A Strategic Analysis of Algorithm Manipulation: a Lending Game perspective

Machine learning models are widely used in many business contexts, but there is a growing concern that strategic individuals may manipulate their features to obtain desirable outcomes from the machine learning models. This paper offers a theoretical analysis of the impact of feature manipulation on the performance of the machine learning models and the payoffs of firms in an online lending context. Contrary to the common belief, our interesting finding is that manipulation may not be harmful to a firm under some circumstances. Instead, it could increase the classification model\u27s performance and raise a firm\u27s payoff and the social welfare when high-quality individuals manipulate more. Overall, our findings suggest that manipulation can bring strategic value to machine learning models instead of just being a harmful activity. Our findings provide useful insights for feature engineering and lay a foundation for future research about optimal strategies to cope with manipulation activities

TIM: Teaching Large Language Models to Translate with Comparison

Open-sourced large language models (LLMs) have demonstrated remarkable efficacy in various tasks with instruction tuning. However, these models can sometimes struggle with tasks that require more specialized knowledge such as translation. One possible reason for such deficiency is that instruction tuning aims to generate fluent and coherent text that continues from a given instruction without being constrained by any task-specific requirements. Moreover, it can be more challenging for tuning smaller LLMs with lower-quality training data. To address this issue, we propose a novel framework using examples in comparison to teach LLMs to learn translation. Our approach involves presenting the model with examples of correct and incorrect translations and using a preference loss to guide the model's learning. We evaluate our method on WMT2022 test sets and show that it outperforms existing methods. Our findings offer a new perspective on fine-tuning LLMs for translation tasks and provide a promising solution for generating high-quality translations. Please refer to Github for more details: https://github.com/lemon0830/TIM

THE IMPACT OF POWER BOUNDARY MANAGEMENT ON THE DESIGN OF COMPANY-INITIATED OPEN INNOVATION PLATFORM

Open innovation recognizes potential opportunities and advantages gained from leveraging knowledge and innovations found outside an organization‟s formal boundaries. With the intensive use of Internet-based tools, organizations are actively involved in using Open Innovation Platform (OIP) to attract external knowledge. However, developing a company-initiated OIP is a challenging task because usage of OIP depends on the voluntary participation of external users, which makes companies cannot follow the protocol of developing traditional IS. Furthermore, a company\u27s institutional properties may also impact the design company-initiated OIP. In this research, we focus on one type of organizational property, namely power boundary, and explore its impact on the design of a company-initiated OIP over time. From qualitative analysis of two versions of OIP in a single company, we develop a theoretical model depicting how the changes of power boundary of a firm influence the design of a company-initiated OIP over time. This result generates theoretical and empirical insights into the OIP design and power boundary and thus has important implications for both scholars and practitioners

The Economics of Hacking

Hacking is becoming more common and dangerous. The challenge of dealing with hacking often comes from the fact that much of our wisdom about conventional crime cannot be directly applied to understand hacking behavior. Against this backdrop, this essay reviews hacking studies, with a focus on discussing the new features of cybercrime and how they affect the application of classical economic theory of crime in the cyberspace. Most findings of hacking studies can be interpreted with a parsimonious demand and supply framework. Hackers decide whether and how much to “supply” hacking by calculating the return on hacking over other opportunities. Defenders optimally tolerate some level of hacking risks because defense is costly. This tolerance can be interpreted as an indirect “demand” for hacking. Variations in law enforcement, hacking benefits, hacking costs, legal alternatives, private defense, and the dual use problem can variously affect the supply or demand for hacking, and in turn the equilibrium observation of hacking in the market. Overall, this essay suggests that the classical economic theory of crime remains a powerful framework to explain hacking behaviors. However, the application of this theory calls for considerations of different assumptions and driving forces, such as psychological motives and economies of scale in offenses, that are often less prevalent in conventional (offline) criminal behaviors, but that tend to underscore hacking in the cyberspace

Photometric Metallicity Calibration with SDSS and SCUSS and its Application to distant stars in the South Galactic Cap

Based on SDSS g, r and SCUSS (South Galactic Cap of u-band Sky Survey) $u$ photometry, we develop a photometric calibration for estimating the stellar metallicity from $u-g$ and $g-r$ colors by using the SDSS spectra of 32,542 F- and G-type main sequence stars, which cover almost $3700$ deg$^{2}$ in the south Galactic cap. The rms scatter of the photometric metallicity residuals relative to spectrum-based metallicity is $0.14$ dex when $g-r<0.4$, and $0.16$ dex when $g-r>0.4$. Due to the deeper and more accurate magnitude of SCUSS $u$ band, the estimate can be used up to the faint magnitude of $g=21$. This application range of photometric metallicity calibration is wide enough so that it can be used to study metallicity distribution of distant stars. In this study, we select the Sagittarius (Sgr) stream and its neighboring field halo stars in south Galactic cap to study their metallicity distribution. We find that the Sgr stream at the cylindrical Galactocentric coordinate of $R\sim 19$ kpc, $\left| z\right| \sim 14$ kpc exhibits a relative rich metallicity distribution, and the neighboring field halo stars in our studied fields can be modeled by two-Gaussian model, with peaks respectively at [Fe/H]$=-1.9$ and [Fe/H]$=-1.5$.Comment: 8 pages, 7 figures, Accepted for publication in MNRA

Src kinase up-regulates the ERK cascade through inactivation of protein phosphatase 2A following cerebral ischemia

<p>Abstract</p> <p>Background</p> <p>The regulation of protein phosphorylation requires a balance in the activity of protein kinases and protein phosphatases. Our previous data indicates that Src can increase ERK activity through Raf kinase in response to ischemic stimuli. This study examined the molecular mechanisms by which Src activates ERK cascade through protein phosphatases following cerebral ischemia.</p> <p>Results</p> <p>Ischemia-induced Src activation is followed by phosphorylation of PP2A at Tyr307 leading to its inhibition in the rat hippocampus. SU6656, a Src inhibitor, up-regulates PP2A activity, resulting in a significant decreased activity in ERK and its targets, CREB and ERα. In addition, the PP2A inhibitor, cantharidin, led to an up-regulation of ERK activity and was able to counteract Src inhibition during ischemia.</p> <p>Conclusion</p> <p>Src induces up-regulation of ERK activity and its target transcription factors, CREB and ERα, through attenuation of PP2A activity. Therefore, activation of ERK is the result of a crosstalk between two pathways, Raf-dependent positive regulators and PP2A-dependent negative regulators.</p