Showing papers by "Georgia Institute of Technology published in 2015"

VQA: Visual Question Answering

Abstract: We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ~0.25M images, ~0.76M questions, and ~10M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines for VQA are provided and compared with human performance.

Advances in molecular quantum chemistry contained in the Q-Chem 4 program package

Abstract: A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Moller–Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly correlated Cr_2 dimer, exploring zeolite-catalysed ethane dehydrogenation, energy decomposition analysis of a charged ter-molecular complex arising from glycerol photoionisation, and natural transition orbitals for a Frenkel exciton state in a nine-unit model of a self-assembling nanotube.

Additive manufacturing technologies : 3D printing, rapid prototyping, and direct digital manufacturing

Abstract: This book covers in detail the various aspects of joining materials to form parts. A conceptual overview of rapid prototyping and layered manufacturing is given, beginning with the fundamentals so that readers can get up to speed quickly. Unusual and emerging applications such as micro-scale manufacturing, medical applications, aerospace, and rapid manufacturing are also discussed. This book provides a comprehensive overview of rapid prototyping technologies as well as support technologies such as software systems, vacuum casting, investment casting, plating, infiltration and other systems. This book also: Reflects recent developments and trends and adheres to the ASTM, SI, and other standards Includes chapters on automotive technology, aerospace technology and low-cost AM technologies Provides a broad range of technical questions to ensure comprehensive understanding of the concepts covered.

Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies: Evaluation Number 18

Abstract: This is the eighteenth in a series of evaluated sets of rate constants, photochemical cross sections, heterogeneous parameters, and thermochemical parameters compiled by the NASA Panel for Data Evaluation. The data are used primarily to model stratospheric and upper tropospheric processes, with particular emphasis on the ozone layer and its possible perturbation by anthropogenic and natural phenomena. The evaluation is available in electronic form from the following Internet URL: http://jpldataeval.jpl.nasa.gov/

Operation, Control, and Applications of the Modular Multilevel Converter: A Review

Abstract: The modular multilevel converter (MMC) has been a subject of increasing importance for medium/high-power energy conversion systems. Over the past few years, significant research has been done to address the technical challenges associated with the operation and control of the MMC. In this paper, a general overview of the basics of operation of the MMC along with its control challenges are discussed, and a review of state-of-the-art control strategies and trends is presented. Finally, the applications of the MMC and their challenges are highlighted.

Progress in triboelectric nanogenerators as a new energy technology and self-powered sensors

Abstract: Ever since the first report of the triboelectric nanogenerator (TENG) in January 2012, its output area power density has reached 500 W m−2, and an instantaneous conversion efficiency of ∼70% and a total energy conversion efficiency of up to 85% have been demonstrated. We provide a comprehensive review of the four modes, their theoretical modelling, and the applications of TENGs for harvesting energy from human motion, walking, vibration, mechanical triggering, rotating tire, wind, flowing water and more as well as self-powered sensors.

Bibliometrics: The Leiden Manifesto for research metrics

Abstract: Nutzen Sie diese zehn Grundsatze um Forschung zu bewerten, drangen Diana Hicks, Paul Wouters und Kollegen.

A regression approach to speech enhancement based on deep neural networks

Abstract: In contrast to the conventional minimum mean square error (MMSE)-based noise reduction techniques, we propose a supervised method to enhance speech by means of finding a mapping function between noisy and clean speech signals based on deep neural networks (DNNs). In order to be able to handle a wide range of additive noises in real-world situations, a large training set that encompasses many possible combinations of speech and noise types, is first designed. A DNN architecture is then employed as a nonlinear regression function to ensure a powerful modeling capability. Several techniques have also been proposed to improve the DNN-based speech enhancement system, including global variance equalization to alleviate the over-smoothing problem of the regression model, and the dropout and noise-aware training strategies to further improve the generalization capability of DNNs to unseen noise conditions. Experimental results demonstrate that the proposed framework can achieve significant improvements in both objective and subjective measures over the conventional MMSE based technique. It is also interesting to observe that the proposed DNN approach can well suppress highly nonstationary noise, which is tough to handle in general. Furthermore, the resulting DNN model, trained with artificial synthesized data, is also effective in dealing with noisy speech data recorded in real-world scenarios without the generation of the annoying musical artifact commonly observed in conventional enhancement methods.

Testing general relativity with present and future astrophysical observations

Abstract: One century after its formulation, Einstein's general relativity (GR) has made remarkable predictions and turned out to be compatible with all experimental tests. Most of these tests probe the theory in the weak-field regime, and there are theoretical and experimental reasons to believe that GR should be modified when gravitational fields are strong and spacetime curvature is large. The best astrophysical laboratories to probe strong-field gravity are black holes and neutron stars, whether isolated or in binary systems. We review the motivations to consider extensions of GR. We present a (necessarily incomplete) catalog of modified theories of gravity for which strong-field predictions have been computed and contrasted to Einstein's theory, and we summarize our current understanding of the structure and dynamics of compact objects in these theories. We discuss current bounds on modified gravity from binary pulsar and cosmological observations, and we highlight the potential of future gravitational wave measurements to inform us on the behavior of gravity in the strong-field regime.

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

Abstract: We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines.

WikiQA: A Challenge Dataset for Open-Domain Question Answering

Abstract: We describe the WIKIQA dataset, a new publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering. Most previous work on answer sentence selection focuses on a dataset created using the TREC-QA data, which includes editor-generated questions and candidate answer sentences selected by matching content words in the question. WIKIQA is constructed using a more natural process and is more than an order of magnitude larger than the previous dataset. In addition, the WIKIQA dataset also includes questions for which there are no correct sentences, enabling researchers to work on answer triggering, a critical component in any QA system. We compare several systems on the task of answer sentence selection on both datasets and also describe the performance of a system on the problem of answer triggering using the WIKIQA dataset.

Ultrahigh-Gain Photodetectors Based on Atomically Thin Graphene-MoS2 Heterostructures

Abstract: Due to its high carrier mobility, broadband absorption, and fast response time, the semi-metallic graphene is attractive for optoelectronics. Another two-dimensional semiconducting material molybdenum disulfide (MoS2) is also known as light- sensitive. Here we show that a large-area and continuous MoS2 monolayer is achievable using a CVD method and graphene is transferable onto MoS2. We demonstrate that a photodetector based on the graphene/MoS2 heterostructure is able to provide a high photogain greater than 10(8). Our experiments show that the electron-hole pairs are produced in the MoS2 layer after light absorption and subsequently separated across the layers. Contradictory to the expectation based on the conventional built-in electric field model for metal-semiconductor contacts, photoelectrons are injected into the graphene layer rather than trapped in MoS2 due to the presence of a perpendicular effective electric field caused by the combination of the built-in electric field, the applied electrostatic field, and charged impurities or adsorbates, resulting in a tuneable photoresponsivity.

Platinum-based nanocages with subnanometer-thick walls and well-defined, controllable facets

Abstract: A cost-effective catalyst should have a high dispersion of the active atoms, together with a controllable surface structure for the optimization of activity, selectivity, or both. We fabricated nanocages by depositing a few atomic layers of platinum (Pt) as conformal shells on palladium (Pd) nanocrystals with well-defined facets and then etching away the Pd templates. Density functional theory calculations suggest that the etching is initiated via a mechanism that involves the formation of vacancies through the removal of Pd atoms incorporated into the outermost layer during the deposition of Pt. With the use of Pd nanoscale cubes and octahedra as templates, we obtained Pt cubic and octahedral nanocages enclosed by {100} and {111} facets, respectively, which exhibited distinctive catalytic activities toward oxygen reduction.

Understanding ENSO Diversity

Abstract: El Nino–Southern Oscillation (ENSO) is a naturally occurring mode of tropical Pacific variability, with global impacts on society and natural ecosystems. While it has long been known that El Nino events display a diverse range of amplitudes, triggers, spatial patterns, and life cycles, the realization that ENSO’s impacts can be highly sensitive to this event-to-event diversity is driving a renewed interest in the subject. This paper surveys our current state of knowledge of ENSO diversity, identifies key gaps in understanding, and outlines some promising future research directions.

Recent developments in heterogeneous photocatalytic water treatment using visible light-responsive photocatalysts: a review

Abstract: Visible light-responsive photocatalytic technology holds great potential in water treatment to enhance purification efficiency, as well as to augment water supply through the safe usage of unconventional water sources. This review summarizes the recent progress in the design and fabrication of visible light-responsive photocatalysts via various synthetic strategies, including the modification of traditional photocatalysts by doping, dye sensitization, or by forming a heterostructure, coupled with π-conjugated architecture, as well as the great efforts made within the exploration of novel visible light-responsive photocatalysts. Background information on the fundamentals of heterogeneous photocatalysis, the pathways of visible light-responsive photocatalysis, and the unique features of visible light-responsive photocatalysts are presented. The photocatalytic properties of the resulting visible light-responsive photocatalysts are also covered in relation to the water treatment, i.e., regarding the photocatalytic degradation of organic compounds and inorganic pollutants, as well as photocatalytic disinfection. Finally, this review concludes with a summary and perspectives on the current challenges faced and new directions in this emerging area of research.

Shape-Controlled Synthesis of Colloidal Metal Nanocrystals: Thermodynamic versus Kinetic Products

Abstract: This Perspective provides a contemporary understanding of the shape evolution of colloidal metal nanocrystals under thermodynamically and kinetically controlled conditions. It has been extremely challenging to investigate this subject in the setting of one-pot synthesis because both the type and number of seeds involved would be changed whenever the experimental conditions are altered, making it essentially impossible to draw conclusions when comparing the outcomes of two syntheses conducted under different conditions. Because of the uncertainty about seeds, most of the mechanistic insights reported in literature for one-pot syntheses of metal nanocrystals with different shapes are either incomplete or ambiguous, and some of them might be misleading or even wrong. Recently, with the use of well-defined seeds for such syntheses, it became possible to separate growth from nucleation and therefore investigate the explicit role(s) played by a specific thermodynamic or kinetic parameter in directing the evolution of colloidal metal nanocrystals into a specific shape. Starting from single-crystal seeds enclosed by a mix of {100}, {111}, and {110} facets, for example, one can obtain colloidal nanocrystals with diversified shapes by adjusting various thermodynamic or kinetic parameters. The mechanistic insights learnt from these studies can also be extended to account for the products of conventional one-pot syntheses that involve self-nucleation only. The knowledge can be further applied to many other types of seeds with twin defects or stacking faults, making it an exciting time to design and synthesize colloidal metal nanocrystals with the shapes sought for a variety of fundamental studies and technologically important applications.

Redesigning photosynthesis to sustainably meet global food and bioenergy demand

Abstract: The world’s crop productivity is stagnating whereas population growth, rising affluence, and mandates for biofuels put increasing demands on agriculture. Meanwhile, demand for increasing cropland competes with equally crucial global sustainability and environmental protection needs. Addressing this looming agricultural crisis will be one of our greatest scientific challenges in the coming decades, and success will require substantial improvements at many levels. We assert that increasing the efficiency and productivity of photosynthesis in crop plants will be essential if this grand challenge is to be met. Here, we explore an array of prospective redesigns of plant systems at various scales, all aimed at increasing crop yields through improved photosynthetic efficiency and performance. Prospects range from straightforward alterations, already supported by preliminary evidence of feasibility, to substantial redesigns that are currently only conceptual, but that may be enabled by new developments in synthetic biology. Although some proposed redesigns are certain to face obstacles that will require alternate routes, the efforts should lead to new discoveries and technical advances with important impacts on the global problem of crop productivity and bioenergy production.

Recent Progress in Electronic Skin.

Abstract: The skin is the largest organ of the human body and can sense pressure, temperature, and other complex environmental stimuli or conditions. The mimicry of human skin's sensory ability via electronics is a topic of innovative research that could find broad applications in robotics, artificial intelligence, and human-machine interfaces, all of which promote the development of electronic skin (e-skin). To imitate tactile sensing via e-skins, flexible and stretchable pressure sensor arrays are constructed based on different transduction mechanisms and structural designs. These arrays can map pressure with high resolution and rapid response beyond that of human perception. Multi-modal force sensing, temperature, and humidity detection, as well as self-healing abilities are also exploited for multi-functional e-skins. Other recent progress in this field includes the integration with high-density flexible circuits for signal processing, the combination with wireless technology for convenient sensing and energy/data transfer, and the development of self-powered e-skins. Future opportunities lie in the fabrication of highly intelligent e-skins that can sense and respond to variations in the external environment. The rapidly increasing innovations in this area will be important to the scientific community and to the future of human life.

National Athletic Trainers' Association Position Statement: Exertional Heat Illnesses

Abstract: Objective: To present best-practice recommendations for the prevention, recognition, and treatment of exertional heat illnesses (EHIs) and to describe the relevant physiology of thermoregulation. ...

SERS Nanoparticles in Medicine: From Label-Free Detection to Spectroscopic Tagging.

Abstract: Spectroscopic Tagging Lucas A. Lane,† Ximei Qian,† and Shuming Nie*,†,‡ †Departments of Biomedical Engineering and Chemistry, Emory University and Georgia Institute of Technology, Health Sciences Research Building, Room E116, 1760 Haygood Drive, Atlanta, Georgia 30322, United States ‡College of Engineering and Applied Sciences, Nanjing University, 22 Hankou Road, Nanjing, Jiangsu Province 210093, China

Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice.

Abstract: Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time (PTT) represents a well-known potential approach for ubiquitous BP monitoring. The goal of this review is to facilitate the achievement of reliable ubiquitous BP monitoring via PTT. We explain the conventional BP measurement methods and their limitations; present models to summarize the theory of the PTT-BP relationship; outline the approach while pinpointing the key challenges; overview the previous work toward putting the theory to practice; make suggestions for best practice and future research; and discuss realistic expectations for the approach.

Multiple Hypothesis Tracking Revisited

Abstract: This paper revisits the classical multiple hypotheses tracking (MHT) algorithm in a tracking-by-detection framework. The success of MHT largely depends on the ability to maintain a small list of potential hypotheses, which can be facilitated with the accurate object detectors that are currently available. We demonstrate that a classical MHT implementation from the 90's can come surprisingly close to the performance of state-of-the-art methods on standard benchmark datasets. In order to further utilize the strength of MHT in exploiting higher-order information, we introduce a method for training online appearance models for each track hypothesis. We show that appearance models can be learned efficiently via a regularized least squares framework, requiring only a few extra operations for each hypothesis branch. We obtain state-of-the-art results on popular tracking-by-detection datasets such as PETS and the recent MOT challenge.

Design for Additive Manufacturing

Abstract: Design for manufacture and assembly (DFM) has typically meant that designers should tailor their designs to eliminate manufacturing difficulties and minimize manufacturing, assembly, and logistics costs. However, the capabilities of additive manufacturing technologies provide an opportunity to rethink DFM to take advantage of the unique capabilities of these technologies. As mentioned in Chap. 16, several companies are now using AM technologies for production manufacturing. For example, Siemens, Phonak, Widex, and the other hearing aid manufacturers use selective laser sintering and stereolithography machines to produce hearing aid shells; Align Technology uses stereolithography to fabricate molds for producing clear dental braces (“aligners”); and Boeing and its suppliers use polymer powder bed fusion (PBF) to produce ducts and similar parts for F-17 fighter jets. For hearing aids and dental aligners, AM machines enable manufacturing of tens to hundreds of thousands of parts, where each part is uniquely customized based upon person-specific geometric data. In the case of aircraft components, AM technology enables low-volume manufacturing, easy integration of design changes and, at least as importantly, piece part reductions to greatly simplify product assembly.

A self-charging power unit by integration of a textile triboelectric nanogenerator and a flexible lithium-ion battery for wearable electronics.

Abstract: A novel integrated power unit realizes both energy harvesting and energy storage by a textile triboelectric nanogenerator (TENG)-cloth and a flexible lithium-ion battery (LIB) belt, respectively. The mechanical energy of daily human motion is converted into electricity by the TENG-cloth, sustaining the energy of the LIB belt to power wearable smart electronics.

Amine-Based CO2 Capture Technology Development from the Beginning of 2013—A Review

Abstract: It is generally accepted by the scientific community that anthropogenic CO2 emissions are leading to global climate change, notably an increase in global temperatures commonly referred to as global warming. The primary source of anthropogenic CO2 emissions is the combustion of fossil fuels for energy. As society’s demand for energy increases and more CO2 is produced, it becomes imperative to decrease the amount emitted to the atmosphere. One promising approach to do this is to capture CO2 at the effluent of the combustion site, namely, power plants, in a process called postcombustion CO2 capture. Technologies to achieve this are heavily researched due in large part to the intuitive nature of removing CO2 from the stack gas and the ease in retrofitting existing CO2 sources with these technologies. As such, several reviews have been written on postcombustion CO2 capture. However, it is a fast-developing field, and the most recent review papers already do not include the state-of-the-art research. Notable am...