examples of how MBF can be used to create other tools. This tutorial will provide an overview of the library, details about how to extend and re-use the library, and demonstrations of the tools released that use the library: The MSR Biology Extension for Excel and the MSR Sequence Assembler. ©2010 Microsoft Corporation. All rights reserved.

In this keynote talk for the Cloud Futures Workshop 2011, Michael Franklin describes the work in the Algorithms, Machines, and People Lab (AMPLab)—an industry-supported collaboration of students, postdocs, and faculty who specialize in data management, cloud computing, statistical machine learning, and other important topics that are necessary for making sense of vast amounts of heterogeneous and unruly data. ©2011 Microsoft Corporation. All rights reserved.

In this keynote talk for the Cloud Futures Workshop 2011, Michael Franklin describes the work in the Algorithms, Machines, and People Lab (AMPLab)—an industry-supported collaboration of students, postdocs, and faculty who specialize in data management, cloud computing, statistical machine learning, and other important topics that are necessary for making sense of vast amounts of heterogeneous and unruly data. ©2011 Microsoft Corporation. All rights reserved.

In this paper we isolate the combinatorial property responsible (at least in part) for the computational speedups recently observed in some quantum walk algorithms. We find that continuous-time quantum walks can exploit the covering space property of certain graphs. We demonstrate that a quantum walk on a graph Y which covers a smaller graph X can be equivalent to a quantum walk on the smaller graph X. This equivalence occurs only when the walk begins on certain initial states, fibre-constant states, which respect the graph covering space structure. We illustrate these observations with walks on Cayley graphs; we show that walks on fibre-constant initial states for Cayley graphs are equivalent to walks on the induced Schreier graph. We also consider the problem of constructing efficient gate sequences simulating the time evolution of a continuous-time quantum walk. For the case of the walk on the m-torus graph T^m on 2^n vertices we construct a gate sequence which uses O(\poly(n)) gates which is independent of the time t the walk is simulated for (and so the sequence can simulate the walk for exponential times). We argue that there exists a wide class of nontrivial operators based on quantum walks on graphs which can be measured efficiently. We introduce a new general class of computational problems, HiddenCover, which includes a variant of the general hidden subgroup problem as a subclass. We argue that quantum computers ought to be able to utilise covering space structures to efficiently solve HiddenCover problems.

Using the methods of quantum trajectories we investigate the effects of dissipative decoherence in a quantum computer algorithm simulating dynamics in various regimes of quantum chaos including dynamical localization, quantum ergodic regime and quasi-integrable motion. As an example we use the quantum sawtooth algorithm which can be implemented in a polynomial number of quantum gates. It is shown that the fidelity of quantum computation decays exponentially with time and that the decay rate is proportional to the number of qubits, number of quantum gates and per gate dissipation rate induced by external decoherence. In the limit of strong dissipation the quantum algorithm generates a quantum attractor which may have complex or simple structure. We also compare the effects of dissipative decoherence with the effects of static imperfections.

Let G=(V,E) be a finite graph, and f:V->N be any function. The Local Search problem consists in finding a local minimum of the function f on G, that is a vertex v such that f(v) is not larger than the value of f on the neighbors of v in G. In this note, we first prove a separation theorem slightly stronger than the one of Gilbert, Hutchinson and Tarjan for graphs of constant genus. This result allows us to enhance a previously known deterministic algorithm for Local Search with query complexity O(\log n)\cdot d+O(\sqrt{g})\cdot\sqrt{n}, so that we obtain a deterministic query complexity of d+O(\sqrt{g})\cdot\sqrt{n}, where n is the size of G, d is its maximum degree, and $g$ is its genus. We also give a quantum version of our algorithm, whose query complexity is of O(\sqrt{d})+O(\sqrt[4]{g})\cdot\sqrt[4]{n}\log\log n. Our deterministic and quantum algorithms have query complexities respectively smaller than the generic algorithms of Aldous and of Aaronson for large classes of graphs, including graphs of bounded genus and planar graphs. Independently from this work, Zhang has recently given a quantum algorithm which finds a local minimum on the planar grid over \{1,...,\sqrt{n}\}^2 using O(\sqrt[4]{n}(\log\log n)^2) queries. Our quantum algorithm can be viewed as a strongly generalized, and slightly enhanced version of this algorithm.

Motivation: In the last few years a growing interest in biology has been shifting towards the problem of optimal information extraction from the huge amount of data generated via large scale and high-throughput techniques. One of the most relevant issues has recently become that of correctly and reliably predicting the functions of observed but still functionally undetermined proteins starting from information coming from the network of co-observed proteins of known functions. Method: The method proposed in this article is based on a message passing algorithm known as Belief Propagation, which takes as input the network of proteins physical interactions and a catalog of known proteins functions, and returns the probabilities for each unclassified protein of having one chosen function. The implementation of the algorithm allows for fast on-line analysis, and can be easily generalized to more complex graph topologies taking into account hyper-graphs, {\em i.e.} complexes of more than two interacting proteins.

Motivation: In the last few years a growing interest in biology has been shifting towards the problem of optimal information extraction from the huge amount of data generated via large scale and high-throughput techniques. One of the most relevant issues has recently become that of correctly and reliably predicting the functions of observed but still functionally undetermined proteins starting from information coming from the network of co-observed proteins of known functions. Method: The method proposed in this article is based on a message passing algorithm known as Belief Propagation, which takes as input the network of proteins physical interactions and a catalog of known proteins functions, and returns the probabilities for each unclassified protein of having one chosen function. The implementation of the algorithm allows for fast on-line analysis, and can be easily generalized to more complex graph topologies taking into account hyper-graphs, {\em i.e.} complexes of more than two interacting proteins.

In this article we overview those aspects of the theory of affine semigroups and their algebras that have been relevant for our own research, and pose several open problems. Answers to these problems would contribute substantially to the further development of the theory. The paper treats two main topics: (1) affine semigroups and several covering properties for them and (2) algebraic properties for the corresponding rings (Koszul, Cohen-Macaulay, different ``sizes'' of the defining binomial ideals). We emphasize the special case when the initial data are encoded into lattice polytopes. The related objects -- polytopal semigroups and algebras -- provide a link with the classical theme of triangulations into unimodular simplices. We have also included an algorithm for checking the semigroup covering property in the most general setting. Our counterexample to certain covering conjectures was found by the application of a small part of this algorithm. The general algorithm could be used for a deeper study of affine semigroups.

Analysis of systematic scan Metropolis algorithms using Iwahori-Hecke algebra techniques

Cubature formulas, asymptotically optimal with respect to accuracy, are derived for calculating multidimensional weakly singular integrals. They are used for developing a universal code for calculating capacitances of conductors of arbitrary shapes.

One of the most basic computational problems is the task of finding a desired item in an ordered list of N items. While the best classical algorithm for this problem uses log_2 N queries to the list, a quantum computer can solve the problem using a constant factor fewer queries. However, the precise value of this constant is unknown. By characterizing a class of quantum query algorithms for ordered search in terms of a semidefinite program, we find new quantum algorithms for small instances of the ordered search problem. Extending these algorithms to arbitrarily large instances using recursion, we show that there is an exact quantum ordered search algorithm using 4 log_{605} N \approx 0.433 log_2 N queries, which improves upon the previously best known exact algorithm.

Grover's database search algorithm, although discovered in the context of quantum computation, can be implemented using any physical system that allows superposition of states. A physical realization of this algorithm is described using coupled simple harmonic oscillators, which can be exactly solved in both classical and quantum domains. Classical wave algorithms are far more stable against decoherence compared to their quantum counterparts. In addition to providing convenient demonstration models, they may have a role in practical situations, such as catalysis.

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Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations. The reparameterization trick is applicable when we can simulate a random variable by applying a differentiable deterministic function on an auxiliary random variable whose distribution is fixed. For many distributions of interest (such as the gamma or Dirichlet), simulation of random variables relies on acceptance-rejection sampling. The discontinuity introduced by the accept-reject step means that standard reparameterization tricks are not applicable. We propose a new method that lets us leverage reparameterization gradients even when variables are outputs of a acceptance-rejection sampling algorithm. Our approach enables reparameterization on a larger class of variational distributions. In several studies of real and synthetic data, we show that the variance of the estimator of the gradient is significantly lower than other state-of-the-art methods. This leads to faster convergence of stochastic gradient variational inference.

We extend the Frank-Wolfe (FW) optimization algorithm to solve constrained smooth convex-concave saddle point (SP) problems. Remarkably, the method only requires access to linear minimization oracles. Leveraging recent advances in FW optimization, we provide the first proof of convergence of a FW-type saddle point solver over polytopes, thereby partially answering a 30 year-old conjecture. We also survey other convergence results and highlight gaps in the theoretical underpinnings of FW-style algorithms. Motivating applications without known efficient alternatives are explored through structured prediction with combinatorial penalties as well as games over matching polytopes involving an exponential number of constraints.

Computing disconnected diagrams in Lattice QCD (operator insertion in a quark loop) entails the computationally demanding problem of taking the trace of the all to all quark propagator. We first outline the basic algorithm used to compute a quark loop as well as improvements to this method. Then, we motivate and introduce an algorithm based on the synergy between hierarchical probing and singular value deflation. We present results for the chiral condensate using a 2+1-flavor clover ensemble and compare estimates of the nucleon charges with the basic algorithm.