Building Familial Spaces for Transition and Work: From the Fantastic to the Normal

Transition for persons with disability is a process of negotiating difficult situations and barriers set by others and by systems. My strategies to overcome those barriers in my personal transitions through education systems and employment included the creations of familiar spaces in which group support plays a major role. This paper tracks my process through the familiar spaces and gives examples of encounters with barriers along my transition through hospital treatments to schools and then work

A Cross-Case Analysis of Migrant Chuukese Families in Hawai‘i and Their Children with Special Needs.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Nonparametric Rank Tests for Non-stationary Panels

This study develops new rank tests for panels that include panel unit root tests as a special case. The tests are unusual in that they can accommodate very general forms of both serial and cross-sectional dependence, including cross-unit cointegration, without the need to specify the form of dependence or estimate nuisance parameters associated with the dependence. The tests retain high power in small samples, and in contrast to other tests that accommodate cross-sectional dependence, the limiting distributions are valid for panels with finite cross-sectional dimensions.Nonparametric rank tests, unit roots, cointegration, cross-sectional dependence

Incremental (1−ϵ)(1-\epsilon)-approximate dynamic matching in O(poly(1/ϵ))O(poly(1/\epsilon)) update time

In the dynamic approximate maximum bipartite matching problem we are given bipartite graph $G$ undergoing updates and our goal is to maintain a matching of $G$ which is large compared the maximum matching size $\mu(G)$. We define a dynamic matching algorithm to be $\alpha$ (respectively $(\alpha, \beta)$)-approximate if it maintains matching $M$ such that at all times $|M | \geq \mu(G) \cdot \alpha$ (respectively $|M| \geq \mu(G) \cdot \alpha - \beta$). We present the first deterministic $(1-\epsilon )$-approximate dynamic matching algorithm with $O(poly(\epsilon ^{-1}))$ amortized update time for graphs undergoing edge insertions. Previous solutions either required super-constant [Gupta FSTTCS'14, Bhattacharya-Kiss-Saranurak SODA'23] or exponential in $1/\epsilon$ [Grandoni-Leonardi-Sankowski-Schwiegelshohn-Solomon SODA'19] update time. Our implementation is arguably simpler than the mentioned algorithms and its description is self contained. Moreover, we show that if we allow for additive $(1, \epsilon \cdot n)$-approximation our algorithm seamlessly extends to also handle vertex deletions, on top of edge insertions. This makes our algorithm one of the few small update time algorithms for $(1-\epsilon )$-approximate dynamic matching allowing for updates both increasing and decreasing the maximum matching size of $G$ in a fully dynamic manner

Incremental (1-?)-Approximate Dynamic Matching in O(poly(1/?)) Update Time

In the dynamic approximate maximum bipartite matching problem we are given bipartite graph G undergoing updates and our goal is to maintain a matching of G which is large compared the maximum matching size ?(G). We define a dynamic matching algorithm to be ? (respectively (?, ?))-approximate if it maintains matching M such that at all times |M | ? ?(G) ? ? (respectively |M| ? ?(G) ? ? - ?). We present the first deterministic (1-?)-approximate dynamic matching algorithm with O(poly(?^{-1})) amortized update time for graphs undergoing edge insertions. Previous solutions either required super-constant [Gupta FSTTCS\u2714, Bhattacharya-Kiss-Saranurak SODA\u2723] or exponential in 1/? [Grandoni-Leonardi-Sankowski-Schwiegelshohn-Solomon SODA\u2719] update time. Our implementation is arguably simpler than the mentioned algorithms and its description is self contained. Moreover, we show that if we allow for additive (1, ??n)-approximation our algorithm seamlessly extends to also handle vertex deletions, on top of edge insertions. This makes our algorithm one of the few small update time algorithms for (1-?)-approximate dynamic matching allowing for updates both increasing and decreasing the maximum matching size of G in a fully dynamic manner. Our algorithm relies on the weighted variant of the celebrated Edge-Degree-Constrained-Subgraph (EDCS) datastructure introduced by [Bernstein-Stein ICALP\u2715]. As far as we are aware we introduce the first application of the weighted-EDCS for arbitrarily dense graphs. We also present a significantly simplified proof for the approximation ratio of weighed-EDCS as a matching sparsifier compared to [Bernstein-Stein], as well as simple descriptions of a fractional matching and fractional vertex cover defined on top of the EDCS. Considering the wide range of applications EDCS has found in settings such as streaming, sub-linear, stochastic and more we hope our techniques will be of independent research interest outside of the dynamic setting

Demo Abstract: EmuLink - Heterogeneous Sensor Network Simulation in Cooja

WISENET (NES)Promo

Introduction: Forensic Fail

Background: About 60% of Pheochromocytoma (PCC) and Paraganglioma (PGL) patients have either germline or somatic mutations in one of the 12 proposed disease causing genes; SDHA, SDHB, SDHC, SDHD, SDHAF2, VHL, EPAS1, RET, NF1, TMEM127, MAX and H-RAS. Selective screening for germline mutations is routinely performed in clinical management of these diseases. Testing for somatic alterations is not performed on a regular basis because of limitations in interpreting the results. Aim: The purpose of the study was to investigate genetic events and phenotype correlations in a large cohort of PCC and PGL tumours. Methods: A total of 101 tumours from 89 patients with PCC and PGL were re-sequenced for a panel of 10 disease causing genes using automated Sanger sequencing. Selected samples were analysed with Multiplex Ligation-dependent Probe Amplification and/or SNParray. Results: Pathogenic genetic variants were found in tumours from 33 individual patients (37%), 14 (16%) were discovered in constitutional DNA and 16 (18%) were confirmed as somatic. Loss of heterozygosity (LOH) was observed in 1/1 SDHB, 11/11 VHL and 3/3 NF1-associated tumours. In patients with somatic mutations there were no recurrences in contrast to carriers of germline mutations (P = 0.022). SDHx/VHL/ EPAS1 associated cases had higher norepinephrine output (P = 0.03) and lower epinephrine output (P<0.001) compared to RET/NF1/H-RAS cases. Conclusion: Somatic mutations are frequent events in PCC and PGL tumours. Tumour genotype may be further investigated as prognostic factors in these diseases. Growing evidence suggest that analysis of tumour DNA could have an impact on the management of these patients