Molecular symmetry and Quantum mechanics: Course Topics

•

0 likes•14 views

Course topics for Molecular symmetry and Quantum mechanics Taught by: Prof. Harjinder Singh. Taught at: IIIT Hyderabad

11
12
2018
l,, ' [!!,, 2a1CD
---- =-MAY
Week 19
'2B 237 ~ Ol Ew_LAB. 9Wr,. "1 c.-,R~u!L_N.ILJI' "'1f.&5s'/i1Ytq; 08--=-== --
. .- - - -
- -- - - - - - - - -
---
- - - - - - - - - - -
t, ~4l_ro_me_tc.~_ C)_bJ_~..__.-~ -------------A
- ! - f2-Ctci-BLOJ___,_,·~ ----- ---------___:___
/I
- - - - - - - - - - - - - -- - - - - - - - -
' 2-• f:) "oop _thwcf .Clod 9txlO-fuVfl_ Uleclnr::u.L...i;· .,_______
JUN
2018
a Lc.1:0 - ~ALL c?proctc__h in ~O ~cf~ -----
S MTWT FS SMTW TFS
3 4 567 89
- - - - - - - - - - -
- - - - - - - -
S M T WT F S S M T W T F S
10 11 12 13 14 15 16 17 18 19 20
21 22 23
s M T WTFS
24 25 26 27 28 2'd 30
)>
C
C)
C
l/l
--l

•
MAY
09---------------- -
Wednesday
• pot•'qj- J&_ ~~_e::h~®-L- - --
9
, V €>r-mcJ.. ~ a_noJy.lCJ ~ vihro.tt'troc.l "Mh'ori - _•
. --~~ (9~10
12
t EXJ::~00~ - · '
5
6
- - - - - -
- - -- - - - - - - - --_-_-_-_-_-_- - --~------
MAY- SS-;:M~ T~ W-:;T~ ~FS-;S::-M~ T-:-:W~ T ---=-F_S_ __ __
2018 • • 1 2 3 4 5 6 S M T W T F S
7 8 9 10 11 12 13 14 15 16 17 18 19
---- -- - - -
S M T W T F- ~ --S-M- TW~f--S
~ 21 2'2 23 24 25 26 'll 28 29-)) 3! ~ ~

More Related Content

More from Subhajit Sahu

A **community** (in a network) is a subset of nodes which are _strongly connected among themselves_, but _weakly connected to others_. Neither the number of output communities nor their size distribution is known a priori. Community detection methods can be divisive or agglomerative. **Divisive methods** use _betweeness centrality_ to **identify and remove bridges** between communities. **Agglomerative methods** greedily **merge two communities** that provide maximum gain in _modularity_. Newman and Girvan have introduced the **modularity metric**. The problem of community detection is then reduced to the problem of modularity maximization which is **NP-complete**. **Louvain method** is a variant of the _agglomerative strategy_, in that is a _multi-level heuristic_. https://gist.github.com/wolfram77/917a1a4a429e89a0f2a1911cea56314d In this paper, the authors discuss **four heuristics** for Community detection using the _Louvain algorithm_ implemented upon recently developed **Grappolo**, which is a parallel variant of the Louvain algorithm. They are: - Vertex following and Minimum label - Data caching - Graph coloring - Threshold scaling With the **Vertex following** heuristic, the _input is preprocessed_ and all single-degree vertices are merged with their corresponding neighbours. This helps reduce the number of vertices considered in each iteration, and also help initial seeds of communities to be formed. With the **Minimum label heuristic**, when a vertex is making the decision to move to a community and multiple communities provided the same modularity gain, the community with the smallest id is chosen. This helps _minimize or prevent community swaps_. With the **Data caching** heuristic, community information is stored in a vector instead of a map, and is reused in each iteration, but with some additional cost. With the **Vertex ordering via Graph coloring** heuristic, _distance-k coloring_ of graphs is performed in order to group vertices into colors. Then, each set of vertices (by color) is processed _concurrently_, and synchronization is performed after that. This enables us to mimic the behaviour of the serial algorithm. Finally, with the **Threshold scaling** heuristic, _successively smaller values of modularity threshold_ are used as the algorithm progresses. This allows the algorithm to converge faster, and it has been observed a good modularity score as well. From the results, it appears that _graph coloring_ and _threshold scaling_ heuristics do not always provide a speedup and this depends upon the nature of the graph. It would be interesting to compare the heuristics against baseline approaches. Future work can include _distributed memory implementations_, and _community detection on streaming graphs_.

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

This is a short review of Community detection methods (on graphs), and their applications. A **community** is a subset of a network whose members are *highly connected*, but *loosely connected* to others outside their community. Different community detection methods *can return differing communities* these algorithms are **heuristic-based**. **Dynamic community detection** involves tracking the *evolution of community structure* over time. https://gist.github.com/wolfram77/09e64d6ba3ef080db5558feb2d32fdc0 Communities can be of the following **types**: - Disjoint - Overlapping - Hierarchical - Local. The following **static** community detection **methods** exist: - Spectral-based - Statistical inference - Optimization - Dynamics-based The following **dynamic** community detection **methods** exist: - Independent community detection and matching - Dependent community detection (evolutionary) - Simultaneous community detection on all snapshots - Dynamic community detection on temporal networks **Applications** of community detection include: - Criminal identification - Fraud detection - Criminal activities detection - Bot detection - Dynamics of epidemic spreading (dynamic) - Cancer/tumor detection - Tissue/organ detection - Evolution of influence (dynamic) - Astroturfing - Customer segmentation - Recommendation systems - Social network analysis (both) - Network summarization - Privary, group segmentation - Link prediction (both) - Community evolution prediction (dynamic, hot field) <br> <br> ## References - [Application Areas of Community Detection: A Review : PAPER](https://ieeexplore.ieee.org/document/8625349)

Application Areas of Community Detection: A Review : NOTES

Application Areas of Community Detection: A Review : NOTES

Application Areas of Community Detection: A Review : NOTES

This paper discusses a GPU implementation of the Louvain community detection algorithm. Louvain algorithm obtains hierachical communities as a dendrogram through modularity optimization. Given an undirected weighted graph, all vertices are first considered to be their own communities. In the first phase, each vertex greedily decides to move to the community of one of its neighbours which gives greatest increase in modularity. If moving to no neighbour's community leads to an increase in modularity, the vertex chooses to stay with its own community. This is done sequentially for all the vertices. If the total change in modularity is more than a certain threshold, this phase is repeated. Once this local moving phase is complete, all vertices have formed their first hierarchy of communities. The next phase is called the aggregation phase, where all the vertices belonging to a community are collapsed into a single super-vertex, such that edges between communities are represented as edges between respective super-vertices (edge weights are combined), and edges within each community are represented as self-loops in respective super-vertices (again, edge weights are combined). Together, the local moving and the aggregation phases constitute a stage. This super-vertex graph is then used as input fof the next stage. This process continues until the increase in modularity is below a certain threshold. As a result from each stage, we have a hierarchy of community memberships for each vertex as a dendrogram. Approaches to perform the Louvain algorithm can be divided into coarse-grained and fine-grained. Coarse-grained approaches process a set of vertices in parallel, while fine-grained approaches process all vertices in parallel. A coarse-grained hybrid-GPU algorithm using multi GPUs has be implemented by Cheong et al. which grabbed my attention. In addition, their algorithm does not use hashing for the local moving phase, but instead sorts each neighbour list based on the community id of each vertex. https://gist.github.com/wolfram77/7e72c9b8c18c18ab908ae76262099329

Community Detection on the GPU : NOTES

Community Detection on the GPU : NOTES

Community Detection on the GPU : NOTES

Survey for extra-child-process package : NOTES

Survey for extra-child-process package : NOTES

Survey for extra-child-process package : NOTES

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

In this paper, the authors describe two approaches for dynamic community detection using the CNM algorithm. CNM is a hierarchical, agglomerative algorithm that greedily maximizes modularity. They define two approaches: BasicDyn and FastDyn. BasicDyn backtracks merges of communities until each marked (changed) vertex is its own singleton community. FastDyn undoes a merge only if the quality of merge, as measured by the induced change in modularity, has significantly decreased compared to when the merge initially took place. FastDyn also allows more than two vertices to contract together if in the previous time step these vertices eventually ended up contracted in the same community. In the static case, merging several vertices together in one contraction phase could lead to deteriorating results. FastDyn is able to do this, however, because it uses information from the merges of the previous time step. Intuitively, merges that previously occurred are more likely to be acceptable later. https://gist.github.com/wolfram77/1856b108334cc822cdddfdfa7334792a

Fast Incremental Community Detection on Dynamic Graphs : NOTES

Fast Incremental Community Detection on Dynamic Graphs : NOTES

Fast Incremental Community Detection on Dynamic Graphs : NOTES

Can you ﬁx farming by going back 8000 years : NOTES

Can you ﬁx farming by going back 8000 years : NOTES

Can you ﬁx farming by going back 8000 years : NOTES

1. Webpages tend to behave as authorities or hubs. 2. An authority represents an research thesis, and a hub represents an encyclopedia. 3. Each page has an authority and a hub score. 4. The graph is based on query, included pointed to and from pages. 5. Authority score is the sum of scores of all hubs pointing to it. 6. Hub score is the sum of scores of all authorities is pointing to. 7. Score are normalized with L2-norm in each iteration (root of sum of squares). 8. Needs to be performed at query time. 9. Two scores are returned, instead of just one. https://gist.github.com/wolfram77/3d9ef6c5a5b63f53caabce4812c7ea81

HITS algorithm : NOTES

HITS algorithm : NOTES

HITS algorithm : NOTES

Computer architectures are facing issues: Memory latencies are far higher. Benefits from instruction level parallelism (ILP) is reducing. With increasing clock rates, power consumption is increasing. Increasing complexity with multi-stage pipelines, intermediate buffers, multi-level caches, out-of-order execution, branch prediction, ... GPUs are parallel computer architectures that are good at some tasks, not so good at others. Running routines with high arithmetic intensity with overlapped memory access is the preferred approach. They may be unsuitable for irregular algorithms, where it is difficult to get high efficiency due to the high latency of accesses. They are less versatile compared to CPUs, using SIMD parallelism, and are dense compute-wise (per currency). NVIDIA's CUDA programming model enables GPUs to be used for general-purpose computing, and hence the term GPGPU. GPU Architectural, Programming, and Performance Models presentation at PPoPP, 2010, Bangalore, India. By Prof. Kishore Kothapalli with Prof. P. J. Narayanan and Suryakant Patidar. https://gist.github.com/wolfram77/43a6660121eef45b78c10d4e652dad6c

Basic Computer Architecture and the Case for GPUs : NOTES

Basic Computer Architecture and the Case for GPUs : NOTES

Basic Computer Architecture and the Case for GPUs : NOTES

For the IPDPS ParSocial event a presentation submission is required by 15th May. The event is on 3rd June. https://gist.github.com/wolfram77/51b15ca09eb28f6909673a2deb1a314d DYNAMIC BATCH PARALLEL ALGORITHMS FOR UPDATING PAGERANK Subhajit Sahut, Kishore Kothapallit and Dip Sankar Banerjeet tInternational Institute of Information Technology Hyderabad, India. tIndian Institute of Technology Jodhpur, India. subhajit.sahu@research. ,kkishore@iiit.ac.in, dipsankarb@iitj.ac.in This work is partially supported by a grant from the Department of Science and Technology (DST), India, under the National Supercomputing Mission (NSM) R&D in Exascale initiative vide Ref. No: DST/NSM/R&D Exascale/2021/16. FACEBOOK 15 TAKING A PAGE OUT OF GOOGLE’S PLAYBOOK 10 STOP FAKE NEWS FROM GOING VIRAL PUBLISHED APR 2015 BY SALVADOR RODRIGUEZ Click-Gap: When is Facebook is driving disproportionate amounts of traffic to websites. Effort to rid fakes news from Facebook’s services. Is a website relying on Facebook to drive significant traffic, but not well ranked by the rest of the web? Also News Citation Graph. PAGERANK APPLICATIONS Ranking of websites. Measuring scientific impact of researchers. Finding the best teams and athletes. Ranking companies by talent concentration. Predicting road/foot traffic in urban spaces. Analysing protein networks. Finding the most authoritative news sources Identifying parts of brain that change jointly. Toxic waste management. PAGERANK APPLICATIONS Debugging complex software systems (Moni torRank) Finding the most original writers (BookRank) Finding topical authorities (TwitterRank) WHAT IS PAGERANK l—-d Plu = Cus + —— UCIiNny Pru u->v = (1-—d) x “us ( ) outdegy, PageRank is a lLink-analysis algorithm. By Larry Page and Sergey Brin in 1996. For ordering information on the web. Represented with a random-surfer model. Rank of a page is defined recursively. Calculate iteratively with power-iteration.

Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDES

Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDES

Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDES

Satellites are usually covered in aluminized polyimide. The yellowish gold color of polyimide with silver aluminium side facing in gives the satellite the appearance of being wrapped in gold. The material is called Multi-layer Insulation (MLI). It helps in radiative insulation of the onboard instruments of satellite. Gold is actually used in electrical contacts to prevent corrosion due to Ultra-violet light or X-rays. https://gist.github.com/wolfram77/8ae2de1a29caf1a2f84babed79943389

Are Satellites Covered in Gold Foil : NOTES

Are Satellites Covered in Gold Foil : NOTES

Are Satellites Covered in Gold Foil : NOTES

This tutorial discusses on tax calculation for long-term and short-term capital gains, as well as for business income that speculative (intraday, BTST) and non-speculative (F&O, BTST). Also a nice concept on tax-loss harvesting. When turnover for business income is more than 5Cr or gains less than 6% an audit is required by a CA. Business income requires maintaining balance sheet and income statement. I may need an audit by a CA.

Taxation for Traders < Markets and Taxation : NOTES

Taxation for Traders < Markets and Taxation : NOTES

Taxation for Traders < Markets and Taxation : NOTES

This paper discusses a method of Generalizing PageRank algorithm for different types of networks. Rank of each vertex is considered to be dependent upon both the in- and out-edges. Each edge can also have differing importance. This solves the problem of dead ends and spider traps without the need of taxation (?). --- Abstract— PageRank is a well-known algorithm that has been used to understand the structure of the Web. In its classical formulation the algorithm considers only forward looking paths in its analysis- a typical web scenario. We propose a generalization of the PageRank algorithm based on both out-links and in-links. This generalization enables the elimination network anomalies- and increases the applicability of the algorithm to an array of new applications in networked data. Through experimental results we illustrate that the proposed generalized PageRank minimizes the effect of network anomalies, and results in more realistic representation of the network. Keywords- Search Engine; PageRank; Web Structure; Web Mining; Spider-Trap; dead-end; Taxation;Web spamming

A Generalization of the PageRank Algorithm : NOTES

A Generalization of the PageRank Algorithm : NOTES

A Generalization of the PageRank Algorithm : NOTES

With biomedical signal processing algorithms, such as the Pan-Tompkins QRS peak detection algorithm, FIR filters are utilized. Raw ECG signal can be fed to a Moving window filter, which helps filter out noise and get the signal of interest. These FIR filters involve the use of multipliers and adders, which take in several input sample and output a single sample. This paper replaces accurate adders in such filters with 10 16-bit signed approximate adders (power ve error parameters) from the EvoApprox library. Functional validation is done in MATLAB with Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) of Moving Window Integration; and Mean Square Error (MSE) of thresholds as error metrics. MIT-BIH Arrythmia database is used as the raw ECG input. In hardware evaluation, a 100-point FIR filter is implemented with a single Multiply-accumulate unit where the exact adder is replaced with selected approximate adder. RTL model is synthesized with 45nm NandGate Open Cell library in Synopsys design compiler. Area, Average power, and Worst-case delay are measured. On average the presented methodology provides an area-saving of 19.71% and power-saving of 19.27%.

ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...

ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...

ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...

1. Old vs New tax regime [Form26AS] 2. Quarterly Tax Deducted at Source (TDS) [Form16] 3. Quarterly Tax Deducted at Source (other income) [Form 15G/H] 4. Quarterly Advance Tax (extra income) [Challan ITNS 280] Extras: - Public Provident Fund scheme (PPF) - National Pension Scheme (NPS) References: - https://www.incometax.gov.in/iec/foportal - https://finshots.in/archive/finshots-money-resolution-4-axe-your-tax/

Income Tax Calender 2021 (ITD) : NOTES

Income Tax Calender 2021 (ITD) : NOTES

Income Tax Calender 2021 (ITD) : NOTES

Youngistaan Foundation: Annual Report 2020-21 : NOTES

Youngistaan Foundation: Annual Report 2020-21 : NOTES

Youngistaan Foundation: Annual Report 2020-21 : NOTES

Youngistaan: Voting awarness-campaign : NOTES

Youngistaan: Voting awarness-campaign : NOTES

Youngistaan: Voting awarness-campaign : NOTES

Highlighted notes on: Cost Efficient PageRank Computation using GPU This paper discusses the use of Aitken extrapolated Power method for PageRank computation. However, the results are unclear whether the performance improvement is due to GPU implementation, or due to use of Aitken extrapolation. The paper mentions a good performance improvement for damping factor values close to 1, and very low tolerance values which are usually not used for PageRank computation. It needs to be cross-checked to see if Aitken extrapolation provides and reduction of iterations on CPU only (as the same effect would be observed on the GPU, only timings change).

Cost Efficient PageRank Computation using GPU : NOTES

Cost Efficient PageRank Computation using GPU : NOTES

Cost Efficient PageRank Computation using GPU : NOTES

This is my report on: Rank adjustment strategies for Dynamic PageRank (v1). While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli. Abstract — To avoid calculating ranks of vertices in a dynamic graph from scratch for every snapshot, the ones computed in the previous snapshot of the graph can be used, with adjustment. Four different rank adjustment strategies for dynamic PageRank are studied here. These include zero-fill, 1/N-fill, scaled zero-fill, and scaled 1/N-fill. Results indicate that the scaled 1/N-fill strategy requires the least number of iterations, on average. As long as the graph has no affected dead ends (including dead ends in the previous snapshot), unaffected vertices can be skipped with this adjustment strategy. Index terms — PageRank algorithm, Dynamic graph, Rank adjustment, Initial ranks.

Rank adjustment strategies for Dynamic PageRank : REPORT

Rank adjustment strategies for Dynamic PageRank : REPORT

Rank adjustment strategies for Dynamic PageRank : REPORT

More from Subhajit Sahu (20)

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

Application Areas of Community Detection: A Review : NOTES

Application Areas of Community Detection: A Review : NOTES

Application Areas of Community Detection: A Review : NOTES

Community Detection on the GPU : NOTES

Community Detection on the GPU : NOTES

Community Detection on the GPU : NOTES

Survey for extra-child-process package : NOTES

Survey for extra-child-process package : NOTES

Survey for extra-child-process package : NOTES

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

Fast Incremental Community Detection on Dynamic Graphs : NOTES

Fast Incremental Community Detection on Dynamic Graphs : NOTES

Fast Incremental Community Detection on Dynamic Graphs : NOTES

Can you ﬁx farming by going back 8000 years : NOTES

Can you ﬁx farming by going back 8000 years : NOTES

Can you ﬁx farming by going back 8000 years : NOTES

HITS algorithm : NOTES

HITS algorithm : NOTES

HITS algorithm : NOTES

Basic Computer Architecture and the Case for GPUs : NOTES

Basic Computer Architecture and the Case for GPUs : NOTES

Basic Computer Architecture and the Case for GPUs : NOTES

Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDES

Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDES

Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDES

Are Satellites Covered in Gold Foil : NOTES

Are Satellites Covered in Gold Foil : NOTES

Are Satellites Covered in Gold Foil : NOTES

Taxation for Traders < Markets and Taxation : NOTES

Taxation for Traders < Markets and Taxation : NOTES

Taxation for Traders < Markets and Taxation : NOTES

A Generalization of the PageRank Algorithm : NOTES

A Generalization of the PageRank Algorithm : NOTES

A Generalization of the PageRank Algorithm : NOTES

ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...

ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...

ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...

Income Tax Calender 2021 (ITD) : NOTES

Income Tax Calender 2021 (ITD) : NOTES

Income Tax Calender 2021 (ITD) : NOTES

Youngistaan Foundation: Annual Report 2020-21 : NOTES

Youngistaan Foundation: Annual Report 2020-21 : NOTES

Youngistaan Foundation: Annual Report 2020-21 : NOTES

Youngistaan: Voting awarness-campaign : NOTES

Youngistaan: Voting awarness-campaign : NOTES

Youngistaan: Voting awarness-campaign : NOTES

Cost Efficient PageRank Computation using GPU : NOTES

Cost Efficient PageRank Computation using GPU : NOTES

Cost Efficient PageRank Computation using GPU : NOTES

Rank adjustment strategies for Dynamic PageRank : REPORT

Rank adjustment strategies for Dynamic PageRank : REPORT

Rank adjustment strategies for Dynamic PageRank : REPORT

Molecular symmetry and Quantum mechanics: Course Topics

1. 11 12 2018 l,, ' [!!,, 2a1CD ---- =-MAY Week 19 '2B 237 ~ Ol Ew_LAB. 9Wr,. "1 c.-,R~u!L_N.ILJI' "'1f.&5s'/i1Ytq; 08--=-== -- . .- - - - - -- - - - - - - - - --- - - - - - - - - - - - t, ~4l_ro_me_tc.~_ C)_bJ_~..__.-~ -------------A - ! - f2-Ctci-BLOJ___,_,·~ ----- ---------___:___ /I - - - - - - - - - - - - - -- - - - - - - - - ' 2-• f:) "oop _thwcf .Clod 9txlO-fuVfl_ Uleclnr::u.L...i;· .,_______ JUN 2018 a Lc.1:0 - ~ALL c?proctc__h in ~O ~cf~ ----- S MTWT FS SMTW TFS 3 4 567 89 - - - - - - - - - - - - - - - - - - - S M T WT F S S M T W T F S 10 11 12 13 14 15 16 17 18 19 20 21 22 23 s M T WTFS 24 25 26 27 28 2'd 30 )> C C) C l/l --l

2. • MAY 09---------------- - Wednesday • pot•'qj- J&_ ~~_e::h~®-L- - -- 9 , V €>r-mcJ.. ~ a_noJy.lCJ ~ vihro.tt'troc.l "Mh'ori - _• . --~~ (9~10 12 t EXJ::~00~ - · ' 5 6 - - - - - - - - -- - - - - - - - --_-_-_-_-_-_- - --~------ MAY- SS-;:M~ T~ W-:;T~ ~FS-;S::-M~ T-:-:W~ T ---=-F_S_ __ __ 2018 • • 1 2 3 4 5 6 S M T W T F S 7 8 9 10 11 12 13 14 15 16 17 18 19 ---- -- - - - S M T W T F- ~ --S-M- TW~f--S ~ 21 2'2 23 24 25 26 'll 28 29-)) 3! ~ ~