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This Other Transaction Solicitation (OTS) Call (70RSAT18R00000002) - Land Border Biometric Exit Facial Recognition Verification Technologies - is issued against Department of Homeland Security (DHS), Science & Technology (S&T), Silicon Valley Innovation Program (SVIP), 5-Year Innovation Other Transaction Solicitation (OTS), HSHQDC-16-R-B0005 (current issue). The OTS describes the overarching program details whereas the call describes the specific problem set. The implementation of a biometric exit system at U.S. Ports of Entry (POEs) along land borders remains a challenge for several reasons including, but not limited to: environmental conditions, speed of travel, and lack of infrastructure. At land-based POEs, infrastructure is a key inhibitor to track the exit of travelers using biographic or biometric data. In addition, the need for uninhibited vehicle traffic flow at land borders is perhaps the greatest challenge for land-border POEs. To avoid having travelers in vehicles stop at border crossings, which could create significant traffic delays, U.S. Customs and Border Protection (CBP) is working to implement a face biometric entry-exit system in a way that poses the least impact on travel and trade. This call is looking for innovative solutions to capture facial recognition quality photos from travelers in order to facilitate identity checks without requiring occupants to leave the vehicle.

NineSigma, representing Ortho Baltic, invites proposals from qualified candidates in the fields of organic 3D shape recognition and the fields of artificial intelligence & machine learning suitable for dealing with organic 3D shapes. Selected candidates will participate in compensated consulting activities, with the potential for additional, long-term partnership opportunities in the future.

The Army Research Laboratory is in the process of developing a new initiative in the area of Cyber Security. The technical focus areas currently under consideration and peer review for this new initiative are: Risk Detection & Recognition Agility Psychosocial Effects The Psychosocial Effects focal area is intended to be crosscutting across multiple research domains. The Draft Program Announcement is expected to be released early 2013. Information regarding this program will be published on this webpage as it comes available.

PECASE: Each year NSF selects nominees for the Presidential Early Career Awards for Scientists and Engineers (PECASE) from among the most meritorious recent CAREER awardees. Selection for this award is based on two important criteria: 1) innovative research at the frontiers of science and technology that is relevant to the mission of NSF, and 2) community service demonstrated through scientific leadership, education, or community outreach. These awards foster innovative developments in science and technology, increase awareness of careers in science and engineering, give recognition to the scientific missions of the participating agencies, enhance connections between fundamental research and national goals, and highlight the importance of science and technology for the Nation's future. Individuals cannot apply for PECASE.

In recognition of the importance of research to the advancement of librarianship and information science, OCLC and ALISE promote independent research that helps integrate new technologies that offer innovative approaches and contributes to a better understanding of the information environment and user expectations and behaviors. Research related (but not limited) to the following areas is encouraged: Impact of digital technology on libraries, museums, and archives Social media, learning, and information-seeking behavior New developments in knowledge organization (metadata, social tagging, linked data, etc.)

Computing systems have undergone a fundamental transformation from the single-core processor-devices of the turn of the century to today's ubiquitous and networked devices with multicore/many-core processors along with warehouse-scale computing via the cloud. At the same time, semiconductor technology is facing fundamental physical limits and single-processor performance has plateaued. This means that the ability to achieve performance improvements through improved processor technologies alone has ended.In recognition of this obstacle, the recent National Strategic Computing Initiative (NSCI) encourages collaborative efforts to develop, "over the next 15 years, a viable path forward for future high-performance computing (HPC) systems even after the limits of current semiconductor technology are reached (the 'post-Moore's Law era')." Exploiting parallelism is one of the most promising directions to meet these performance demands. While parallelism has already been studied extensively and is a reality in today's computing technology, the expected scale of future systems is unprecedented. At extreme scales, factors that have small impacts today can become highly significant.

Computing systems have undergone a fundamental transformation from the single-core processor-devices of the turn of the century to today's ubiquitous and networked devices with multi-core/many-core processors along with warehouse-scale computing via the cloud. At the same time, semiconductor technology is facing fundamental physical limits and single-processor performance has plateaued. This means that the ability to achieve performance improvements through improved processor technologies alone has ended. In recognition of this obstacle, the recent National Strategic Computing Initiative (NSCI) encourages collaborative efforts to develop, "over the next 15 years, a viable path forward for future high-performance computing (HPC) systems even after the limits of current semiconductor technology are reached (the 'post-Moore's Law era')."

Computing systems have undergone a fundamental transformation from the single-core processor-devices of the turn of the century to today's ubiquitous and networked devices with multi-core/many-core processors along with warehouse-scale computing via the cloud. At the same time, semiconductor technology is facing fundamental physical limits and single-processor performance has plateaued. This means that the ability to achieve performance improvements through improved processor technologies alone has ended. In recognition of this obstacle, the recent National Strategic Computing Initiative (NSCI) encourages collaborative efforts to develop, "over the next 15 years, a viable path forward for future high-performance computing (HPC) systems even after the limits of current semiconductor technology are reached (the 'post-Moore's Law era')."

A grand challenge in computing is the creation of machines that can proactively interpret and learn from data in real time, solve unfamiliar problems using what they have learned, and operate with the energy efficiency of the human brain. While complex machine-learning algorithms and advanced electronic hardware (henceforth referred to as 'hardware') that can support large-scale learning have been realized in recent years and support applications such as speech recognition and computer vision, emerging computing challenges require real-time learning, prediction, and automated decision-making in diverse domains such as autonomous vehicles, military applications,healthcare informatics and business analytics. A salient feature of these emerging domains is the large and continuously streaming data sets that these applications generate, which must be processed efficiently enough to support real-time learning and decision making based on these data. This challenge requires novel hardware techniques and machine-learning architectures.This solicitation seeks to lay the foundation for next-generation co-design of RTML algorithms and hardware, with the principal focus on developing novel hardware architectures and learning algorithms in which all stages of training (including incremental training, hyperparameter estimation, and deployment) can be performed in real time. The National Science Foundation (NSF) and the Defense Advanced Research Projects Agency (DARPA) are teaming up through this Real-Time Machine Learning (RTML) program to explore high-performance, energy-efficient hardware and machine-learning architectures that can learn from a continuous stream of new data in real time, through opportunities for post-award collaboration between researchers supported by DARPA and NSF.

A grand challenge in computing is the creation of machines that can proactively interpret and learn from data in real time, solve unfamiliar problems using what they have learned, and operate with the energy efficiency of the human brain. While complex machine-learning algorithms and advanced electronic hardware (henceforth referred to as 'hardware') that can support large-scale learning have been realized in recent years and support applications such as speech recognition and computer vision, emerging computing challenges require real-time learning, prediction, and automated decision-making in diverse domains such as autonomous vehicles, military applications, healthcare informatics and business analytics.

Hardware and/or software technologies to obtain the following kinds of information from a human face through cameras or other devices. Any types of measurement hardware including devices employing infrared, sonic, voice recognition or other technologies should be in the scope.

NSF has long supported transformative research in artificial intelligence (AI) and machine learning (ML). The resulting innovations offer new levels of economic opportunity and growth, safety and security, and health and wellness, intended to be shared across all segments of society. Broad acceptance and adoption of large-scale deployments of AI systems rely critically on their trustworthiness which, in turn, depends on the ability to assess and demonstrate the fairness (including broad accessibility and utility), transparency, explainability, and accountability of such systems. For example, the behavior of algorithms for face recognition, speech, and language, especially when integrated into decision support systems applied across different segments of society, would benefit from new foundational research in fairness of AI systems. NSF and Amazon are partnering to jointly support computational research focused on fairness in AI, with the goal of contributing to trustworthy AI systems that are readily accepted and deployed to tackle grand challenges facing society. Specific topics of interest include, but are not limited to transparency, explainability, accountability, potential adverse biases and effects, mitigation strategies, algorithmic advances, fairness objectives, validation of fairness, and advances in broad accessibility and utility. Funded projects will enable broadened acceptance of AI systems, helping the U.S. further capitalize on the potential of AI technologies. Although Amazon provides partial funding for this program, it will not play a role in the selection of proposals for award.

Computing systems have undergone a fundamental transformation from the single-core processor-devices of the turn of the century to today's ubiquitous and networked devices with multicore/many-core processors along with warehouse-scale computing via the cloud. At the same time, semiconductor technology is facing fundamental physical limits and single-processor performance has plateaued. This means that the ability to achieve performance improvements through improved processor technologies alone has ended. In recognition of this obstacle, the recent National Strategic Computing Initiative (NSCI) encourages collaborative efforts to develop, "over the next 15 years, a viable path forward for future high-performance computing (HPC) systems even after the limits of current semiconductor technology are reached (the 'post-Moore's Law era')."

The NSF ADVANCE program provides grants to enhance the systemic factors that support equity and inclusion and to mitigate the systemic factors that create inequities in the academic profession and workplaces. Systemic (or organizational) inequities may exist in areas such as policy and practice as well as in organizational culture and climate. For example, practices in academic departments that result in the inequitable allocation of service or teaching assignments may impede research productivity, delay advancement, and create a culture of differential treatment and rewards. Similarly, policies and procedures that do not mitigate implicit bias in hiring, tenure, and promotion decisions could lead to women and racial and ethnic minorities being evaluated less favorably, perpetuating historical under-participation in STEM academic careers and contributing to an academic climate that is not inclusive. All NSF ADVANCE proposals are expected to use intersectional approaches in the design of systemic change strategies in recognition that gender, race and ethnicity do not exist in isolation from each other and from other categories of social identity. The solicitation includes four funding tracks: Institutional Transformation (IT), Adaptation, Partnership, and Catalyst, in support of the NSF ADVANCE program goal to broaden the implementation of systemic strategies that promote equity for STEM faculty in academic workplaces and the academic profession.