An official website of the United States government
Feeding value improvement of corn-ethanol co-product and soybean hull by fungal fermentation: Fiber degradation and digestibility improvement
- Award ID(s):
- 1804702
- PAR ID:
- 10387698
- Date Published:
- Journal Name:
- Food and Bioproducts Processing
- Volume:
- 130
- Issue:
- C
- ISSN:
- 0960-3085
- Page Range / eLocation ID:
- 143 to 153
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
We consider the problem of fairly allocating a set of indivisible goods among n agents with additive valuations, using the popular fairness notion of maximin share (MMS). Since MMS allocations do not always exist, a series of works provided existence and algorithms for approximate MMS allocations. The Garg-Taki algorithm gives the current best approximation factor of (3/4 + 1/12n). Most of these results are based on complicated analyses, especially those providing better than 2/3 factor. Moreover, since no tight example is known of the Garg-Taki algorithm, it is unclear if this is the best factor of this approach. In this paper, we significantly simplify the analysis of this algorithm and also improve the existence guarantee to a factor of (3/4 + min(1/36, 3/(16n-4))). For small n, this provides a noticeable improvement. Furthermore, we present a tight example of this algorithm, showing that this may be the best factor one can hope for with the current techniques.more » « less
-
null (Ed.)We describe a data mining pipeline to convert data from educational systems into knowledge component (KC) models. In contrast to other approaches, our approach employs and compares multiple model search methodologies (e.g., sparse factor analysis, covariance clustering) within a single pipeline. In this preliminary work, we describe our approach's results on two datasets when using 2 model search methodologies for inferring item or KCs relations (i.e., implied transfer). The first method uses item covariances which are clustered to determine related KCs, and the second method uses sparse factor analysis to derive the relationship matrix for clustering. We evaluate these methods on data from experimentally controlled practice of statistics items as well as data from the Andes physics system. We explain our plans to upgrade our pipeline to include additional methods of finding item relationships and creating domain models. We discuss advantages of improving the domain model that go beyond model fit, including the fact that models with clustered item KCs result in performance predictions transferring between KCs, enabling the learning system to be more adaptive and better able to track student knowledge.more » « less
-
Large cloud providers including AWS, Azure, and Google Cloud offer two tiers of network services to their customers: one class uses the providers' private wide area networks (WAN-transit) to carry a customer's traffic as much as possible, and the other uses the public internet (inet-transit). Little is known about how each cloud provider configures its network to offer different transit services, how well these services work, and whether the quality of those services can be further improved. In this work, we conduct a large-scale study to answer these questions. Using RIPE Atlas probes as vantage points, we explore how traffic enters and leaves each cloud's WAN. In addition, we measure the access latency of the WAN-transit and the inet-transit service of each cloud and compare it with that of an emulated performance-based routing strategy. Our study shows that despite the cloud providers' intention to carry customers' traffic on its WAN to the maximum extent possible, for about 12% (Azure) and 13% (Google) of our vantage points, traffic exits the cloud WAN early at cloud edges more than 5000km away from the vantage points' nearest cloud edges. In contrast, more than 84% (AWS), 78% (Azure), and 81% (Google) of vantage points enter a cloud WAN within a 500km radius of their respective locations. Moreover, we find that cloud providers employ different routing strategies to implement the inet-transit service, leading to transit policies that may deviate from their advertised service descriptions. Finally, we find that a performance-based routing strategy can significantly reduce latencies in all three cloud providers for 4% to 85% of vantage point and cloud region pairs.more » « less
-
We study the problem of learning personalized decision policies from observational data while accounting for possible unobserved confounding in the data-generating process. Unlike previous approaches that assume unconfoundedness, i.e., no unobserved confounders affected both treatment assignment and outcomes, we calibrate policy learning for realistic violations of this unverifiable assumption with uncertainty sets motivated by sensitivity analysis in causal inference. Our framework for confounding-robust policy improvement optimizes the minimax regret of a candidate policy against a baseline or reference "status quo" policy, over an uncertainty set around nominal propensity weights. We prove that if the uncertainty set is well-specified, robust policy learning can do no worse than the baseline, and only improve if the data supports it. We characterize the adversarial subproblem and use efficient algorithmic solutions to optimize over parametrized spaces of decision policies such as logistic treatment assignment. We assess our methods on synthetic data and a large clinical trial, demonstrating that confounded selection can hinder policy learning and lead to unwarranted harm, while our robust approach guarantees safety and focuses on well-evidenced improvement.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.fbp.2021.09.013
