Challenging minds daycare: Challenging Minds | SUMMERSET SD

Challenging Minds | SUMMERSET SD

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About the Provider

Description: Challenging Minds, Inc. allows children to discover, pretend, dawdle, and play. The program is child-centered, and for the most part, children driven.It does not place values of good or bad on children’s efforts or the products of those efforts. The goal is to encourage children to try new things, recognize their efforts and facilitate and reward their successes.

Program and Licensing Details

• License Number:
  29251
• Capacity:
  77
• Enrolled in Subsidized Child Care Program:
  No
• District Office:
  Child Care Licensing District #1
• District Office Phone:
  1-800-644-2914 (Note: This is not the facility phone number.)

Location Map

Inspection/Report History

Hugs Kollege Inc – Bronx NY School-Age Child Care

Where possible, ChildcareCenter provides inspection reports as a service to families. This information is deemed reliable,
but is not guaranteed. We encourage families to contact the daycare provider directly with any questions or concerns,
as the provider may have already addressed some or all issues. Reports can also be verified with your local daycare licensing office.

If you are a provider and you believe any information is incorrect, please contact us. We will research your concern and make corrections accordingly.

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only the opinion of the writer. We ask that users follow our
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the review and decide the appropriate next step. Please note – we will not remove a review simply because it is
negative. Providers are welcome to respond to parental reviews, however we ask that they identify themselves as
the provider.

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Challenged Minds Learning Center Inc

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About the Provider

Description: Challenged Minds Learning Center is a child center that offers early education to the Fort Myers, FL area. We provide child care learning service to all children from 1 year old to 12 years old. We are open all year round to provide a loving environment for your child.

Additional Information: Provider First Licensed on: 1/7/10;

Program and Licensing Details

• License Number:
  C20LE0060
• Capacity:
  165
• Enrolled in Subsidized Child Care Program:
  Yes
• Type of Care:
  VPK Provider; After School;Before School;Drop In;Food Served;Full Day;Half Day;Infant Care;Transportation
• Current License Expiration Date:
  Jan 06, 2023
• District Office:
  Judicial Circuit 20
  2295 Victoria Avenue
  Ft. Myers, Florida 33906
• District Office Phone:
  (239) 895-0331 (Note: This is not the facility phone number. )
• Licensor:
  Chemenda Sawyer

Location Map

Inspection/Report History

Hugs Kollege Inc – Bronx NY School-Age Child Care

Where possible, ChildcareCenter provides inspection reports as a service to families. This information is deemed reliable,
but is not guaranteed. We encourage families to contact the daycare provider directly with any questions or concerns,
as the provider may have already addressed some or all issues. Reports can also be verified with your local daycare licensing office.

If you are a provider and you believe any information is incorrect, please contact us. We will research your concern and make corrections accordingly.

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Reviews

Be the first to review this childcare provider.
Write a review about Challenged Minds Learning Center Inc. Let other families know what’s great, or what could be improved.
Please read our brief review guidelines to make your review as helpful as possible.

Email address (will not be published):

Display name:

Which best describes your experience?:

Select from belowI have used this provider for more than 6 monthsI have used this provider for less than 6 monthsI have toured this provider’s facility, but have not used its servicesI am the ownerI am an employeeOther

Rating (1=poor, 5=excellent):

Select your Rating1 star2 star3 star4 star5 star

Review Policy:

ChildcareCenter.us does not actively screen or monitor user reviews, nor do we verify or edit content. Reviews reflect
only the opinion of the writer. We ask that users follow our
review guidelines. If you see a review that does not reflect these guidelines, you can email us. We will assess
the review and decide the appropriate next step. Please note – we will not remove a review simply because it is
negative. Providers are welcome to respond to parental reviews, however we ask that they identify themselves as
the provider.

Write a Review

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Movie “Dangerous Minds”

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suitable for your screen

• Description

• Persons and teams

• 7 reviews

1995, Drama, Foreign, USA

Lou Ann Jackson’s outward fragility hides an experienced officer in the elite Marine Corps. Leaving the service, she decided to devote herself to teaching. Instead of hand-to-hand combat, Ann took up teaching English. True, she had to keep her fighting skills at the ready, since her listeners are very difficult teenagers.

The film Dangerous Minds has a real basis. It was created based on the autobiographical book by Lou Ann Jackson. The main character was embodied by actress Michelle Pfeiffer. The plot is based on a topic that is always and everywhere relevant – how to find a common language with uncontrollable teenagers. It is of particular interest if it is presented in the form of a well-made film. And here, director John N. Smith, a former Oscar nominee, did not disappoint the audience.

The picture, with musical accompaniment by rapper Coolio – it’s about the hit Gangsta’s Paradise, which was nominated for the MT Katal Award as the best song of the film – burst into the minds of the audience. This is evidenced by rental fees – they amounted to 179.5 million dollars against 23 million of the budget.

Confirmation of the audience’s love is the nomination of the film for the MTV channel award. Also, Michelle Pfeiffer herself was twice nominated for this award – for the best role and as the most desirable woman.

Lou Ann Jackson’s outward fragility hides an experienced officer in the elite Marine Corps. Leaving the service, she decided to devote herself to teaching. Instead of hand-to-hand combat, Ann took up teaching English. True, she had to keep her fighting skills at the ready, since her listeners are very difficult teenagers.
The film Dangerous Minds has a real basis. It was created by mo

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Available on devices

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• Smart TV

• Consoles

• Attachments

Translation

English, Russian

Subtitles

English, Russian, Turkish

Discovered a universal method for sorting complex information This information would help you better understand your competitors.

This is an example of the “nearest neighbor” problem that is widely studied in computer science. Given a set of information and a new point, and you want to find which of the existing points it will be closest to? This question comes up in many everyday situations in areas such as genome research, image searches, and recommendations on Spotify.

But, unlike the cafe example, questions about the nearest neighbor are often very complex. Over the past few decades, some of the greatest minds in computer science have been looking for the best way to solve a problem like this. In particular, they tried to deal with the complications that arise from the fact that different datasets can have very different definitions of the “proximity” of points to each other.

Now, a team of computer scientists have come up with a whole new way to solve the nearest neighbor problem. In a pair of papers, five scientists detailed the first generalized method for solving the nearest neighbor problem for complex data.

“This is the first result to cover a large set of spaces with a single algorithmic technique,” said Piotr Indik, a computer scientist at the Massachusetts Institute of Technology and an influential figure in the development of this problem.

Distance difference

We are so attached to a single way of measuring distance that it is easy to lose sight of the possibility of other ways. We usually measure distance like Euclidean – as a straight line between two points. However, there are situations in which other definitions make more sense. For example, the Manhattan distance forces you to make 9 turns.0 degrees, as if moving along a rectangular network of roads. To measure a distance that could be 5 kilometers in a straight line, you may need to cross a city in one direction for 3 kilometers and then in the other direction for 4.

It is also possible to think of distances in a non-geographical sense. What is the distance between two people on a social network, or two films, or two genomes? In these examples, the distance indicates the degree of similarity.

There are dozens of distance metrics, each of which is suitable for a specific task. Take, for example, two genomes. Biologists compare them using the “Levenshtein distance”, or edit distance. The editing distance between two genome sequences is the number of additions, deletions, insertions, and substitutions required to change one of them into the other.

Edit distance and Euclidean distance are two different concepts of distance, and one cannot be reduced to the other. This incompatibility exists for many pairs of distance metrics, and is a hurdle for computer scientists trying to develop nearest neighbor algorithms. This means that an algorithm that works for one type of distance will not work for another – or rather, it was until now, until a new kind of search appeared.

Alexander Andoni

Squaring the circle

The standard approach to finding the nearest neighbor is to divide the existing data into subgroups. If, say, your data describes the location of cows in a pasture, then you can enclose each of them in a circle. Then we place a new cow in the meadow and ask the question: in which of the circles does she fall? It is practically guaranteed that the nearest neighbor of the new cow will be in the same circle.

Then repeat the process. Divide the circles into subcircles, divide these cells further, and so on. As a result, there is a cell with only two points: the existing one and the new one. This existing point will be your nearest neighbor.

Above – division of data into cells. Below – using an expanding graph

Algorithms draw cells, good algorithms draw them quickly and well – that is, so that there is no situation in which a new cow falls into a circle, and its nearest neighbor ends up in another circle. “In these cells, we want near points to be more often in the same disk as each other, and far points rarely,” said Ilya Razenshtein, a computer scientist at Microsoft Research, co-author of the new work, which also contributed to Alexander Andoni from Columbia University. , Assaf Naor of Princeton, Alexander Nikolov of the University of Toronto and Eric Weingarten of Columbia University.

For many years, computer scientists have come up with various algorithms for drawing such cells. For low-dimensional data – where one point is determined by a small number of values, for example, the location of a cow in a pasture – algorithms create the so-called. “Voronoi diagrams” that accurately answer the nearest neighbor question.

For multidimensional data, where one point can be defined by hundreds or thousands of values, Voronoi diagrams require too much computing power. Instead, programmers draw cells using “locally sensitive hashing” first described by Indik and Rajiv Motwani in 1998. LCH algorithms draw cells randomly. Therefore, they are faster, but less accurate – instead of finding the exact nearest neighbor, they guarantee to find a point that is no further than a given distance from the real nearest neighbor. Think of it like a Netflix movie recommendation that isn’t perfect, but good enough.

Since the late 1990s, computer scientists have come up with such LCH algorithms that give approximate solutions to the nearest neighbor problem for given distance metrics. The algorithms were very specialized, and an algorithm developed for one distance metric could not be applied to another.

“You can come up with a very efficient algorithm for Euclidean or Manhattan distance, for some specific cases. But we did not have an algorithm that would work on a large class of distances, ”said Indik.

Since algorithms that worked with one metric could not be used for another, programmers came up with a workaround strategy. Through a process called “embedding,” they overlay a metric for which they didn’t have a good algorithm onto a metric for which they did. However, the correspondence of metrics was usually inaccurate – something like a square peg in a round hole. In some cases, the embedding did not take place at all. It was necessary to come up with a universal way to answer the question about the nearest neighbor.

Ilya Razenstein

An unexpected result

In the new work, scientists abandoned the search for special algorithms. Instead, they asked a broader question: what prevents the development of an algorithm on a certain distance metric?

They decided that the reason for this was the unpleasant situation associated with the expanding graph, or expander. An expander is a special kind of graph, a set of points connected by edges. Graphs have their own distance metric. The distance between two points on a graph is the minimum number of lines required to go from one point to another. You can imagine a graph representing the connections between people in a social network, and then the distance between people will be equal to the number of connections separating them. If, for example, Julian Moore has a boyfriend who has a boyfriend whose boyfriend is Kevin Bacon, then the distance from Moore to Bacon is three.

An expander is a special type of graph that has two seemingly contradictory properties. It is highly connected, meaning two points cannot be separated without cutting several edges. At the same time, most of the points are connected to a very small number of others. Because of this, most of the points are far from each other.

The unique combination of these properties – good connectivity and a small number of edges – makes it impossible to perform a fast nearest neighbor search on expanders. Any attempts to divide it into cells will separate points that are close to each other.

“Any way of cutting the expander into two parts requires cutting many edges that separate many closely spaced vertices,” said Weingarten, co-author of the paper.

By the summer of 2016, Andoni, Niokoloff, Razenshtein, and Wanjgarten knew that it was impossible to come up with a good algorithm for finding the nearest neighbor for expanders. But they wanted to prove that such algorithms could not be built for many other distance metrics—metrics for which computer scientists have been stymied when looking for good algorithms.

To find a proof of the impossibility of such algorithms, they had to find a way to embed the expander metric into other distance metrics. With this method, they could determine that other metrics have properties similar to those of expanders, which prevents them from making good algorithms for them.

Eric Weingarten

Four computer scientists went to Assaf Naor, a mathematician at Princeton University whose previous work was well suited to the topic of expanders. They asked him to help prove that expanders could be built into different types of metrics. Naor quickly returned with an answer – but not the one they expected from him.

“We asked Assaf to help us with this claim, and he proved otherwise,” Andoni said.

Naor proved that expanders do not fit into the large class of distance metrics known as “normed spaces” (which includes such metrics as Euclidean and Manhattan space). Based on Naor’s evidence, the scientists followed the following logical chain: if expanders do not fit into the distance metric, then there must be a good way to break them into cells (since, as they have shown, the only barrier to this is the properties of the expanders).