Networks and Business Cycles

The speed at which the US economy has recovered from recessions ranges from months to years. We propose a model incorporating the innovation network, the production network, and cross-sectional shocks and show that their interactions jointly explain large variations in the recovery speed across recessions in the US. In the model, besides the production linkages, firms learn insights on production from each other through the innovation network. We show when the innovation network takes a low-rank structure, there exists one key direction: the impact a shock becomes persistent only if the shock is parallel to this key direction; in contrast, the impact declines quickly if the shock follows other directions. Empirically, we estimate the model in a state-space form and document a set of new stylized facts of the US economy. First, the innovation network among sectors takes a low-rank structure. Second, the innovation network has non-negligible overlap with the production network. Third, recessions with slow recovery are those witnessing sizable negative shock to sectors in the center of the innovation network. Such network structures and the time-varying sectoral distribution of the shocks can well explain the large variation in the recovery speed across recessions in the US. Finally, to emphasize the prevalence of the channel, we explore the application of the theory in asset pricing

A Cooperative Game Framework for the Joint Operation of Natural Gas Storage and Electric Power Generation

We develop a cooperative game-theoretic framework for analyzing the impact of natural gas storage on interconnected gas and electricity markets. While increased utilization of gas storage has been proposed as a policy solution to fuel-security concerns in the electric power grid, the mode of interaction between gas storage units and electric power markets has not been investigated and some potential for cross-market manipulation exists. We investigate the potential for collusive behavior between gas storage units and power plants, whereby joint profits in the electricity and gas markets are increased by a strategy that involves the cooperative agents taking a loss in one market to the benefit of the other market. In a static game context, we find that such a strategy increases joint profits in scenarios when peak demand natural gas prices are high and the power plant(s) involved in the cooperative arrangement have relatively low marginal costs. The value of cooperation is not affected by whether gas storage units are physically connected to gas-fired power plants or if gas storage units inject gas into existing pipeline systems. While additional research into the nature of these competitive effects is needed, particularly in a repeated game context, our results point to the need to carefully consider the competitive effects of fuel security measures. A mechanism for monitoring of interactions between gas storage and power plants is likely warranted

Palladium-catalyzed difluoromethylation of heteroaryl chlorides, bromides and iodides.

A palladium-catalyzed difluoromethylation of a series of heteroaryl chlorides, bromides and iodides under mild conditions is described. A wide range of heteroaryl halides such as pyridyl, pyrimidyl, pyrazyl, funanyl, thienyl, pyazolyl, imidazolyl, thiazolyl, and oxazolyl halides were efficiently difluoromethylated, thus providing medicinal chemists an alternative choice for the preparation of drug candidates with the difluoromethylated heteroarene unit

An optimal feedback model to prevent manipulation behaviours in consensus under social network group decision making

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A novel framework to prevent manipulation behaviour in consensus reaching process under social network group decision making is proposed, which is based on a theoretically sound optimal feedback model. The manipulation behaviour classification is twofold: (1) ‘individual manipulation’ where each expert manipulates his/her own behaviour to achieve higher importance degree (weight); and (2) ‘group manipulation’ where a group of experts force inconsistent experts to adopt specific recommendation advices obtained via the use of fixed feedback parameter. To counteract ‘individual manipulation’, a behavioural weights assignment method modelling sequential attitude ranging from ‘dictatorship’ to ‘democracy’ is developed, and then a reasonable policy for group minimum adjustment cost is established to assign appropriate weights to experts. To prevent ‘group manipulation’, an optimal feedback model with objective function the individual adjustments cost and constraints related to the threshold of group consensus is investigated. This approach allows the inconsistent experts to balance group consensus and adjustment cost, which enhances their willingness to adopt the recommendation advices and consequently the group reaching consensus on the decision making problem at hand. A numerical example is presented to illustrate and verify the proposed optimal feedback model

Inhibition of Bacterial Ammonia Oxidation by Organohydrazines in Soil Microcosms

Hydroxylamine oxidation by hydroxylamine oxidoreductase (HAO) is a key step for energy-yielding in support of the growth of ammonia-oxidizing bacteria (AOB). Organohydrazines have been shown to inactivate HAO from Nitrosomonas europaea, and may serve as selective inhibitors to differentiate bacterial from archaeal ammonia oxidation due to the absence of bacterial HAO gene homolog in known ammonia-oxidizing archaea (AOA). In this study, the effects of three organohydrazines on activity, abundance, and composition of AOB and AOA were evaluated in soil microcosms. The results indicate that phenylhydrazine and methylhydrazine at the concentration of 100 μmol g−1 dry weight soil completely suppressed the activity of soil nitrification. Denaturing gradient gel electrophoresis fingerprinting and sequencing analysis of bacterial ammonia monooxygenase subunit A gene (amoA) clearly demonstrated that nitrification activity change is well paralleled with the growth of Nitrosomonas europaea-like AOB in soil microcosms. No significant correlation between AOA community structure and nitrification activity was observed among all treatments during the incubation period, although incomplete inhibition of nitrification activity occurred in 2-hydroxyethylhydrazine-amended soil microcosms. These findings show that the HAO-targeted organohydrazines can effectively inhibit bacterial nitrification in soil, and the mechanism of organohydrazine affecting AOA remains unclear

pH is the primary determinant of the bacterial community structure in agricultural soils impacted by polycyclic aromatic hydrocarbon pollution

Acidification and pollution are two major threats to agricultural ecosystems; however, microbial community responses to co-existed soil acidification and pollution remain less explored. In this study, arable soils of broad pH (4.26–8.43) and polycyclic aromatic hydrocarbon (PAH) gradients (0.18–20.68 mg kg−1) were collected from vegetable farmlands. Bacterial community characteristics including abundance, diversity and composition were revealed by quantitative PCR and high-throughput sequencing. The bacterial 16S rRNA gene copies significantly correlated with soil carbon and nitrogen contents, suggesting the control of nutrients accessibility on bacterial abundance. The bacterial diversity was strongly related to soil pH, with higher diversity in neutral samples and lower in acidic samples. Soil pH was also identified by an ordination analysis as important factor shaping bacterial community composition. The relative abundances of some dominant phyla varied along the pH gradient, and the enrichment of a few phylotypes suggested their adaptation to low pH condition. In contrast, at the current pollution level, PAH showed marginal effects on soil bacterial community. Overall, these findings suggest pH was the primary determinant of bacterial community in these arable soils, indicative of a more substantial influence of acidification than PAH pollution on bacteria driven ecological processes

Prompt-aligned Gradient for Prompt Tuning

Thanks to the large pre-trained vision-language models (VLMs) like CLIP, we can craft a zero-shot classifier by "prompt", e.g., the confidence score of an image being "[CLASS]" can be obtained by using the VLM provided similarity measure between the image and the prompt sentence "a photo of a [CLASS]". Therefore, prompt shows a great potential for fast adaptation of VLMs to downstream tasks if we fine-tune the prompt-based similarity measure. However, we find a common failure that improper fine-tuning may not only undermine the prompt's inherent prediction for the task-related classes, but also for other classes in the VLM vocabulary. Existing methods still address this problem by using traditional anti-overfitting techniques such as early stopping and data augmentation, which lack a principled solution specific to prompt. We present Prompt-aligned Gradient, dubbed ProGrad, to prevent prompt tuning from forgetting the the general knowledge learned from VLMs. In particular, ProGrad only updates the prompt whose gradient is aligned (or non-conflicting) to the "general direction", which is represented as the gradient of the KL loss of the pre-defined prompt prediction. Extensive experiments demonstrate the stronger few-shot generalization ability of ProGrad over state-of-the-art prompt tuning methods. Codes are available at https://github.com/BeierZhu/Prompt-align.Comment: Accepted by ICCV202

Efficient Multicore Collaborative Filtering

This paper describes the solution method taken by LeBuSiShu team for track1 in ACM KDD CUP 2011 contest (resulting in the 5th place). We identified two main challenges: the unique item taxonomy characteristics as well as the large data set size.To handle the item taxonomy, we present a novel method called Matrix Factorization Item Taxonomy Regularization (MFITR). MFITR obtained the 2nd best prediction result out of more then ten implemented algorithms. For rapidly computing multiple solutions of various algorithms, we have implemented an open source parallel collaborative filtering library on top of the GraphLab machine learning framework. We report some preliminary performance results obtained using the BlackLight supercomputer.Comment: In ACM KDD CUP Workshop 201