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NATE VAYNE CLASSIC R Group

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Jacob Wilson
Jacob Wilson

Photos Concept With Maze Rar


Inspired by their work on the soundtrack for the 2016 film Swiss Army Man, band members Andy Hull and Robert McDowell set out to create an album that was a reset from the band's previous studio albums Cope and Hope (both 2014). A Black Mile to the Surface is an alternative rock and Americana record, interweaving a concept album set in a South Dakota mining town with reflections on Hull's young daughter. The album received generally positive reviews from music critics, who praised Hull's storytelling and the album's cinematic stylings. Commercially, it performed well, debuting at number 33 on the US Billboard 200.




photos concept with maze rar



A Black Mile to the Surface has been classified as indie rock, alternative rock,[12] and Americana, with comparisons to Fleet Foxes,[13] Band of Horses,[14][15] and Mumford & Sons.[15][16] It began life as a concept album set in the mining town of Lead, South Dakota, the home of the Sanford Underground Research Facility (SURF).[17] As the album developed, the South Dakota narrative began to interweave with Hull's complicated emotions about the birth of his daughter, and his associated feelings of life and death.[18] The opening track, "The Maze", was written a month after the birth of Hull's daughter.[19] While the first four tracks set "a light and dark theme", the narrative part of the record begins on "The Alien", which follows a boy from his childhood abuse into his adult life.[6]


Day 13 is looking at a series of buses that are running on their own time cycles, and trying to find times where the buses arrive in certain patterns. It brings in a somewhat obscure number theory concept called the Chinese Remainder Theorem, which has to do with solving a series of modular linear equations that all equal the same value.


In a similar category to hidden object games are "find the difference" games, where someone is presented with two photos that look similar with the goal of finding all the ways the photos are unique from each other. Tiny Lands takes this concept and adds a third dimension.


Consider a collection of finitely many polygons in $\mathbb C$, such that for each side of each polygon, there exists another side of some polygon in the collection (possibly the same) that is parallel and of equal length. A translation surface is the surface formed by identifying these opposite sides with one another. The $\mathcalH(1, 1)$ stratum consists of genus two translation surfaces with two singularities of order one. A circle packing corresponding to a graph $G$ is a configuration of disjoint disks such that each vertex of $G$ corresponds to a circle, two disks are externally tangent if and only if their vertices are connected by an edge in $G$, and $G$ is a triangulation of the surface. It is proven that for certain circle packings on $\mathcalH(1, 1)$ translation surfaces, there are only a finite number of ways the packing can vary without changing the contacts graph, if two disks along the slit are fixed in place. These variations can be explicitly characterized using a new concept known as \textitsplitting bigons. Finally, the uniqueness theorem is generalized to a specific type of translation surfaces with arbitrary genus $g \geq 2$.


Physarum Polycephalum is a unicellular slime mold that has been intensely studied due to its ability to solve mazes, find shortest paths, generate Steiner trees, share knowledge, remember past events, and its applications to unconventional computing. The CELL model is a unicellular automaton introduced in the recent work of Gunji et al. in 2008, that models Physarum's amoeboid motion, tentacle formation, maze solving, and network creation. In the present paper, we extend the CELL model by spawning multiple CELLs, allowing us to understand the interactions between multiple cells, and in particular, their mobility, merge speed, and cytoplasm mixing. We conclude the paper with some notes about applications of our work to modeling the rise of present day civilization from the early nomadic humans and the spread of trends and information around the world. Our study of the interactions of this unicellular organism should further the understanding of how Physarum Polycephalum communicates and shares information.


Unsupervised generative models have been a popular approach to representing molecules. These models extract salient molecular features to create compact vec- tors that can be used for downstream prediction tasks. However, current generative models for molecules rely mostly on structural features and do not fully capture global biochemical features. Here, we propose a multi-view generative model that integrates low-level structural features with global chemical properties to create a more holistic molecular representation. In proof-of-concept analyses, compared to purely structural latent representations, multi-view latent representations improve model accuracy on various tasks when used as input to feed-forward prediction networks. For some tasks, simple models trained on multi-view representations perform comparably to more complex supervised methods. Multi-view represen- tations are an attractive method to improve representations in an unsupervised manner, and could be useful for prediction tasks, particularly in contexts where data is limited.


A necessary characteristic for the deployment of deep learning models in real world applications is resistance to small adversarial perturbations while maintaining accuracy on non-malicious inputs. While robust training provides models that exhibit better adversarial accuracy than standard models, there is still a significant gap in natural accuracy between robust and non-robust models which we aim to bridge. We consider a number of ensemble methods designed to mitigate this performance difference. Our key insight is that model trained to withstand small attacks, when ensembled, can often withstand significantly larger attacks, and this concept can in turn be leveraged to optimize natural accuracy. We consider two schemes, one that combines predictions from several randomly initialized robust models, and the other that fuses features from robust and standard models.


In this expository paper we discuss a relatively new counterfeit coin problem with an unusual goal: maintaining the privacy of, rather than revealing, counterfeit coins in a set of both fake and real coins. We introduce two classes of solutions to this problem --- one that respects the privacy of all the coins and one that respects the privacy of only the fake coins --- and give several results regarding each. We describe and generalize 6 unique strategies that fall into these two categories. Furthermore, we explain conditions for the existence of a solution, as well as showing proof of a solution's optimality in select cases. In order to quantify exactly how much information is revealed by a given solution, we also define the revealing factor and revealing coefficient; these two values additionally act as a means of comparing the relative effectiveness of different solutions. Most importantly, by introducing an array of new concepts, we lay the foundation for future analysis of this very interesting problem, as well as many other problems related to privacy and the transfer of information.


Given a graph, an acyclic orientation of the edges determines a partial ordering of the vertices. This partial ordering has a number of linear extensions, i.e. total orderings of the vertices that agree with the partial ordering. The purpose of this paper is twofold. Firstly, properties of the orientation that induces the maximum number of linear extensions are investigated. Due to similarities between the optimal orientation in simple cases and the solution to the Max-Cut Problem, the possibility of a correlation is explored, though with minimal success. Correlations are then explored between the optimal orientation of a graph G and the comparability graphs with the minimum number of edges that contain G as a subgraph, as well as to certain graphical colorings induced by the orientation. Specifically, small cases of non-comparability graphs are investigated and compared to the known results for comparability graphs. We then explore the optimal orientation for odd anti-cycles and related graphs, proving that the conjectured orientations are optimal in the odd anti-cycle case. In the second part of this paper, the above concepts are extended to random graphs, that is, graphs with probabilities associated with each edge. New definitions and theorems are introduced to create a more intuitive system that agrees with the discrete case when all probabilities are 0 or 1, though complete results for this new system would be much more difficult to prove.


Engaging students in practicing a wide range of problems facilitates their learning. However, generating fresh problems that have specific characteristics, such as using a certain set of concepts or being of a given difficulty level, is a tedious task for a teacher. In this paper, we present PuzzleJAR, a system that is based on an iterative constraint-based technique for automatically generating problems. The PuzzleJAR system takes as parameters the problem definition, the complexity function, and domain-specific semantics-preserving transformations. We present an instantiation of our technique with automated generation of Sudoku and Fillomino puzzles, and we are currently extending our technique to generate Python programming problems. Since defining complexities of Sudoku and Fillomino puzzles is still an open research question, we developed our own mechanism to define complexity, using machine learning to generate a function for difficulty from puzzles with already known difficulties. Using this technique, PuzzleJAR generated over 200,000 Sudoku puzzles of different sizes (9x9, 16x16, 25x25) and over 10,000 Fillomino puzzles of sizes ranging from 2x2 to 16x16. .


Don't be scared by their long names; a lot of these are actually pretty intuitive and easy to grasp. Of course, since you're not doing any actual modelling in this unit, you might be tempted to skip ahead, and that's completely fine! Just know that understanding these concepts well will help you a lot in the long run, and proceeding through tutorials in order will build a strong foundation for you to build on. Prior knowledge also plays a huge part in this, so if you're coming from other 3D software, you should already be familiar with these concepts. 350c69d7ab


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