Assessing the Impacts of Market Failures on Innovation Investment in Uruguay

This paper analyzes the effects of financial and nonfinancial obstacles to innovation on Uruguayan firms. We contribute to the literature by including the role of systemic and institutional factors affecting the different stages of the innovation process. The empirical analysis is based on four waves of national innovation surveys covering firms in the industry and services sector. In line with recent studies, we confine our analysis to the relevant sample of potentially innovative firms. Our results show that market, financial, knowledge, and context obstacles are the most important factors reducing innovation propensity and the amount invested in innovation activities. The effects are similar for firms in the industry and services sectors. We do not find evidence that institutional factors hamper innovation. Investment in equipment and investment in R&D and other intangible activities are affected differently by obstacles. On the other hand, innovation outcomes are affected mainly by financial and market-related barriers. We do not find evidence that obstacles to innovation have a significant impact on labor productivity

Short-Term Memory in Orthogonal Neural Networks

We study the ability of linear recurrent networks obeying discrete time dynamics to store long temporal sequences that are retrievable from the instantaneous state of the network. We calculate this temporal memory capacity for both distributed shift register and random orthogonal connectivity matrices. We show that the memory capacity of these networks scales with system size.Comment: 4 pages, 4 figures, to be published in Phys. Rev. Let

An Evaluation of the Anchor-Site Phase of Family to Family

Examines updated outcomes of Casey's initiative to help states improve the quality of foster care while reducing its prevalence through community partnerships; team decision making; foster family recruitment, development, and support; and self-evaluation

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.Comment: Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical Sciences

Using cfRNA as a tool to evaluate clinical treatment outcomes in patients with metastatic lung cancers and other tumors

Aim: We report an exploratory analysis of cfRNA as a biomarker to monitor clinical responses in non-small cell lung cancer (NSCLC), breast cancer, and colorectal cancer (CRC). An analysis of cfRNA as a method for measuring PD-L1 expression with comparison to clinical responses was also performed in the NSCLC cohort. Methods: Blood samples were collected from 127 patients with metastatic disease that were undergoing therapy, 52 with NSCLC, 50 with breast cancer, and 25 with CRC. cfRNA was purified from fractionated plasma, and following reverse transcription (RT), total cfRNA and gene expression of PD-L1were analyzed by real-time polymerase chain reaction (qPCR) using beta-actin expression as a surrogate for relative amounts of cfDNA and cfRNA. For the concordance study of liquid biopsies and tissue biopsies, the isolated RNA was analyzed by RNAseq for the expressions of 13 genes. We had to close the study early due to a lack of follow-up during the Covid-19 pandemic. Results: We collected a total of 373 blood samples. Mean cfRNA PCR signals after RT were about 50-fold higher than those of cfDNA. cfRNA was detected in all patients, while cfDNA was detected in 88% of them. A high concordance was found for the expression levels of 13 genes between blood and solid tumor tissue. Changes in cfRNA levels followed over the course of treatments were associated with response to therapy, increasing in progressive disease (PD) and falling when a partial response (PR) occurred. The expression of PD-L1 over time in patients treated with immunotherapy decreased with PR but increased with PD. Pre-treatment levels of PD-L1 were predictive of response in patients treated with immunotherapy. Conclusion: Changes in cfRNA correlate with clinical response to the therapy. Total cfRNA may be useful in predicting clinical outcomes. PD-L1 gene expression may provide a biomarker to predict response to PD-L1 inhibition

Community-Centered Approaches for Food Systems Transformation

The materials you will find here were produced for the Food Sovereignty Workshop held at the Ohio State University on October 13th and 14th 2022. "Community-Centered Approaches for Food Systems Transformation" focused on university-community partnerships and the importance of learning from communities about their local food systems, both historically and in the present moment while conducting research. Several communities involved in food sovereignty efforts were represented at the workshop in addition to 27 different units at Ohio State University. Speakers also represented Historically Black University, Central State, and Menominee Tribal College. Five government offices and 6 non-profit organizations were also represented. The food sovereignty workshop resulted in new connections between practitioner, government, and university networks. Contacts were made between urban farming and gardening organizations such as Growing and Growth Collective and Southside Family Farms, Bronzeville Growers Market, city and county agencies such as the Center for Public Health and the Franklin County Board of Commissioners. A team formed at the food sovereignty workshop that is doing work directly relevant to Goal B-10 of the City's Food System's Plan, "Engaging cultural communities in Columbus". The purpose of Goal B-10 is to engage cultural communities in Columbus and address inequities. The team is creating "A Cultural Heritage Learning Place" focused on Black, Indigenous and People of Color (BIPOC) communities. The workshop opened with an acknowledgment that Ohio State University owes the many tribal communities impacted by its land grant establishment and continued benefits from tribal lands. The workshop served as a call to action to right those historic wrongs and the ongoing harms from disrupted native food systems. We emphasized community-partnered research that puts local communities in the forefront of research and that includes local practitioners. Presentations showed that this approach offers paths forward in various areas of food systems research and university outreach that are both ethical and sustainable. Food sovereignty served as a lens for exploring how these types of research can work and how they can impact our knowledge and mindsets. A full report is included among the items here.Globally, 27% of people faced moderate or severe food insecurity in 2019, representing more than 2 billion people. Community-Centered Approaches for Food Systems Transformation discussed the question: "How we might reprioritize research and teaching based on community partnership rather than "expertise?" In other words, how might learning from communities take precedence over learning about them. The workshop began with a call to action as we discussed the cultural contexts of local food systems locally and around the world. In her keynote, Dr. Phipps shed light on research and grassroots projects that address this urgent crisis, highlighting the work currently being done by communities to produce nutrient-dense, culturally relevant cuisine. Dr. Phipps' keynote was followed by examples of community food systems work being done in the U.S., Belize, and Yemen. The conversation was moderated by culturally-relevant food systems expert, Associate Professor Mary Rodriguez, who has done work in the Middle East and with diaspora communities in Columbus. Conversations that followed centered on the types of actions that might be taken, and were continued at Ohio State's STEAM Factory the next day, including representatives of local non-profit organizations such Ohio Food Banks, South Side Family Farms, Bronzeville Growers Market. A special thanks to our sponsors and to the speakers, consultants, hosts, and facilitators who helped us make the workshop a success. Thanks to the participants who came to the workshop and joined us in lively conversations. We are so grateful to all of you for sharing your expertise, experiences, and energy with us.Ohio State University Middle East Studies CenterOhio State University Global Water InstituteOhio State University LibrariesThe Newark Earthworks Center (with support from an Indigenous Arts and Humanities Grant, Global Arts + Humanities Discovery Theme)The STEAM FactoryThe Department of Near Eastern and South Asian Languages and CulturesCFAES AgroEcosystems Management Program (AMP)The Department of Food, Agricultural and Biological EngineeringThe Growing and Growth CollectiveThe Department of AnthropologyThe Knowlton School of ArchitectureThe Department of Comparative StudiesThe Center for Latin American StudiesThe CFAES Rattan Lal Center for the Study of Carbon Management and Sequestration (C-MASC)Keynote (Audio Recording) / Brandy Phipps -- Keynote Transcript / Brandy Phipps -- Keynote Slides / Brandy Phipps -- Presentation Slides / Brian Kowalkowski -- Presentation Slides / Daniel Varisco -- Thought Piece / Daniel Varisco -- Picture of Opening Remarks / Casey Hoy -- Handout of Keywords / Melinda McClimans -- Handout of Program / Rebecca Gianotti -- Bibliography of Related Sources (Libguide) / Magda El-Sherbini -- Event Page from MESC Website with Speaker Bios, etc. -- Report on the Workshop / Melinda McClimans -- Flyer of Even

Measurement-based Classical Computation

Measurement-based quantum computation (MBQC) is a model of quantum computation, in which computation proceeds via adaptive single qubit measurements on a multiqubit quantum state. It is computationally equivalent to the circuit model. Unlike the circuit model, however, its classical analog is little studied. Here we present a classical analog of MBQC whose computational complexity presents a rich structure. To do so, we identify uniform families of quantum computations [refining the circuits introduced by Bremner Proc. R. Soc. A 467, 459 (2010)] whose output is likely hard to exactly simulate (sample) classically. We demonstrate that these circuit families can be efficiently implemented in the MBQC model without adaptive measurement and, thus, can be achieved in a classical analog of MBQC whose resource state is a probability distribution which has been created quantum mechanically. Such states (by definition) violate no Bell inequality, but, if widely held beliefs about computational complexity are true, they, nevertheless, exhibit nonclassicality when used as a computational resource—an imprint of their quantum origin

Severity Index for Suspected Arbovirus (SISA) : machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

Funding: This study was supported, in part, by the Department of Defense Global Emerging Infection Surveillance (https://health.mil/Military-Health-Topics/Combat-Support/Armed-Forces-Health-Surveillance-Branch/Global-Emerging-Infections-Surveillance-and-Response) grant (P0220_13_OT) and the Department of Medicine of SUNY Upstate Medical University (http://www.upstate.edu/medicine/). D.F., M.H. and P.H. were supported by the Ben Kean Fellowship from the American Society for Tropical Medicine and Hygeine (https://www.astmh.org/awards-fellowships-medals/benjamin-h-keen-travel-fellowship-in-tropical-medi). S.J.R and A.M.S-I were supported by NSF DEB EEID 1518681, NSF DEB RAPID 1641145 (https://www.nsf.gov/), A.M.S-I was additionally supported by the Prometeo program of the National Secretary of Higher Education, Science, Technology, and Innovation of Ecuador (http://prometeo.educacionsuperior.gob.ec/).Background: Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. Methodology/Principal findings: Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to select the final algorithm for each model. After eliminating those with missing data, the SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed that SISA and SISAL are not directly comparable to one another. Conclusions/Significance: Both SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in our dataset. These algorithms will need to be tested and validated on new data from future patients. Machine learning is a powerful prediction tool and provides an excellent option for new management tools and clinical assessment of arboviral infection.Publisher PDFPeer reviewe