A regional computable general equilibrium model with applications for the Pacific Northwest

Many important policy problems entail linkages among multiple economic sectors, and require the use of a general equilibrium economic modeling framework. This economic approach is appropriate when the market for any one good or service is linked to numerous other goods and services, and back to fundamental inputs such as labor and capital. In this dissertation, a computable general equilibrium (CGE) model for the Pacific Northwest region is developed. It describes all parts of Pacific Northwest economy simultaneously and how its industries, households, government institutions, and factors of production interact with each other. The model is used to address two policy issues in the Northwest: development of a new biofuels supply chain, and the impact of future events such as climate change on Pacific Northwest farmers. Before these applications are carried out, a major effort is made to estimate the parameters of the general equilibrium model, and to validate that the model is representative of the regional economy. Techniques from the literature on calibration of macro-economic models are employed, in conjunction with historical agricultural price and quantity data for the Northwest. These methods allow greater confidence to be placed in the analyses that follow. Once the model is parameterized and validated, the first application concerns the potential of an oilseed crop, camelina, to be used as a new biofuel for the aviation sector. The aim of this study is to identify conditions and policies under which a supply chain could be developed within the Northwest. Several policy options are examined within the model with regard to meeting stated targets by the aviation sector for using camelina as a biofuel. Model results indicate that a regional supply chain for biofuels is unlikely to develop unless subsidies are targeted to particular activities, including farming and processing. Particular estimates of these subsidies are derived. The second application of the model concerns how the Pacific Northwest wheat economy will be affected by long-run changes in climate, population growth, input costs, and other phenomena. A series of possible future scenarios, called Representative Agricultural Pathways (RAPs), are developed to describe trends in key drivers at the regional and global scales. These RAPs are quantified and integrated as simulations into the CGE model, the first time this has been done within the literature. In general the health of the Pacific Northwest wheat sector, as represented by wheat prices, exports quantities, and producer economic welfare, appears to be quite viable under a range of alternative future scenarios

The elasticity of excess demand for United States crop exports

The elasticity of excess demand held by foreign buyers of United States agricultural products is critical for understanding the impacts of changes in farm policy and is a parameter that is much debated. The objective of my study is to estimate this parameter for major U.S. export crops, including wheat, corn, and soybeans. I first formulate an economic model of U.S. exports including country-specific, crop-specific price transmission elasticities, supply elasticities, and demand elasticities for each of the major importers of U.S. crops. I then regress each model with updated time-series data sources, carry out extensive diagnostic tests, and incorporate these estimates into an economic model of U.S. export markets to calculate the excess demand elasticities. I provide a systematic comparison to previous estimates in the literature and find that the foreign demand for corn and soybeans tends to be fairly elastic, while the demand for wheat is relatively inelastic

Red-shift effect on the zero field splitting for negatively charged nitrogen-vacancy centers in diamond

The zero field splitting (ZFS) quantifies the energy difference for the ground electron spin-triplet of a nitrogen-vacancy center in the absence of external fields. The values of the ZFS play a key role in determining the Larmor precession of the Bloch sphere and the Rabi oscillation of a spin system. The ZFS is generally detected using coherent spin manipulation by sweeping microwaves (MWs) at frequencies close to resonance with the ZFS. In this letter, we report our experimental observations of the red-shift effect on the ZFS as a function of the MW power for two different thermal environments of a sample. We find an asymptotic property of the red shifts of the ZFS. Given the identical initial thermal equilibrium states of the sample, the differences in the raw values of the ZFS between the two cases randomly vary from 47 kHz to 1505 kHz over the entire experimental range. According to the asymptotic approximation, the differences are reduced to 29-166 kHz with a standard deviation of 49 kHz, suggesting a significant elimination of the red-shift effect. To the best of our knowledge, no other study has addressed the quantification and elimination of the red shift-effect of the MW field dependence using the asymptotic approximation

Self-Supervised Visual Representation Learning with Semantic Grouping

In this paper, we tackle the problem of learning visual representations from unlabeled scene-centric data. Existing works have demonstrated the potential of utilizing the underlying complex structure within scene-centric data; still, they commonly rely on hand-crafted objectness priors or specialized pretext tasks to build a learning framework, which may harm generalizability. Instead, we propose contrastive learning from data-driven semantic slots, namely SlotCon, for joint semantic grouping and representation learning. The semantic grouping is performed by assigning pixels to a set of learnable prototypes, which can adapt to each sample by attentive pooling over the feature and form new slots. Based on the learned data-dependent slots, a contrastive objective is employed for representation learning, which enhances the discriminability of features, and conversely facilitates grouping semantically coherent pixels together. Compared with previous efforts, by simultaneously optimizing the two coupled objectives of semantic grouping and contrastive learning, our approach bypasses the disadvantages of hand-crafted priors and is able to learn object/group-level representations from scene-centric images. Experiments show our approach effectively decomposes complex scenes into semantic groups for feature learning and significantly benefits downstream tasks, including object detection, instance segmentation, and semantic segmentation. Code is available at: https://github.com/CVMI-Lab/SlotCon.Comment: Accepted at NeurIPS 202

On the secrecy performance of land mobile satellite communication systems

In this paper, we investigate the secrecy performance against eavesdropping of a land mobile satellite (LMS) system, where the satellite employs the spot beam technique, and both the terrestrial user and eavesdropper are equipped with multiple antennas and utilize maximal ratio combining (MRC) to receive the confidential message. Specifically, in terms of the availability of the eavesdropper’s CSI at the satellite, we consider both passive (Scenario I) and active (Scenario II) eavesdropping. For Scenario I where the eavesdropper’s channel state information (CSI) is unknown to the satellite, closed-form expressions for the probability of non-zero secrecy capacity and secrecy outage probability are derived. Furthermore, expressions for the asymptotic secrecy outage probability are also presented to reveal the secrecy diversity order and array gain of the considered system. For Scenario II where the eavesdropper’s CSI is available at the satellite, novel expressions for the exact and asymptotic average secrecy capacity are obtained. Based on a simple asymptotic formula, we can characterize the high signalto- noise ratio (SNR) slope and high SNR power offset of the LMS systems. Finally, simulations are provided to validate our theoretical analysis and show the effect of different parameters on the system performance

Towards Understanding the Adoption and Social Experience of Digital Wallet Systems

For millions around the globe, digital wallets are replacing cash and credit cards. These services support user-to-user payments, and add a social component to transactions. However, there is little understanding of the key factors behind digital wallets’ rapid growth in US (Venmo) and China (WeChat Pay). What are the factors that led to their success? How social relationships play a role in their adoption? We conduct a mixed methods study, using a comprehensive survey (N=879) and semi-structured interviews (N=41) to explore the interplay of the two roles of these digital wallets, i.e., a payment system and a social platform. Our analysis suggests that the network effect does benefit their adoption and retention, but through different mechanisms. In return, transaction activities performed in digital wallets help strengthen existing social ties. We also present design implications for future social payment services

Performance Analysis of NOMA-Based Land Mobile Satellite Networks

Non-orthogonal multiple access (NOMA) scheme, which has the ability to superpose information in the power domain and serve multiple users on the same time/frequency resource, is regarded as an effective solution to increase transmit rate and fairness. In this paper, we introduce the NOMA scheme in a downlink land mobile satellite (LMS) network and present a comprehensive performance analysis for the considered system. Specifically, we first obtain the power allocation coefficients by maximizing the sum rate while meeting the predefined target rates of each NOMA user. Then, we derive the theoretical expressions for the ergodic capacity and the energy efficiency of the considered system. Moreover, the outage probability (OP) and average symbol error rate performances of NOMA users are derived analytically. To gain further insights, we derive the asymptotic OP at the high signal-to-noise ratio regime to characterize the diversity orders and coding gains of NOMA users. Finally, simulation results are provided to validate the theoretical analysis as well as the superiority of employing the NOMA scheme in the LMS system, and show the impact of key parameters, such as fading configurations and user selection strategy on the performance of NOMA users

Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

The emergent few-shot reasoning capabilities of Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear. In this work, we hypothesize that the learned \textit{semantics} of language tokens do the most heavy lifting during the reasoning process. Different from human's symbolic reasoning process, the semantic representations of LLMs could create strong connections among tokens, thus composing a superficial logical chain. To test our hypothesis, we decouple semantics from the language reasoning process and evaluate three kinds of reasoning abilities, i.e., deduction, induction and abduction. Our findings reveal that semantics play a vital role in LLMs' in-context reasoning -- LLMs perform significantly better when semantics are consistent with commonsense but struggle to solve symbolic or counter-commonsense reasoning tasks by leveraging in-context new knowledge. The surprising observations question whether modern LLMs have mastered the inductive, deductive and abductive reasoning abilities as in human intelligence, and motivate research on unveiling the magic existing within the black-box LLMs. On the whole, our analysis provides a novel perspective on the role of semantics in developing and evaluating language models' reasoning abilities. Code is available at {\url{https://github.com/XiaojuanTang/ICSR}}

Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students

The increasing use of Artificial Intelligence (AI) by students in learning presents new challenges for assessing their learning outcomes in project-based learning (PBL). This paper introduces a co-design study to explore the potential of students' AI usage data as a novel material for PBL assessment. We conducted workshops with 18 college students, encouraging them to speculate an alternative world where they could freely employ AI in PBL while needing to report this process to assess their skills and contributions. Our workshops yielded various scenarios of students' use of AI in PBL and ways of analyzing these uses grounded by students' vision of education goal transformation. We also found students with different attitudes toward AI exhibited distinct preferences in how to analyze and understand the use of AI. Based on these findings, we discuss future research opportunities on student-AI interactions and understanding AI-enhanced learning.Comment: Conditionally accepted by CHI '2

Hybrid satellite terrestrial relay networks with cooperative non-orthogonal multiple access

In this letter, we investigate the outage probability (OP) and ergodic capacity of the downlink hybrid satellite terrestrial relay networks (HSTRNs) with a cooperative non-orthogonal multiple access (C-NOMA) scheme, in which a user with better channel condition acts as a relay node and forwards information to other users, thus alleviating the masking effect of users with poor channel conditions in heavy shadowing. Specifically, the exact analytical expression for the OP of the considered system is derived. Furthermore, the ergodic capacity expression is also developed to facilitate performance evaluation of the proposed framework. Finally, the simulations are provided to show the impact of key parameters on the considered system and the superiority of introducing the C-NOMA scheme to the HSTRNs