Showing results for false Vector Vector Vector
GitHub Repo
https://github.com/mtumilowicz/java17-mesi-false-sharing-processor-optimisations-workshop
mtumilowicz/java17-mesi-false-sharing-processor-optimisations-workshop
Introduction to cache coherence: false sharing, MESI protocol and vectorization
GitHub Repo
https://github.com/mainlp/False-Refusal-Mitigation
mainlp/False-Refusal-Mitigation
Code for paper: [ICLR 2025] Surgical, Cheap, and Flexible: Mitigating False Refusal in Language Models via Single Vector Ablation
GitHub Repo
https://github.com/mansiwalke/Vector
mansiwalke/Vector
#----Vectors---- # A vector is a one-dimensional array. # We can create a vector with all the basic data type # The simplest way to build a vector is to use the c command. # Creating a Numerical Vector: vec_num <- c(1, 10, 49) vec_num class(vec_num) # Creating a Character Vector: vec_chr <- c("a", "b", "c") vec_chr class(vec_chr) # Creating a Boolean Vector vec_bool <- c(TRUE, FALSE, TRUE) vec_bool class(vec_bool) vec_random <- c(1,3.0,TRUE) vec_random class(vec_random) # Object Properties #vector v1= 1:100 class(v1) ; typeof(v1) v2=letters[1:10] class(v2) ; typeof(v2) length(v2) # Arithmetic calculations on vectors: # Create the vectors vect_1 <- c(1, 3, 5) vect_2 <- c(2, 4, 6) # Take the sum of A_vector and B_vector sum_vect <- vect_1 + vect_2 # Print out total_vector sum_vect # Slicing Vector:Slice the first five rows of the vector slice_vector <- c(1,2,3,4,5,6,7,8,9,10) slice_vector[1:5] # Faster way to create a vector with adjacent values is: c(1:10) # Applying Logical Operators on Vectors: # Create a vector from 1 to 10 logical_vector <- c(1:10) logical_vector>5 # Print value strictly above 5 logical_vector[(logical_vector>5)] # Print 5 and 6 logical_vector <- c(1:10) logical_vector[(logical_vector>4) & (logical_vector<7)] #access elements (x = seq(0,10,by=2)) x[3] # access 3rd element x[c(2, 4)] # access 2nd and 4th element x[-1] # access all but 1st element x[c(2, -4)] # cannot mix positive and negative integers x[c(2.4, 3.54)] # real numbers are truncated to integers #modify (x = -3:2) x[2] <- 0; # modify 2nd element x x[x<0] = 5; # modify elements less than 0 x x = x[1:4]; # truncate x to first 4 elements x #delete vector (x = seq(1,5, length.out = 10)) x = NULL x x[4]
GitHub Repo
https://github.com/SESELOVSKYDarian/TPN-78-vectores-07
SESELOVSKYDarian/TPN-78-vectores-07
se ingresan datos a un vector de enteros de 8 elementos. escribir la función bool BuscaVal(vector <int> v , int val); la cual recibirá el valor por referencia y la variable val, dicha funcion devolvera true o false si val existe en el vector.
GitHub Repo
https://github.com/nickhuang99/Intent-Aware-RAG
nickhuang99/Intent-Aware-RAG
Why Pure Vector Search is a "False Proposition" for RAG?
GitHub Repo
https://github.com/FaresSofiane/Is-Vectoriel-Py
FaresSofiane/Is-Vectoriel-Py
Fonction Python, Prend en entré un fichier pdf est retourne True si il est vectoriel sinon False
GitHub Repo
https://github.com/metinaktas/Acoustic-direction-finding-using-single-acoustic-vector-sensor-under-high-reverberation
metinaktas/Acoustic-direction-finding-using-single-acoustic-vector-sensor-under-high-reverberation
We propose a novel and robust method for acoustic direction finding, which is solely based on acoustic pressure and pressure gradient measurements from single Acoustic Vector Sensor (AVS). We do not make any stochastic and sparseness assumptions regarding the signal source and the environmental characteristics. Hence, our method can be applied to a wide range of wideband acoustic signals including the speech and noise-like signals in various environments. Our method identifies the “clean” time frequency bins that are not distorted by multipath signals and noise, and estimates the 2D-DOA angles at only those identified bins. Moreover, the identification of the clean bins and the corresponding DOA estimation are performed jointly in one framework in a computationally highly efficient manner. We mathematically and experimentally show that the false detection rate of the proposed method is zero, i.e., none of the time-frequency bins with multiple sources are wrongly labeled as single-source, when the source directions do not coincide. Therefore, our method is significantly more reliable and robust compared to the competing state-of-the-art methods that perform the time-frequency bin selection and the DOA estimation separately. The proposed method, for performed simulations, estimates the source direction with high accuracy (less than 1 degree error) even under significantly high reverberation conditions.
GitHub Repo
https://github.com/RashmiS5/SAM-Classification-of-satellite-images
RashmiS5/SAM-Classification-of-satellite-images
The project aims at explaining the usage of SAM algorithm for satellite image classification. Hyperspectral Image provides pixel spectrum that fetches detailed information about a surface to identify and distinguish between spectrally similar (but unique) materials. The Hyperspectral Image sensor placed on board the Remote Sensing Satellite captures Hyperspectral Images with various bands of spectrum. Experiments are carried out for the implementation of Spectral Angle Mapper (SAM) on Hyper- spectral Images for classification of pixels on the surface. The false color composite of the image is also obtained for better visualization of surface differences. The Hyperspectral Images of various bands are stacked one after the other to form three-dimensional Cube of images for SAM implementation. SAM is a supervised classification algorithm which identifies the various classes in the image based on the calculation of the spectral angle. The spectral angle is calculated between the test vector built for each pixel and the reference vector built for each reference classes selected by the user. Results are obtained to read and reorganize multiple 2-D datasets into a single compact 3D dataset cube. The reference vector is built for performing SAM classification and the angle between the reference vector and pixel vector is calculated to compare with the determined threshold angle value. The color coding is then applied to distinguish between the various classes that have been recognized by the SAM algorithm. Hence using SAM, Hyperspectral images are analyzed to extract thematic information such as land-cover, water bodies, and clouds.
GitHub Repo
https://github.com/AkibCoding/False-Information-Propagation-Detection-Leveraging-SVM-and-Text-Vectorization-COMP-BERT
AkibCoding/False-Information-Propagation-Detection-Leveraging-SVM-and-Text-Vectorization-COMP-BERT
No repository description available.
GitHub Repo
https://github.com/Adityagupta2590/Feature-level-fusion-of-Palm-print-and-Palm-vein