The Complementary Role of Exports and R&D Investments as Sources of Productivity Growth

This paper examines two potential channels of knowledge acquisition that underlie firm productivity growth in the Taiwanese electronics industry: participation in the export market and investments in R&D and/or worker training. We focus on the argument that a firm's own investments in R&D are necessary for the firm to assimilate knowledge or expertise gained from foreign contacts and thus are an important component of the process of learning-by-exporting. Firm-level panel data from 1986, 1991, and 1996 is used to investigate a firm's decision to invest in these two activities and to assess the effects of these investments on the firm's future total factor productivity. The empirical model consists of four equations. The firm's decisions to export and invest in R&D and/or worker training are modeled with a bivariate probit model that recognizes the interdependence of the decisions. We then estimate how participation in these investment activities alters the firm's future productivity trajectory while controlling for the potential selection bias introduced by endogenous firm exit. The primary empirical findings are that, on average, firms that export but do not invest in R&D and/or worker training have significantly higher future productivity than firms that do not participate in either activity. In addition, firms that export and invest in R&D and/or worker training have significantly higher future productivity than firms that only export. These findings are consistent with the hypothesis that export experience is an important source of productivity growth for Taiwanese firms and that firm investments in R&D and worker training facilitate their ability to benefit from their exposure to the export market.

Ramsey Theory Problems over the Integers: Avoiding Generalized Progressions

Two well studied Ramsey-theoretic problems consider subsets of the natural numbers which either contain no three elements in arithmetic progression, or in geometric progression. We study generalizations of this problem, by varying the kinds of progressions to be avoided and the metrics used to evaluate the density of the resulting subsets. One can view a 3-term arithmetic progression as a sequence $x, f_n(x), f_n(f_n(x))$, where $f_n(x) = x + n$, $n$ a nonzero integer. Thus avoiding three-term arithmetic progressions is equivalent to containing no three elements of the form $x, f_n(x), f_n(f_n(x))$ with $f_n \in\mathcal{F}_{\rm t}$, the set of integer translations. One can similarly construct related progressions using different families of functions. We investigate several such families, including geometric progressions ($f_n(x) = nx$ with $n > 1$ a natural number) and exponential progressions ($f_n(x) = x^n$). Progression-free sets are often constructed "greedily," including every number so long as it is not in progression with any of the previous elements. Rankin characterized the greedy geometric-progression-free set in terms of the greedy arithmetic set. We characterize the greedy exponential set and prove that it has asymptotic density 1, and then discuss how the optimality of the greedy set depends on the family of functions used to define progressions. Traditionally, the size of a progression-free set is measured using the (upper) asymptotic density, however we consider several different notions of density, including the uniform and exponential densities.Comment: Version 1.0, 13 page

Towards Hardware-Based Application Fingerprinting with Microarchitectural Signals for Zero Trust Environments

The interactions between software and hardware are increasingly important to computer system security. This research collects sequences of microprocessor control signals to develop machine learning models that identify software tasks. The proposed approach considers software task identification in hardware as a general problem with attacks treated as a subset of software tasks. Two lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during real (i.e., non-simulated) system operation. Second, experimental design is used to select hardware and software configuration to train and evaluate machine learning models. The machine learning models significantly outperform a Naive classifier based on Euclidean distances from class means. Various configurations produce balanced accuracy scores between 26.08% and 96.89%