The University of California, Merced , is the tenth and newest of the University of California campuses. Located in the San Joaquin Valley in unincorporated Merced County, California, near Merced. UC Merced is the first American research university to be built in the 21st century. Most UC Merced students are from California with enrollment nearly evenly divided between Southern California, Central Valley, and Northern California.UC Merced claims to be the only institution in the United States to have all of its buildings on campus to be LEED certified. Its Triple Net Zero Commitment is expected to create zero net landfill waste and zero net greenhouse gas emissions by the year 2020. Wikipedia.
Tian L.,University of California at Merced
Physical Review Letters | Year: 2012
Optomechanical systems with strong coupling can be a powerful medium for quantum state engineering of the cavity modes. Here, we show that quantum state conversion between cavity modes of distinctively different wavelengths can be realized with high fidelity by adiabatically varying the effective optomechanical couplings. The conversion fidelity for Gaussian states is derived by solving the Langevin equation in the adiabatic limit. Meanwhile, we also show that traveling photon pulses can be transmitted between different input and output channels with high fidelity and the output pulse can be engineered via the optomechanical couplings. © 2012 American Physical Society.
Tian L.,University of California at Merced
Physical Review Letters | Year: 2013
Entanglement is a key element in quantum information processing. Here, we present schemes to generate robust photon entanglement via optomechanical quantum interfaces in the strong coupling regime. The schemes explore the excitation of the Bogoliubov dark mode and the destructive quantum interference between the bright modes of the interface, similar to electromagnetically induced transparency, to eliminate leading-order effects of the mechanical noise. Both continuous-variable and discrete-state entanglements that are robust against the mechanical noise can be achieved. The schemes can be used to generate entanglement in hybrid quantum systems between, e.g., microwave photon and optical photon. © 2013 American Physical Society.
Westerling A.L.,University of California at Merced
Philosophical Transactions of the Royal Society B: Biological Sciences | Year: 2016
Prior work shows western US forest wildfire activity increased abruptly in the mid-1980s. Large forest wildfires and areas burned in them have continued to increase over recent decades, with most of the increase in lightning-ignited fires. Northern US Rockies forests dominated early increases in wildfire activity, and still contributed 50% of the increase in large fires over the last decade. However, the percentage growth in wildfire activity in Pacific northwestern and southwestern US forests has rapidly increased over the last two decades. Wildfire numbers and burned area are also increasing in non-forest vegetation types. Wildfire activity appears strongly associated with warming and earlier spring snowmelt. Analysis of the drivers of forest wildfire sensitivity to changes in the timing of spring demonstrates that forests at elevations where the historical mean snow-free season ranged between two and four months, with relatively high cumulative warm-season actual evapotranspiration, have been most affected. Increases in large wildfires associated with earlier spring snowmelt scale exponentially with changes in moisture deficit, and moisture deficit changes can explain most of the spatial variability in forest wildfire regime response to the timing of spring. © 2016 The Author(s) Published by the Royal Society. All rights reserved.
Kelley A.M.,University of California at Merced
Annual Review of Physical Chemistry | Year: 2010
This article reviews the experimental and theoretical aspects of vibrational hyper-Raman scattering from molecules. Particular emphasis is placed on hyper-Raman scattering enhanced by nanostructured metal surfaces and by two-photon electronic resonance. Copyright © 2010 by Annual Reviews. All rights reserved.
Agency: NSF | Branch: Standard Grant | Program: | Phase: EVOLUTIONARY GENETICS | Award Amount: 500.00K | Year: 2016
Humans have adapted to many challenging environments during our evolution. For example, different temperatures, diets, pathogens and altitudes have led to local adaptations. Adaptations occur through the selection and increase of beneficial mutations in genes in a population, which can arise spontaneously by random mutation or can be acquired through admixture with another population. Analysis of human genomes has shown that some of our beneficial mutations have come from archaic human populations like the Neanderthals and Denisovans, facilitated by interbreeding between modern humans and those groups tens of thousands of years ago. This process is referred to as adaptive introgression. While there are a few examples of beneficial mutations in genes arising from adaptive introgression, in this project tools will be built to scan human genomes to identify more candidate genes for adaptive introgression and to fully characterize the importance of this process in many human populations. Importantly, the project will involve training of undergraduate and graduate students in computational science and genomics, and the tools created will be freely accessible for others to use. In addition, a new interdisciplinary course that bridges data analysis of human genetic variation, programming, statistics and biology will be offered to undergraduate students.
Detecting and characterizing adaptation has mostly been approached through two models: selection on de novo mutations (SDN) or selection on standing variation (SSV). Therefore it is assumed that a population either has to wait for a beneficial mutation to arise de novo or it harbors enough neutral standing variation that can become beneficial under a change in environment. However, most populations do not live in isolation and have exchanged genetic variants with other populations through admixture (gene-flow from a donor population to a recipient population). This process is an evolutionary force that may accelerate adaptation in the recipient population. The PI will model positive selection and gene-flow jointly to investigate the patterns of genetic variation under this model, to compare and contrast to the two other models of positive selection (SDN, SSV), to determine what summaries of the data accurately distinguishes adaptive gene flow from SDN and SSV, to develop novel statistics that accurately detect this type of selection and to develop statistical and computational tools to scan genomes to identify candidate regions. These tools will be applied to real data sets in humans and in other organisms. The broader impacts include training a postdoc, students, building a project-oriented course that integrates multiple disciplines (programming, statistics and modeling) that will teach students to visualize biological data sets for exploratory analyses, to test hypothesis and to fit models to the data, and bringing science to high school students through a series of lectures. Finally, all computational tools developed under this grant will be made freely available to the scientific community.
Agency: NSF | Branch: Standard Grant | Program: | Phase: SEES Coastal | Award Amount: 1.75M | Year: 2016
Coastal upwelling in eastern boundary currents drives some of the Earths most productive and biodiverse marine ecosystems. While the contributions of upwelling to marine ecosystems are well-recognized, critical implications of upwelling for coastal terrestrial ecosystems are not. The main hypothesis of this study is that ocean-atmosphere-land interactions, mediated by coastal fog, cause upwelling to drive one of the Earths most productive terrestrial ecosystems, coast redwood forests. The study further hypothesizes that learning about climate change impacts to this iconic species can influence perceptions of climate change. The future resilience of coast redwoods is now of critical concern due to the detection of a decline in coastal fog that may be associated with anthropogenic climate change and expanding urban heat islands. However, this coastal ocean-atmosphere-land system has received relatively little attention. This is largely due to the fact that until recently, earth system models were not capable of simulating the coastal fog that links the component systems, making it difficult to interpret historical observations or to project climate change impacts on these integrated systems. Furthermore, fundamental ecological measurements are obscured by the presence of fog, making it very difficult to understand how coast redwoods will respond to changes in fog. Understanding this coastal ocean-atmosphere-land system will not only provide much needed information for coast redwood resilience, but will also establish a foundation for future work on critical fog-mediated vulnerabilities to a range of coastal terrestrial, riparian, and intertidal ecosystems, and human-affected sectors including irrigated agriculture, wildfire management, public health, air and ground traffic, tourism, and urban energy and water consumption.
In this project, an interdisciplinary team will leverage recent advances in regional ocean-atmosphere-land modeling and laser spectrometry to provide an unprecedented exploration of this coastal integrated natural-human system. Activities to broaden the impacts of the project include outreach to land managers and interpreters, interactions between modeling and public outreach, participation in a climate change documentary, media outreach, and interdisciplinary training of Hispanic Serving Institution undergraduates and two postdoctoral scholars.
The results of this project will be (1) a process-level understanding of the coastal fog-mediated interactions between ocean-atmosphere circulation and coast redwood ecophysiology, (2) projections of fog, coast redwood resilience, and upwelling under anthropogenic scenarios of global greenhouse gas forcing and local urban heat islands along with the ocean-atmosphere-land feedbacks to this forcing, and (3) an understanding of how the projected vulnerabilities of this iconic coastal species can influence human perceptions about climate change and climate-friendly behaviors. The project will focus on three activities with essential linkages across the team: (1) U.S. Pacific Coast simulations of ocean and atmospheric circulation will be used to understand how the timing and strength of upwelling interacts with the atmosphere and coastal land systems to produce and maintain coastal fog (Samelson, Skyllingstad, de Szoeke, Oregon State Univ.; OBrien, Lawrence Berkeley National Laboratory). (2) Laser spectrometer measurements of atmospheric carbonyl sulfide in coast redwood forests will provide the unique capability of measuring primary productivity and physiological regulation in the presence of fog (Campbell, UC Merced; Berry, Carnegie Institution; Dawson, UC Berkeley; Seibt, UCLA). The resulting ecological information will be used to develop regional simulations of coast redwood physiology under current and projected fog regimes. (3) The new scientific understanding of coast redwood resilience will form the foundation of surveys measuring human attitudes, knowledge, values, place connections, and current climate-related behaviors with regard to coast redwoods (Ardoin, Stanford Univ.). These survey data, along with the ecological data from ongoing research, will create a foundation for educational interventions that build on peoples current place relationships, understandings, and existing behaviors.
Agency: NSF | Branch: Standard Grant | Program: | Phase: Campus Cyberinfrastrc (CC-NIE) | Award Amount: 422.46K | Year: 2017
As the nations first new research university of the 21st Century, the University of California Merced (UC Merced) has quickly established itself as a regional leader in emerging research across a wide variety of areas. Many of these research activities involve very large data sets, data-intensive computation and data collaboration.
These endeavors require a reliable, robust and fast network that is different in its construction, management and use from existing internet networks. This new type of network is built for science data flows, has appropriate security measures and is dedicated to its special purpose: research. The Energy Sciences Network (ESNet) has developed a model for such a network called a Science DMZ. UC Merced is building a Science DMZ network, distinct from the regular network and dedicated for these advanced science applications. This new network cuts data transfer times, allows for seamless data collaboration and sets the stage for new discoveries on the UC Merced campus and beyond.
The project includes a dedicated Science DMZ campus edge router which allows for 10, 40 and 100 Gbps connections across campus and out to the wider scientific community. Each academic building has dedicated fiber optic cables connecting many devices (data servers, clusters and even science instruments) at speeds hundreds of times faster and much more reliably than current networks provide. This project will employ a different security model: by carefully controlling the physical topology of the network, security can be provided without the use of a firewall, vastly improving performance.
Agency: NSF | Branch: Standard Grant | Program: | Phase: NSF Research Traineeship (NRT) | Award Amount: 2.92M | Year: 2016
The world is bursting with data, not just in sheer amounts of it, but also in terms of complexity. Interdependencies among variables abound, and their relationships can change over time in intricate, nonlinear ways. Such complexities are common in nature and intelligent systems have evolved in biological organisms to adapt to these interdependencies and nonlinearities. More recently, engineers have begun to build intelligent systems for applications in health, security, and industry that can similarly adapt. The scientists who study intelligent adaptive systems in nature, as well as the engineers who build them in the lab, are increasingly in need of conceptual and technical abilities to deal with large, complex systems and datasets. These abilities provide a common basis for exchanging hypotheses and theories among mathematicians, physicists, biologists, cognitive scientists, computer scientists and engineers; all of whom work on common problems of adaptation, learning, regulation, and prediction. This National Science Foundation Research Traineeship (NRT) award to the University of California, Merced, will help the next generation of PhD students make interdisciplinary breakthroughs in theories and applications of intelligent adaptive systems. The project anticipates training 100 PhD students, including 50 funded trainees, from doctoral programs in applied mathematics, cognitive and information sciences, electrical engineering and computer science, mechanical engineering, physics, and quantitative and systems biology.
Prior research in cybernetics, connectionism, and complex adaptive systems focused on general principles of intelligent adaptive systems that cut across disciplines and domains. The NRT program will advance the next wave of research in this area, by delving more deeply into principles of learning and adaptation as they manifest across a wider range of biological, human, and technological systems. The training program includes an intensive computational basecamp, custom course modules on intelligent adaptive systems, lab rotations, communication skills development workshops, and industry networking opportunities. Taken together, these NRT activities will enable the trainees to achieve conceptual and technical capabilities for dealing with large, complex datasets. All NRT trainees will have the opportunity to learn about entrepreneurship, network with industry mentors, engage in professional development, and engage with the local community to educate, disseminate research, and develop outreach partnerships. The NRT program will transform the capacity for interdisciplinary research and education at UC Merced. At the institutional level, the NRT program will serve as a model for collaborative, interdisciplinary graduate education. An extensive recruitment plan will connect with and enhance resources and programs at other UC campuses and a number of Hispanic-Serving Institutions to increase the diversity of scientists and engineers working on intelligent adaptive systems. Finally, the NRT program will have a direct and transformative economic impact in Californias Central Valley, by fostering a culture of innovation and higher education in under-privileged communities.
The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.
Kelley A.M.,University of California at Merced
ACS Nano | Year: 2011
Phonon frequencies and eigenvectors, electron-phonon couplings, and the associated resonance Raman spectra have been calculated for approximately spherical, wurtzite form CdSe nanocrystals having radii of 1.4 to 2.3 nm and containing 318 to 1498 atoms. Calculations of the equilibrium geometries and phonon modes are carried out using an empirical force field, and the electron and hole wave functions are calculated as particle-in-a-sphere envelope functions multiplying the Bloch functions, with valence-band mixing included for the hole functions. The coupling of each phonon mode to the 1S e-1S3/2 and 1Se-2S3/2 excitations is evaluated directly from the change in Coulombic energy along the phonon coordinate. Ten to 50 different modes in each crystal have significant Huang-Rhys factors, clustered around two frequency regions: acoustic phonons at 20-40 cm-1 depending on crystal size, and optical phonons at 185-200 cm-1. The Huang-Rhys factors are larger for the acoustic modes than for the optical modes and decrease with increasing crystal size, and the Huang-Rhys factors for each group of modes are smaller for the 1S e-2S3/2 than for the 1Se-1S3/2 excitation. These results are compared with measurements of electron-phonon coupling in CdSe nanocrystals using different experimental techniques. © 2011 American Chemical Society.
Agency: NSF | Branch: Continuing grant | Program: | Phase: CENTERS FOR RSCH EXCELL IN S&T | Award Amount: 1.00M | Year: 2016
Center for Cellular and Biomolecular Machines
With National Science Foundation support, the University of California Merced will establish the Center for Cellular and Biomolecular Machines. The Center will use an interdisciplinary approach cutting across scientific and engineering methodologies to (i) pursue a fundamental understanding of the structure, dynamics and functioning of multi-scale biomolecular and cellular assemblies; (ii) use these fundamental principles for designing and developing novel bio-inspired functioning machines ranging from designer cells and tissue to diagnostic and therapeutic devices; and (iii) develop an integrated, interdisciplinary training program for graduate students that will combine physical and biological components with supervision of research and training experiences for undergraduate students and high school teachers.
Center research is organized around three subprojects based on the scales of the assemblies and processes involved. Subproject 1, entitled Biomolecular Machines, investigates circadian molecular clocks to develop sensors capable of monitoring specific biological processes inside cells in real time. Center researchers will investigate the molecular mechanisms of the cyanobacterial oscillator that is composed of three monomeric proteins that autonomously maintains time in vitro. Researchers will also develop proofs of concept for single-molecule biosensors and apply them to investigate the cellular events that occur during cardiac dysfunction in an in vivo model.
Entitled Macromolecular Assemblies and Hybrid Devices, subproject 2 will study material properties of innovative assemblies of biomolecules, inorganic matter and/or their mixtures to produce enhanced functionality and devices. This method will be applied to develop a sensor for Valley Fever that is caused by a fungal infection.
Subproject 3, entitled Cellular and Multicellular Systems, investigates large scale assemblies composed of multiple cells. Center research focuses on bacterial community motility and stem cell differentiation. Emphasis will be placed on the effect of mechanical force on cell fate.
Progress in these areas may lead to therapeutic improvements in human health and the implementation of design principles for building bio-inspired materials and machines.