9,263 research outputs found
The regional promotion through Moe about the potentialities and its limitations of Moe-Okoshi
No full textIn this paper, the athor tries to discuss Moe-Okoshi, a kind of regional promotion through Moe.
Moe is an emotion of attachment to fictitious objects, like characters in animations, comics, or computer games. Originally, Moe was a slang of the Japanese OTAKU culture, but is now globally known as a term for a more common affection for sweeties. In fact, the market of Moe is becoming larger and larger, and attracting much attention of those who have had no connection with the pop culture before. The regional promotion, the main subject of this paper, is also having an intimate relation with Moe. Many novel promotion plans, in which practitioners try to draw the public notice by means of the fascination or curiosity of Moe, have already appeared. Such plans are commonly called Moe-Okoshi.
However, a lot of practices which we have bundled with the name of Moe-Okoshi can be classified into some models in spite of the superficial similarities; the frequent use of pretty animation-like pictures and so on. And each model has a different feature and method. A few of them have already been highlighted as individual examples, but they have been given no opportunity to be discussed systematically in the context of regional promotion.
These circumstances duly considered, it may be very meaningful to analyze the strategies which a practitioner can take toward each Moe-Okoshi model. For this purpose, the author investigates the potentialities and limitations of Moe-Okoshi.論文(Article)departmental bulletin pape
Adaptive Blind Chip-Level Multiuser Detection in Multi-Rate Synchronous DS-CDMA System with Partial Loading
No full textThis paper addresses an adaptive blind multiuser detection strategy for a multi-rate direct sequence CDMA downlink channel. Specifically, two chip-level equalisation schemes, using either variable spreading length (VSL) or multi-code (MCD) multi-rate access modes, are proposed and tested. Both equalisers can be updated by minimising a hybrid CM/MSE cost function based on the constant modulus (CM) criterion for active users and a mean square error (MSE) criterion for inactive users in a partially loaded system. The BER performance abd the convergence of the proposed algorithms are analysed and compared through various simulations in both partially and fully loaded systems. Essentially, our results show that the VSL algorithm exhibits faster convergence than the MCD scheme while similar BER performance over different multi-path channels is achieved
DA-MoE: Addressing depth-sensitivity in graph-level analysis through mixture of experts
No full textGraph neural networks (GNNs) are gaining popularity for processing graph data. In real-world scenarios, graph data within the same dataset can vary significantly in scale. This variability leads to depth-sensitivity, where the optimal depth of GNN layers depends on the scale of the graph data. Empirically, fewer layers are sufficient for message passing in smaller graphs, while larger graphs typically require deeper networks to capture long-range dependencies and global features. However, existing methods generally use a fixed number of GNN layers to generate representations for all graphs, overlooking the depth-sensitivity issue in graph data. To address this challenge, we propose the depth adaptive mixture of expert (DA-MoE) method, which incorporates two main improvements to GNN backbone: 1) DA-MoE employs different GNN layers, each considered an expert with its own parameters. Such a design allows the model to flexibly aggregate information at different scales, effectively addressing the depth-sensitivity issue in graph data. 2) DA-MoE utilizes GNN to capture the structural information instead of the linear projections in the gating network. Thus, the gating network enables the model to capture complex patterns and dependencies within the data. By leveraging these improvements, each expert in DA-MoE specifically learns distinct graph patterns at different scales. Furthermore, comprehensive experiments on the TU dataset and open graph benchmark (OGB) have shown that DA-MoE consistently surpasses existing baselines on various tasks, including graph, node, and link-level analyses. The code are available at https://github.com/Celin-Yao/DA-MoE.No Full Tex
Township of Moe, Parishes of Moe & Yarragon, County of Buln Buln [cartographic material] /
No full textCadastral map of Moe in Victoria showing land holdings.; "Surveyed by John Lardner assistant surveyor."; In upper right corner: L.5352.; In upper left corner: M 498B.; In lower right corner: Price 1/-.; Also available in an electronic version via the internet at: http://nla.gov.au/nla.map-rm2740-29; Library copy has ms. annotations. Inset: Locality plan.Moe, Parishes of Moe & Yarragon, County of Buln Bul
Tim Moe Named U of M Crookston's Teambacker of the Year for 2013
No full textTollefson, Elizabeth. (2013). Tim Moe Named U of M Crookston's Teambacker of the Year for 2013. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/223036
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Dose estimates for the 1104 m APS storage ring
No full textThe estimated dose equivalent rates outside the shielded storage ring, and the estimated annual dose equivalent to members of the public due to direct radiation and skyshine from the ring, have been recalculated. The previous estimates found in LS-84 (MOE 87) and cited in the 1987 Conceptual Design Report of the APS (ANL 87) required revision because of changes in the ring circumference and in the proposed location of the ring with respect to the nearest site boundary. The values assumed for the neutron quality factors were also overestimated (by a factor of 2) in the previous computation, and the correct values have been used for this estimate. The methodology used to compute dose and dose rate from the storage ring is the same as that used in LS-90 (MOE 87a). The calculations assumed 80 cm thick walls of ordinary concrete (or the shielding equivalent of this) and a roof thickness of 1 meter of ordinary concrete. The circumference of the ring was increased to 1,104 m, and the closest distance to the boundary was taken as 140 m. The recalculation of the skyshine component used the same methodology as that used in LS-84
Accurate estimation of log MOE from non-destructive standing tree measurements
No full textKey message: A novel non-destructive method has been developed to predict modulus of elasticity (MOE) of logs using measurements taken from cores extracted from discs. The trees were felled and cut into logs to allow validation of our method; however, similar results would be obtained if the cores were extracted from standing trees. The method shows that a single core from breast height is sufficient to predict MOE of logs, allowing early grading and sorting of logs for optimal use and processing. • Context: Early estimation of log MOE allows efficient sorting and grading of logs which can improve the financial return and reduce wastage of wood. • Aims: This work aims to predict the MOE of logs accurately from measurements taken on cores obtained from trees. • Methods: The MOE of the logs was predicted using ultrasound measurements conducted on small segments obtained from cores using two different approaches: segment average and integral average. Sixty-eight trees from locally developed F1 and F2 hybrid pines (slash pine × Caribbean pine hybrids, Pinus elliottii var. elliottii × P. caribaea var. hondurensis (PEE × PCH cross)) were felled and cut into logs to validate the results. The Beam Identification by Non-destructive Grading (BING) method was used to measure a reference dynamic MOE (BING-MOE) for each log, and this was compared with the estimated log MOE. • Results: Strong correlations (r=0.79 to 0.91) between measured log MOE and estimated log MOE were obtained. This study revealed that a single core from the breast height (1.3 m) of a tree allows a good prediction of the log MOE. Tree height, spacing, and diameter had no significant effect on the log MOE prediction. The segment average MOE under predicts the BING-MOE, whereas the integral average method provides very little bias in the prediction. Furthermore, the prediction errors from the regression analysis for all logs were greater in the segment average method compared with the integral average method. • Conclusion: This paper presented a novel non-destructive evaluation method capable of predicting the MOE of the whole log by combining data available from a single breast-height core extracted from standing trees with our integral average MOE approach. The integral average method predicted the BING-MOE more accurately with lower bias compared with other existing tools without any complex equipment, analysis, and statistical calibration for segregating out individual trees or stands. The method can potentially be used to predict the log MOE of other tree species and extended to predict MOE of individual boards that can be sawn from a log
Recovering Reed-Solomon codes privately
No full textWe investigate the problems of privately repairing erasures and evaluating their linear combinations for Reed-Solomon codes with low communication bandwidths. We propose two approaches: one based on hiding subspaces used to form parity-check equations, and another based on multiplying parity-check equations with random polynomials. We also derive a lower bound on the repair bandwidth for the single erasure case under reasonable assumptions about the schemes being used and demonstrate the optimality of the proposed schemes for codes of specific lengths.Ministry of Education (MOE)National Research Foundation (NRF)Submitted/Accepted versionThis research is supported by the National Research Foundation, Singapore under its Strategic Capability Research Centres Funding Initiative, Ministry of Education, Singapore, under its MOE Academic Research Fund Tier 2 Grants MOE-T2EP20121-0007 and MOE-000623-00, Tier 1 Grant RG19/23, Australia Research Council (ARC) DECRA Grant DE180100768, Israel Science Foundation (ISF) under Grant 2462/24. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
No full textDeep learning for time series forecasting has seen significant advancements over the past decades. However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capable forecasting models in real-world applications. In response, we introduce Time-MoE, a scalable and unified architecture designed to pre-train larger, more capable forecasting foundation models while reducing inference costs. By leveraging a sparse mixture-of-experts (MoE) design, Time-MoE enhances computational efficiency by activating only a subset of networks for each prediction, reducing computational load while maintaining high model capacity. This allows Time-MoE to scale effectively without a corresponding increase in inference costs. Time-MoE comprises a family of decoder-only transformer models that operate in an auto-regressive manner and support flexible forecasting horizons with varying input context lengths. We pre-trained these models on our newly introduced large-scale data Time-300B, which spans over 9 domains and encompassing over 300 billion time points. For the first time, we scaled a time series foundation model up to 2.4 billion parameters, achieving significantly improved forecasting precision. Our results validate the applicability of scaling laws for training tokens and model size in the context of time series forecasting. Compared to dense models with the same number of activated parameters or equivalent computation budgets, our models consistently outperform them by large margin. These advancements position Time-MoE as a state-of-the-art solution for tackling real-world time series forecasting challenges with superior capability, efficiency, and flexibility. Code is available at https://github.com/Time-MoE/Time-MoEFull Tex
joycesim/MOE: Publication
No full text<p>A python library for modeling thermal evolution of magma oceans in terrestrial planets.</p>
<p>Accompanying manuscript: "Volatile and Trace Element Storage in a Crystallizing Martian Magma Ocean", S. J. Sim, M. M. Hirschmann and S. Hier-Majumder</p>
<p>Start with Simulations.py and make sure to have a data folder when running it as this will be where all the model outputs get saved.</p>
<p>If you do not want to run the simulations, please request for the zipped data file as it is too large to store on here. You can unzip and use that to generate the plots in the manuscript.</p>
<p>After all the data files are generated, run shhm_plots.py script to generate all the plots for the manuscript.</p>
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