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1. The Resilience of Dene Generative Geography, Considering "the Nen' Yese' Ensemble."

   Kari, James (October 2019, Alaska journal of anthropology)

   ¶Kari 2019 introduces a theory of Na-Dene prehistory, "the Proto-Dene Lex Loci" that derives from Lexware dictionary files and cumulative place name for seven adjacent Alaska Dene languages. To investigate Dene prehistory in the Copper River and circum–Glacial Lake Atna (GLA) region, we discuss a selection of 67 Dene place names from seven Dene languages in four Alaska river basins. Dene geolinguistic data are information-rich; highly noticeable are various watershed tenure devices (hydronymic districts, patterned duplications, geoduplicates) that reflect ancient Dene vernacular collaborations to facilitate landscape recognition. ¶As few as three Dene place names establish that the Dene occupation of Tanana River preceded the initial Dene names in the Copper River Basin. A group of about 20 Ahtna names termed "the Nen’ Yese’ Ensemble (NYE)" are overtly descriptive of the geology and hydrology at the Tyone Spillway. One subset of 9 to 10 names plausibly were coined during "a first season on the NYE" (starting at Hogan Hill, K'ey Tsaaygha). Another subgroup of 11 names has four pairs of patterned duplications that indicate spatial-temporal seriation. Plausibly this group of names were coined prior to, during, and after the names of the NYE (11,000 to 9000 years ago) in the time frame of major GLA drainage shift. As geological, paleo-ecological records for GLA become refined, geo-temporal benchmarks may be attainable. Alaska Dene geolinguistic data sets can broaden discussions of Alaska and Beringian prehistory as well as theories of language change. 

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2. Northern Dene Constellations as Worldview Projections with Case Studies from the Ahtna, Gwich’in, and Sahtúot’ı̨nę

   https://doi.org/10.3368/aa.56.2.1  

   Cannon, C.; Justin, W.; Herbert, P.; Hubbard, C.; Neyelle, C. (January 2019, Arctic anthropology)

   null (Ed.)

   The sky is routinely overlooked in Northern Dene ethnology as a meaningful domain of linguistic and cultural knowledge. However, a decade of comparative ethnological research in Alaska and Canada has shown that Dene stellar knowledge is largely tied to sacred and covert knowledge systems. In this paper, we describe an Ahtna, Gwich'in, and Sahtúot'ı̨nę constellation identified as the incarnated spirit of an ancient Traveler-Transformer figure who circled the world in Distant Time. Although this Traveler is widely known in mythology, his enigmatic transformation to the sky embodies a specialized domain of knowledge rooted in the traditional beliefs and practices of medicine people. This "Traveler" constellation is not only a world custodian and archetype of an idealized medicine person, but it is also a teacher, ally, gamekeeper, and the embodiment of the world. We identify variations of this constellation throughout the Northern Dene region. 

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3. A Newly Documented Whole‑Sky Circumpolar Constellation in Alaskan Gwich’in

   Cannon, Chris; Holton, Gary (January 2014, Arctic anthropology)

   Traditional approaches to the documentation of indigenous astronomical knowledge often assume a one-to-one or near one-to-one correspondence between indigenous and classical constellation terms. Only a single constellation, equated with the Big Dipper, is robustly attested across the Northern Dene languages. Here we provide evidence from Gwich’in (Dene) that shows that the equation of this single Gwich’in constellation with the classical constellation is only partial. The Gwich’in constellation yahdii is actually a whole-sky constellation that maps nearly the entire sky. The Big Dipper is the tail of yahdii, and the remaining stars in the constellation are identified by other Gwich’in body-part terms, forming a unified functional conceptualization of the sky. Our work demonstrates how observational and cultural biases can prejudice the description of cultural astronomy. Dene astronomy is much richer than has been previously claimed and provides the first well-documented indigenous example of a whole-sky constellation. 

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4. Cross-Referenced Dictionaries and the Limits of Write Optimization

   https://doi.org/10.1137/1.9781611974782.99  

   Afshani, Peyman; Bender, Michael; Farach-Colton, Martin; Fineman, Jeremy; Goswami, Mayank; Tsai, Meng-Tsung Tsai (January 2017, Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms)

   Dictionaries remain the most well studied class of data structures. A dictionary supports insertions, deletions, membership queries, and usually successor, predecessor, and extract-min. In a RAM, all such operations take O(log n) time on n elements. Dictionaries are often cross-referenced as follows. Consider a set of tuples {〈ai,bi,ci…〉}. A database might include more than one dictionary on such a set, for example, one indexed on the a ‘s, another on the b‘s, and so on. Once again, in a RAM, inserting into a set of L cross-referenced dictionaries takes O(L log n) time, as does deleting. The situation is more interesting in external memory. On a Disk Access Machine (DAM), B-trees achieve O(logB N) I/Os for insertions and deletions on a single dictionary and K-element range queries take optimal O(logB N + K/B) I/Os. These bounds are also achievable by a B-tree on cross-referenced dictionaries, with a slowdown of an L factor on insertion and deletions. In recent years, both the theory and practice of external- memory dictionaries has been revolutionized by write- optimization techniques. A dictionary is write optimized if it is close to a B-tree for query time while beating B-trees on insertions. The best (and optimal) dictionaries achieve a substantially improved insertion and deletion cost of amortized I/Os on a single dictionary while maintaining optimal O(log1+B∊ N + K/B)- I/O range queries. Although write optimization still helps for insertions into cross-referenced dictionaries, its value for deletions would seem to be greatly reduced. A deletion into a cross- referenced dictionary only specifies a key a. It seems to be necessary to look up the associated values b, c … in order to delete them from the other dictionaries. This takes Ω(logB N) I/Os, well above the per-dictionary write-optimization budget of So the total deletion cost is In short, for deletions, write optimization offers an advantage over B-trees in that L multiplies a lower order term, but when L = 2, write optimization seems to offer no asymptotic advantage over B-trees. That is, no known query- optimal solution for pairs of cross-referenced dictionaries seem to beat B-trees for deletions. In this paper, we show a lower bound establishing that a pair of cross-referenced dictionaries that are optimal for range queries and that supports deletions cannot match the write optimization bound available to insert-only dictionaries. This result thus establishes a limit to the applicability of write-optimization techniques on which many new databases and file systems are based. Read More: http://epubs.siam.org/doi/10.1137/1.9781611974782.99 

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5. Learning to Refine 3D Human Pose Sequences

   Mei, Jieru; Chen, Xingyu; Wang, Chunyu; Yuille, Alan; Lan, Xuguang; Zeng, Wenjun (January 2019, 3DV)

   We present a basis approach to refine noisy 3D human pose sequences by jointly projecting them onto a non-linear pose manifold, which is represented by a number of basis dictionaries with each covering a small manifold region. We learn the dictionaries by jointly minimizing the distance between the original poses and their projections on the dictionaries, along with the temporal jittering of the projected poses. During testing, given a sequence of noisy poses which are probably off the manifold, we project them to the manifold using the same strategy as in training for refinement. We apply our approach to the monocular 3D pose estimation and the long term motion prediction tasks. The experimental results on the benchmark dataset shows the estimated 3D poses are notably improved in both tasks. In particular, the smoothness constraint helps generate more robust refinement results even when some poses in the original sequence have large errors. 

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