mi'o za'ure'u ru'u cadzu i mi'o ca lo nu le te batci za'o se cortu cu co'a xanka
We walked around some more and when the bite kept hurting, we began to worry.


lu .iesai ku'i mi ba'e ca djica le nu mi sipna .i mi mutce tatpi li'u se cusku la teris.
"Oh, yes! But what I really want right now is some sleep. I'm very tired," said Terry.


lo ka gasnu lo zekri cu datni le nanmu lo ka zgana lo se zukte be ce'u
Being a criminal are the data about the man collected by monitoring his actions.


mi pu finti lo lisri lo ka jai gau zdile le verba vau lo se vedli be mi
I created a story out of my real experience to amuse the child.


mi pu friti fi le mamta fe lo ka sidju lo verba lo ka klama lo cnita
I offered the mother to help the child to go down.


lo ka gasnu lo zekri cu info le nanmu lo ka zgana lo se zukte be ce'u
That he is a criminal is the information about the man collected by monitoring his actions.


lu xu le prenu noi sanli ne'a lo va dinju cu ninmu li'u lu jo'a i mi na djuno li'u
"Is the person standing near that building a woman?" "Good question! I don't know! (there is a person standing near the building, the question of whether it's a woman is applicable)."


le nixli pu retsku fi lo pulji fe lu ta'i ma klama la palma noi xotli li'u
The girl asked a police officer "How does one get to Palm hotel?"


lo rilti be lo pu se sanga be le nakni cu simsa lo'u la la cu cu la la le'u
The rhythm of the phrase said by him was similar to "la la tcu tcu la la".


lo cmaci cu saske lo du'u lo namcu cu simxu lo ka ckini vau lo nu lanli le namcu
Mathematic is a science of how numbers are related to each other based on their analysis.


mi pu na zvati le tcadu i se ni'i bo na ka'e ku mi penmi la kevin
I wasn't in the city so I couldn't meet Kevin.


mi jinvi lo du'u lo vi uenci be lo nu farvi fa le bangu cu mutce lo ka vajni
I think that these documents containing the development of the language are very important.


aigne
fu'ivla x_{1} is an eigenvalue (or zero) of linear transformation/square matrix x_{2}, associated with/'owning' all vectors in generalized eigenspace x_{3} (implies neither nondegeneracy nor degeneracy; default includes the zero vector) with 'eigenspacegeneralization' power/exponent x_{4} (typically and probably by cultural default will be 1), with algebraic multiplicity (of eigenvalue) x_{5} For any eigenvector v in generalized eigenspace x_{3} of linear transformation x_{2} for eigenvalue x_{1}, where I is the identity matrix/transformation that works/makes sense in the context, the following equation is satisfied: ((x_{2}  x_{1} I)^x_{4})v = 0. When the argument of x_{4} is 1, the generalized eigenspace x_{3} is simply a strict/simple/basic eigenspace; this is the typical (and probable cultural default) meaning of this word. x_{4} will typically be restricted to integer values k > 0. x_{2} should always be specified (at least implicitly by context), for an eigenvalue does not mean much without the linear transformation being known. However, since one usually knows the said linear transformation, and since the basic underlying relationship of this word is "eigenness", the eigenvalue is given the primary terbri (x_{1}). When filling x_{3} and/or x_{4}, x_{2} and x_{1} (in that order of importance) should already be (at least contextually implicitly) specified. x_{3} is the set of all eigenvectors of linear transformation x_{2}, endowed with all of the typical operations of the vector space at hand. The default includes the zero vector (else the x_{3} eigenspace is not actually a vector space); normally in the context of mathematics, the zero vector is not considered to be an eigenvector, but by this definition it is included. Thus, a Lojban mathematician would consider the zero vector to be an (automatic) eigenvector of the given (in fact, any) linear transformation (particularly ones represented by a square matrix in a given basis). This is the logically most basic definition, but is contrary to typical mathematical culture. This word implies neither nondegeneracy nor degeneracy of eigenspace x_{3}. In other words there may or may not be more than one linearly independent vector in the eigenspace x_{3} for a given eigenvalue x_{1} of linear transformation x_{2}. x_{3} is the unique generalized eigenspace of x_{2} for given values of x_{1} and x_{4}. x_{1} is not necessarily the unique eigenvalue of linear transformation x_{2}, nor is its multiplicity necessarily 1 for the same. Beware when converting the terbri structure of this word. In fact, the set of all eigenvalues for a given linear transformation x_{2} will include scalar zero (0); therefore, any linear transformation with a nontrivial set of eigenvalues will have at least two eigenvalues that may fill in terbri x_{1} of this word. The 'eigenvalue' of zero for a proper/nice linear transformation will produce an 'eigenspace' that is equivalent to the entire vector space at hand. If x_{3} is specified by a set of vectors, the span of that set should fully yield the entire eigenspace of the linear transformation x_{2} associated with eigenvalue x_{1}, however the set may be redundant (linearly dependent); the zero vector is automatically included in any vector space. A multidimensional eigenspace (that is to say a vector space of eigenvectors with dimension strictly greater than 1) for fixed eigenvalue and linear transformation (and generalization exponent) is degenerate by definition. The algebraic multiplicity x_{5} of the eigenvalue does not entail degeneracy (of eigenspace) if greater than 1; it is the integer number of occurrences of a given eigenvalue x_{1} in the multiset of eigenvalues (spectrum) of the given linear transformation/square matrix x_{2}. In other words, the characteristic polynomial can be factored into linear polynomial primes (with root x_{1}) which are exponentiated to the power x_{5} (the multiplicity; notably, not x_{4}). For x_{4} > x_{5}, the eigenspace is trivial. x_{2} may not be diagonalizable. The scalar zero (0) is a naturally permissible argument of x_{1} (unlike some cultural mathematical definitions in English). Eigenspaces retain the operations and properties endowing the vectorspaces to which they belong (as subspaces). Thus, an eigenspace is more than a set of objects: it is a set of vectors such that that set is endowed with vectorspace operators and properties. Thus klesi alone is insufficient. But the set underlying eigenspace x_{3} is a type of klesi, with the property of being closed under linear transformation x_{2} (up to scalar multiplication). The vector space and basis being used are not specified by this word. Use this word as a seltau in constructions such as "eigenket", "eigenstate", etc. (In such cases, te aigne is recommended for the typical English usages of such terms. Use zei in lujvo formed by these constructs. The term "eigenvector" may be rendered as cmima be le te aigne). See also gei'ai, klesi, daigno


ca lo prulamdei lo bruna be mi cu klama la fukucimas. mu'i lo nu sarji le xabju .i mi xanka lo nu zenba lo ni seldi'e
Yesterday my brother went to Fukushima to help the inhabitants. I'm afraid that the radiation will increase.


ba lo nu facki lo du'u ba'o viska ro zo'e ku kei ra cpacu lo ri relxilma'e gi'e klama le zdani
When he saw that there was nothing more to see, he took his bike and rode home.


zo .djaberuakis. cmene le kriceto be la'o ly. Teddy Bear .ly. be'o dalpe'o be ko'a be'o noi clani je sloska se kerfa
His longhaired, blond, Teddy Bear hamster was called Jabberwocky.
