HP HPE2-Z40 : Delta - Applying Aruba Switching Fundamentals for Mobility Exam
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Test Name : Delta - Applying Aruba Switching Fundamentals for Mobility
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Format | HPE2-Z40 Course Contents | HPE2-Z40 Course Outline | HPE2-Z40 test
Syllabus | HPE2-Z40 test
Exam ID : HPE2-Z40
Exam Title : Delta - Applying Aruba Switching Fundamentals for Mobility
Exam type : Web based
Exam duration : 1 hour
Exam length : 34 questions
Passing score : 71%
Delivery languages : English, Japanese
tests your knowledge of the features, benefits, and
functions of Aruba networking components and technologies used in
the Aruba Mobile-First architecture. This test
tests your ability to
implement and validate a basic secured wired and wireless network
with the use of ArubaOS switches, CLI, various technologies, and Aruba
Instant Access Points (IAPs). This test
also tests your ability to
manage and monitor the network with AirWave.
Typical candidate is a technical professional who has at least six
months experience with small to medium enterprise level network
deployments. The candidate also demonstrates knowledge of wired
technologies used in edge and simple core environments, and
fundamental knowledge of wireless technologies; 802.11a/b/g/n/ac,
basic RF interactions and issues, and basic wireless security methods.
The candidate also has the HP ATP - FlexNetwork Solutions V3 certification.
Course Outline | Syllabus | test
21% Identify, describe, and apply foundational networking architectures and technologies.
- Identify the role of TFTP, SFTP, FTP, Telnet, SNMPv2, and SNMPv3 in the management of Aruba network devices, and apply the appropriate security for these features.
- Describe Layer 2 redundancy technologiessuch as STP, RSTP, MSTP and VSF, and recognize the benefits of each.
- Describe, identify, and explain wirelesstechnologies.
23% Identify, describe, and differentiate the functions and features of Aruba products and solutions.
- Identify, describe, and differentiate the functions, features, and management options of Aruba products and solutions, and explain how Aruba, a Hewlett Packard Enterprise company, deliverssolutionsthat enable a digital workplace.
23% Install, configure, set up, and validate Aruba Networking solutions.
- Configure basicfeatures on ArubaOS switchesto include initial settings and management access, and validate the installed solution with the use of debug technology, logging, and show and display commands. Manage the software and configuration files on ArubaOS switches, and manage ArubaOS switches and APs with Aruba AirWave.
15% Tune, optimize, and upgrade Aruba solutions.
Optimize Layer 2 and Layer 3 infrastructuresthrough broadcast domain reduction, VLANs, and VSF.
18% Manage, monitor, administer, and operate Aruba solutions.
Performnetwork management in accordance with best practices.
Performadministrative taskssuch as moves, adds, changes, deletions, and password resetsfor managed devices.
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HP Delta Practice Test
within the marker selection difficulty, \(x_i\in \BbbR^d\) is gene expression measurements of the ith cellphone for d distinct genes. They count on the subset of the cells used for practicing encompass labels.
Setup. They model the marker option problem as a label-mindful dimension reduction system inspired by means of compressive classification and largest margin nearest neighbor algorithms13. One such method, SqueezeFit, goals to find a projection to the lowest-dimensional subspace for which samples with different labels continue to be farther aside than samples with the identical label. consider a dataset \(\mathcalD=\(x_i,y_i)\_i\in \mathcalI\) in \(\BbbR^d\instances [k]\), here xi is a pattern and yi is its corresponding label. They denote \(\mathcalZ(\mathcalD):= \x_i-x_j:i,j\in \mathcalI,y_i\,\ne\, y_j\\) because the vector change between samples with distinctive labels.
right here optimization issue corresponds to discovering the orthogonal projection to the bottom-dimensional area that continues a prescribed separation Δ > 0 between samples with distinctive labels:
$$\startarrayl\,\textlower\,\quad \rmrank\ \Pi \\ \,\texts.t.\quad \parallel \Pi z\parallel \ge \Delta \quad\forall z\in \mathcalZ(\mathcalD),\quad \Pi ^\properly =\Pi ,\quad \Pi ^2=\Pi .\endarray$$
right here, Π is the low-dimensional projection, and Δ > 0 is the desired minimal distance between projected samples Πxi and Πxj with different labels. This parameter displays a fundamental anxiety in compressive classification: Δ may still be enormous to be able to allow ample separation of samples with distinctive labels in the low-dimensional house, and simultaneously the projected area rank Π may still be of low dimension in order that this projection comfortably reduces the dimension of the pattern. To address the intractability of the optimization in Eq. (1), a convex leisure approach is used13:
$$\beginarrayll&\,\textual contentcut\,\quad \rmtr\ M\\ \,\texts.t.\,\quad z^\desirable Mz\ge &\Delta ^2\,\,\forall z\in \mathcalZ(\mathcalD),\quad 0\preceq M\preceq I.\conclusionarray$$
The leisure extends the feasible set from the set of orthogonal projections, the place optimization is intractable—matrices Π that satisfy the constraints in Eq. (1)—to the set of wonderful semidefinite matrices—matrices M that satisfy the constraints in Eq. (2)—where you possibly can use average optimization toolboxes to discover the world most fulfilling in polynomial time15. The trace norm of M corresponds to the ℓ1-norm of the vector of eigenvalues of M. hence, minimizing the trace norm \(\mathrmtr\,M\) encourages M to be low rank16.
scGeneFit finds a prescribed variety of gene markers, so that when the samples are projected onto these marker dimensions they reveal the equal separation of cells with diverse labels as in the common gene house. The purpose of picking a handful of marker genes in mathematical terms translates to finding a projection onto a subset of the coordinates; peculiarly, M is a diagonal matrix with entries α1, …, αd. This constraint simplifies the optimization to a linear program:
$$\beginarrayll\textlower\quad \parallel \!\alpha \parallel _1\\ \texts.t.\,\quad \mathop\sum \limits_j=1^d\alpha _jz_j^2\ge \Delta ^2\,\;\forall z\in \mathcalZ(\mathcalD),\quad 0\le \alpha _j\le 1.\conclusionarray$$
The objective’s same ℓ1 hint norm promotes sparsity within the matrix M (ref. sixteen). consequently, numerical experiments exhibit that the solution of Eq. (three) is in fact sparse, and the dimension of the projection—the number of chosen markers—is smaller than the dimension of the long-established space.
to ensure that this system to be beneficial in apply, they regulate the optimization components Eq. (3) to enable for outliers, and they specify the dimension of the projected area (i.e., the number of markers) s, resulting in the scGeneFit optimization issue:
$$\startarrayll\textual contentreduce\quad \parallel\! \beta \parallel _1\\ \texts.t.\quad \mathop\sum \limits_j=1^d\alpha _jz_j^2\ge \Delta ^2-\beta _z\,\;\forall z\in \mathcalZ(\mathcalD),\\ \qquad\parallel \!\alpha \parallel _1\le s,\quad 0\le \alpha _i\le 1,\quad \beta _z\ge 0.\conclusionarray$$
right here β is a slack vector that quantifies how a good deal the margin between units with diverse labels is violated for every constraint17. β is indexed via the features \(z\in \mathcalZ(\mathcalD)\) and its dimension equals that of the constraint set \(\mathcalZ(\mathcalD)\).
Incorporating label hierarchies
believe a hierarchical partition of the samples denoted with the aid of Tσ, the place σ is an ordered set of indices. Say \(T_\sigma ^\best \subset T_\sigma \), if σ is a prefix of \(\sigma ^\top\) (for example Tijk ⊂ Tij ⊂ Ti, akin to a three-degree hierarchy; see Fig. three for a concrete illustration).
Fig. 3: illustration of hierarchical partition explaining the notation.
during this instance, they now have three classes (T1, T2, and T3) on the first level of the hierarchy. at the 2d stage of the hierarchy, T1 is divided into three courses (T11, T12, and T13), and T2 is divided in two classes (T21 and T23).
When provided with the structured relationship of the labels, scGeneFit solves the optimization problem (Eq. (4)), changing the set of constraints \(\mathcalZ(\mathcalD)\) to \(\mathcalZ_T(\mathcalD)\) to reflect the hierarchical suggestions. In element,
$$\mathcalZ_T(\mathcalD):= \x_i-x_j:x_i\in T_\sigma a,x_j\in T_\sigma b,a\,\ne\, b,\,\textprefix\;\sigma \$$
option optimization constraints
The optimization difficulty described above for scGeneFit is positive when the label structure is a flat (famous person fashioned) hierarchy; besides the fact that children, when the label structure has further layers, they would want to add an additional constraint to encourage labels that are closer in hierarchical house to even be closer in the projected (marker) space. In selected, let \(\mathcalI_t\) be the set of indices i ∈ 1, …, n of phone profiles with label t, and let nt be the variety of cells in that set. The projected middle of the profiles labeled t is Πct, with \(c_t=\frac1n_t\sum _i\in \mathcalI_tx_i\). The favored constraints can as a consequence be formally encoded as \(\parallel \Pi x_i-\Pi c_t^\top \parallel ^2-\parallel \Pi x_i-\Pi c_t\parallel ^2\ge \Delta ^2\), if yi = t and \(t\,\ne\, t^\leading\).
The hierarchical scGeneFit goal encodes the intuition that the distance between labeled cells should replicate the label distance within the given label hierarchy (Supplementary assistance). here's given through the linear application
$$\startarrayll&\,\textcut\,\quad \parallel\! \beta \parallel _1\\ &\,\textual contents.t.\,\ \ \mathop\sum \limits_j=1^d\alpha _j\left[(x_i-c_t^\prime )_j^2-(x_i-c_t)_j^2\right]\ge \Delta ^2-\beta _i,t^\leading \\ &\,\,\forall t,t^\best \,\ne\, t,i\in \mathcalI_t,\parallel \!\alpha \parallel _1\le s,0\le \alpha _i\le 1,\beta _i,t^\major \ge 0,\conclusionarray$$
where, as earlier than, β is a slack vector.
The optimization process permits the analysis of thousands of genes at a time as follows. As earlier than, let ct be the empirical gene expression imply of a class t and accept as true with constraints of the kind constraints of the kind ∥ct − cs∥ > Δ. To insure that the variety of constraints remains inside the order of the number of courses, they handiest accept as true with constraints over the cells with gene expression profiles closest to the mobilephone class centroids in the decrease-dimensional house. Their latest implementation finds 50 markers in a simulated dataset with 10,000 cells (40 synthetic mobile labels), with 10,000 genes in ~15 min (the use of a typical MacBook seasoned laptop)
In scGeneFit, both main hyperparameters are: s (the goal variety of markers) and Δ (the target separation of samples with distinct labels or centers of different classes). In their code, they put in force a twin annealing formula that optimize for the cost of Δ for a given practising set, examine set, and classifier. The different hyperparameters scGeneFit uses are set to make the issue smaller in case the computational power doesn’t allow the person to run the optimization within the whole dataset (like capping the number of constraints to be used or sampling the dataset to generate fewer constraints). Such hyperparameters are absolutely described in the Supplementary fabric.
Optimization of the linear application and scalability
The optimization difficulty (Eq. (four)) is a linear software that they resolve with scipy linear programming solver (scipy.opitimize.linprog). The computational bottleneck of the linear software is the variety of constraints in \(\mathcalZ(\mathcalD)\), which a priori scales quadratically with the number of cells. as a way to resolve this difficulty and make the optimization extra effective, they use a couple of strategies. The easiest one is to opt for essentially the most relevant constraints in Eq. (four) by considering, for each and every sample, the k-nearest neighbors from each and every of the other courses. one other approach they use is to randomly opt for a subsample, run scGeneFit on the subsample, and undertaking the held-out samples the usage of the markers chosen on the subsample.
The most useful approach they use is to set constraints in keeping with the empirical centroids of clusters, as discussed in the high-dimensional instance above. despite the fact, such a strategy has the underlying assumption that classes are linearly separable.
a detailed comparison amongst all editions of their formulation is documented in their software release18.
The computational complexity of linear programming is an open difficulty in optimization, however it is commonly used to be asymptotically higher bounded by way of O(N2.5), where N is the dimension of the difficulty (variety of variables plus variety of constraints)19. For the selected experiments they perform, they remedy scGeneFit with 4000 variables and 6000 constraints in <40 s, on Matlab 2018a working on an Intel Xeon CPU 1.90 GHz using <4 Gb of memory.
Dataset description and preprocessing
Cells within the mouse somatosensory cortex (S1) and hippocampal CA1 vicinity had been categorized in accordance with 3005 single-mobile transcriptomes by way of scRNA-seq. The nine foremost molecularly diverse classes of cells (layer 1) were acquired via a divisive biclustering method, and corresponding subclasses of cells (layer 2) had been got via repeating the biclustering formulation within every foremost class14.
The CBMCs had been produced with CITE-seq4. Single-mobilephone RNA statistics processing and filtering had been carried out as distinct in ref. 4. In selected, the records are sparse and normalized through \(\mathrmlog\,_2(1+X)\).
to be able to evaluate the efficiency of scGeneFit, they first split the statistics in working towards (70%) and examine (30%). They educate a okay-nearest neighbor classifier on the practising information (for okay = three, 5, 15) after projection to the corresponding markers (computed on the complete dataset). They consider the classifier on the examine facts and record the misclassification error with respect to the ordinary courses (Supplementary desk S1). They also evaluate the performance of k-capability clustering, the use of ok-skill++, reporting the smallest misclassification error among ten random initializations. For the hierarchical dataset in Zeisel, they evaluate the performance on the second degree. finally, they deliver precision, bear in mind, and f1-metrics for the classification initiatives of both synthetic and true datasets (Supplementary advice).
extra information on research design is obtainable within the Nature research Reporting abstract linked to this article.