text:
title: Configuring Continuous Queries
newversionredirect: query_language/continuous_queries/
menu:
  influxdb_09:
    weight: 41
    parent: query_language
---

## What is a continuous query?
A continuous query (CQ) is an InfluxQL query that the system periodically executes in batches.

Each batch contains one or more queries where each query covers a single `GROUP BY time()` interval.
InfluxDB always generates a query for the current `GROUP BY time()` interval (the one containing `now()`) and it generates zero or more queries for recent `GROUP BY time()` intervals.

The time ranges of the generated queries have round-number boundaries.
For example, if the CQ's `GROUP BY time()` interval is 30 minutes long, the start and end times for each query (in `MM:SS`) are always `00:00` or `30:00`.

## The CQ schedule
The rate at which InfluxDB generates batches of queries and the number of queries generated per batch depend on the CQ's `GROUP BY time()` interval and on the following configuration options (shown with their default settings):

* `compute-runs-per-interval = 10`
* `compute-no-more-than = "2m0s"`
* `recompute-previous-n =  2`
* `recompute-no-older-than = "10m0s"`

### Frequency of batch creation

InfluxDB compares `compute-runs-per-interval` and `compute-no-more-than` to determine the rate at which it generates batches for a CQ.
`compute-runs-per-interval` is the upper bound of the number of incremental queries generated per `GROUP BY time()` interval.
`compute-no-more-than` is the maximum frequency at which InfluxDB generates batches.
`compute-no-more-than` takes precedence over `compute-runs-per-interval`.

In the default settings, InfluxDB generates batches for a CQ as often as  `10` times per the `GROUP BY time()` interval.
However, it will not generate batches more often than every two minutes, regardless of the value of `GROUP BY  time()` divided by 10.

For example, if the CQ has a `GROUP BY time(30m)` interval, then `compute-runs-per-interval` sets a maximum batch frequency of `3m`, since `30m` / 10 = `3m`.
This is less frequent than the maximum frequency of `2m`, as set by `compute-no-more-than`, so the system generates a batch of that CQ every `3m`.

If the CQ has a `GROUP BY time(30s)` interval, then `compute-runs-per-interval` sets a maximum batch frequency of `3s`, since `30s` / 10 = `3s`.
However, `compute-no-more-than` takes precedence and thus the system generates batches every `2m`.

### Number of queries per batch
InfluxDB uses `recompute-previous-n` and `recompute-no-older-than` to determine the number queries generated per batch for a CQ.
All batches include at least one generated query with the time range that spans `now()`.
The maximum number of additional queries with time ranges that span intervals prior to `now()` is determined by `recompute-previous-n`.
Therefore `recompute-previous-n` + 1 is the maximum number of queries per batch.
`recompute-no-older-than` puts a limit on the age of the previous query interval, such that the upper boundary  of each generated query must be greater than `now()` - `recompute-no-older-than`.
`recompute-no-older-than` takes precedence over `recompute-previous-n`.

In the default settings, InfluxDB generates at most three queries per batch: one spanning `now()` and up to two queries spanning older time intervals, as set by `recompute-previous-n`.
In addition, the upper boundary of the query time range for each generated query must be less than `10m` in the past.

For example, if the CQ has a `GROUP BY time(30s)` interval, then InfluxDB generates three queries for each batch.
However, if the CQ has a `GROUP BY time(30m)` interval, then InfluxDB generates only one or two queries for a given batch, since the third query would have an upper boundary at least `30m` in the past, which exceeds the `recompute-no-older-than` setting.

Depending on your CQ's `GROUP BY time()` interval you may want to make changes to the default configuration settings.
The next sections present example CQs and discuss how they work with the configuration options:

* [CQ 1](/influxdb/v0.9/query_language/continuous_queries_config/#cq-1-4-minute-group-by-time-interval) is an introductory example CQ that shows how the default configuration settings work with a CQ's `GROUP BY time()` interval.
* [CQ 2](/influxdb/v0.9/query_language/continuous_queries_config/#cq-2-30-second-group-by-time-interval) is an example of a CQ with a shorter term `GROUP BY time()` interval that doesn't work well with the default configuration settings.
This section also covers how to alter the configuration settings to fix the issue.
* [CQ 3](/influxdb/v0.9/query_language/continuous_queries_config/#cq-3-1-hour-group-by-time-interval) is an example of a CQ with a longer term `GROUP BY time()` interval.
This section shows how to alter the configuration settings to take into account lagged data.

## CQ 1: 4 minute `GROUP BY time()` interval
### Create CQ 1
```sql
CREATE CONTINUOUS QUERY cq1 ON telegraf BEGIN SELECT MEAN(value) INTO cq_4m FROM cpu_usage_idle WHERE cpu='cpu-total' GROUP BY time(4m) END
```

For our purposes the only important part of that statement is the `4m` `GROUP BY time()` interval.
For more on the syntax for creating CQs, see [Continuous Queries](/influxdb/v0.9/query_language/continuous_queries/).

### The default CQ schedule
Default configuration settings:
`compute-runs-per-interval = 10`
`compute-no-more-than = "2m0s"`
`recompute-previous-n = 2`
`recompute-no-older-than = "10m0s"`

*InfluxDB generates batches of CQ 1 every 2 minutes.*

`compute-runs-per-interval` sets a maximum batch frequency of `0.4m`, since `4m` /  `10` = `0.4m`.
This is more frequent than the maximum frequency of `2m`, as set by `compute-no-more-than`, so the system generates a batch of CQ 1 every `2m`.

*InfluxDB generates 3 queries per batch of CQ 1.*

InfluxDB generates one query that spans `now()` and two additional queries that span older time intervals because `recompute-no-older-than=10m` always exceeds the recent time range covered by `recompute-previous-n=2`: `2` intervals * `4m` = `8m`, which is less than the `10m` maximum.

### CQ 1 in practice
A sample of CQ 1's frequency of batch creation and the number of queries per batch on 2015/11/24 starting at 18:19:45 (UTC):

InfluxDB generates three queries at 18:19:45:
```sql
SELECT MEAN(value) INTO "telegraf"."default".cq_4m FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T18:16:00Z' AND time < '2015-11-24T18:20:00Z' GROUP BY time(4m)

SELECT MEAN(value) INTO "telegraf"."default".cq_4m FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T18:12:00Z' AND time < '2015-11-24T18:16:00Z' GROUP BY time(4m)

SELECT MEAN(value) INTO "telegraf"."default".cq_4m FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T18:08:00Z' AND time < '2015-11-24T18:12:00Z' GROUP BY time(4m)
```
InfluxDB generates another three queries two minutes later at 18:21:45:
```sql
SELECT MEAN(value) INTO "telegraf"."default".cq_4m FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T18:20:00Z' AND time < '2015-11-24T18:24:00Z' GROUP BY time(4m)

SELECT MEAN(value) INTO "telegraf"."default".cq_4m FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T18:16:00Z' AND time < '2015-11-24T18:20:00Z' GROUP BY time(4m)

SELECT MEAN(value) INTO "telegraf"."default".cq_4m FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T18:12:00Z' AND time < '2015-11-24T18:16:00Z' GROUP BY time(4m)
```

In both cases, the first query has the relevant time range that spans `now()`: 18:19:45 falls within the four minute interval between 18:16:00 and 18:20:00, and 18:21:45 falls within the four minute interval between 18:20:00 and 18:24:00.
The second two queries span older four minute time intervals.
Notice that the queries that cover the time ranges 18:16:00 through 18:20:00 and 18:12:00 through 18:16:00 appear in both the first batch of generated queries and in the second batch of generated queries.
The more recent CQ results overwrite previously calculated values.
This ensures that if data points arrive with a timestamp a few minutes in the past they are still incorporated into the CQ results.

## CQ 2: 30 second `GROUP BY time()` interval
### Create CQ 2
```sql
CREATE CONTINUOUS QUERY cq2 ON telegraf BEGIN SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' GROUP BY time(30s) END
```
For our purposes the only important part of that statement is the `30s` `GROUP BY time()` interval.
For more on the syntax for creating CQs, see [Continuous Queries](/influxdb/v0.9/query_language/continuous_queries/).

### The default CQ schedule
Default configuration settings:
`compute-runs-per-interval = 10`
`compute-no-more-than = "2m0s"`
`recompute-previous-n = 2`
`recompute-no-older-than = "10m0s"`

*InfluxDB generates batches of CQ 2 every 2 minutes.*

`compute-runs-per-interval` sets a maximum batch frequency of `3s`, since `30s` /  `10` = `3s`.
This is more frequent than the maximum frequency of `2m`, as set by `compute-no-more-than`, so the system generates a batch of CQ 2 every `2m`.

*InfluxDB generates 3 queries per batch of CQ 2.*

InfluxDB generates one query that spans `now()` and two additional queries that span older time intervals because `recompute-no-older-than=10m` always exceeds the recent time range covered by `recompute-previous-n=2`: `2` intervals * `30s` = `1m`, which is less than the `10m` maximum.

### CQ 2 in practice
A sample of CQ 2's frequency of batch creation and the number of queries per batch on 2015/11/24 starting at 19:41:17 (UTC):

InfluxDB generates three queries at 19:41:17:
```sql
SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:41:00Z' AND time < '2015-11-24T19:41:30Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:40:30Z' AND time < '2015-11-24T19:41:00Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:40:00Z' AND time < '2015-11-24T19:40:30Z' GROUP BY time(30s)
```
InfluxDB generates another three queries two minutes later at 19:43:17:
```sql
SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:43:00Z' AND time < '2015-11-24T19:43:30Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:42:30Z' AND time < '2015-11-24T19:43:00Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:42:00Z' AND time < '2015-11-24T19:42:30Z' GROUP BY time(30s)
```

In both cases, the first query has the revelant time range that spans `now()`: 19:41:17 falls within the 30 second interval between 19:41:00 and 19:41:30, and 19:43:17 falls within the 30 second interval between 19:43:00 and 19:43:30.
The second two queries span older 30 second time intervals.

Because of CQ 2's small `GROUP BY time()` interval, leaving the default configuration settings as-is causes InfluxDB to skip a 30 second interval.
In the example above, the system never queries the time range between 19:41:30 and 19:42:00.
The next two sections offer different ways to solve the missing interval problem.

### Change the frequency of batch creation
New configuration settings:
`compute-runs-per-interval = 10`
`compute-no-more-than = "1m30s"`
`recompute-previous-n = 2`
`recompute-no-older-than = "10m0s"`

*InfluxDB now generates batches of CQ 2 every 1.5 minutes.*

`compute-runs-per-interval` sets a maximum batch frequency of `3s`, since `30s` /  `10` = `3s`.
This is more frequent than the maximum frequency of `1.5m`, as set by `compute-no-more-than`, so the system generates a batch of CQ 2 every `1.5m`.

Now a sample of CQ 2's frequency of batch creation and the number of queries per batch on 2015/11/24 starting at 19:41:17 (UTC) looks like this:

InfluxDB generates three queries at 19:41:17:
```sql
SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:41:00Z' AND time < '2015-11-24T19:41:30Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:40:30Z' AND time < '2015-11-24T19:41:00Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:40:00Z' AND time < '2015-11-24T19:40:30Z' GROUP BY time(30s)
```
InfluxDB generates another three queries 1.5 minutes later at 19:42:47:
```sql
SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:42:30Z' AND time < '2015-11-24T19:43:00Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:42:00Z' AND time < '2015-11-24T19:42:30Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:41:30Z' AND time < '2015-11-24T19:42:00Z' GROUP BY time(30s)
```

Notice that because of the new frequency of batch creation, InfluxDB no longer misses the query that covers the time range from 19:41:30 through 19:42:00.

### Change the number of queries per batch
New configuration settings:
`compute-runs-per-interval = 10`
`compute-no-more-than = "2m0s"`
`recompute-previous-n = 3`
`recompute-no-older-than = "10m0s"`

*InfluxDB generates 4 queries per batch of CQ 2.*

InfluxDB generates one query that spans `now()` and three additional queries that span older time intervals because `recompute-no-older-than=10m` always exceeds the recent time range covered by `recompute-previous-n=3`: `3` intervals * `30s` = `1.5m`, which is less than the `10m` maximum.

Now a sample of CQ 2's frequency of batch creation and the number of queries per batch on 2015/11/24 starting at 19:41:17 (UTC) looks like this:

InfluxDB generates four queries at 19:41:17:
```sql
SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:41:00Z' AND time < '2015-11-24T19:41:30Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:40:30Z' AND time < '2015-11-24T19:41:00Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:40:00Z' AND time < '2015-11-24T19:40:30Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:39:30Z' AND time < '2015-11-24T19:40:00Z' GROUP BY time(30s)
```
InfluxDB generates another four queries two minutes later at 19:43:17:
```sql
SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:43:00Z' AND time < '2015-11-24T19:43:30Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:42:30Z' AND time < '2015-11-24T19:43:00Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:42:00Z' AND time < '2015-11-24T19:42:30Z' GROUP BY time(30s)

SELECT MEAN(value) INTO "telegraf"."default".cq_30s FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T19:41:30Z' AND time < '2015-11-24T19:42:00Z' GROUP BY time(30s)
```

Notice that because of the new number of queries per batch, InfluxDB no longer misses the query that covers the time range from 19:41:30 through 19:42:00.

## CQ 3: 1 hour `GROUP BY time()` interval
### Create CQ 3
```sql
CREATE CONTINUOUS QUERY cq3 ON telegraf BEGIN SELECT MEAN(value) INTO cq_1h FROM cpu_usage_idle WHERE cpu='cpu-total' GROUP BY time(1h) END
```
For our purposes the only important part of that statement is the `1h` `GROUP BY time()` interval.
For more on the syntax for creating CQs, see [Continuous Queries](/influxdb/v0.9/query_language/continuous_queries/).

### The default CQ schedule
Default configuration settings:
`compute-runs-per-interval = 10`
`compute-no-more-than = "2m0s"`
`recompute-previous-n = 2`
`recompute-no-older-than = "10m0s"`

*InfluxDB generates batches of CQ 3 every 6 minutes.*

`compute-runs-per-interval` sets a maximum batch frequency of `6m`, since `60m` /  `10` = `6m`.
This is less frequent than the maximum frequency of `2m`, as set by `compute-no-more-than`, so the system generates a batch of CQ 3 every `6m`.

*InfluxDB generates at most 2 queries per batch of CQ 3.*

InfluxDB generates one query that spans `now()` and at most 1 additional query that spans an older time interval because the third query would have an upper boundary at least `1h` in the past, which exceeds the `recompute-no-older-than` (`10m`) setting.

### CQ 3 in practice
A sample of CQ 3's frequency of batch creation and the number of queries per batch on 2015/11/24 starting at 22:09:55 (UTC):

InfluxDB generates two queries at 22:09:55:
```sql
SELECT MEAN(value) INTO "telegraf"."default".cq_1h FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T22:00:00Z' AND time < '2015-11-24T23:00:00Z' GROUP BY time(1h)

SELECT MEAN(value) INTO "telegraf"."default".cq_1h FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T21:00:00Z' AND time < '2015-11-24T22:00:00Z' GROUP BY time(1h)
```
InfluxDB generates one query six minutes later at 22:15:55:
```sql
SELECT MEAN(value) INTO "telegraf"."default".cq_1h FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T22:00:00Z' AND time < '2015-11-24T23:00:00Z' GROUP BY time(1h)
```

At 22:09:55, InfluxDB generates two queries; one query covers the time range that spans `now()` and one query covers the previous `GROUP BY time()` interval.
InfluxDB generates the second query because  `now()` - `10m` falls in the previous `GROUP BY time()` interval (22:09:55 -  `10m` = 21:59:55).

At 22:15:55, InfluxDB generates only one query.
That query covers the time range that spans `now()`.
InfluxDB doesn't generate a second query because `now()` - `10m` falls in the interval that spans `now()` (22:15:55 - `10m` = 22:05:55).

If you have lagged data, generating, at most, one query for a previous interval may not take into account all of your data.
The next section offers one way to adjust the configuration settings so that a query batch always includes a query for one previous time interval.

### Change the number queries per batch
New configuration settings:
`compute-runs-per-interval = 10`
`compute-no-more-than = "2m0s"`
`recompute-previous-n = 1`
`recompute-no-older-than = 120m0s`

*InfluxDB generates 2 queries per batch of CQ 3.*

InfluxDB generates one query that spans `now()` and one additional query that spans an older time interval because `recompute-no-older-than=120m` always exceeds the recent time range covered by `recompute-previous-n=1`: `1` interval * `1h` = `60m`, which is less than the `120m` maximum.

Now, a sample of CQ 3's frequency of batch creation and the number of queries per batch on 2015/11/24 starting at 22:09:55 (UTC) looks like this:

InfluxDB generates two queries at 22:09:55:
```sql
SELECT MEAN(value) INTO "telegraf"."default".cq_1h FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T22:00:00Z' AND time < '2015-11-24T23:00:00Z' GROUP BY time(1h)

SELECT MEAN(value) INTO "telegraf"."default".cq_1h FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T21:00:00Z' AND time < '2015-11-24T22:00:00Z' GROUP BY time(1h)
```
InfluxDB generates two queries six minutes later at 22:15:55:
```sql
SELECT MEAN(value) INTO "telegraf"."default".cq_1h FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T22:00:00Z' AND time < '2015-11-24T23:00:00Z' GROUP BY time(1h)

SELECT MEAN(value) INTO "telegraf"."default".cq_1h FROM "telegraf"."default".cpu_usage_idle WHERE cpu = 'cpu-total' AND time >= '2015-11-24T21:00:00Z' AND time < '2015-11-24T22:00:00Z' GROUP BY time(1h)
```

Because of the new settings, InfluxDB always includes in a batch one query that spans an older time interval in addition to the query that spans `now()`.


text:
Home / Jazznews / CD from abroad / Jack DeJohnette - Sound Travels
Jack DeJohnette - Sound Travels
Drum legend Jack DeJohnette will release a new album, Sound Travels (a co-release between Golden Beams and eOne), on January 17, 2012.
DeJohnette, who will turn 70 in August, is also set to receive a National Endowment for the Arts Jazz Master Fellowship, the highest U.S. honor for jazz musicians.
Speaking about the upcoming latin-influenced release, DeJohnette said: "I wanted to make something that would make people move, to make them relax and forget their troubles."
The album features a long list of guest artists, including Bobby McFerrin and Bruce Honsby (Vocals), Ambrose Akinmusire (Trumpet), Lionel Loueke and Esperanza Spalding (Bass), Tim Ries (Sax), Luisito Quintero (Percussion), and Jason Moran (Piano).

redpajama_set_name: RedPajamaCommonCrawl


So today I woke up and decided I was going to venture to the UK… well, the UK in Taiwan festival that is, HA! >_< The event was held in Huashan Creative Park, another one of my favourite spaces to visit in Taipei. The park, initially a brewery, has long since been re-created and transformed into another historical gem in Taiwan. It is definitely a great place to celebrate the arts and culture in the community!


70% of digital orders are from restaurant-branded apps or websites.
Restaurant digital orders have grown at an average annual rate of 23% since 2013 and will triple in volume by the end of 2020, and the majority of digital orders, 6 out of 10, are by mobile apps, reports The NPD Group.
Restaurant digital orders have grown at an average annual rate of 23 percent since 2013 and will triple in volume by the end of 2020, and the majority of digital orders, 6 out of 10, are by mobile apps, reports The NPD Group. A restaurant's app or website represents 70 percent of digital orders and the remaining orders are through third-party apps or other types of apps or websites, according to the findings of NPD's recently released "Delivering Digital Convenience" report.
Among the reasons why restaurant branded apps are accessed the most is that customers of some of these brands are more drawn to rewards and savings, while other restaurant brands appeal to consumers who want to customize their order or take time and friction out of the ordering process. Frequency of use varies by brand, though deals and discounts contribute to greater use, finds the NPD study.
Although not as popular as restaurant-branded apps, third-party apps, like DoorDash, Grubhub/Seamless, and UberEATS, account for more than their fair share (40%) of the 20 most-used apps. The primary reasons for using a third-party app versus a restaurant branded app, according to the consumers surveyed, are looking up items, paying for meals, and checking prices. For the smaller and independent operators, third-party apps level the playing field and give them a piece of the growing digital ordering sector without having to make expensive investments in their IT infrastructure.


The Stalker project started with 31" tires, so we lowered the gear ratio in the differentials to make up for any loss in power that the larger tires caused. The freshly rebuilt motor took full advantage of the gearing as it pushed the little 4x4 through the interstate traffic with ease. But although the engine seemed to thrive now at higher rpm's, we wanted to bring the stress on the motor down a little and maybe prolong this newfound energy. This installment will show how we dropped the rpm's a little when in 5th gear (highway driving) and then gave the clutch a bit more 'gription'.


text:
Research round-up
In the December Nature Biotechnology Selinger et al. use an Escherichia coli oligonucleotide array with 30-base-pair resolution to detect antisense transcripts, new open reading frames (ORFs), and transcription starts and stops (Nat Biotechnol 2000, 18:1262-1268). The 295,936 elements of the array do not come without their problems. The sheer size and complexity of the array means that there is a huge amount of cross hybridization detected by missense probes. But the use of many probes within th
William Wells(wells@biotext.com)
In the December Nature Biotechnology Selinger et al. use an Escherichia coli oligonucleotide array with 30-base-pair resolution to detect antisense transcripts, new open reading frames (ORFs), and transcription starts and stops (Nat Biotechnol 2000, 18:1262-1268). The 295,936 elements of the array do not come without their problems. The sheer size and complexity of the array means that there is a huge amount of cross hybridization detected by missense probes. But the use of many probes within the same gene allows Selinger et al. to identify many genes that are up- or down-regulated in stationary phase. They also confirm transcription stop and start sites for two genes. Scanning for antisense transcripts suggests that there is a low level of transcription throughout the genome, probably from replication-induced and readthrough transcription.
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text:
"My Best Friend"
CD, Vinyl
Rivers Cuomo
COR#
Weezer, Rick Rubin, Chad Bamford
Make Believe track listing
"Pardon Me"
(8) "My Best Friend"
(9) "The Other Way"
"My Best Friend" (previously known as "You're My Best Friend") is the ninth track from Make Believe.
2.1 Band Commentary (originally featured on weezer.com)
4 Audio
Make Believe (2005)
Yogi Bear (2010 film)
"My Best Friend" was a song that Rivers Cuomo wrote and was in contention for the album that would become Make Believe, but was shelved for several months during the album's production. It was excluded from the final track list until being brought back mere months before the album's release[1]. When the band was asked to submit a track to the soundtack to the film Shrek 2, Cuomo offered up the song, believing that a song written about a big, funny, "kind of ogre-ish" friend of his was perfect for the film. The producers, however, felt that the song was too obvious, so Cuomo rewrote the lyrics, and the band reworked the song before re-submitted it. Ultimately, the song was not used for the film. The band would, however, contribute a cover of the Monkees song "I'm a Believer" to the soundtrack of the 2010 film Shrek Forever After. "My Best Friend" would eventually appear during the credits to the 2010 film Yogi Bear.
Band Commentary (originally featured on weezer.com)
Scott: That's a wild…that's one song that's, like, kind of, like a wild card on the record, I gotta say. Cuz…that was…yeah, that's a wild track. And we left it -
Pat: No one thought about that song for the longest time, the next thing I know, 'we're doing that song!'
Scott: Yeah, and when we kind of went back and tightened everything up, it was one of the songs that I think I left all of the bass from when we tracked it. That's just nuts.
Pat: It's on fire. That song sounds like it's on fire to me.
Rivers: I'm trying to think of something interesting…
Pat: That it didn't go with Shrek?
Rivers: It's such a complicated story, I don't know if it's that interesting.
Scott: I'm glad that it didn't, I can say that much.
Rivers: Yeah, that song has some weird karma with Shrek. I remember I originally wrote it about some guy I met, and then [the producers of the sequel to the film] Shrek asked us if we had a song, and then I thought, 'oh yeah, I remember that song, let me think about showing it to them', and they actually thought it sounded too much like it was written for Shrek. 'Cuz this guy was kind of ogre-ish, and he made me laugh…
Brian: And you changed the lyrics for them, right?
Rivers: So I had to change the lyrics so it sounded less Shrek-like
Pat: Shrek-y?
Rivers: Shrek-ish!
Pat: Shrek-ish? Puck-ish?
Scott: It will live on longer than Shrek 2 will.
Rivers: I don't know if it's that interesting.
Brian: But it was also…I think at the moment when the Shrek people wanted it, it wasn't complete either. And that would have been the version that would have been the first Weezer song people heard in 3 years, and it wasn't actually finished to the best it could have been, so it's a blessing that we got to work on it a little longer.
Rivers: Yeah, I feel the same way. It got so much better. I resang it, put the organ on it, it sounds way better. We put the drum sounds on it…
Rivers Cuomo – lead guitar, lead vocals
Patrick Wilson – percussion
Brian Bell – rhythm guitar, synth, backing vocals
Scott Shriner – bass guitar, vocals
Rick Rubin - producer
Josh Abraham - mixing
When everything is wrong I'll come talk to you
You make things alright when I'm feeling blue
You are such a blessing and I wont be messing
With the one thing that brings light to all of my darkness
You're my best friend
and I love you, and I love you
There is no other one who can take your place
I feel happy inside when I see your face
I hope you believe me
'Cause I speak sincerely
And I mean it when I tell you that I need you
I'm here right beside you
I will never leave you
And I feel the pain you feel when you start crying
Teenage Victory Songs entry for "My Best Friend"
Retrieved from "https://www.weezerpedia.com/w/index.php?title=My_Best_Friend&oldid=101357"
Songs that appeared on the 4 and 5 Star Demos of Rivers Cuomo list
Songs on Make Believe
Self-produced Weezer songs
Weezer songs produced by Rick Rubin
Weezer songs produced by Chad Bamford


text:
Beavers are USPBL's best team at 8-5
Birmingham-Bloomfield Beavers are USBPL's best with 8-5 mark after a quarter-season
Beavers are USPBL's best team at 8-5 Birmingham-Bloomfield Beavers are USBPL's best with 8-5 mark after a quarter-season Check out this story on HometownLife.com: http://www.hometownlife.com/story/sports/2016/07/03/beavers-usbpls-best-quarter-season/86604750/
Marty Budner, hometownlife.com Published 12:09 a.m. ET July 3, 2016 | Updated 7:08 a.m. ET July 3, 2016
Manager Chris Newell (left) and bench coach Rich Donnelly have the Birmingham-Bloomfield Beavers in first place 13 games into the 48-game USPBL season.(Photo: John Stormzand | Staff Photographer)Buy Photo
Chris Newell is one-quarter of a way through his inaugural season as head coach of the United Shore Professional Baseball League's Birmingham-Bloomfield Beavers.
The former pro scout is highly satisfied with the way things are playing out, both on the field with his team's recent play and off the field with the USPBL's ultimate mission of developing players.
The Beavers stood in first place in the three-team USPBL with an 8-5 record after 13 games of their 48-game schedule. After dropping their first three contests, Newell's squad has rebounded nicely by winning eight of its last 10.
"I really like where we're at. We've made some roster moves. We're always trying to get better," Newell said prior to the holiday weekend. "We have a great clubhouse. We have a group of guys who really work their butts off and pay attention to details. Our bats are really starting to come around.
"I said after opening day that I wasn't worried about our bats, that we'd be fine. I let the first three games go and just kind of wanted to see where we were at and see what adjustments that they needed to make in my opinion.
"As I told them the first day, I said I'd let them do their thing because that's what got them here," he added. "But as soon as your thing isn't working, we were going to start doing things my way. They have been super-receptive to that."
Moving to next level
While the winning record has been a welcome turnaround, Newell takes just as much pride in the fact the USPBL already has sent two of his players to major league-affiliated clubs.
The first was left-handed pitcher Ross Vance, who signed on with the St. Louis Cardinals' Class A affiliate. The second was infielder Aaron Bossi, who went to the New York Yankees organization.
Both went unselected in the recent MLB entry draft and then made USPBL history as the first two from Andy Appleby's independent minor league baseball circuit to move on to the next level.
"Although our record is great and our guys our playing good and I want to win every game and I've been on record as saying that multiple times as I love winning," Newell said. "But the one constant thing that I've never lost focus on is my ultimate job description.
"Winning games is truly secondary. The primary responsibility I have is to develop as many guys or get as many guys signed into affiliated ball as possible. There have been two guys in this league who have experienced that and, thankfully, they've both been from my team.
"That's what's been gratifying, more than anything," he added. "That's what this league stands for. That's what I signed up for and it's been nice. I 100 percent agree with the vision of this league."
Beavers are hitting
Newell promised Birmingham-Bloomfield would be fundamentally sound and play with energy and the Beavers have been adhering to that plan lately.
The Beavers have the highest team batting average (.276) of the three teams and also lead the league in slugging percentage (.441), total runs (81), hits (12), home runs (16), RBIs (71) and bases (198).
Nic Wilson, a 23-year-old first baseman from Georgia, is tearing up the league offensively. The 6-foot-6, 240-pound heavy hitter is chasing the USPBL triple crown as he leads the league in average (.381), RBIs (14) and home runs (seven).
Left-handed lead-off hitter David Pepe and outfielder Miles Williams are two other players who have turned it on since opening day.
Pepe's average is .327, up 110 points over the past 10 games, and he is tied for the league-lead with 17 hits. Williams, who previously played in the Florida Marlins system, is batting .310 with 11 RBIs.
Daniel Kennon (.356) and Ramon Ortega (.321) are other Beavers who have produced well at the plate.
The pitching staff is led by Forest Koumas and local product Alex Kinch.
Koumas has two wins and a sparkling 0.60 earned-run average, which leads the league. Koumas was one of Newell's first pickups as he helped lead the University of South Carolina to back-to-back college World Series appearances.
Kinch, an Ann Arbor native who attended Wayne State University, has a 2.89 ERA with 29 strikeouts, which ranks second in the league.
"I have a group of guys who want to get better and they want to listen," Newell said. "They are hungry to get better and anytime you surround yourself with a group of guys who want to truly get better, everybody wins.
"I love coming to the ball park every day. They treat each other very well and it's just a great clubhouse. Even the guys we had to release, it's been hard because they are great kids. It's not personal, it's business and they get it.
"But they are believing in their abilities and they are playing with confidence," he added. "Luckily, it's worked out and our guys have been doing their job, which is, ultimately, why up to this point, I think we're the best team in the league and why we've had the most guys — the only guys — move on to affiliated ball."
Slobin lifts the culture in North Farmington sports
Farmington takes second in Rockford Invite
Jan. 18, 2020, noon
Salem uses perimeter consistency, defeats Plymouth
Stevenson's Seth Lause wins Athlete of the Week
Salem hockey shines in mercy win against Franklin
Kelbert finds home at Northwood


- Climate Research Division, National Institute of Meteorological Sciences, 33 Seohobuk-ro, Seogwipo-si, Jeju-do, Korea.
National Institute of Meteorological Sciences (NIMS) is participating in the CORDEX with a regional climate model, HadGEM3-RA which is based on the global atmospheric HadGEM3 of the Met Office Hadley Centre (MOHC). Configuration of HadGEM3-RA is almost same as the HadGEM3-A (See sections 3 and 4), except that the dynamic settings are taken from the operational limited area model. Detailed descriptions for dynamics core and physical packages are described in Davies et al. (2005) and Martin et al. (2006).
The configuration of the model domain follows a protocol of the Coordinated Regional climate Downscaling Experiment (CORDEX) for Asia (Giorgi et al., 2009). The model domain includes East Asia, India, and the Western Pacific Ocean, as shown in Fig. 1. The number of grid points is 396 (west-east) by 251 (north-south), with a horizontal resolution 0.22 degree (approximately 25km). The buffer zone for lateral boundary conditions is 11 grids at each direction, where is located between outer dotted line in Fig. 1.
The dynamic core of HadGEM3-RA is a nonhydrostatic, fully compressible, deep atmosphere formulation using a terrain-following, height-based vertical coordinate. It includes semi-Lagrangian advection of all prognostic variables except density, permitting relatively long time steps to be used at high resolution. The model uses the Arakawa-C grid in which the zonal and meridional wind components are staggered as well as the momentum and thermodynamic variables. HadGEM3-RA runs on a Charney-Phillips vertical grid in which the momentum and thermodynamic variables are staggered (Davies et al., 2005; Martin et al., 2006).
Model physics are summarized in Table 1. Detailed descriptions for dynamics core and physical packages are described in Davies et al. (2005) and Martin et al. (2006).
The experiments are conducted for 131 years; 36-year simulation for the current (1970-2005) climate and two types of 95-year simulations for the future (2006-2100) climate. The current climate simulation is driven from the historical run of the Atmosphere-Ocean coupled Hadley Center Global Environmental Model version 2 (HadGEM2-AO) simulation of the National Institute of Meteorological Research (NIMR) (Baek et al., 2012). For the future climate simulations, two different boundary conditions from the Representative Concentration Pathways (RCP) 2.6 and 8.5 scenarios of HadGEM2-AO. The RCP 2.6 scenario form the low end of the scenario literature in terms of emissions and radiative forcing (DP van Vuuren et al., 2011). The RCP 8.5 scenario is characterized by increasing greenhouse gas emissions over time and is representative of scenarios in the literature which result in high greenhouse gas concentration levels (Riahi et al., 2011).
Table 1. HadGEM3-RA configurations for the dynamical downscaling.
Davies, T., M. J. P. Cullen, A. J. Malcolm, M. H. Mawson, A. Staniforth, A. A. White, and N. Wood, 2005 : A new dynamical core for the Met Office's global and regional modeling of the atmosphere. Quart. J. Roy. Meteor. Soc., 131,1759-1782.
DP van Vuuren et al., 2011: RCP2.6: exploring the possibility to keep global mean temperature increase below 2℃. Climatic Change, 109, 95-116, doi:10.1007/s10584-011-0152-3.
Giorgi, F., C. Jones, and G. R. Asrar, 2009 : Addressing climate information needs at the regional level: The CORDEX framework. World Meteorological Organization (WMO) Bulletin, 58, 175-183.
Martin, G. M., M. A. Ringer, V. D. Pope, A. Jones, C. Dearden, and T. J. Hinton, 2006 : The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model (HadGEM1). Part I: Model description and global climatology. J. Clim 19, 1274-1302.
Riahi K., S. Rao, V. Krey, C. Cho, V. Chirkov, G. Fischer, G. Kindermann, N. Nakicenovic, and P. Rafaj, 2011 : RCP 8.5 - A scenario of comparatively high greenhouse gas emissions. Climatic Change, 109, 33-57, doi:10.1007/s10584-011-0149-y.
Thomson, A., and Coauthors, 2011 : RCP4.5: a pathway for stabilization of radiative forcing by 2100. Climatic Change, 109, 77-94. doi: 10.1007/s10584-011-0151-4.
Seoul National University Regional Climate Model (SNURCM) (Lee et al., 2004) are based on version 5 of the Penn State University/National Center for Atmospheric Research Meso-scale Model (MM5) (Grell et al., 1994) and the community land model version 3 (CLM3) as an advanced and comprehensive land surface model. The spectral nudging technique (von Storch et al., 2000) was performed as an alternative to the modified relaxation method (Liang et al., 2001) reducing systematic bias. Planetary boundary layer scheme of the Yonsei University (YSU) was added to the SNURCM for better simulation of precipitation over the ocean (Cha et al., 2008). SNURCM and Seoul National University Mesol-scale Model version 5 (SNU-MM5) are the same models.
The model domain (Fig.1) follows a protocol of the Coordinated Regional climate Downscaling Experiment (CORDEX) for East Asia, including north-south direction from the Indochina Peninsula to Russia and east-west direction from India to western Pacific. The number of grid points is 405 (west-east) by 260 (north-south) horizontal grids centering at 34.60°N and 116.57°E with a nominal horizontal resolution of 0.22°. A 24-level terrain-following (sigma) vertical grid from the surface to the model top of 70 hPa is used.
SNURCM dynamics is based on the MM5. The MM5 uses a nonhydrostatic primitive equation system with a terrain-following sigma vertical coordinate, which is a well-studied mesoscale model that has been applied widely in the atmospheric study. A spectral nudging technique of von Storch et al. (2000) was implemented for lateral boundary handling. The technique is applied to the entire model domain, while the relaxation method is applied only to the lateral buffer zone.
For evaluation experiment, present 37-year regional climate (1979-2015) forced by ERA-Interim is produced. For the historical experiment, present 26-year regional climate (1980-2005) forced by HadGEM2-AO is produced. Also, 95-year future climate simulations (2006-2100) were produced forced by HadGEM2-AO and representative concentration pathways (RCP) 4.5 and 8.5 scenarios. These scenarios are the latest emission scenarios recommended to use for the Fifth Assessment Report (AR5) of IPCC. For historical and RCP experiments, NOAH LSM is used as a land surface model.
Table 1. SNURCM configurations for the dynamical downscaling.
Bonan, G. B., Oleson, K. W., Vertenstein, M., Levis, S., Zeng, X. B., Dai, Y. J., Dickinson, R. E., and Yang, Z. L.: The land surface climatology of the community land model coupled to the NCAR community climate model, Journal of Climate, 15, 3123-3149, Doi 10.1175/1520-0442(2002)015<3123:Tlscot>2.0.Co;2, 2002.
Briegleb, B. P.: Delta‐Eddington approximation for solar radiation in the NCAR Community Climate Model, Journal of Geophysical Research: Atmospheres, 97, 7603-7612, https://doi.org/10.1029/92JD00291 1992.
Cha, D. H., Lee, D. K., and Hong, S. Y.: Impact of boundary layer processes on seasonal simulation of the East Asian summer monsoon using a Regional Climate Model, Meteorology and Atmospheric Physics, 100, 53-72, 10.1007/s00703-008-0295-6, 2008.
Grell, G. A., Dudhia, J., and Stauffer, D. R.: A description of the fifth-generation Penn State/NCAR mesoscale model (MM5), 1994.
Hong, S. Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes, Monthly Weather Review, 134, 2318-2341, Doi 10.1175/Mwr3199.1, 2006.
Kain, J. S., and Fritsch, J. M.: A One-Dimensional Entraining Detraining Plume Model and Its Application in Convective Parameterization, Journal of the Atmospheric Sciences, 47, 2784-2802, Doi 10.1175/1520-0469(1990)047<2784:Aodepm>2.0.Co;2, 1990.
Lee, D. K., Cha, D. H., and Kang, H. S.: Regional climate simulation of the 1998 summer flood over East Asia, J Meteorol Soc Jpn, 82, 1735-1753, DOI 10.2151/jmsj.82.1735, 2004.
Liang, X. Z., Kunkel, K. E., and Samel, A. N.: Development of a regional climate model for US midwest applications. Part I: Sensitivity to buffer zone treatment, Journal of Climate, 14, 4363-4378, Doi 10.1175/1520-0442(2001)014<4363:Doarcm>2.0.Co;2, 2001.
Reisner, J., Rasmussen, R. M., and Bruintjes, R. T.: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model, Quarterly Journal of the Royal Meteorological Society, 124, 1071-1107, DOI 10.1256/smsqj.54803, 1998.
von Storch, H., Langenberg, H., and Feser, F.: A Spectral Nudging Technique for Dynamical Downscaling Purposes, Monthly Weather Review, 128, 3664-3673, 10.1175/1520-0493(2000)128<3664:Asntfd>2.0.Co;2, 2000.
- Department of Atmospheric Sciences, Kongju National University, 182 Shinkwan-dong, Gongju-city 314-701, ChungCheongnam-do, South Korea.
The Regional Climate Model version 4 (RegCM4) used in this study, developed by the International Centre for Theoretical Physics (ICTP), is the latest version with some noteworthy improvements, such as the coupling of a sophisticated land surface model, community land model 3 (http://www.ictp.it/research/esp/models/regcm4.aspx). Compared to RegCM3, RegCM4 includes new land surface, planetary boundary layer, and air-sea flux schemes, a mixed convection and tropical band configuration, modifications to the pre-existing radiative transfer and boundary layer schemes, and a full upgrade of the model code aimed towards improved flexibility, portability, and user friendliness. A detailed description of RegCM4 is given by Giorgi et al. (2012).
The model domain (Fig. 1), set based on the CORDEX Ⅱ East Asia domain, covers most of Asia, the western Pacific, the Bay of Bengal, and the South China Sea. The number of grid points in Lambert Conformal map projection is 394 (west-east) by 249 (north-south), with a horizontal resolution of 25 km. A 23-sigma (50 hPa) vertical grid is used. A summary for the simulation environment including the physical schemes is shown in Table 1.
The basic model dynamics have remained the same as in RegCM3, which was essentially the same as that of the previous version RegCM2 (Giorgi et al. 1993a, b). RegCM4 is thus a hydrostatic, compressible, sigma-p vertical coordinate model run on an Arakawa B-grid in which wind and thermodynamical variables are horizontally staggered. A time-splitting explicit integration scheme is used in which the 2 fastest gravity modes are first separated from the model solution and then integrated with smaller time steps. This allows the use of a longer time step for the rest of the model. Essentially, the model dynamics are the same as that of the hydrostatic version of MM5 (Grell et al. 1994).
in this study, the MIT-Emanuel (1991) cumulus parameterization scheme and the NCAR CLM3.5 (Oleson et al., 2008) land surface model, which showed relatively better simulation skills in South Korea, were selected based on the 25 years sensitivity experiments (Kim et al., 2017). The Holtslag et al. (1990) scheme and NCAR CCM3 (Kiehl et al., 1996) radiation scheme were used for the planetary boundary layer. To minimize the systematic errors that occur in long-term simulations over large domain, spectral nudging was applied to RegCM4 (Von Storch et al., 2000). We used the default of RegCM4 for the other physics.
In this study, two types of simulations using RegCM4 were performed. One is a current climate, that is the evaluation and historical simulation of RegCM4. Evaluation simulation was forced by ERA-Interim data, and historical simulation was forced by HadGEM2-AO produced by the National Institute of Meteorological Research (NIMR)/Korea Meteorological Administration (KMA). The other is future climate simulation for the projection of future climate under the two emission scenarios, representative concentration pathways (RCP) 4.5 and 8.5. The simulation periods for the current and future climate simulations are 27-year (from 1979 to 2005) and 95-year (from 2006 to 2100), respectively. RCP scenarios are the latest emission scenarios recommended to use for the Fifth Assessment Report (AR5) of IPCC. To prepare the Sixth Assessment Report (AR6) with new global climate change scenarios based on RCP, the NIMR/ KMA simulated the several experiments such as preindustrial control run, historical run, and RCP scenarios (4.5, 8.5) runs for the long term projections recommended by CMIP5 using the coupled global atmosphere-ocean model, HadGEM2-AO. Details of HadGEM2-AO are given by Collins et al. (2011), and the results of the HadGEM2-AO CMIP5 experiment by NIMR/KMA are shown in Baek et al. (2013).
Figure 1. Model domain and topography (m) for the 25km grid spacing.
Table . RegCM4 configurations for the dynamical downscaling.
Baek, H.-J., Lee, J. H., Lee, H.-S., Hyun, Y.-K., Cho, C. H., Kwon, W.-T., Marzin, C., Gan, S.-Y., Kim, M.-J., Choi, D.-H., Lee, J. H., Lee, J., C., Boo, K.-O., Kang, H.-S. and Byun, Y.-H., 2013: Climate change in the 21st Century simulated by HadGEM2-AO under representative concentration pathways, Asia-Pacific J. Atmos. Sci., 49(5), 603-618.
Collins, W. J., N. Bellouin, M. Doutriaux-Boucher, N. Gedney, P. Halloran, T. Hinton, J. Hughes, C. D. Jones, M. Joshi, S. Liddicoat, G. Martin, F. O'Connor, J. Rae, C. Senior, S. Sitch, I. Totterdell, A. Wiltshire, and S. Woodward, 2011 : Development and evaluation of an Earth-system model – HadGEM2, Geosci. Model Dev. Discuss., 4(2), 997–1062, doi:10.5194/gmdd-4-997-2011.
Emanuel, K. A., 1991 : A scheme for representing cumulus convection in large-scale models. J. of the Atmos. Sci., 48(21), 2313-2335.
Giorgi, F., M. R. Marinucci, and G. T. Bates, 1993a : Development of a second-generation regional climate model (RegCM2). Part I: Boundary-layer and radiative transfer processes. Mon. Wea. Rev. 121, 2794-2813.
Giorgi, F., M. R. Marinucci, G. de Canio, and G. T. Bates, 1993b : Development of a second-generation regional climate model (RegCM2). Part II: Convective processes and assimilation of lateral boundary conditions. Mon. Wea. Rev. 121, 2814-2832.
Giorgi, F., E. Coppola, F. Solmon, L. Mariotti, M. B. Sylla, X. Bi, N. Elguindi, G. T. Diro, V. Nair, G. Giuliani, U. U. Turuncoglu, S. Cozzini, I. Güttler, T. A. O'Brien, A. B. Tawfik, A. Shalaby, A. S. Zakey, A. L. Steiner, F. Stordal, L. C. Sloan, and C. Brankovic, 2012 : RegCM4: model description and preliminary test over multi CORDEX domain, Clim. Res., 52, 7-29.
Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994 : A description of the fifth generation Penn State/NCAR Mesoscale Model (MM5). National Center for Atmospheric Research Tech Note NCAR/TN-298 + STR, NCAR, Boulder, CO.
Holtslag, A. A. M., E. I. F. De Bruijin, and H.-L. Pan, 1990 : A high resolution air mass transformation model for short-range weather forecasting. Mon. Wea. Rev., 118, 1561-1575.
Kiehl, J. T., J. J. Hack, G. B. Bonan, B. A. Boville, B. P. Briegleb, D. L. Williamson, and P. J. Rasch, 1996 : Description of NCAR Community Climate Model(CCM3). NCAR Tech. Note NCAR/TN-420+STR, 152 pp.
Kim T.-J., M.-S. Suh, and S.-K. Oh, 2017 : Impact of land surface and cumulus parameterization schemes on the simulation skills of RegCM4.0 over CORDEX-East Asia Phase 2 domain. J. Clim. Res., 12(2), 181-197.
Oleson, K. W., G.-Y. Niu, Z.-L. Yang, D. M. Lawrence, P. E. Thornton, P. J. Lawrence, R. Stöckli, R. E. Dickinson, G. B. Bonan, S. Levis, A. Dai, and T. Qian, 2008 : Improvements to the community land model and their impact on the hydrological cycle. J. Geophys. Res., 113, G01021, doi:10.1029/2007JG000563.
Von Storch, H., H. Langerberg, and F. Feser, 2000: A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev., 128(10), 3664-3673, doi: 1175/1520-0493.
- Department of Atmospheric Sciences, Pusan National University, Jangjeon 2-dong, Geumjeonggu, Busan 46241, Korea.
In this study, Weather Research and Forecasting (WRF, Skamarock et al. 2008) model version 3.7 is used for regional climate simulation over CORDEX-East Asia phase 2 domain. The WRF is a state-of-the-art atmospheric modeling system which is widely used for both meteorological studies and real-time numerical weather prediction. The model serves a wide range of meteorological applications in the range of spatial scales from tens of meters to thousands of kilometers. The detailed description of WRF model can be found in the website http://www2.mmm.ucar.edu/wrf/users/model.html.
Following the CORDEX East Asia phase 2 framework, the model domain covers East Asia, the Bay of Bengal, South China sea and western North Pacific regions with 25 km horizontal resolution (Figure 1). The model has 30 vertical levels, with the model top at 50 hPa. The number of grid points is 395 (west-east) by 250 (north-south) centering at 34.40°N and 116.57°E. For lateral boundary forcing, 15 grid points at each direction are used for the buffer zone.
In this study, we used the Advanced Research WRF (WRF-ARW) core version 3.7 that is maintained by NCAR's Mesoscale and Microscale Meteorology (MMM) laboratory. The equation set for WRF-ARW is fully compressible non-hydrostatic. It uses mass-based terrain-following coordinate, and Arakawa C-grid staggering. The time integration scheme in the model adopts the third-order Runge-Kutta scheme coupled with a split-explicit 2nd-order time integration scheme for the acoustic and gravity-wave modes. More detailed description of WRF-ARW can be found in the website http://www2.mmm.ucar.edu/wrf/users/docs/wrf-dyn.html.
The physical parameterizations used in this study are the WRF Single-Moment 3-class microphysics scheme (Hong et al., 2004), Betts-Miller-Janjic convection parameterization scheme (BM, Betts and Miller, 1986; Janjic, 1994), Community Atmospheric Model radiation scheme (CAM, Collins et al., 2004), Noah land surface model for land surface process (Chen and Dudhia, 2001), and Yonsei University (YSU) PBL scheme (Hong et al., 2006).
In order to assess the performance of the WRF model in simulating the present-day climate over CORDEX East Asia phase 2 domain, we performed dynamical downscaling using WRF model driven by two lateral boundary forcings: European Centre for Medium-Range Weather Forecast Interim Reanalysis (ERA-Interim, Simmons et al., 2007) and Max-Planck-Institute Earth System Model at low vertical resolution (MPI-ESM-LR, Giorgetta et al., 2013). For the future climate simulations, MPI-ESM-LR GCM is downscaled using WRF model under the Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) within the framework of the CORDEX East Asia phase 2. Model simulations are available for the reference period 1979-2005 and future period 2006-2100. The first two years (1979-1980) for reference simulation is considered as the spin-up period.
Betts, A., and M. Miller, 1986: A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets. Quart. J. Roy. Meteor. Soc., 112, 693-709.
Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569-585, doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.
Collins, W. D., J. K. Hackney, and D. P. Edwards, 2002: An updated parameterization for infrared emission and absorption by water vapor in the National Center for Atmospheric Research Community Atmosphere Model. J. Geophys. Res., 107, 1-20, doi:10.1029/2001JD001365.
Giorgetta, M. A., and Coauthors, 2013: Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst., 5, 572–597, doi:https://doi.org/10.1002/jame.20038.
Hong, S. Y., J. Dudhia, and S. H. Chen, 2004: A Revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103-120, doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.
Hong, S. Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318-2341, doi:10.1175/MWR3199.1.
Janjic, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927-945, doi:10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. Gill, D. Barker, M. Dudhia, X. Y. Huang, W. Wang, and J. G. Powers, 2008: A description of the Advanced Research WRF Version 3, NCAR Tech. Note, NCAR/TN-475+STR, 113pp.
Simmons, A., S. Uppala, D. Dee, and S. Kobayashi, 2007: ERA-Interim: New ECMWF reanalysis products from 1989 onwards. ECMWF Newsl., 110 , 25-35.
The Consortium for Small-scale Modeling (COSMO) was formed in October 1998. Its general goal is to develop, improve and maintain a non-hydrostatic limited-area atmospheric model, to be used both for operational and for research applications by the members of the consortium. The CLM Community extended the COSMO-Model to be able to run long-term simulations, the so-called climate mode. The resulting modeling system is called the COSMO-CLM (or CCLM) (https://www.clm-community.eu/). CCLM is internationally used in CORDEX East Asia study (Wang et al., 2013; Huang et al., 2015; Huang et al., 2017). Here we used CCLM 5.0 for downscaling.
Spatial domain of the simulations covers East Asia, India, and Wester Pacific Ocean as shown in figure 1. This configuration follows a protocol of the Coordinated Regional Climate Downscaling Experiment (CORDEX) for Asia (http://www.cordex.org/domains/region-7-east-asia/). The number of horizontal grid points is 396 (west-east) by 251 (north-south) having 25 km resolution. A 40-level height based hybrid vertical grid (Gal-Chen and Somerville, 1975) is used.
CCLM is based on non-hydrostatic, compressible dynamical equations to avoid restrictions on the spatial scales and the domain size, and application of an efficient numerical method of solution. Coordinate system in CCLM is generalized terrain-following height coordinated with rotated geographical coordinates and user defined grid stretching in vertical.
Grid and sub-grid scale physics are described in various equations based on theoretical assumptions. Table 1 show details in physical parameterization of the CCLM CORDEX East Asia simulations.
We simulated the current climate with CCLM using the boundary forcing of the ERA-Interim reanalysis data (CCLM-ERA) for 37 years (1979-2015) and using the boundary forcing of MPI-ESM-LR historical run (CCLM-MPI) for 27 years (1979-2005). Future climate projections with CCLM were produced using the MPI-ESM-LR boundary forcing for two AR5 representative concentration pathways (RCP) 4.5 and 8.5 scenarios during 95 years (2006-2100).
Figure 1. Simulation domain and geometric height above sea level (m).
Davies H. C., R. E. Turner, 1977: Updating prediction models by dynamical relaxation – examination of technique. QJR Meteorol. Soc., 103, 225-245.
Gal-Chen, T., and R. C. Somerville, 1975: On the use of a coordinate transformation for the solution of the Navier-Stokes equations. J. Computational Phys., 17(2), 209-228.
Huang, B., S. Planski, and U. Cubasch, 2015: Assessment of precipitation climatology in an ensemble of CORDEX-East Asia regional climate model simulations. Clim. Res., 64, 141-158.
Huang, J., J. Zhai, T. Jiang, Y. Wang, X. Li, R. Wang, M. Xiong, B. Su, and T. Fischer, 2017: Analysis of future drought characteristics in China using the regional climate model CCLM. Clim. Dyn., 50(1-2), 507-525.
Ritter, B., and J. F. Geleyn, 1992: A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon. Wea. Rev., 120, 303-325.
Seifert, A., and K. D. Beheng, 2001: A double-moment parameterization for simulating autoconversion, accretion and selfcollection. Atmos. Res. 59-60, 265-281.
Schrodin, R., and E. Heise, 2002: A new multi-layer soil-model. COSMO Newsl., 2: 139-151.
Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117(8), 1179-1800.
Von Stortch H., H. Langenberg, and F. Feser, 2000: A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev., 128, 3664-3673.
Wang, D., C. Menz, T. Simon, C. Simmer, and C. Ohlwein, 2013: Regional dynamical downscaling with CCLM over East Asia. Meteorol. Atmos. Phys., 121(1-2), 39-53.


1. YOUR PURPOSE. YOUR LIFE. YOUR L EGACY. YOUR MONEY.
Some people won't spend money on coaching, classes, services or programs that will take them to where they ought to be. Yet, they will easily spend 5x that amount on hair, dress, designer shoes or whatever rocks their boat.
If you despise how you are living right now, then do something about it.
Which steps are you taking to come out of the dungeon of poverty? Do you know that there are many solutions but you just need to activate something and get moving?
For example, if you love to spend so much time on Facebook, morning till night, then go and learn how to monetize your time on Facebook!
Take certification classes to help you get to the next level and increase your income. Start building your legacy. Do something wonderful with your life and think about the next generation. They are watching you.
Ladies, you want a man who is settled, knows what he is doing and loves the Lord, right? Guess what? Men don't want liability either. They want the best for their lives too.
Many people are looking for freebies all the time, If you are one of them, STOP. Learn to spend money [wisely] to make money. Know your strengths, get help for your weak areas. People waste too much time trying to figure out stuff they do not know.
For example, if graphic design isn't your thing, hire a great graphic designer to do the task. If web design isn't your lane, stay in your lane, save money, find money and hire a web designer.
If putting words together on paper isn't your thing, hire a great copywriter to help you bring out what's in you. Your reputation is at stake when you launch sub par products or services.
If you have a toothache and you know that bad tooth needs to be pulled out, would you do it yourself? Wouldn't you go to a dentist?
People ruin things by being cheap; then they become disappointed. First impressions do matter. Your destiny is not "let me try my luck." You don't gamble with your allocated time on earth.
It's time to invest in your elevation. Grow your business. Elevate your business. Jumpstart your ministry.
A while back, I sowed a seed into the ministry of a preacher. I didn't sow cash but in kind – I helped with strategic visibility which usually costs thousands of dollars.
A few days later after the project completion and delivery, someone unexpectedly sowed into my life [in kind; more than I sowed into the preacher's ministry]. He provided access to his online course which costs thousands of dollars.
I knew what the preacher needed for his next step, and I sowed. The person who sowed into my life knew what I needed for my next step and sowed. These two people do not know each other in any way. The common denominator is their love for Jesus Christ.
Understand the principle of seed sowing. Stop being stingy; and if all you do is take, receive, take, you better start giving. Givers never lack.
What can one learn from these lessons? Take action; don't sit down and expect manna to fall down from heaven. Don't expect to receive a harvest when you did not plant anything.
Can you add some points to this "real talk?" I appreciate your comments.


Larger than life. A legend in his own time. A hillbilly, a rocker, a lover, a fighter, a father, a songwriter beyond compare, Steve Earle is all of these tags and much more. With his latest album So You Wanna be an Outlaw? going steady on the charts and a lengthy tour ahead, including a stop in Thunder Bay at the Thunder Bay Community Auditorium on September 24, Earle, even in his early 60s, is doing what he loves for some very good reasons.
The Walleye: Given everything you've been through—addictions, marriages, bankruptcies, living as a touring musician—what does being an outlaw mean to you today?
Steve Earle: It means what it always did, but it's also about rehabilitating that term. A lot of people that weren't there when outlaw country came out thought that it was about lifestyle, but it wasn't just that. It was about country artists figuring out that rock artists had more artistic freedom, and they wanted to do that, to make the records that they wanted to. And I was there, and that's what I always did, always trying to make the best records that I could. That was my calling and I did whatever it took to get them made.
American singer-songwriter Steve Earle, photographed at Dyckman Farmhouse in New York. Photo by Chad Batka.
TW: After a few passion projects the last few years, this record seems to be a return to hard country, hard rock. What was the motivation there?
SE: Well, you gotta make a record about something, you know? I mean, I'm writing a memoir, so maybe that prompted a bit of it, but a lot of it had to do with timing. I was asked to write a song for the TV show Nashville, "If Mama Coulda Seen Me," which got used in the first season, then in the second season they also asked me for another song…so I wrote "Looking for a Woman," which gave me two songs, then I went on to make the blues record (Terraplane) and the record with Shawn Colvin (Colvin & Earle), then in the middle of the Colvin tour it occurred to me that I'll have to make another record, cause I got alimony money to pay, so I got the songs out, dusted them off, then they seemed to fit together well enough, then I realized—I've been listening to a Waylon Jennings record, Honky Tonk Heroes, about every few years whether I need to or not—but maybe that's what this record will be.
SE: Well, I came up at the same time, I started making records then, but I got to Nashville when I was 19 right in the middle of all that, and the window closed, and I didn't get a record deal in time. But that's still what I'm about, what the record means.
TW: Do you see yourself as being remembered as part of that group: Waylon, Willie, and Steve?
SE: Oh yeah, I always have. That's just how I came about as an artist.
TW: Not sure if you know, but classic rock radio will play "Copperhead Road" about twice a day. That song's pretty awesome and a great gateway into your vast catalogue. Do you still play that song? Do you like it?
SE: Yeah, we still play that song every night. There's three or four songs we play every night—"Copperhead Road," "Guitar Town," "The Galway Girl"—and there's some songs you just have to play every night, and I'm okay with that. I still get something outta playing them, the fans love them, and why would you not be proud of a song that connected with a lot of people? That's not a reason to not play a song… to me that's a reason to play it every night.
TW: So what makes a good song to you? What makes you say, "Yeah, people have to hear this"?
SE: A good song lets you find what you have in common with the audience—you're no different from anyone watching you play. Everybody loves, everybody misses their kids, everybody celebrates, those are the things that people relate to in songs, so those are the good ones. This job is about empathy as much as anything else. To me, good songs make people feel like they're not alone, and that someone else feels like they do.
TW: Is that one of the reasons you're still touring?
SE: Well, that and I have to make a living. I can't afford to not tour. I need the money, so… I mean, I like to tour and it's a good thing I do, cause I don't have a choice. There's no real record royalties anymore and I've been married a lot, so I don't really have any money, so I just have to keep working.
TW: Along with all that, you're a father. Your son, Justin Townes Earle, is coming into his own as an artist, and you have another son who has some unique challenges. How does that influence your life?
SE: Justin's doing real well, and he's got a little girl now who I just met, and she's like six weeks old, and my middle son has kids, so I have three grandchildren. My youngest son, John Henry, is seven. He has autism, and kinda everything I do now is for him. I think he's gonna be fine, but I want him to be well, and it costs money to get him the care that he needs to give him the best chance that he possibly can. He's doing better than a lot of kids with autism cause he lives in a place where he can get those services, in New York City, and I make sure that he gets them. He's the main reason I wake up in the morning every day.
TW: You're working really hard but you're also famously clean. What defines a good time for you now?
SE: Baseball and live theatre. Those are two things that I really love—oh, and fishing with a fly rod, which I'm gonna do tomorrow in Idaho. The rest of the band will wait for me , I'll buy them a steak, then we'll play Billings, Montana. We get a break in a few weeks and I'll take John Henry to the beach.
TW: It's hard to ignore that there's a lot going on in your country now. You've been an activist, but you've also written some songs from some contrary perspectives. Are you getting more material out of all this, or do you just want to leave the country?
SE: I was in Canada the night Trump was elected. I was playing in Ottawa on the Guitar Town 30th Anniversary Tour, and we didn't know this was going to happen. It blindsided everybody. I wrote this record thinking that we were going to elect Hillary Clinton. She wasn't my favourite choice, but I voted for her, and I thought I'd walk off stage and find that we'd elected the first woman president, and instead I find out we elected the first orangutan president. People have asked me why the record isn't more political, but most of the songs were written before all this happened, so I decided to just let it be what it is, and the next record will be just as country as this one, but way more political.


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text:
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TEF Comments
Commentary / TEF
ITEF Comment Vol. XIII Issue 6
by admin · Published September 6, 2007 · Updated September 4, 2015
Illinois Taxpayers Beat Back State Income Tax Increase 14 Years In a Row
June 30, 1989, is a date that remains indelibly etched in the memories-and wallets-of Illinois taxpayers. That was the day of the Illinois "Tax Massacre," the day the Illinois General Assembly passed the largest state tax increases in the nation, under the aegis of Gov. James R. "Big Jim" Thompson (R).
For strange and perverse reasons, Republican Governors and Republican legislators have manhandled Illinois taxpayers more brutally than Democrats, and June 30, 1989, was the ultimate fiscal mauling that energized the pro-taxpayer, anti-tax movements in the state, and for the best of reasons: self-preservation. The $2 billion tax increases passed on that day included a 20% hike in the state income tax, a 46% hike in the state gasoline tax, a 50% hike in the state cigarette tax, and a new sales tax on computer software. Huge state tax increases were passed in the final hour that day, preventing any debate or testimony against the increases. To reward themselves for their efforts, the members of the General Assembly voted themselves a salary increase of 17%.
These tax increases caused thousands of angry taxpayers to attend meetings and tax protests in the summer of 1989, sponsored by National Taxpayers United of Illinois. In an event covered on national TV, embarrassed tax-raiser Rep. Daniel "Dan" Rostenkowski (D-Ill.), Chairman of the House Ways and Means Committee, found himself trapped in his car surrounded by enraged tax protesters, led by NTUI activists. In 1990, notorious Du Page County tax-raiser, Jack T. Knuepfer (R), chairman of the Du Page County Board, and scores of other tax-raisers, were defeated in that year's primary.
Although state tax increases have been passed since 1989, taxpayers have defeated increases in the state income tax for the past 14 years. Figures just released by the non-partisan Tax Foundation, Washington D.C., show that relative to other states, Illinois is no longer in the high-tax-state category. Using Tax Burden (taxes as a percentage of income) as a measure, the Tax Foundation shows Illinois' state and local taxes, at 10.8%, the 22nd highest in the nation. At 10.8% of income, Illinois' state/local tax burden ranks just below the national average of 11.0%. Illinois taxpayers pay $4,594 per capita for state and local taxes on per-capita state income of $42,428. (http://www.taxfoundation.org/taxdata/show/452.html)
To be sure, Illinois is not a low-tax-state, and state spending, especially for the benefit of powerful special interests, continues to rise. Public-employee pensions increasingly eat away taxpayers' dollars, in the form of gigantic, multi-million-dollar pensions enjoyed by retired government employees, especially retired public school teachers and administrators. Teachers outside the Chicago Public School system can retire after 34 years of employment, and receive, the first year, 75% of their salaries, based on their last four years before retirement, and these benefits increase each year by 3%.
A report on May 16, 2007, by ABC7 News Team investigative reporter, Chuck Goudie, illustrated the staggering costs to taxpayers: Over 1,100 retired teachers have pensions of over $111,000 a year, and were eligible to retire at age 55. The average pay for a teacher in Illinois is $57,000 a year, and almost 3,500 teachers make more than $100,000 a year-for 9 months employment. Eighteen former state employees, all retired university or public school educators, receive, each year, over $200,000 in pension benefits.
Despite these examples of egregious spending, Illinois, relative to other states, has benefited from the successful activities of taxpayer organizations, and its economy now is reaping the benefits.
Click here for the ITEF comment
Next story Taxpayers Say: Fire The 630 Overpaid CTA Employees!
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Taxpayers Education Foundation (TEF) was founded in 1976. We have saved Illinois taxpayers literally billions of dollars, and our influence and activities are now felt in other states and in Washington.
Taxpayer Education Foundation 205 W. Randolph Street, Suite 1305 Chicago, IL 60606 P: (312) 427-5128 F: (312) 427-5139 E: info@taxpayersunited.org
All content © 2015 Taxpayer Education Foundation


Exploris provides Artificial Intelligence based Diagnostic and Therapeutic lab tests, allowing for higher precision of results and increased convenience and safety for patients.
We enhance lab tests with Artificial Intelligence based predictive modeling for improved interpretation of lab results. The technology platform comprises clinical data sources, knowledge and a proprietary software engine which is capable for so far unmet capabilities in detecting new combinations of markers.
A change in health status is reflected in physiological factors. Identifying the relevant factors and determining their relationship leads to new diagnostic approaches and improved therapies.
As a pioneer in Artificial Intelligence based eDiagnostics and eTherapeutics we are one of the first companies which have successfully passed clinical studies for our novel, groundbreaking tests.
Bringing the power of our extensive Artificial Intelligence software suite, the internet and the huge source of information in collected data together, turns out to be one of the most important technological advances in health care.
How will AI change healthcare?
Can AI support doctors and help to avoid the ressource crisis?


text:
"""
Created on Jan 1, 2016

@author: Gregory Kramida
"""

import cv2
import numpy as np

def generate_board_object_points(board_height, board_width, board_square_size):
    board_dims = (board_width, board_height)
    object_points = np.zeros((board_height * board_width, 3), np.float32)
    object_points[:, :2] = np.mgrid[0:board_width, 0:board_height].T.reshape(-1, 2)
    # convert square sizes to meters
    object_points *= board_square_size
    return object_points

def homogenize_4vec(vec):
    return np.array([vec[0] / vec[3], vec[1] / vec[3], vec[2] / vec[3], 1.0]).T

class Pose(object):
    def __init__(self, transform=None, inverse_transform=None, rotation=None, translation_vector=None):
        if translation_vector is not None:
            if type(translation_vector) != np.ndarray:
                translation_vector = np.array(translation_vector)
            if translation_vector.shape != (3, 1):
                translation_vector = translation_vector.reshape(3, 1)
        if rotation is not None:
            if type(rotation) != np.ndarray:
                rotation = np.array(rotation)
            if rotation.size == 9:
                rotation_vector = cv2.Rodrigues(rotation)[0]
                rotation_matrix = rotation
            elif rotation.size == 3:
                rotation_matrix = cv2.Rodrigues(rotation)[0]
                rotation_vector = rotation
            else:
                raise ValueError(
                    "Wrong rotation size: {:d}. Expecting a 3-length vector or 3x3 matrix.".format(rotation.size))
        if transform is None:
            if translation_vector is None or rotation is None:
                raise (ValueError("Expecting either the transform matrix or both the rotation & translation vector"))
            self.T = np.vstack((np.append(rotation_matrix, translation_vector, axis=1), [0, 0, 0, 1]))
        else:
            self.T = transform
            if translation_vector is None:
                translation_vector = transform[0:3, 3].reshape(3, 1)
            if rotation is None:
                rotation_matrix = transform[0:3, 0:3]
                rotation_vector = cv2.Rodrigues(rotation_matrix)[0]
        if inverse_transform is None:
            rot_mat_inv = rotation_matrix.T
            inverse_translation = -rot_mat_inv.dot(translation_vector)
            inverse_transform = np.vstack((np.append(rot_mat_inv, inverse_translation, 1), [0, 0, 0, 1]))

        self.rmat = rotation_matrix
        self.tvec = translation_vector
        self.rvec = rotation_vector
        self.T_inv = inverse_transform

    def dot(self, other_pose):
        return Pose(self.T.dot(other_pose.T))

    def diff(self, other_pose):
        """
        Find difference between two poses.
        I.e. find the euclidean distance between unit vectors after being transformed by the poses.
        """
        unit_vector = np.array([1., 1., 1., 1.]).T
        p1 = self.T.dot(unit_vector)
        p2 = other_pose.T.dot(unit_vector)
        # no need to homogenize, since the last entry will end up being one anyway
        return np.linalg.norm(p1 - p2)  # it will also not contribute to the norm, i.e. 1 - 1 = 0

    @staticmethod
    def invert_pose_matrix(transform_matrix):
        translation_vector = transform_matrix[0:3, 3].reshape(3, 1)
        rotation_matrix = transform_matrix[0:3, 0:3]
        rotation_matrix_inverse = rotation_matrix.T
        translation_vector_inverse = -rotation_matrix_inverse.dot(translation_vector)
        return np.vstack((np.append(rotation_matrix_inverse, translation_vector_inverse, 1), [0, 0, 0, 1]))

    def __str__(self):
        return "================\nPose rotation: \n" + str(self.rmat) + "\nTranslation:\n" + str(
            self.tvec) + "\n===============\n"


text:
Dr Ranbir Singh
Upping the Ante
As the centre's calls for reforms in the system get strident, questions of primacy of the executive over the judiciary have raised concerns. Objections by the executive betray a sense of insecurity.
A Sound, Basic Document
India Legal - November 29, 2022
As India renews its commitment to the Constitution, it's time to assess how it has stood the test of time. Experts have said that while it is a sound document, it should also be flexible.


As you know I have done a lot of traveling. You absolutely stand out as a guide - for your knowledge, for your companionship, for all the wonderful group photos! I know you must have done this tour many times, yet your enthusiasm never wavers. You are a delight!
Everything was nice and we enjoyed a lot. I specially recommend the invisible Ljubljana tour. This was very interessting..
Uns hat der Aufenthalt in Sloweninen sehr gut gefallen und wir haben wirklich schoene und sehr interessante Eindrucke bekommen. Vielen Dank das Sie sich fuer uns so viel Zeit genommen haben und uns alles so anschaulich erklaert haben. Der Aufenthalt in Slowenien wird uns immer in bester Erinnerung bleiben. Danke noch mals Marija und viele Gruesse!
Thank you again for your excellent guiding in Slovenia !
Thanks so much Mateja. Wanted you to know How much we enjoyed our adventure and especially your lovely city. You were the best trip leader ever!
We wanted to thank you for leading us with excellence. You know your business! We hope to visit Ljubljana in the future.There is much we could add but simply put: You're the Best!
I want to let you know that this trip was the most memorable for me. Traveling five continents except Arctic and Antarctic, I can say without doubt that the trip was wonderful because we have an extraordinary tour guide as you. As a professor for many years, I know the importance to let people know how well they have done their "assignment" . Your knowledge not only of the region but also in historic terms including other subjects and your enthusiasm are part of the wonderful memories of this trip to the Adriatic countries. Regards to your lovely family. Thanks for making our trip so wonderful.
Zahvaljujem se, bilo je sve super.
Thanks Mateja for a wonderful trip. We saw the other caves yesterday-they were amazing! Take care!
Ich möchte mich nochmals namens unserer Gruppe für die sehr interessante und informative Laibacher Stadtführung am letzten Samstag bedanken. Ich war das erste Mal in Laibach und es hat mir sehr gut gefallen.
Vielen Dank für die schöne Führung. Durch Ihre interessanten Erklärungen ist uns gar nicht aufgefallen, dass es geregnet hat. Vielen Dank auch für Ihre herzliche Art mit Gästen umzugehen. Bis zum nächsten Mal!
Wir bedanken uns für die kompetente Betreuung.Gern kommen wir wieder auf Sie zurück, sollten wir wieder einmal vor Ort bei Ihnen sein!
Thank you for a wonderful tour, we all really enjoyed it. Daniel was a wonderful host.
Najlepše se zahvaljujemo za prijetno in zanimivo vodenje. Otroci, čeprav ne poslušajo ves čas vsi, kot vedno,so bili zelo zadovoljni in zagotovo pridemo v Ljubljano spet čez kaki dve leti. Uspešno leto in vse lepo želimo tudi vam!
Študenti in profesorica so bili vsi zelo zadovoljni z ogledom. Videli so veliko zanimivega in še ne poznanega, posebaj pa so pohvalili prijaznost iz Vaše strani jaz pa temu dodajam še pohvalo (in zahvalo) za prilagodljivost pri sestavi programa in super izvedbo, kjub časovni omejenosti.
I thoroughly enjoyed the trip because you made it very special. Thank you!
I really enjoyed the trip with you, learned a lot and got a lot of exercise. Thanks for a great trip!
Those were wonderful moments! Thank you for taking good care of us.Thanks!
Many THX 4 all your information as well as good advise+trips! Mateja you are a gem: a friendly person+ a wonderful professional in every way.
Thank you very much for a 6 star trip!
We had a wonderful time on our food tour. Danijel was a great guide. He answered all our questions and provided a lot of information on the city, culture and history of Slovenia. We had a great time and will recommend to friends.
Gli ospiti sono stati soddisfatti Grazie per il servizio Alla prossima!
Thank you so much for all the learning and discovery you shared with us. Before we came on this trip I had a vague notion of place and history. NOw I have a much better idea what happened and why. Thank you for sharing the beauties of these countries. I am excited to share my new knowledge with our friends and family!


text:
Philip Braham (18 June 1881 – 2 May 1934) was an English composer of the early twentieth century, chiefly associated with theatrical work.  From 1914, he composed music for such musicals and revues as Theodore & Co (1916) and London Calling! (1923), including several revues produced by André Charlot.  His best-known song is "Limehouse Blues," which has been recorded by many artists.  He wrote for film in the 1930s.

Biography
Braham studied at Cambridge University. He began to compose music for the theatre in 1913 with Alice up to Date at the London Pavilion and became musical director of The Comedy Theatre in London. In World War I, Braham volunteered for medical work, being unfit for active service and worked as a casualty dresser at Charing Cross Hospital. He contributed additional music to musicals such as The Girl in the Train (1914), and Theodore & Co and See Saw, both in 1916.  He wrote the music for the hit revue Tails Up! (1918), together with his frequent collaborator Douglas Furber, which played at the Comedy Theatre in London for 467 performances.

The best-remembered show on which Braham worked was probably London Calling! (1923) on which he collaborated with Noël Coward.  In 1925, he collaborated with Coward in On with the Dance and with John Hastings Turner on Bubbly, starring Cyril Ritchard.  He wrote for several revues produced by André Charlot. His best-known song is the jazz standard "Limehouse Blues", which he co-wrote with Douglas Furber. It was introduced by Teddie Gerard in the 1921 West End revue A to Z, but it was soon closely associated with Gertrude Lawrence and was subsequently recorded by several artists and used in films.

In the early 1930s, Braham turned to film music, working as musical director at Wembley Studios and writing music for such films as Money for Speed (1933).  He was managing director of Philip Braham and Campbell and was on the board of directors of the London Pavilion. He was known in the theatrical world as "Pa" and was noted for his hospitality. He died suddenly in his office in London, aged 52.

Notes

External links

Braham discography at All Music Guide
Braham's Victor recordings

1881 births
1934 deaths
English musical theatre composers
English male composers
20th-century British male musicians

redpajama_set_name: RedPajamaWikipedia


text:
Springville's Lauren Wilson breaks her cousin's single-game scoring record
By Jack Lido
Published: Jan. 13, 2022 at 12:51 AM CST
SPRINGVILLE, Iowa (KCRG) - "I didn't really know until thirty-ish (points). My coaches and teammates were all cheering me on."
Lauren Wilson flew past "thirty-ish," and broke the Springville single game scoring record with 40.
The record was previously held by Oriole great Rylee Menster, who also happens to be Wilson's cousin.
"I wanted her to be better than me!" Menster said. "She's already broken records of mine so that's happy I'm happy to see that and especially to keep it in the family that's an extra plus."
Menster now plays at Minnesota State, back in high school, her little cousin Lauren was a team manager.
"I practiced with them when I was a manager," Wilson said "I think growing up with them and just improving my game a lot."
"Feeding Lauren inside and Riley was one we always fed outside," said Springville head coach Christina Zaruba "Growing up watching Riley play, is what Lauren kind of was witness to and she has always loved being right behind her."
Like most great players, Wilson is unselfish. But anyone who's watched her play knows she's a special talent, capable of breaking records anytime she steps on the court.
"Sometime she has the ball in there and you're like what she gonna do there's no way she's gonna get around three defenders and she scores it," Menster said.
When asked if she could ever get more than 40, Wilson said to keep watching the Orioles.
"I don't know, I guess we'll see."


text:
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