ESTIMATING EFFECTS OF AGRICULTURAL RESEARCH AND EXTENSION EXPENDITURES ON PRODUCTIVITY: A TRANSLOG PRODUCTION FUNCTION APPROACH

The effects of agricultural research and extension expenditures on productivity in the United States are estimated during the period 1949-81 using data for ten production regions. The large time-series cross-sectional data base allows the translog production function to be estimated directly. Results from the translog and Cobb-Douglas production functions are compared. The results indicate that use of the Cobb-Douglas production function would overestimate the internal rate of return of agricultural research and extension expenditures in the United States and eight production regions. The total marginal product and internal rate of return for the United States are $8.11 and 66 percent, respectively.Productivity Analysis,

Tunneling magnetoresistance in diluted magnetic semiconductor tunnel junctions

Using the spin-polarized tunneling model and taking into account the basic physics of ferromagnetic semiconductors, we study the temperature dependence of the tunneling magnetoresistance (TMR) in the diluted magnetic semiconductor (DMS) trilayer heterostructure system (Ga,Mn)As/AlAs/(Ga,Mn)As. The experimentally observed TMR ratio is in reasonable agreement with our result based on the typical material parameters. It is also shown that the TMR ratio has a strong dependence on both the itinerant-carrier density and the magnetic ion density in the DMS electrodes. This can provide a potential way to achieve larger TMR ratio by optimally adjusting the material parameters.Comment: 5 pages (RevTex), 3 figures (eps), submitted to PR

A Novel Local Community Detection Method Using Evolutionary Computation.

The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local community detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local community detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local community detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained

Extracted BERT Model Leaks More Information than You Think!

The collection and availability of big data, combined with advances in pre-trained models (e.g. BERT), have revolutionized the predictive performance of natural language processing tasks. This allows corporations to provide machine learning as a service (MLaaS) by encapsulating fine-tuned BERT-based models as APIs. Due to significant commercial interest, there has been a surge of attempts to steal remote services via model extraction. Although previous works have made progress in defending against model extraction attacks, there has been little discussion on their performance in preventing privacy leakage. This work bridges this gap by launching an attribute inference attack against the extracted BERT model. Our extensive experiments reveal that model extraction can cause severe privacy leakage even when victim models are facilitated with advanced defensive strategies

Intrinsic non-uniqueness of the acoustic full waveform inverse problem

SUMMARY In the context of seismic imaging, full waveform inversion (FWI) is increasingly popular. Because of its lower numerical cost, the acoustic approximation is often used, especially at the exploration geophysics scale, both for tests and for real data. Moreover, some research domains such as helioseismology face true acoustic media for which FWI can be useful. In this work, an argument that combines particle relabelling and homogenization is used to show that the general acoustic inverse problem based on band-limited data is intrinsically non-unique. It follows that the results of such inversions should be interpreted with caution. To illustrate these ideas, we consider 2-D numerical FWI examples based on a Gauss–Newton iterative inversion scheme and demonstrate effects of this non-uniqueness in the local optimization context.</jats:p