We prove a fractional version of the Erdős–Szekeres theorem: for any k there is a constant ck > 0 such that any sufficiently large finite set X ⊂ R2 contains k subsets Y1, . . . ,Yk , each of size ≥ ck |X |, such that every set {y1, . . . , yk} with yi ∈ Yi is in convex position. The main tool is a lemma stating that any finite set X ⊂ Rd contains “large” subsets Y1, . . . ,Yk such that all set...

We study the error detection problem in q-ary asymmetric channels wherein every input symbol xi is mapped to an output symbol yi satisfying yi ≥ xi . A general setting is assumed where the noise vectors are (potentially) restricted in: 1) the amplitude, yi − xi ≤ a, 2) the Hamming weight, ∑n i=1 {yi 6=xi} ≤ h 6 6 , and 3) the total weight, ∑n i=1 (yi − xi) ≤ t. Optimal codes detecting these typ...

Bu ara?t?rmada Devine, Fahie ve McGillicuddy (2013) taraf?ndan geli?tirilen “?yi Ö?retmen Ölçe?i”nin Türk kültürüne uyarlanmas? amaçlam??t?r. amaçla çal??man?n verileri bir yüksekö?retim kurumunun e?itim fakültesinin farkl? bölümlerinde ö?renim gören 350 ö?renciden elde edilmi?tir. Ara?t?rmada ilk olarak orijinal ölçek uzman görü?leri do?rultusunda dil kültürel aç?dan Türkçeye uyarlanm?? ölçe?i...

Let δi = 1 if the i time Yi is an observed death and δi = 0 if it was a right-censored event: That is, the individual was alive at time Yi, but was last seen at that time. If Ti (1 ≤ i ≤ n) are the true survival or failure times, then Yi = Ti if δi = 1 and Yi < Ti if δi = 0, in which case the true failure time Ti is unknown. We also assume d-dimensional covariate vectors X1, X2, . . . , Xn for ...

The traditional Chinese herbal medicine yi-gan san has been used to cure neuropsychological disorders. Schizophrenia can be one of the target diseases of yi-gan san. We aimed at evaluating the possible use of yi-gan san in improving the schizophrenic symptoms of an animal model. Yi-gan san or distilled water was administered to mice born from pregnant mice injected with polyinosinic-polycytidil...

Remarks on transformations in pre-processing step: For all i ∈ {1, ..., k}, after applying the transformation (shift correction), we pool (Xi, yi) together to estimate β∗. Note that in general the transformation (shift correction) should not depend on the responses yi, otherwise we get a dependence on the noise. To see this, notice that yi = Xiβi + i where Xi is the transformed set of features....

Proof to Lemma 2. First, yi ≥ 0. For each i, we construct |V | affinely independent points in C with yi = 0. Fix i, then one solution is obviously x = 0, the empty subgraph. Next, for all p 6= i, obtain one solution by setting only yp = 1, and for all j 6= p set yj = 0. Clearly, yj = 0 and the |V | − 1 solutions thus obtained are affinely independent. In total we have |V | solutions with yi = 0...