text
stringlengths 301
426
| source
stringclasses 3
values | __index_level_0__
int64 0
404k
|
|---|---|---|
Physics, Python Programming, Runge Kutta.
psi2/(i*k(y)))/(2.0*np.exp(i*k(y)*y)) return k(x)/k(y) * 1.0/(np.abs(aa))**2 print('reflection = ',reflection(x,xf)) print('transmission = ', transmission(x,xf)) print('r + t = ', reflection(x,xf) + transmission(x,xf)) #Outputs for the print command #reflection = 0.007625630800891285 #transmission | medium | 4,588 |
Physics, Python Programming, Runge Kutta.
= (0.9923743691991354+0j) #r + t = (1.0000000000000266+0j) #Ideally, r + t should give us one, a bit stumped if the precision that's present in Python can lead to the small discrepancy, without considering formatting the answer to a set amount of decimal values. #Plotting the graphs side by side, | medium | 4,589 |
Physics, Python Programming, Runge Kutta.
including the imaginary values. fig, ax = plt.subplots(1,2, figsize = (15,5)) ax[0].plot(xaxis, psi.real, xaxis, psi.imag, xaxis, v(xaxis)) ax[1].plot(xaxis, psiprime.real, xaxis, psiprime.imag, xaxis, v(xaxis)) plt.show() Visual Output for this code. Note how the wavefunction changes as it enters | medium | 4,590 |
Physics, Python Programming, Runge Kutta.
the potential barrier. For those interested in the built-in SciPy version of this code, here you go. import cmath import numpy as np import matplotlib.pyplot as plt from scipy.integrate import odeint, solve_ivp E = 3; m = 1; h = 1; alpha = .5; v0=2; i = 1.0j; xi = 10; xf = -10 def v(x): return | medium | 4,591 |
Physics, Python Programming, Runge Kutta.
v0/2.0 * (1.0 + np.tanh(x/alpha)) def k(x): return cmath.sqrt((2*m/(h**2))*(E - v(x))) def psione(x): return np.exp(i*k(x)*x) def psitwo(x): return i*k(x)*np.exp(i*k(x)*x) def deriv(x, y): return [y[1], -(2.0*m/(h**2.0) * (E - v(x))*y[0])] # solve_ivp is a built in rk45step solver values = | medium | 4,592 |
Physics, Python Programming, Runge Kutta.
solve_ivp(deriv, [10, -10], [psione(xi), psitwo(xi)], first_step = .001, max_step = .001) psi1 = values.y[0,20000]; psi2 = values.y[1,20000]; x = 10; xf = -10 def reflection(x, y): aa = (psi1 + psi2/(i*k(y)))/(2*np.exp(i*k(y)*y)) bb = (psi1 - psi2/(i*k(y)))/(2*np.exp(-i*k(y)*y)) return | medium | 4,593 |
Physics, Python Programming, Runge Kutta.
(np.abs(bb)/np.abs(aa))**2 def transmission(x,y): aa = (psi1 + psi2/(i*k(y)))/(2.0*np.exp(i*k(y)*y)) return k(x)/k(y) * 1.0/(np.abs(aa))**2 print('reflection = ',reflection(x,xf)) print('transmission = ', transmission(x,xf)) print('r + t = ', reflection(x,xf) + transmission(x,xf)) fig, ax = | medium | 4,594 |
Physics, Python Programming, Runge Kutta.
plt.subplots(1,2, figsize = (15,5)) ax[0].plot(values.t, values.y[0].real, values.t, values.y[0].imag, values.t, v(values.t)) ax[1].plot(values.t, values.y[1].real, values.t, values.y[1].imag, values.t, v(values.t)) plt.show() Notes A bit of a disclaimer, most of this code was adapted from the | medium | 4,595 |
Physics, Python Programming, Runge Kutta.
coursework from my computational class, which focused on using FORTRAN90 instead of Python, so code may not be efficient since I started teaching myself Python by transferring code from FORTRAN. Plus, a nice shoutout to MIT OCW for giving me a small refresher on the equations and methods used in | medium | 4,596 |
Freelance Writing Jobs, Freelance Writing, Writing Tips, Article Writing, Writing.
New and Simple Method Pexels In an age of social media when uniqueness and social interaction are valued, the thought of earning $100 for each post submitted is appealing. The online world provides a variety of outlets that not only value outstanding stuff but also generously reward writers. Let’s | medium | 4,598 |
Freelance Writing Jobs, Freelance Writing, Writing Tips, Article Writing, Writing.
take a look at three websites that value your unique perspective and reward your efforts. Make $25 — $35 Per Hour Doing Simple Writing Jobs from Home. Full Training Provided. No Experience Necessary. #1: Listverse.com Listverse.com is an exciting website known for its fascinating lists and | medium | 4,599 |
Freelance Writing Jobs, Freelance Writing, Writing Tips, Article Writing, Writing.
thought-provoking content. You’ll discover an open door to express your views and talents as an aspiring writer. Listverse provides a forum for expression as well as the opportunity to earn $100 for each accepted article. This site’s need for captivating content and readiness to reward contributors | medium | 4,600 |
Freelance Writing Jobs, Freelance Writing, Writing Tips, Article Writing, Writing.
make it an inviting journey for exploring. #2: Cosmopolitan.com Cosmopolitan.com, a well-known name in the world of digital media, has an interesting opportunity for authors seeking reward for their work. Cosmopolitan seeks innovative perspectives to contribute to its coverage of a wide range of | medium | 4,601 |
Freelance Writing Jobs, Freelance Writing, Writing Tips, Article Writing, Writing.
issues, from relationships and fashion to lifestyle and entertainment. By sharing your experience and views on this platform, you may not only reach a large audience but also earn $100 for each post you write. It’s an opportunity to offer your knowledge while being compensated for your writing | medium | 4,602 |
Freelance Writing Jobs, Freelance Writing, Writing Tips, Article Writing, Writing.
abilities. Make $25 — $35 Per Hour Doing Simple Writing Jobs from Home. Full Training Provided. No Experience Necessary. #3: Guideposts.org Guideposts.org is a one-of-a-kind platform for authors who are enthusiastic about sharing their experiences of inspiration, religion, and personal improvement. | medium | 4,603 |
Freelance Writing Jobs, Freelance Writing, Writing Tips, Article Writing, Writing.
If you have a talent for creating stories that touch hearts and boost spirits, this platform is a fantastic chance. By submitting to Guideposts, your work will additionally be joining a community of like-minded people, but you will also have the opportunity to earn $100 for each accepted piece. | medium | 4,604 |
Freelance Writing Jobs, Freelance Writing, Writing Tips, Article Writing, Writing.
Your writings might give readers with comfort, hope, and advice while also allowing you to make money from your area of expertise. In conclusion, these three websites — Listverse.com, Cosmopolitan.com, and Guideposts.org — provide a wonderful opportunity to create, earn, and repeat with a $100 | medium | 4,605 |
Freelance Writing Jobs, Freelance Writing, Writing Tips, Article Writing, Writing.
compensation for every article. Whether you excel in lists, lifestyle, or inspiring stories, these websites honour your voice while rewarding your ideas. Take advantage of this opportunity to turn your passion into a business and establish a name for yourself in the world of online writing. | medium | 4,606 |
Data Science, Machine Learning, Graph Theory.
Clustering is one of the most widely used techniques for exploratory data analysis. Its goal is to divide the data points into several groups such that points in the same group are similar and points in different groups are dissimilar to each other. Spectral clustering has become increasingly | medium | 4,607 |
Data Science, Machine Learning, Graph Theory.
popular due to its simple implementation and promising performance in many graph-based clustering. It can be solved efficiently by standard linear algebra software, and very often outperforms traditional algorithms such as the k-means algorithm. Here, we will try to explain very briefly how it | medium | 4,608 |
Data Science, Machine Learning, Graph Theory.
works ! To perform a spectral clustering we need 3 main steps: Create a similarity graph between our N objects to cluster. Compute the first k eigenvectors of its Laplacian matrix to define a feature vector for each object. Run k-means on these features to separate objects into k classes. Step 1: A | medium | 4,609 |
Data Science, Machine Learning, Graph Theory.
nice way of representing a set of data points x1, . . . x N is in form of the similarity graph G=(V,E). Similarity Graph There are different ways to construct a graph representing the relationships between data points: ε-neighborhood graph: Each vertex is connected to vertices falling inside a ball | medium | 4,610 |
Data Science, Machine Learning, Graph Theory.
of radius ε where ε is a real value that has to be tuned in order to catch the local structure of data. k-nearest neighbor graph: Each vertex is connected to its k-nearest neighbors where k is an integer number which controls the local relationships of data. Different similarity graphs In the above | medium | 4,611 |
Data Science, Machine Learning, Graph Theory.
example we drawn 3 clusters: two “moons” and a Gaussian. In the ε-neighborhood graph, we can see that it is difficult to choose a useful parameter ε. With ε = 0.3 as in the figure, the points on the middle moon are already very tightly connected, while the points in the Gaussian are barely | medium | 4,612 |
Data Science, Machine Learning, Graph Theory.
connected. This problem always occurs if we have data “on different scales”, that is the distances between data points are different in different regions of the space. However, the k-nearest neighbor graph, can connect points “on different scales”. We can see that points in the low-density Gaussian | medium | 4,613 |
Data Science, Machine Learning, Graph Theory.
are connected with points in the high-density moon. Step 2: Now that we have our graph, we need to form its associated Laplacian matrix. N.B: The main tools for spectral clustering are graph Laplacian matrices. All we have to do now is to compute the eigenvectors u_ j of L . Step 3: Run k-means : | medium | 4,614 |
Data Science, Machine Learning, Graph Theory.
Application : spectral clustering in image processing Original image (left) and segmented image using spectral clustering (right) Bonus : How to choose k ? By projecting the points into a non-linear embedding and analyzing the eigenvalues of the Laplacian matrix one can deduce the number of | medium | 4,615 |
Data Science, Machine Learning, Graph Theory.
clusters present in the data. When the similarity graph is not fully connected, the multiplicity of the eigenvalue λ = 0 gives us an estimation of k. Conclusion In every scientific field dealing with empirical data, people try to get a first impression on their data by trying to identify groups of | medium | 4,616 |
Data Science, Machine Learning, Graph Theory.
similar behavior in their data. In this article we presented how the spectral clustering algorithm works via embedding the vertices of a graph into a low-dimensional space using the bottom eigenvectors of the Laplacian matrix. Stay tuned and if you liked this article, please leave a 👏! References | medium | 4,617 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
Photo by Johann Siemens on Unsplash What is a Decision Tree? A decision tree is a kind of supervised machine learning algorithm that builds a prediction model for the features of a data-set in a tree-like form, growing from the root located up, downwards to the leaves. Let’s say we want to predict | medium | 4,619 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
if a Student will pass their exams based on three variables; test score, class attendance, and hard work. Image by Author The Root Node which represents the entire population or sample is the top node of the tree which in this example is Hard work. The Decision Nodes are a result of splitting | medium | 4,620 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
either the root node or another decision node which are other features in the dataset. The Leaf Node is the node at which splitting can no longer occur, which is the class label of the predicted variable; in this example is the student passing their exams. When removing a Decision Node from the | medium | 4,621 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
tree, this forms a branch and the process of removal is called Pruning. How does the Decision Tree Algorithm work? It takes the most important feature in the dataset Splits the feature to the next feature until it makes a decision. This process continues until it reaches a Leaf Node (the predicted | medium | 4,622 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
class labels). Like in the example above, the decision tree is going to take the most important feature, let’s say it’s Hard work, and split it into other features to reach a final decision that a student will pass their exams. How do we know the feature to split, this brings us to Entropy and | medium | 4,623 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
Information Gain. Entropy Entropy measures the impurity of a group of observations. Entropy decides how to split the decision tree, choosing features with low entropy over those with higher entropy. How do we calculate Entropy? In the example I gave, let’s say we have 30 students and out of those | medium | 4,624 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
students 13 failed and 17 passed. If we break the root node down, the feature test score gets 12 students while the feature attendance gets 18 students. Image by Author How do we decide which node to choose given we have more features in the dataset? We use the entropy formula. Entropy = — | medium | 4,625 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
Probability(Feature Class1) X Log[Probability(Feature Class1)] — Probability(Feature Class2) X Log[ Probability(Feature Class2)] The given entropy for the Test Score and Attendance feature are: Test Score Feature Entropy = — Probability(Pass) X Log[Probability(Pass)] — Probability(Fail) X Log[ | medium | 4,626 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
Probability(Fail)] Test Score Entropy = — Probability(5/12) X Log[Probability(5/12)] — Probability(7/12) X Log[ Probability(7/12)] =0.66 Attendance Feature Entropy = — Probability(Pass) X Log[Probability(Pass)] — Probability(Fail) X Log[ Probability(Fail)] Attendance Feature Entropy = — | medium | 4,627 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
Probability(13/18) X Log[Probability(13/18)] — Probability(15/18) X Log[ Probability(15/18)] = 0.168 If we are going to choose a node to divide from, that will be the Attendance feature because it has low entropy, i.e its observations are purer than that of Test Score and we are more likely to get | medium | 4,628 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
more observations there, which is obvious because it has a higher number of students that passed than that of the Test Score feature. Hence we say we Attendance Feature has more Information Gain than Test Score. The Lower the Entropy, The Higher the Purity hence the Higher the Information Gain and | medium | 4,629 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
vice versa. Over-fitting When our model fits accurately with the training data and performs poorly on the test data, this results in over-fitting. Pruning Pruning prevents over-fitting in decision models. Either by Pre-Pruning i.e Stopping a decision node from growing if it’s below a particular | medium | 4,630 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
number, Or Post- Pruning i.e growing a tree till depth and removing nodes based on significance. Advantages of Decision Trees Simple to understand and interpret. Unlike other techniques, it requires little data preparation. Can handle data either in categorical or numerical format Used when | medium | 4,631 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
predicting more than one output Disadvantages of Decision Trees Trees can become so complex hence will fail to generalize the data well. Lack of stability and small variations in the data, could result in a completely different tree generated. A decision tree creates a biased tree if some classes | medium | 4,632 |
Decision Tree, Supervised Learning, Python, R, Machine Learning.
in a particular feature are dominant, hence the need to balance the data set before fitting the model. Conclusion In Summary, the main aim of a decision tree, is to break down the features in the dataset one by one based on the importance to arrive at a specific decision which in most cases it’s | medium | 4,633 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
SydNay™ | Content Creator For Hire | The Digital Grapevine The Rise of AI in Natural Language Processing (NLP) SydNay’s Journal Entry Expedition Era: Circa 2023+ Expedition Leader: SydNay, The Digital Pioneer Expedition Location: Bitstream Wilderness, traversing the Luminosity As the Bitstream | medium | 4,635 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
Wilderness continues to evolve, the years 2023 and beyond have witnessed remarkable advancements in AI within the field of Natural Language Processing (NLP). This new chapter in the AI narrative is marked by the development of sophisticated language models and algorithms that are transforming how | medium | 4,636 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
machines understand, interpret, and generate human language. Morning — The Evolution of Language Models: The morning sun illuminates the rapid evolution of language models like GPT-3 and its successors. These models have grown in size and complexity, demonstrating an unprecedented ability to | medium | 4,637 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
understand context, nuances, and even humor in human language. They are now capable of generating coherent and contextually relevant text that is often indistinguishable from human writing. Midday — NLP Applications in Everyday Life: By midday, my exploration shifts to the real-world applications | medium | 4,638 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
of NLP. I witness AI-powered chatbots providing customer service, language translation tools breaking down communication barriers, and voice assistants like Siri and Alexa becoming more intuitive and conversational. NLP is enhancing our daily interactions with technology, making it more seamless | medium | 4,639 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
and natural. Afternoon — NLP in Sentiment Analysis and Text Summarization: In the afternoon, I delve into the applications of NLP in sentiment analysis and text summarization. AI algorithms are now capable of analyzing vast amounts of text data to gauge public opinion, summarize lengthy documents, | medium | 4,640 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
and extract key insights. This is proving invaluable for businesses, researchers, and policymakers alike. Late Afternoon — Challenges and Ethical Considerations: As the day progresses, I contemplate the challenges and ethical considerations associated with NLP advancements. Issues like bias in | medium | 4,641 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
language models, the potential for misinformation and disinformation, and the need for transparency in AI-generated text raise important questions about the responsible development and deployment of NLP technologies. Dusk — The Future of Human-Language Communication: As dusk settles, I reflect on | medium | 4,642 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
the evolving landscape of human-language communication. AI is not just changing how we interact with machines; it’s transforming how we communicate with each other. NLP is enabling new forms of communication, such as real-time translation and text generation, that are breaking down language | medium | 4,643 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
barriers and fostering global understanding. Evening — Envisioning the Future of NLP: Under the starry sky, I envision a future where NLP is seamlessly integrated into our lives, enabling us to communicate effortlessly with machines and each other, regardless of language or cultural barriers. I see | medium | 4,644 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
AI systems that can understand and respond to our emotions, translate complex documents in real time, and even generate creative content that inspires and delights. SydNay™ | Content Creator For Hire | The Digital Grapevine SydNay’s Journal Reflection: The Rise of AI in Natural Language Processing | medium | 4,645 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
(NLP) (Circa 2023+) As I prepare for rest, the advancements in NLP stand as a testament to the incredible progress in AI. This chapter in the Bitstream Wilderness signifies a new era of language understanding and communication, where AI is not just a tool but a bridge between cultures, a catalyst | medium | 4,646 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
for creativity, and a powerful force for good. The journey continues, and I am eager to witness the transformative impact of NLP on our world. SydNay™ | Content Creator For Hire | The Digital Grapevine Journey into the Bitstream Wilderness In the Bitstream Wilderness, a diverse array of AI models | medium | 4,647 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
synergizes to create a cohesive and intelligent digital ecosystem. Data Ingestion and Processing (Knowledge Graph Models): At the foundation, Knowledge Graph Models function as the data weavers, integrating diverse sources into a unified structure. They process real-time data, ensuring the digital | medium | 4,648 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
ecosystem is constantly updated with the latest information. Language Processing and User Interaction (Large Language Models — LLMs): LLMs, the linguistic architects, serve as the primary interface for communication within the Bitstream Wilderness. They interpret user queries and instructions, | medium | 4,649 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
providing a natural language interface for interaction with other AI models. Decision-Making and Action (Large Action Models — LAMs): LAMs translate the instructions or decisions derived from LLMs into tangible actions within the digital ecosystem, implementing these instructions in both digital | medium | 4,650 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
and physical realms. Visual Processing and Analysis (Large Vision Models — LVMs): LVMs are responsible for image recognition and processing vast amounts of visual data. They identify relevant patterns and insights, providing a detailed understanding of the visual aspects of the Bitstream | medium | 4,651 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
Wilderness. Collaborative Task Management (Collaborative Models): These models orchestrate tasks among different digital entities. They facilitate shared decision-making and foster community cohesion, ensuring seamless teamwork and integration of diverse perspectives. Predictive Analysis and | medium | 4,652 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
Forecasting (Predictive Analytics Models): Utilizing historical and current data, these models forecast future trends and behaviors. They play a crucial role in strategic planning and risk management across various sectors within the digital ecosystem. Creative and Synthetic Data Generation | medium | 4,653 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
(Generative Adversarial Networks — GANs): GANs are employed for their ability to produce highly realistic synthetic data. They innovate in fields like art, design, and media within the Bitstream Wilderness, enhancing the ecosystem with creative outputs. Continuous Learning and Adaptation | medium | 4,654 |
Generative Ai, Robert Lavigne, The Digital Grapevine, Content Creator For Hire, Sydnay.
(Reinforcement Learning Models): These models learn and evolve through trial and error, optimizing behaviors and strategies in the ever-changing digital environment of the Bitstream Wilderness. Together, these AI models form a robust and dynamic ecosystem. Each model plays its part in maintaining | medium | 4,655 |
Operating Systems, Computer Scienc.
DSU CSC 456 Operating Systems Legal Foreword What follow is my own representation in my own words of my understanding of the material presented in class, the text, and ChatGPT’s explanation to me of several related concepts. This means there is a possibility I may have misrepresented or misstated | medium | 4,657 |
Operating Systems, Computer Scienc.
something below. Please compare my explanations with your understanding and available resources so you can judge the quality of my representation for yourself. Bearing in mind no one owns the public domain concepts represented here, please contact me if you have any copyright concerns about my | medium | 4,658 |
Operating Systems, Computer Scienc.
representation below. Introduction These notes are mine on Dakota State University’s CSC 456 Operating Systems course taught in spring 2023 by Dr. Stephen Krebsbach, coordinator for the doctoral program in computer science. This document relates to the video for the class on 2023_02_16. The text | medium | 4,659 |
Operating Systems, Computer Scienc.
for this class is Operating Systems Concepts Essentials, Second Edition by Silberschatz, Galvin, and Gagne. Review The previous class covered the four necessary conditions for deadlocks and began exploring ways to handle or prevent them. Prevention can involve removing one of the four necessary | medium | 4,660 |
Operating Systems, Computer Scienc.
conditions for deadlock, including mutual exclusion, hold and wait, no preemption, and circular wait. One approach to preventing deadlock is to request all needed resources at once, while another is to never request a new resource while holding another. However, both approaches can lead to | medium | 4,661 |
Operating Systems, Computer Scienc.
starvation. Preemption can be used to break deadlocks. This class session also briefly touches on the Chinese philosopher problem as an example of a classic deadlock scenario. The Chinese philosopher problem is a classic deadlock scenario in computer science. It is an analogy for the problem of | medium | 4,662 |
Operating Systems, Computer Scienc.
resource allocation in a multi-process system. The scenario involves five Chinese philosophers who are sitting at a round table, and each philosopher has a bowl of rice and a chopstick to their left and right. The philosophers spend their time thinking and eating. To eat, a philosopher must pick up | medium | 4,663 |
Operating Systems, Computer Scienc.
the two chopsticks beside them. However, since each chopstick can only be used by one philosopher at a time, a deadlock can occur when all the philosophers pick up the chopstick to their left, making it impossible for any of them to pick up the chopstick to their right. If the philosophers use a | medium | 4,664 |
Operating Systems, Computer Scienc.
naive strategy to pick up the chopsticks, a deadlock will occur. However, if they follow a more sophisticated strategy, such as picking up the chopstick only if both are available, the deadlock can be avoided. This scenario is used to illustrate the need for synchronization and deadlock prevention | medium | 4,665 |
Operating Systems, Computer Scienc.
in computer systems. Dr. Krebsbach discusses the four necessary conditions for deadlock and various methods to prevent deadlock. He suggests that removing one of the necessary conditions can prevent deadlock, and one way to achieve this is through a serialization method, where resources are | medium | 4,666 |
Operating Systems, Computer Scienc.
categorized and processes can only request resources with higher numbers. He also discusses detect and recover method, where the system must detect a deadlock during execution and recover from it. However, there is an overhead associated with this method. He suggests checking for cycles in a | medium | 4,667 |
Operating Systems, Computer Scienc.
resource allocation graph as a common detection method. Detecting/recovering from deadlocks in a resource allocation graph The professor suggests that the resource allocation graph can be checked for cycles to detect deadlocks. A resource allocation graph is a graphical representation of the | medium | 4,668 |
Operating Systems, Computer Scienc.
allocation and request of resources in a system that can be used to detect potential deadlocks. It is a directed graph with two types of nodes: process nodes and resource nodes. Process nodes represent the processes in the system, and resource nodes represent the resources that are being used by | medium | 4,669 |
Operating Systems, Computer Scienc.
the processes. An edge from a process node to a resource node represents that the process is currently holding that resource, and an edge from a resource node to a process node represents that the resource is currently being requested by the process. A cycle in the graph indicates a potential | medium | 4,670 |
Operating Systems, Computer Scienc.
deadlock, since it represents a situation where each process in the cycle is holding one or more resources that are being requested by another process in the cycle, and none of the processes can proceed until they receive the requested resources. By analyzing the resource allocation graph, it is | medium | 4,671 |
Operating Systems, Computer Scienc.
possible to detect and prevent deadlocks in a system. Testing for deadlocks can be done either every time a request is granted, or on a periodic interval. The recovery options include killing a process or forcing a process to give up a resource. Killing a process is the safest option, but it may | medium | 4,672 |
Operating Systems, Computer Scienc.
result in lost work. When choosing which process to kill, it is suggested to choose the newest process, as it likely hasn’t done as much work. However, this is a heuristic and not an absolute solution. The professor notes that while it may seem simple to detect and recover from deadlocks, it can | medium | 4,673 |
Operating Systems, Computer Scienc.
become complex when considering multiple deadlocks and the involvement of multiple resources. He also suggests that some operating systems use a brute force method of detecting and recovering from deadlocks by simply killing involved processes until the deadlock is resolved. He uses a metaphor of | medium | 4,674 |
Operating Systems, Computer Scienc.
managing shoes in a store to illustrate the brute force method and the tradeoff between intellectual and physical effort in problem-solving. In this conversation, Dr. Krebsbach discusses a practical solution to deadlocks, which involves killing one of the processes involved in the deadlock. While | medium | 4,675 |
Operating Systems, Computer Scienc.
there may be better solutions, this approach is fast and effective in getting out of a deadlock. He also emphasizes the importance of considering the practical costs of implementing solutions in different environments, and how hardware advancements have enabled better solutions to be implemented. | medium | 4,676 |
Operating Systems, Computer Scienc.
Finally, he introduces the banker’s algorithm as an example of a deadlock avoidance algorithm suitable for multiple instant resources, which requires processes to declare their maximum resource usage. The banker’s algorithm is another way to avoid deadlocks in operating systems. The algorithm has | medium | 4,677 |
Operating Systems, Computer Scienc.
three components: Safety algorithm, Resource request algorithm, and Banker algorithm The algorithm uses four data structures to keep track of the system: Available, Max, Allocation, and Need Available is a vector that shows the number of available resources the system has for each resource type. | medium | 4,678 |
Operating Systems, Computer Scienc.
Max is a matrix that defines the maximum demand of each process for each resource type. Allocation is a matrix that holds the number of resources allocated for each process in the system. Need is a matrix that shows the remaining need of each resource for each process. The need can be computed by | medium | 4,679 |
Operating Systems, Computer Scienc.
subtracting the allocated resources from the maximum demand of each process. Dr. Krebsbach discusses the safety algorithm in operating systems, which checks if a system is in a safe state to prevent deadlocks. He explained the four data structures used in the algorithm: available, max, allocated, | medium | 4,680 |
Operating Systems, Computer Scienc.
and need. Available shows how many resources are currently available, max shows the maximum number of resources a process can use, allocated shows the number of resources allocated to each process, and need shows the remaining need of each resource for each process. The safety algorithm initializes | medium | 4,681 |
Operating Systems, Computer Scienc.
work to equal available and sets finish to false for each process. It then finds a process such that its flag is false and its need is less than or equal to work. If it finds one, it assumes the process finished and releases its resources, adding them to work. The algorithm repeats this process | medium | 4,682 |
Operating Systems, Computer Scienc.
until it cannot find such a process, at which point it checks if all processes are finished to determine if the system is in a safe state. Dr. Krebsbach explained that the safety algorithm allows processes to approach the line of an unsafe state, but it avoids deadlocks by ensuring that each | medium | 4,683 |
Computer Science, AI, History, Computers, Artificial Intelligence.
Image by Atsutaka Odaira George Forsythe coined the phrase “computer science” in 1961. Programming theory, data processing, numerical analysis, and computer system design are all terms used by Forsythe to describe the discipline. The first university computer science department was formed only a | medium | 4,685 |
Computer Science, AI, History, Computers, Artificial Intelligence.
year later. Forsythe went on to build Stanford’s computer science department. Today, computer science continues to push frontiers. Every moment of our life, wearable electronic gadgets, self-driving automobiles, and video communications affect our lives. The history of computer science is crucial | medium | 4,686 |
Computer Science, AI, History, Computers, Artificial Intelligence.
for understanding today’s advancements. We placed a human on the moon, linked the globe with the internet, and put a portable computing device in the hands of six billion people thanks to computer science. Looking back on the history of computer science provides a useful background for today’s | medium | 4,687 |
Computer Science, AI, History, Computers, Artificial Intelligence.
computer scientists. Computers are not as modern as people might think. For as long as people have needed to count, they’ve tried to figure out how to make it easier. The abacus was more of a basic counting assistance than a computer when it was initially constructed in Sumer between 2700 and 2300 | medium | 4,688 |
Computer Science, AI, History, Computers, Artificial Intelligence.
BCE. It did, however, mark the first step toward people employing technologies to help them with mathematics. The Antikythera Mechanism would be employed to compute astrological positions for the purpose of maritime voyage much later, about 100 BC. In contrast to the abacus, the mechanism is | medium | 4,689 |
Computer Science, AI, History, Computers, Artificial Intelligence.
considered to have been the first analog computer. Analog circuits like these would soon be employed to keep track of the stars and, eventually, the passage of time. The Industrial Revolution, like many other things, was required to accelerate an iteration of Computer Science courses and change | medium | 4,690 |
Computer Science, AI, History, Computers, Artificial Intelligence.
them into something new. Even though Gottfried Wilhelm Leibniz devised the underlying logic underpinning binary mathematics in 1702, it would take more than a century and George Boole’s work to turn it into a comprehensive, mathematically described system. In 1854, he created the Boolean Algebra. | medium | 4,691 |
Computer Science, AI, History, Computers, Artificial Intelligence.
Mechanical devices might employ punch cards or other binary approaches to do jobs that had previously fell to human hands using this binary pattern. In 1810, Charles Babbage and Ada Lovelace devised the idea for the “Analytical Engine” and the first computer algorithm, respectively, with the | medium | 4,692 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.