Epistemic agency for costructuring expansive knowledge-building practices

As a hallmark of authentic science practices, students need to enact epistemic agency to shape/reshape the key aspects of their inquiry work as a collaborative community. This study elaborates an emergent temporal mechanism for engaging students\u27 epistemic agency: “reflective structuration” by which members of a classroom community coconstruct ever-evolving inquiry directions and group structures as their collective inquiry work proceeds. Using an interactional ethnography method, we examined how students (n = 22) in a Grade 5 classroom coconstructed shared inquiry directions and flexible group structures to guide their sustained inquiry about human body systems over 7 months supported by a collaborative online environment. Rich data were collected to trace the work of the eye inquiry group as a telling case. With their teacher\u27s support, students took agentic moves to construct an evolving set of wondering areas as a way to frame what their whole class needed to investigate. Flexible groups, such as the eye inquiry group, emerged and evolved in the various areas, leading to progressively deepening inquiry and extensive idea exchanges among students. Implications for research and practice are discussed

The Relation of Gas Seepage and Coal Body Damage Under the True Three Dimension Stress

AbstractThe law of gas flow is the basis of coal and gas outburst prevention and gas drainage rate increase. Thus the relation of gas seepage and coal body damage under the true three dimension stress is studied. The research results show that when volume stress is not change with the change of pore pressure the permeability of coal body change with the parabolic law. The relation of damage and permeability of coal body is established. It can be known that during load of coal body the greater the damage occur, the more the permeability of coal body after unload decrease than primary permeability of coal body

Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio

Automatic designing computationally efficient neural networks has received much attention in recent years. Existing approaches either utilize network pruning or leverage the network architecture search methods. This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs, so that under each network configuration, one can estimate the FLOPs utilization ratio (FUR) for each layer and use it to determine whether to increase or decrease the number of channels on the layer. Note that FUR, like the gradient of a non-linear function, is accurate only in a small neighborhood of the current network. Hence, we design an iterative mechanism so that the initial network undergoes a number of steps, each of which has a small `adjusting rate' to control the changes to the network. The computational overhead of the entire search process is reasonable, i.e., comparable to that of re-training the final model from scratch. Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach, which consistently outperforms the pruning counterpart. The code is available at https://github.com/danczs/NetworkAdjustment

Bayes Risk Transducer: Transducer with Controllable Alignment Prediction

Automatic speech recognition (ASR) based on transducers is widely used. In training, a transducer maximizes the summed posteriors of all paths. The path with the highest posterior is commonly defined as the predicted alignment between the speech and the transcription. While the vanilla transducer does not have a prior preference for any of the valid paths, this work intends to enforce the preferred paths and achieve controllable alignment prediction. Specifically, this work proposes Bayes Risk Transducer (BRT), which uses a Bayes risk function to set lower risk values to the preferred paths so that the predicted alignment is more likely to satisfy specific desired properties. We further demonstrate that these predicted alignments with intentionally designed properties can provide practical advantages over the vanilla transducer. Experimentally, the proposed BRT saves inference cost by up to 46% for non-streaming ASR and reduces overall system latency by 41% for streaming ASR

Ti4O7/g-C3N4 for Visible Light Photocatalytic Oxidation of Hypophosphite: Effect of Mass Ratio of Ti4O7/g-C3N4

Hypophosphite wastewater treatment is still a critical issue in metallurgical processes and the oxidation of hypophosphite to phosphate followed by the precipitation of phosphate is an important strategy for hypophosphite wastewater treatment. Herein, Ti4O7/g-C3N4 photocatalysts with various mass ratios (Ti4O7 (m): g-C3N4 (m) = 0.5, 0.2, 0.1, and 0.05) were synthesized by a hydrolysis method and the effect of the mass ratio of Ti4O7 (m): g-C3N4 (m) on Ti4O7/g-C3N4 visible light photocatalytic oxidation of hypophosphite was evaluated. The as-prepared Ti4O7/g-C3N4 were characterized and confirmed by SEM, XPS, XRD and FTIR. Moreover, the specific surface area and the distribution of pore size of Ti4O7/g-C3N4 was also analyzed. Our results showed that Ti4O7/g-C3N4 exhibited remarkably improved photocatalytic performance on hypophosphite oxidation compared with g-C3N4 and meanwhile 1:2-Ti4O7/g-C3N4 with a mass ratio of 0.5 showed the best photocatalytic performance with the highest oxidation rate constant (17.7-fold and 91.0-fold higher than that of pure g-C3N4 and Ti4O7, respectively). The enhanced performance of photocatalytic oxidation of hypophosphite was ascribed to the heterojunction structure of Ti4O7/g-C3N4 with broader light absorption and significantly enhanced efficiency of the charge carrier (e−-h+) generation and separation. Additionally, the generated ·OH and ·O2- radicals contributed to the hypophosphite oxidation during the photocatalytic system

Co-Check: Collaborative Outsourced Data Auditing in Multicloud Environment

With the increasing demand for ubiquitous connectivity, wireless technology has significantly improved our daily lives. Meanwhile, together with cloud-computing technology (e.g., cloud storage services and big data processing), new wireless networking technology becomes the foundation infrastructure of emerging communication networks. Particularly, cloud storage has been widely used in services, such as data outsourcing and resource sharing, among the heterogeneous wireless environments because of its convenience, low cost, and flexibility. However, users/clients lose the physical control of their data after outsourcing. Consequently, ensuring the integrity of the outsourced data becomes an important security requirement of cloud storage applications. In this paper, we present Co-Check, a collaborative multicloud data integrity audition scheme, which is based on BLS (Boneh-Lynn-Shacham) signature and homomorphic tags. According to the proposed scheme, clients can audit their outsourced data in a one-round challenge-response interaction with low performance overhead. Our scheme also supports dynamic data maintenance. The theoretical analysis and experiment results illustrate that our scheme is provably secure and efficient