Binghamton University State University of New York

www.binghamton.edu
Binghamton, NY, United States

Binghamton University, or Binghamton University, State University of New York, locally referred to as BU, is a public research university in the U.S. state of New York. The university is one of the four university centers in the State University of New York system. Since its establishment in 1946, the university has grown from a small liberal arts college, Harpur College, to a large doctoral-granting institution, presently consisting of six colleges and schools, and is now home to more than 16,000 undergraduate and graduate students. The legal and official name of the university is the State University of New York at Binghamton.Binghamton University is currently ranked 88th among the 201 national universities in U.S. News & World Report '​s 2015 America's Best Colleges and Universities ranking, and internationally, it is ranked 701+ according to QS University Rankings. It has been called a "Public Ivy" by Greenes' Guide. The Carnegie Foundation for the Advancement of Teaching has classified the university as Research University with high research activity. Binghamton University is famous for the quality of education given the affordable price. For many years, the university has been ranked as one of the top 10 best-value public colleges.Although the university's mailing address is in Binghamton, its main campus is actually located in the town of Vestal, with a secondary education center located in downtown Binghamton. The Vestal campus is listed as a census-designated place for statistical purposes and had a residential population of 6,177 as of the 2010 census. Wikipedia.

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Spear L.P.,Binghamton University State University of New York
Neurotoxicology and Teratology | Year: 2014

Adolescence is an evolutionarily conserved developmental period characterized by notable maturational changes in the brain along with various age-related behavioral characteristics, including the propensity to initiate alcohol and other drug use and consume more alcohol per occasion than adults. After a brief review of adolescent neurobehavioral function from an evolutionary perspective, the paper will turn to assessment of adolescent alcohol sensitivity and consequences, with a focus on work from our laboratory. After summarizing evidence showing that adolescents differ considerably from adults in their sensitivity to various effects of alcohol, potential contributors to these age-typical sensitivities will be discussed, and the degree to which these findings are generalizable to other drugs and to human adolescents will be considered. Recent studies are then reviewed to illustrate that repeated alcohol exposure during adolescence induces behavioral, cognitive, and neural alterations that are highly specific, replicable, persistent and dependent on the timing of the exposure. Research in this area is in its early stages, however, and more work will be necessary to characterize the extent of these neurobehavioral alterations and further determine the degree to which observed effects are specific to alcohol exposure during adolescence. © 2013 Elsevier Inc.


Spear L.P.,Binghamton University State University of New York
Physiology and Behavior | Year: 2015

There are two key alcohol use patterns among human adolescents that confer increased vulnerability for later alcohol abuse/dependence, along with neurocognitive alterations: (a) early initiation of use during adolescence, and (b) high rates of binge drinking that are particularly prevalent late in adolescence. The central thesis of this review is that lasting neurobehavioral outcomes of these two adolescent exposure patterns may differ. Although it is difficult to disentangle consequences of early use from later binge drinking in human studies given the substantial overlap between groups, these two types of problematic adolescent use are differentially heritable and hence separable to some extent. Although few studies using animal models have manipulated alcohol exposure age, those studies that have have typically observed timing-specific exposure effects, with more marked (or at least different patterns of) lasting consequences evident after exposures during early-mid adolescence than late-adolescence/emerging adulthood, and effects often restricted to male rats in those few instances where sex differences have been explored. As one example, adult male rats exposed to ethanol during early-mid adolescence (postnatal days [P] 25-45) were found to be socially anxious and to retain adolescent-typical ethanol-induced social facilitation into adulthood, effects that were not evident after exposure during late-adolescence/emerging adulthood (P45-65); exposure at the later interval, however, induced lasting tolerance to ethanol's social inhibitory effects that was not evident after exposure early in adolescence. Females, in contrast, were little influenced by ethanol exposure at either interval. Exposure timing effects have likewise been reported following social isolation as well as after repeated exposure to other drugs such as nicotine (and cannabinoids), with effects often, although not always, more pronounced in males where studied. Consistent with these timing-specific exposure effects, notable maturational changes in brain have been observed from early to late adolescence that could provide differential neural substrates for exposure timing-related consequences, with for instance exposure during early adolescence perhaps more likely to impact later self-administration and social/affective behaviors, whereas exposures later in adolescence may be more likely to influence cognitive tasks whose neural substrates (such as the prefrontal cortex [PFC]) are still undergoing maturation at that time. More work is needed, however to characterize timing-specific effects of adolescent ethanol exposures and their sex dependency, determine their neural substrates, and assess their comparability to and interactions with adolescent exposure to other drugs and stressors. Such information could prove critical for informing intervention/prevention strategies regarding the potential efficacy of efforts directed toward delaying onset of alcohol use versus toward reducing high levels of use and risks associated with that use later in adolescence. late adolescent alcohol exposure may differ. © 2015 Elsevier Inc.


Spear L.P.,Binghamton University State University of New York
Neuroscience and Biobehavioral Reviews | Year: 2016

Studies using animal models of adolescent exposure to alcohol, nicotine, cannabinoids, and the stimulants cocaine, 3,4-methylenedioxymethampethamine and methamphetamine have revealed a variety of persisting neural and behavioral consequences. Affected brain regions often include mesolimbic and prefrontal regions undergoing notable ontogenetic change during adolescence, although it is unclear whether this represents areas of specific vulnerability or particular scrutiny to date. Persisting alterations in forebrain systems critical for modulating reward, socioemotional processing and cognition have emerged, including apparent induction of a hyper-dopaminergic state with some drugs and/or attenuations in neurons expressing cholinergic markers. Disruptions in cognitive functions such as working memory, alterations in affect including increases in social anxiety, and mixed evidence for increases in later drug self-administration has also been reported. When consequences of adolescent and adult exposure were compared, adolescents were generally found to be more vulnerable to alcohol, nicotine, and cannabinoids, but generally not to stimulants. More work is needed to determine how adolescent drug exposure influences sculpting of the adolescent brain, and provide approaches to prevent/reverse these effects. © 2016 Elsevier Ltd


Whittingham M.S.,Binghamton University State University of New York
Proceedings of the IEEE | Year: 2012

Advanced energy storage has been a key enabling technology for the portable electronics explosion. The lithium and Ni-MeH battery technologies are less than 40 years old and have taken over the electronics industry and are on the same track for the transportation industry and the utility grid. In this review, energy storage from the gigawatt pumped hydro systems to the smallest watt-hour battery are discussed, and the future directions predicted. If renewable energy, or even lower cost energy, is to become prevalent energy storage is a critical component in reducing peak power demands and the intermittent nature of solar and wind power. An electric economy will demand more electrification of the transportation sector and it is likely that all vehicles sold by the end of this decade will have some level of hybridization. Energy storage capabilities in conjunction with the smart grid are expected to see a massive leap forward over the next 25 years. © 2012 IEEE.


Whittingham M.S.,Binghamton University State University of New York
Chemical Reviews | Year: 2014

There is a growing demand for energy storage, for intermittent renewable energy such as solar and wind power, for transportation, and for the myriad portable electronic devices. The presence of carbon leads creates a reducing atmosphere, preventing the oxidation of the ferrous formed to ferric. The transient change of lattice constant during the two-phase reaction is clearly observed by the time-resolved X-ray diffraction measurement. Once the lithium is disordered, then the reaction will proceed by a single phase so long as the ordering time is longer than the reaction time. The wider the single-phase regions, the narrower is the miscibility gap and the smaller is the lattice mismatch between the lithium-rich and lithium-poor materials. In addition, once the lithium disorder is generated, then if the time it takes to order is greater than the reaction time, phase separation will not occur and the system will stay disordered throughout the reaction.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: CROSS-EF ACTIVITIES | Award Amount: 867.54K | Year: 2016

Yams (Dioscorea species) are among the primary agricultural commodities and major staple crop in Africa, India, South East Asia, South America and the Caribbean. However, the crop is laden with serious production problems arising from factors such as anthracnose disease, pests, and declines in soil fertility. This project seeks to revolutionize yam production in developing countries through the development and field-testing of low-cost genotyping/phenotyping biosensors that contain an integrated paper-based biobattery. These proof-of-concept devices will provide self-sustainable, field-tested sensing systems that can be used to detect the presence of disease causing pathogens by smallholder farmers in the developing world. The project will provide international interdisciplinary research training for students and postdoctoral researchers. All project outcomes will be available for use by the scientific community. DNA sequences will be available through GenBank. Information about biosensors will be provided through multiple mechanisms that include peer-reviewed publications as well as organized field-testing by smallholder farmers using standard protocols and handouts.

This project will address some of the fundamental research and constraint issues associated with decreased yam production in developing countries. This project aims to develop and characterize the next generation of low-cost biosensors that can be used to assess differences in virulence and inter- and/or intraspecific genetic diversity in fungal isolates that cause anthracnose diseases in Dioscorea alata and Dioscorea cayanensis, two species of yams. Specific objectives are to (i) conduct molecular and biochemical characterization of healthy and fungal-infected yam varieties to identify DNA markers associated with up to 150-180 field-collected fungal isolates; ii) use markers as the basis for designing a multiplex SNP paper-based bioassay to detect the presence of fungal pathogens; iii) attach biosensor that is powered by a novel microbial-based bio-battery to the back of a smartphone; sensor signals could be read visually or via custom-based applications (apps) to be developed for smartphones; and, iv) develop standardized protocols for screening yams for anthracnose resistance using the new biosensing tools as a first step to deploying the technology in the field. It is anticipated that the proposed devices can serve as a model for transforming low-cost tools into precision agriculture to promote increased food production in developing countries in developing countries. In addition, the project can potentially shift the paradigm for flexible and stackable paper-based batteries by enabling, through their architecture and design, exceptional electrical characteristics and functionalities.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: Secure &Trustworthy Cyberspace | Award Amount: 575.88K | Year: 2016

The project focuses on advancing the field of digital image steganography -- a covert way of communication in which information is hidden in other objects, such as digital media files, to assure privacy. For a secure steganographic system, it should be impossible to prove the presence of hidden data. Achieving this level of security in practice is extraordinarily difficult because digital media is hard to describe using statistical models with accuracy necessary to guarantee perfect security.

This project works with devices that acquire an image to learn a sufficiently accurate model within which it becomes possible to construct steganography with a verifiable level of security and performance that can be contrasted with theoretically achievable limits. The result is a novel advanced privacy tool needed in countries that prohibit the use of encryption or in a hostile environment when there is need to communicate without attracting attention via channels in control of an adversary or via public, insecure channels. On the other hand, a deeper understanding of the limits of covert communication will facilitate better defense against such methods of deception, an effort recognized as steganalysis. The project explores commercial applications of data hiding include signal authentication, integrity verification, and secure data dissemination. This research contributes to trusted information exchange, data mining, information assurance, network and computer security, counter-deception, and intrusion detection and its prevention.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: CAREER: FACULTY EARLY CAR DEV | Award Amount: 500.00K | Year: 2016

This Faculty Early Career Development (CAREER) award will enable a novel additive manufacturing methodology for printing thin-films of nanoparticles. In additive manufacturing, objects with complex shapes are built up by depositing materials layer-by-layer. This approach has revolutionized the creation of large three-dimensional prototypes. However, it is not yet feasible to use this technology to print well-ordered layers at fine length scales. The ability to precisely control the position and orientation of the nanoparticles (i.e. the microstructure) within a thin-film is essential since this governs the electrical, mechanical, and optical properties of the film. The next-generation of high-performance devices for use in energy production, health care, and security will require a high-throughput manufacturing methodology that provides fine feature control. This award supports fundamental research on electrospray printing, a process that has the potential to offer precise control over thin-film structure while maintaining high-volume production. With this technique, electric fields are used to control the production of thin functional layers from nanoparticle solutions. This research will contribute to the acceleration of manufacturing innovation in the United States by enabling the creation of high technology jobs in the area of printed functional nanomaterials. Additionally, this effort will train and motivate students of varied levels and backgrounds in additive manufacturing through their engagement in interdisciplinary research and international partnerships.

Establishing the processing-structure-property relationships for thin-films of functional materials produced by electrospray printing will enable the process to become a viable manufacturing method. Key findings include identifying how the excess nanoparticle electric charge imparted by electrospray governs the structure of a printed deposit. This new knowledge will facilitate the creation of a novel printing technique incorporating substrate-level Coulombic intervention to control the microstructure at a scale that is yet to be achieved. Coulombic intervention uses a fringing field created in the vicinity of a target substrate to accurately steer and position the charged particles emitted by electrospray. Experimental studies and simulations will be used to establish a comprehensive framework for electrospray printing. Probabilistic modeling will provide critical insight into the evolution of an electrospray printed deposit that is difficult to obtain experimentally. This model will ultimately be used to design the structure of printed deposits for targeted functionality. The influence of substrate topology and material properties on film structure will also be elucidated to advance the versatility of electrospray printing.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: COMPUTER SYSTEMS | Award Amount: 488.37K | Year: 2016

The number of users streaming mobile video over the Internet has increased at an unprecedented rate throughout the past decade. Because video streaming is a mainstay of mobile computing, it is important that the best possible experience is delivered to users. Many mathematical algorithms have been proposed to improve quality in video-streaming-related domains. Typically, the parameters of these algorithms are established based on machine-centric indicators of video quality. Although researchers have attempted to connect these indicators with true user-perceived quality, in practice, there is often a disconnect. This project aims to improve directly-measurable indicators of user satisfaction in mobile video streaming by taking into account both individual user preferences as well as a users tolerance for less than perfect quality in a specific video.

The proposed research will improve user-specific indicators by connecting problems in video streaming with problems in multi-task learning and collaborative filtering. These machine learning strategies are especially effective for prediction when large amounts of data are available. This effectiveness on large-scale learning fits well into this projects proposed context of improving user-facing indications of satisfaction. These indications include video abandonment, video session times, and collected navigation commands. Unlike user-surveys, these metrics can be collected at large scales through automated tooling in the video player. This research will investigate strategies that combine these large scale measurements with predictions from machine learning approaches toward selecting algorithm parameters that produce the most improvement in user-facing quality. This project will explore such parameter selection strategies in the context of improving user-perceived dynamic adaptive streaming quality. It will also explore such strategies to maintain a fixed level of user-facing quality while reducing mobile display power consumption via backlight scaling. Demonstrations of the approaches produced by this project will be featured in courses at the PIs institution and will be used to draw undergraduate interest toward computer science research.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: COMPUTING RES INFRASTRUCTURE | Award Amount: 483.58K | Year: 2016

This project will extend and sustain a widely-used data infrastructure for studying human emotion, hosted at the lead investigators university and available to the research community. The first two versions of the dataset (BP4D and BP4D+) contain videos of people reacting to varied emotion-eliciting situations, their self-reported emotion, and expert annotations of their facial expression. Version 1, BP4D (n=41), has been used by over 100 research groups and supported a successful community competition around recognizing emotion. The second version (BP4D+) adds participants (n = 140), thermal imaging, and measures of peripheral physiology. The current project greatly broadens and extends this corpus to produce a new dataset (BP4D++) that enables deep-learning approaches, increases generalizability, and builds research infrastructure and community in computer and behavioral science. The collaborators will (1) increase participant diversity; 2) add videos of pairs of people interacting to the current mix of individual and interviewer-mediated video; 3) increase the number of participants to meet the demands of recent advances in big data approaches to machine learning; and 4) expand the size and scope of annotations in the videos. They will also involve the community through an oversight and coordinating consortium that includes researchers in computer vision, biometrics, robotics, and cognitive and behavioral science. The consortium will be composed of special interest groups that focus on various aspects of the corpus, including groups responsible for completing the needed annotations, generating meta-data, and expanding the database application scope. Having an infrastructure to support emotion recognition research matters because computer systems that interact with people (such as phone assistants or characters in virtual reality environments) will be more useful if they react appropriately to what people are doing, thinking, and feeling.

The team will triple the number of participants in the combined corpora to 540. They will develop a dyadic interaction task and capture data from 100 interacting dyads to support dynamic modeling of interpersonal influence across expressive behavior and physiology, as well as analysis of emotional synchrony. They will increase the density of facial annotations to about 15 million frames in total, allowing the database to become sufficiently large to support deep-learning approaches to multimodal emotion detection. These annotations will be accomplished through a hybrid approach that combines expert coding using the Facial Action Coding System, automated face analysis, and crowdsourcing with expert input from the research community. Finally, the recorded data will be augmented with a wide range of meta-data derived from 2D videos, 3D videos, thermal videos, and physiological signals. To ensure the community is involved in sustaining the infrastructure, in addition to the governance consortium described above, the investigators will involve the community in jointly building both APIs that allow adding meta-data and annotations and tools to support the submission and evaluation of new recognition algorithms, then organizing community-wide competitions using those tools. The research team will also reach out to new research communities around health computing, biometrics, and affective computing to widen the utility of the enhanced infrastructure, grow the community of expert annotators through training workshops, and build an educational community around the infrastructure that facilitates the development and sharing of course materials that use it. Long-term, the infrastructure will be funded through a combination of commercial licensing and support from the lead universitys system administration group.

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