et al. in that a smaller lateral tibial plateau indicated a trend towards having a positive pivotshift. As suggested by Musahl, it would be logical to assume that the AP width of the femur and tibia would have a greater influence on the pivotshift outcome rather than the ML width. However, in the study by Musahl et al., a correlation between ML diam- eter of the tibial plateau and pivotshift grade was shown, but no correlation was found with the AP diameter. The current study did find a correlation using the AP diameter as a portion of a combined indice describing the bony anat- omy. The Musahl paper investigated a different patient population (grade 1 pivotshift vs. grade 2 pivotshift) than the current study which compared reconstructed knees without a pivot and reconstructed knees with a residual pivotshift. The current study also uses multiple AP mea- surements to create a ratio which may be more sensitive to pick up differences between groups. The current study also includes the femoral condyle length which was not mea- sured in the Musahl paper. Ratios that included multiple structural characteristics proved to be more specific than individual characteristics in separating patients in the Pivot group and No Pivot group.
This study has several limitations. Beside the limited number of specimens, one limitation of this study is that loads applied by the examiner during the pivotshift could not be assessed. Analysis of the loads would be es- pecially important with regards to the valgus torque ap- plied at the start of the reduction event, since more valgus rotation correlated with increased anterior trans- lation of the lateral knee compartment. In addition, the grading of the pivotshifttest is influenced by the hip position, possibly due to tensioning of the iliotibial band (Bach et al. 1988). However, the hip position was not an- alyzed during this study. The results of the present study are based on biomechanical testing on cadaveric speci- mens without muscle activation and guarding, so care must be taken when transferring results into clinical set- tings. Also, the precision of electromagnetic tracker sys- tems is limited with an accuracy of 0.5 mm for translation and 0.5° for rotation.
instability (Hewison et al., 2015; Slette et al., 2016; Sonnery-Cottet et al., 2017; Sonnery-Cottet et al., 2015). However, no reliable diagnostic tool is available for identi- fying patients with ALRI of the knee, while such a tool is essential in order to perform a reliable diagnosis and evaluation of the possible effectiveness of such treatments. At this moment, the main clinical tests to diagnose ALRI are the pivotshifttest (Galway & MacIntosh, 1980) and anterior drawer test with the foot in 30° of internal ro- tation (Larson, 1983; Slocum & Larson, 1968). Other tests, such as Slocum’s test (Slocum et al., 1976), Losee test (Losee et al., 1978) and jerk test (Hughston et al., 1976a) are comparable to the pivotshifttest. These tests mainly demonstrate anterior subluxation of the lateral tibia plat- eau on the lateral femoral condyle. The pivotshifttest is an accurate diagnostic test for rupture of the ACL with a sensitivity ranging from 0% to 93% and specificity ranging from 82% to 100% (Benjaminse et al., 2006; Leblanc et al., 2015). Even though the pivotshifttest assesses rotatory laxity of the knee additional to anteroposterior laxity, no distinction can be made between the amount of antero- posterior or rotatory laxity. To which extent the pivotshift contributes in specifically diagnosing ALRI as a conse- quence of injury to secondary constraints is unclear (Bonanzinga et al., 2017).
In particular with regard to controlled load application, reliable and objective measurements of anterior and rota- tional knee stability could only be achieved in-vitro, so far. Most of these in-vitro studies where using robotic sys- tems, evaluating anterior tibial translation during an- terior force application (88 N or 134 N) as well as during simulated pivotshifttest (10 Nm valgus torque + 4, 5 or 10 Nm internal rotational torque [22–32]. Some studies additionally considered the rotational range [22, 23, 25, 30, 32]. Robotic devices equipped with force/torque sensors are able to apply exactly re- producible loadings while measuring the motion of the tibia in 6° of freedom with high accuracy. However, using in-vitro experiments, no conclusions can be made on the performance after healing or even a long- term period and it is not possible to relate biomechan- ical parameters to patient satisfaction or quality of life.
Quicksort studied in many books such as   and . It is an exhaustively anatom- ize sorting algorithm and following the idea of divide-and-conquer on an input con- sisting of n items . Quicksort used a pivot item to divide its input items into two partitions; the items in one sublist seem diminutive or identically tantamount to the pivot; the items in the other sublist seem more sizably voluminous than or equipollent to the pivot, after then it uses recursion to order these sublists. It is prominent that the input consists of n items with different keys in arbitrary order and the pivot is picked by just picking an item, and then on average Quicksort utilizes 2 ln n n + O n ( ) com- parisons between items from the input. The Partial Quicksort algorithm analyzed by Ragab   and  depends on the idea of the standard Quicksort. It uses a smart strategy to find the l smallest elements out of n distinct elements and sort them. Yaroslavskiy declared in 2009 that he had made some improvements for the Quicksort algorithm, the demand being drawn by experiments.
The randomized-select algorithm has a linear execution time in the average case, but quadratic in the worst case. It’s possible to make the algorithm deterministic and maintaining the complexity linear even in the worst case. This imply a burdening of the algorithm that in the randomized case was extremely simple. The method that we will apply is just an example of a more general technique, known as derandomization technique that can also be applied to many other algorithms. The critical point of the randomized-quick-sort algorithm is related to the choice of the pivot that may fall over the minimal or maximal element, deteriorating the performances. The ideal choice would be to choose a pivot as much as possible close to the median. Here’s how it’s possible to improve the choice of the pivot to limit the impact of the worse case:
While other comparison-efficient in-place sorting methods are known (e.g. [18, 12, 9]), the ones based on QuickXsort and elementary methods X are particularly easy to implement 1 since one can adapt existing implementations for X. In such an implementation, the tried and tested optimization to choose the pivot as the median of a small sample suggests itself to improve QuickXsort. In previous works [1, 5, 3, 6], the influence of QuickXsort on the performance of X was either studied by ad-hoc techniques that do not easily apply with general pivot sampling
Fig. 5: Covariate shift in the EEG dataset 2A-subject A03, between training and testing input distribution for different frequency bands. (a) Mu band [8-12] Hz, and (b) Beta band [14-30] Hz. The red circles denote the features of the left hand motor imagery, and blue crosses denote the features of the right hand motor imagery. The black and red lines represent the decision boundaries obtained by the training data and test data respectively.
ing could be ruled out for all countries under review as well as a parallel shift of the reporting thresholds, implying that the assessment of the same health catego- ries will differ between countries. Ziebarth  presents a comparison of different health measures in case of reporting heterogeneity and analyses their impact on an indicator of inequality (concentration index as a form of inequality measure). He finds that self-assessed health goes along with the highest degree of inequality. If alternatively a variable like doctor visits is used, the concentration index is signif- icantly lower, i.e. it is reduced by the factor ten if the SF-12 health indicator is used. Summarizing, Ziebarth argues that income-related reporting heterogeneity is a complex problem in generic health measures.
The pivot method is a widely used technique for extract- ing paraphrases from bilingual parallel corpora (Bannard and Callison-Burch, 2005). Previous work extracted para- phrases from monolingual parallel corpora by identifying divergent strings in identical surrounding contexts occuring in aligned monolingual sentences (Barzilay and McKeown, 2001; Barzilay and Lee, 2003). The pivot method uses in- stead phrases in the foreign language of a bilingual parallel corpus as pivots to identify paraphrases in the source lan- guage. The method exploits information in the translation table of phrase-based Statistical Machine Translation sys- tems (Koehn et al., 2003). Source language phrases are considered to be potential paraphrases of each other if they share translations in the other language. Using this tech- nique, multiple candidate paraphrases can be extracted for each source phrase.
Since the pivot element is not fixed, we select the column (row in case of LSF-2), associated with the numbers adjacent to the pivot element, assign it as the diagonal elements and arrange the other row (column) elements in an orderly manner to get a new matrix satisfying the property
As an answer to the corpus-based method’s biggest disadvantage, namely the need for a large bilingual corpus, in the 1990’s Tanaka and Umemura (1994) presented a new approach. As a resource, they only use dictionaries to and from a pivot language to generate a new dictionary. These so-called pivot language based methods rely on the idea that the lookup of a word in an uncommon language through a third, intermedi- ated language can be automated. Tanaka and Umemura’s method uses bidirectional source- pivot and pivot-target dictionaries (harmonized dictionaries). Correct translation pairs are se- lected by means of inverse consultation, a method that relies on counting the number of pivot language definitions of the source word, through which the target language definitions can be identified (Tanaka and Umemura, 1994).
In this proposed system,the test compression method which changes the number of shift cycles dynamically to alleviate the low encoding capacity issue which may occur in high compression configurations. By changing the number of shift cycles dynamically per pattern, a different number of free variables can be supplied from the ATE to meet the encoding requirements from different test cubes such that it increases the encoding capacity as well as improves the encoding efficiency. Also the scan cells are modified as single cycle access with hold mode and without hold mode ,it may help to detect the faults in the flip flops with a clock cycle .Therefore it reduces the test access time.
Sorting an array of objects such as integers, bytes, floats, etc. is considered as one of the most im- portant problems in Computer Science. Quicksort is an effective and wide studied sorting algo- rithm to sort an array of n distinct elements using a single pivot. Recently, a modified version of the classical Quicksort was chosen as standard sorting algorithm for Oracles Java 7 routine library due to Vladimir Yaroslavskiy. The purpose of this paper is to present the different behavior of the classical Quicksort and the Dual-pivot Quicksort in complexity. In Particular, we discuss the con- vergence of the Dual-pivot Quicksort process by using the contraction method. Moreover we show the distribution of the number of comparison done by the duality process converges to a unique fixed point.
around 2,000 clusters of near-duplicate images which we have identified, allowing both sensitivity and specificity to be accurately tested for different metrics and thresholds . The GIST representations can be used for this purpose with any of Euclidean, Cosine, or Jensen-Shannon distances. A key aspect of such classification functions with very large collections is their specificity, which must be high to avoid very large numbers of false positives. All of these tests maintain high specificity up to a sensitivity of around 50%; to test the efficiency gains of the Hilbert Exclusion we therefore tested searches over the collection at five different thresholds, representing for each metric sensitivity of 10% to 50%, after which point a fast drop-off in specificity occurs. Table 4 gives, for each metric, the intrinsic dimensionality, the mean cost per distance measurement in milliseconds, and the thresholds used to search.
methodology, there is no need to use external test equipment sincea self-testable circuitry is built on the chip itself. The BIST technique usually combines a built-in Pseudo-Random Test-Sequence (PRTS) generation with an output response analyzer. This methodology relief us from the complex task of test- sequence generation and decreases the storage requirements of test-sequences and response