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Examples of vectors in nature are velocity, momentum, force, electromagnetic fields and weight. A quantity or phenomenon that exhibits magnitude only, with no specific direction, is called a scalar. Examples of scalars include speed, mass, electrical resistance and hard drive storage capacity.
Vectors are typically represented by an arrow with a beginning, or tail, and an end, or head, that is usually represented by an arrowhead. Vectors delineate the movement from point A to point B and can be defined as an entity with a designation, such as vector a.
In mathematics, the Cartesian coordinate system depicts vectors using a number pair as shown in Figure 1. In this example, the vector is a directed line segment defined as (0,0), (7,7) using its numbered pairs. Vectors and scalars can be used in mathematical processes and vector operations, such as vector addition, vector subtraction and vector multiplication.
Vectors can be depicted graphically in two or three dimensions. The magnitude of the vector is shown as the length of a line segment. The direction of the vector is shown by the orientation of the line segment and by an arrowhead at one end.
When creating images in vector graphics, the graphics file is a sequence of vector statements describing a series of points that connect to form the image. Examples of vector graphics software include Adobe Illustrator and CorelDraw.
raster graphics map individual bits -- each of which has its own qualities, such as color -- into an image via components called pixels, or picture elements. Raster images typically have a fixed number of pixels and are less scalable than vector images. As an image gets larger, individual pixels can become visible, resulting in the image being not as sharp or high quality as an equivalent vector image.
With raster images, computers must store each pixel, rather than a series of vector points. This often results in raster files being larger than vector graphics files. Adobe Illustrator and Adobe Photoshop are examples of software that is used to convert raster images into vector graphics files and vice versa.
In cybersecurity, the pathway that a threat actor or hacker uses to deliver their payload, such as a virus or ransomware, to a system or network is called an attack vector. This is how hackers exploit vulnerabilities in a system or network.
An attack vector can also be human, in the case of social engineering, where the perpetrator uses clever communication techniques to mislead users into giving out valuable information such as passwords. Typical attack vectors include malware, email attachments, instant messages and pop-up windows.
Cyber threat detection and mitigation systems include firewalls, intrusion detection systems, intrusion prevention systems and antivirus software. Numerous products and services are available to prevent threat actors from using attack vectors and to address threats.
If mode = "any", is.vector may return TRUE forthe atomic modes, list and expression.For any mode, it will return FALSE if x has anyattributes except names. (This is incompatible with S.) On the otherhand, as.vector removes all attributes including namesfor results of atomic mode.
For vector, a vector of the given length and mode. Logicalvector elements are initialized to FALSE, numeric vectorelements to 0, character vector elements to "", rawvector elements to nul bytes and list/expression elements toNULL.
For as.vector, a vector (atomic or of type list or expression).All attributes are removed from the result if it is of an atomic mode,but not in general for a list or expression result. The default method handles 24input types and 12 values of type: the details of mostcoercions are undocumented and subject to change.
Non-vector modes "symbol" (synonym "name") and"pairlist" are accepted but have long been undocumented: theyare used to implement as.name andas.pairlist, and those functions should preferably beused directly. None of the description here applies to thosemodes: see the help for the preferred forms.
The Vector Core at the University of North Carolina at Chapel Hill (UNC Vector Core) was founded in 1993 as a full-service viral vector production organization. We have extensive experience in manufacturing research grade vectors utilize our unique expertise to ensure your project is on time and on budget.
We have developed a suspension cell based production platform that utilizes serum-free media to increase efficiency and purity. Our proprietary system decreases the amount of processing steps necessary in methods using adherent cells while also reducing the variability and shortcomings associated with using FBS, giving our clients superior vectors at a lower cost.
The absence of animal components in the manufacturing of our vectors reduces the risk of contamination from adventitious agents. In addition, our production platform uses Wave bioreactors to minimize the number of open steps and cross-contamination.
Vectors, as defined by the California Department of Public Health, are any insect or other arthropod, rodent or other animal of public health significance capable of harboring or transmitting the causative agents of human disease, or capable of causing human discomfort and injury." Under this definition of a vector, the Orange County Mosquito and Vector Control District (District) provides surveillance and control measures for rats, mosquitoes, flies, and Red Imported Fire Ants.
The VBL is one-of-its-kind biocontainment facility to conduct research on vector (ticks and mosquitoes)-borne and zoonotic pathogens of public health threat. The VBL has three levels of containment labs and each one is designed for a specific research activity.
Provide an initial iteration of an incubator module,jdk.incubator.vector, to express vector computations that reliably compile at runtime tooptimal vector hardware instructions on supported CPU architectures and thus achieve superiorperformance to equivalent scalar computations.
Clear and concise API:The API shall be capable of clearly and concisely expressing a wide range ofvector computations consisting of a sequence of vector operations often composedwithin loops and possibly with control flow. It should be possible to express acomputation that is generic to vector size (or the number of lanes per vector)thus enabling such computations to be portable across hardware supportingdifferent vector sizes (as detailed in the next goal).
Platform agnostic:The API shall be architecture agnostic, enabling support for runtimeimplementations on multiple CPU architectures that support vector hardwareinstructions.As is usual in Java APIs, where platform optimization and portabilityconflict, the bias will be to making the Vector API portable, even if someplatform-specific idioms cannot be directly expressed in portable code.The next goal of x64 and AArch64 performance is representative of appropriateperformance goals on all platforms where Java is supported.The ARM Scalable Vector Extension (SVE) is of special interest in thisregard to ensure the API can support this architecture, even though as ofwriting there are no known production hardware implementations.
It is not a goal for HotSpot to support vector hardware instructions on CPUarchitectures other than x64 and AArch64. Such support is left for later JEPs.However, it is important to state, as expressedin the goals, that the API must not rule out such implementations. Further,work performed may naturally leverage and extend existing abstractions inHotSpot for auto-vectorization vector support making such a task easier.
It is not a goal to support strict floating point calculations as defined bythe Java strictfp keyword. The results of floating point operations performedon floating point scalars may differ from equivalent floating point operationsperforming on vectors of floating point scalars. However, this goal does notrule out options to express or control the desired precision or reproducibilityof floating point vector computations.
Vector computations consist of a sequence of operations on vectors. A vectorcomprises a (usually) fixed sequence of scalar values, where the scalarvalues correspond to the number of hardware-defined vector lanes. A binary operation appliedto two vectors with the same number of lanes would, for each lane, apply theequivalent scalar operation on the corresponding two scalar values from eachvector. This is commonly referred to asSingle Instruction Multiple Data (SIMD).
Vector operations express a degree of parallelism that enables more work to beperformed in a single CPU cycle and thus can result in significant performancegains. For example, given two vectors each covering a sequence of eightintegers (eight lanes), then the two vectors can be added together using asingle hardware instruction. The vector addition hardware instruction operateson sixteen integers, performing eight integer additions, in the time it wouldordinarily take to operate on two integers, performing one integer addition.
HotSpot supports auto-vectorization where scalar operations are transformed intosuperword operations, which are then mapped to vector hardware instructions.The set of transformable scalar operations are limited and fragile to changes inthe code shape. Furthermore, only a subset of available vector hardwareinstructions might be utilized limiting the performance of generated code.
A developer wishing to write scalar operations that are reliably transformedinto superword operations needs to understand HotSpot's auto-vectorizationsupport and its limitations to achieve reliable and sustainable performance.
In some cases it may not be possible for the developer to write scalaroperations that are transformable. For example, HotSpot does not transform thesimple scalar operations for calculating the hash code of an array (see theArrays::hashCode method implementations in the JDK source code), nor can itauto-vectorize code to lexicographically compare two arrays (which is why anintrinsic was added to perform lexicographical comparison, see8033148).
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