URBAN STRUCTURE: ITS ROLE IN URBAN GROWTH, NET NEW BUSINESS FORMATION AND INDUSTRIAL CHURN

Cities are the “engines of growth” because entrepreneurial and cre-ative activities are concentrated in cities. This suggests that cities grow by host-ing new businesses and “churning” industries advantageously. In so doing, cities need to adapt their spatial structure to mitigate negative externalities. Our previous paper (Lee and Gordon 2007) found that the links between urban structure and growth vary across metro size: more clustering in small metros and more dispersion in large metros were associated with faster employment growth. In this paper, we extend our research to investigate to what extent ur-ban spatial structure variables – dispersion and polycentricity – influence net new business formation (NNBF) and industrial “churning” in a cross-section of 79 U.S. metropolitan areas in the 2000s. The results of least squares regression and locally weighted regression analyses are mixed. OLS results for recent years fail to replicate our results for the 1990s. But applying a more powerful LOESS approach does give results for spatial impacts on NNBF and industrial churning that are consistent with the links between spatial structure and urban growth found in the earlier paper.URBAN SPATIAL STRUCTURE, URBAN GROWTH, NET NEW BUSINESS FORMATION (NNBF), INDUSTRIAL CHURN

Airline Booking Limit Competition Game Under Differentiated Fare Structure

We address a two-firm booking limit competition game in the airline industry. We assume aggregate common demand, and differentiated ticket fare and capacity, to make this study more realistic. A game theoretic approach is used to analyze the competition game. The optimal booking limits and the best response functions are derived. We show the existence of a pure Nash equilibrium and provide the closed-form equilibrium solution. The location of the Nash equilibrium depends on the relative magnitude of the ratios of the full and discount fares. We also show that the sum of the booking limits of the two firms remains the same regardless of the initial allocation proportion of the demand

Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning

Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data. However, previous works mainly focused on understanding single graph inputs while many real-world applications require pair-wise analysis for graph-structured data (e.g., scene graph matching, code searching, and drug-drug interaction prediction). To this end, recent works have shifted their focus to learning the interaction between pairs of graphs. Despite their improved performance, these works were still limited in that the interactions were considered at the node-level, resulting in high computational costs and suboptimal performance. To address this issue, we propose a novel and efficient graph-level approach for extracting interaction representations using co-attention in graph pooling. Our method, Co-Attention Graph Pooling (CAGPool), exhibits competitive performance relative to existing methods in both classification and regression tasks using real-world datasets, while maintaining lower computational complexity.Comment: Published at IEEE Acces

Investigation of Advanced Cathode Contacting Solutions in SOFC

Contacting solutions for air electrode in Solid Oxide Cells stacks often implement a ceramic paste made of electronic conducting perovskite, comparable or same as the electro-active material. This contacting layer, is applied in a green state by wet-powder-spray or screen-printing, and in situ fired during stack commissioning. The low level of necking between ceramic particles causes increased ohmic losses. Moreover the shrinkage usually observed during long term operation in temperature of this layer, due to sintering effect, lead to cracks and contact losses which hinder the cell performance. Increasing cell’s footprint, performance and lifetime at the stack level requires appropriate contacting solution. In this paper we reports the investigation of a new advanced monolithic contacting solution, easy to handle, soft and flexible, highly porous and highly conductive. Two different compositions have been investigated, with respect of their compatibility with Crofer (SEM, XRD). In addition, solid oxide cells contacted with this solution as well as with a ceramic paste have also been electrochemically tested up to 1000 hours in order to compare and assess the impact of this contacting solution on cell’s performance. Results will be presented and discussed

DeepCompass: AI-driven Location-Orientation Synchronization for Navigating Platforms

In current navigating platforms, the user's orientation is typically estimated based on the difference between two consecutive locations. In other words, the orientation cannot be identified until the second location is taken. This asynchronous location-orientation identification often leads to our real-life question: Why does my navigator tell the wrong direction of my car at the beginning? We propose DeepCompass to identify the user's orientation by bridging the gap between the street-view and the user-view images. First, we explore suitable model architectures and design corresponding input configuration. Second, we demonstrate artificial transformation techniques (e.g., style transfer and road segmentation) to minimize the disparity between the street-view and the user's real-time experience. We evaluate DeepCompass with extensive evaluation in various driving conditions. DeepCompass does not require additional hardware and is also not susceptible to external interference, in contrast to magnetometer-based navigator. This highlights the potential of DeepCompass as an add-on to existing sensor-based orientation detection methods.Comment: 7page with 3 supplemental page

The attributes of residence/workplace areas and transit commuting

JTLU vol 4, no 3, pp 43-63 (2011)Area type matters when we try to explain variations in public transit commuting; workplace (commuting destination) type matters more than residence (origin) type. We found this statistical link over a sample of all census tracts in the four largest California metropolitan areas: Los Angeles, San Francisco, San Diego, and Sacramento. In this research, we used a statistical cluster analysis to identify twenty generic residence neighborhood types and fourteen workplace neighborhood types. The variables used in the analysis included broad indicators of lo- cation and density, street design, transit access, and highway access. Once identified, the denser neighborhoods had higher transit commuting, other things equal. Yet what distinguishes this research is that we did not use a simple density measure to differentiate neighborhoods. Rather, density was an important ingredient of our neighborhood-type definition, which surpassed simple density in explanatory power

Pairing fluctuations and pseudogaps in the attractive Hubbard model

The two-dimensional attractive Hubbard model is studied in the weak to intermediate coupling regime by employing a non-perturbative approach. It is first shown that this approach is in quantitative agreement with Monte Carlo calculations for both single-particle and two-particle quantities. Both the density of states and the single-particle spectral weight show a pseudogap at the Fermi energy below some characteristic temperature T*, also in good agreement with quantum Monte Carlo calculations. The pseudogap is caused by critical pairing fluctuations in the low-temperature renormalized classical regime $\omega < T$ of the two-dimensional system. With increasing temperature the spectral weight fills in the pseudogap instead of closing it and the pseudogap appears earlier in the density of states than in the spectral function. Small temperature changes around T* can modify the spectral weight over frequency scales much larger than temperature. Several qualitative results for the s-wave case should remain true for d-wave superconductors.Comment: 20 pages, 12 figure

Ref-1 redox activity alters cancer cell metabolism in pancreatic cancer: exploiting this novel finding as a potential target

Background: Pancreatic cancer is a complex disease with a desmoplastic stroma, extreme hypoxia, and inherent resistance to therapy. Understanding the signaling and adaptive response of such an aggressive cancer is key to making advances in therapeutic efficacy. Redox factor-1 (Ref-1), a redox signaling protein, regulates the conversion of several transcription factors (TFs), including HIF-1α, STAT3 and NFκB from an oxidized to reduced state leading to enhancement of their DNA binding. In our previously published work, knockdown of Ref-1 under normoxia resulted in altered gene expression patterns on pathways including EIF2, protein kinase A, and mTOR. In this study, single cell RNA sequencing (scRNA-seq) and proteomics were used to explore the effects of Ref-1 on metabolic pathways under hypoxia. Methods: scRNA-seq comparing pancreatic cancer cells expressing less than 20% of the Ref-1 protein was analyzed using left truncated mixture Gaussian model and validated using proteomics and qRT-PCR. The identified Ref-1's role in mitochondrial function was confirmed using mitochondrial function assays, qRT-PCR, western blotting and NADP assay. Further, the effect of Ref-1 redox function inhibition against pancreatic cancer metabolism was assayed using 3D co-culture in vitro and xenograft studies in vivo. Results: Distinct transcriptional variation in central metabolism, cell cycle, apoptosis, immune response, and genes downstream of a series of signaling pathways and transcriptional regulatory factors were identified in Ref-1 knockdown vs Scrambled control from the scRNA-seq data. Mitochondrial DEG subsets downregulated with Ref-1 knockdown were significantly reduced following Ref-1 redox inhibition and more dramatically in combination with Devimistat in vitro. Mitochondrial function assays demonstrated that Ref-1 knockdown and Ref-1 redox signaling inhibition decreased utilization of TCA cycle substrates and slowed the growth of pancreatic cancer co-culture spheroids. In Ref-1 knockdown cells, a higher flux rate of NADP + consuming reactions was observed suggesting the less availability of NADP + and a higher level of oxidative stress in these cells. In vivo xenograft studies demonstrated that tumor reduction was potent with Ref-1 redox inhibitor similar to Devimistat. Conclusion: Ref-1 redox signaling inhibition conclusively alters cancer cell metabolism by causing TCA cycle dysfunction while also reducing the pancreatic tumor growth in vitro as well as in vivo

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts