University of Massachusetts Lowell

The HITRAN database is a compilation of molecular spectroscopic parameters. It was established in the early 1970s and is used by various computer codes to predict and simulate the transmission and emission of light in gaseous media (with an emphasis on terrestrial and planetary atmospheres). The HITRAN compilation is composed of five major components: the line-by-line spectroscopic parameters required for high-resolution radiative-transfer codes, experimental infrared absorption cross-sections (for molecules where it is not yet feasible for representation in a line-by-line form), collision-induced absorption data, aerosol indices of refraction, and general tables (including partition sums) that apply globally to the data. This paper describes the contents of the 2020 quadrennial edition of HITRAN. The HITRAN2020 edition takes advantage of recent experimental and theoretical data that were meticulously validated, in particular, against laboratory and atmospheric spectra. The new edition replaces the previous HITRAN edition of 2016 (including its updates during the intervening years). All five components of HITRAN have undergone major updates. In particular, the extent of the updates in the HITRAN2020 edition range from updating a few lines of specific molecules to complete replacements of the lists, and also the introduction of additional isotopologues and new (to HITRAN) molecules: SO, CH3F, GeH4, CS2, CH3I and NF3. Many new vibrational bands were added, extending the spectral coverage and completeness of the line lists. Also, the accuracy of the parameters for major atmospheric absorbers has been increased substantially, often featuring sub-percent uncertainties. Broadening parameters associated with the ambient pressure of water vapor were introduced to HITRAN for the first time and are now available for several molecules. The HITRAN2020 edition continues to take advantage of the relational structure and efficient interface available at www.hitran.org and the HITRAN Application Programming Interface (HAPI). The functionality of both tools has been extended for the new edition.

Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or “temporally deep”, are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are “doubly deep” in that they can be compositional in spatial and temporal “layers”. Such models may have advantages when target concepts are complex and/or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.

This paper describes the development of an evaluative outcome measure for patients with upper extremity musculoskeletal conditions. The goal is to produce a brief, self-administered measure of symptoms and functional status, with a focus on physical function, to be used by clinicians in daily practice and as a research tool. This is a joint initiative of the American Academy of Orthopedic Surgeons (AAOS), the Council of Musculoskeletal Specialty Societies (COMSS), and the Institute for Work and Health (Toronto, Ontario). Our approach is consistent with previously described strategies for scale development. In Stage 1, Item Generation, a group of methodologists and clinical experts reviewed 13 outcome measurement scales currently in use and generated a list of 821 items. In Stage 2a, Initial Item Reduction, these 821 items were reduced to 78 items using various strategies including removal of items which were generic, repetitive, not reflective of disability, or not relevant to the upper extremity or to one of the targeted concepts of symptoms and functional status. Items not highly endorsed in a survey of content experts were also eliminated. Stage 2b, Further Item Reduction, will be based on results of field testing in which patients complete the 78-item questionnaire. This field testing, which is currently underway in 20 centers in the United States, Canada, and Australia, will generate the final format and content of the Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire. Future work includes plans for validity and reliability testing.

Part I discusses the Job Content Questionnaire (JCQ), designed to measure scales assessing psychological demands, decision latitude, social support, physical demands, and job insecurity. Part II describes the reliability of the JCQ scales in a cross-national context using 10,288 men and 6,313 women from 6 studies conducted in 4 countries. Substantial similarity in means, standard deviations, and correlations among the scales, and in correlations between scales and demographic variables, is found for both men and women in all studies. Reliability is good for most scales. Results suggest that psychological job characteristics are more similar across national boundaries than across occupations.

Research Article| July 01, 1992 Chemical subdivision of the A-type granitoids:Petrogenetic and tectonic implications G. Nelson Eby G. Nelson Eby 1Department of Earth Sciences, University of Massachusetts, Lowell, Massachusetts 01854 Search for other works by this author on: GSW Google Scholar Author and Article Information G. Nelson Eby 1Department of Earth Sciences, University of Massachusetts, Lowell, Massachusetts 01854 Publisher: Geological Society of America First Online: 02 Jun 2017 Online ISSN: 1943-2682 Print ISSN: 0091-7613 Geological Society of America Geology (1992) 20 (7): 641–644. https://doi.org/10.1130/0091-7613(1992)020<0641:CSOTAT>2.3.CO;2 Article history First Online: 02 Jun 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation G. Nelson Eby; Chemical subdivision of the A-type granitoids:Petrogenetic and tectonic implications. Geology 1992;; 20 (7): 641–644. doi: https://doi.org/10.1130/0091-7613(1992)020<0641:CSOTAT>2.3.CO;2 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyGeology Search Advanced Search Abstract The A-type granitoids can be divided into two chemical groups. The first group (A1) is characterized by element ratios similar to those observed for oceanic-island basalts. The second group (A2) is characterized by ratios that vary from those observed for continental crust to those observed for island-arc basalts. It is proposed that these two types have very different sources and tectonic settings. The A1 group represents differentiates of magmas derived from sources like those of oceanic-island basalts but emplaced in continental rifts or during intraplate magmatism. The A2 group represents magmas derived from continental crust or underplated crust that has been through a cycle of continent-continent collision or island-arc magmatism. This content is PDF only. Please click on the PDF icon to access. First Page Preview Close Modal You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark. Fine-tuning deep models in a new domain can require a significant amount of data, which for many applications is simply not available. We propose a new CNN architecture which introduces an adaptation layer and an additional domain confusion loss, to learn a representation that is both semantically meaningful and domain invariant. We additionally show that a domain confusion metric can be used for model selection to determine the dimension of an adaptation layer and the best position for the layer in the CNN architecture. Our proposed adaptation method offers empirical performance which exceeds previously published results on a standard benchmark visual domain adaptation task.

The HITRAN database is a compilation of molecular spectroscopic parameters. It was established in the early 1970s and is used by various computer codes to predict and simulate the transmission and emission of light in gaseous media (with an emphasis on terrestrial and planetary atmospheres). The HITRAN compilation is composed of five major components: the line-by-line spectroscopic parameters required for high-resolution radiative-transfer codes, experimental infrared absorption cross-sections (for molecules where it is not yet feasible for representation in a line-by-line form), collision-induced absorption data, aerosol indices of refraction, and general tables (including partition sums) that apply globally to the data. This paper describes the contents of the 2020 quadrennial edition of HITRAN. The HITRAN2020 edition takes advantage of recent experimental and theoretical data that were meticulously validated, in particular, against laboratory and atmospheric spectra. The new edition replaces the previous HITRAN edition of 2016 (including its updates during the intervening years). All five components of HITRAN have undergone major updates. In particular, the extent of the updates in the HITRAN2020 edition range from updating a few lines of specific molecules to complete replacements of the lists, and also the introduction of additional isotopologues and new (to HITRAN) molecules: SO, CH3F, GeH4, CS2, CH3I and NF3. Many new vibrational bands were added, extending the spectral coverage and completeness of the line lists. Also, the accuracy of the parameters for major atmospheric absorbers has been increased substantially, often featuring sub-percent uncertainties. Broadening parameters associated with the ambient pressure of water vapor were introduced to HITRAN for the first time and are now available for several molecules. The HITRAN2020 edition continues to take advantage of the relational structure and efficient interface available at www.hitran.org and the HITRAN Application Programming Interface (HAPI). The functionality of both tools has been extended for the new edition.

Chronic traumatic encephalopathy is a progressive tauopathy that occurs as a consequence of repetitive mild traumatic brain injury. We analysed post-mortem brains obtained from a cohort of 85 subjects with histories of repetitive mild traumatic brain injury and found evidence of chronic traumatic encephalopathy in 68 subjects: all males, ranging in age from 17 to 98 years (mean 59.5 years), including 64 athletes, 21 military veterans (86% of whom were also athletes) and one individual who engaged in self-injurious head banging behaviour. Eighteen age- and gender-matched individuals without a history of repetitive mild traumatic brain injury served as control subjects. In chronic traumatic encephalopathy, the spectrum of hyperphosphorylated tau pathology ranged in severity from focal perivascular epicentres of neurofibrillary tangles in the frontal neocortex to severe tauopathy affecting widespread brain regions, including the medial temporal lobe, thereby allowing a progressive staging of pathology from stages I-IV. Multifocal axonal varicosities and axonal loss were found in deep cortex and subcortical white matter at all stages of chronic traumatic encephalopathy. TAR DNA-binding protein 43 immunoreactive inclusions and neurites were also found in 85% of cases, ranging from focal pathology in stages I-III to widespread inclusions and neurites in stage IV. Symptoms in stage I chronic traumatic encephalopathy included headache and loss of attention and concentration. Additional symptoms in stage II included depression, explosivity and short-term memory loss. In stage III, executive dysfunction and cognitive impairment were found, and in stage IV, dementia, word-finding difficulty and aggression were characteristic. Data on athletic exposure were available for 34 American football players; the stage of chronic traumatic encephalopathy correlated with increased duration of football play, survival after football and age at death. Chronic traumatic encephalopathy was the sole diagnosis in 43 cases (63%); eight were also diagnosed with motor neuron disease (12%), seven with Alzheimer's disease (11%), 11 with Lewy body disease (16%) and four with frontotemporal lobar degeneration (6%). There is an ordered and predictable progression of hyperphosphorylated tau abnormalities through the nervous system in chronic traumatic encephalopathy that occurs in conjunction with widespread axonal disruption and loss. The frequent association of chronic traumatic encephalopathy with other neurodegenerative disorders suggests that repetitive brain trauma and hyperphosphorylated tau protein deposition promote the accumulation of other abnormally aggregated proteins including TAR DNA-binding protein 43, amyloid beta protein and alpha-synuclein.

Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning methods. Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being ``frustratingly easy'' to implement. However, in practice, the target domain is often unlabeled, requiring unsupervised adaptation. We propose a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL). CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. Even though it is extraordinarily simple--it can be implemented in four lines of Matlab code--CORAL performs remarkably well in extensive evaluations on standard benchmark datasets.

How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.

The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifacts, which can adversely affect the radiologists' judgment and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed nature. Over the past years, various low-dose CT methods have produced impressive results. However, most of the algorithms developed for this application, including the recently popularized deep learning techniques, aim for minimizing the mean-squared error (MSE) between a denoised CT image and the ground truth under generic penalties. Although the peak signal-to-noise ratio is improved, MSE- or weighted-MSE-based methods can compromise the visibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory and promises to improve the performance of GAN. The perceptual loss suppresses noise by comparing the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN focuses more on migrating the data noise distribution from strong to weak statistically. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task and is capable of not only reducing the image noise level but also trying to keep the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.

Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent are effective for tasks involving sequences, visual and otherwise. We describe a class of recurrent convolutional architectures which is end-to-end trainable and suitable for large-scale visual understanding tasks, and demonstrate the value of these models for activity recognition, image captioning, and video description. In contrast to previous models which assume a fixed visual representation or perform simple temporal averaging for sequential processing, recurrent convolutional models are "doubly deep" in that they learn compositional representations in space and time. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Differentiable recurrent models are appealing in that they can directly map variable-length inputs (e.g., videos) to variable-length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent sequence models are directly connected to modern visual convolutional network models and can be jointly trained to learn temporal dynamics and convolutional perceptual representations. Our results show that such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined or optimized.

ABSTRACT The use of the learning curve has been receiving increasing attention in recent years. Much of this increase has been due to learning curve applications other than in the traditional learning curve areas. A comprehensive survey of developments in the learning curve area has never been published. The closest thing to a survey was by Asher in 1956. His study focused exclusively on military applications during and immediately after World War II. This paper summarizes the learning curve literature from World War II to the present, emphasizing developments since the study by Asher. Particular emphasis is given to identifying the new directions into which the learning curve has made recent inroads and identifying fruitful areas for future research.

Real-world videos often have complex dynamics, methods for generating open-domain video descriptions should be sensitive to temporal structure and allow both input (sequence of frames) and output (sequence of words) of variable length. To approach this problem we propose a novel end-to-end sequence-to-sequence model to generate captions for videos. For this we exploit recurrent neural networks, specifically LSTMs, which have demonstrated state-of-the-art performance in image caption generation. Our LSTM model is trained on video-sentence pairs and learns to associate a sequence of video frames to a sequence of words in order to generate a description of the event in the video clip. Our model naturally is able to learn the temporal structure of the sequence of frames as well as the sequence model of the generated sentences, i.e. a language model. We evaluate several variants of our model that exploit different visual features on a standard set of YouTube videos and two movie description datasets (M-VAD and MPII-MD).

Let T be a (possibly nonlinear) continuous operator on Hilbert space . If, for some starting vector x, the orbit sequence {Tkx,k = 0,1,...} converges, then the limit z is a fixed point of T; that is, Tz = z. An operator N on a Hilbert space is nonexpansive (ne) if, for each x and y in , Even when N has fixed points the orbit sequence {Nkx} need not converge; consider the example N = −I, where I denotes the identity operator. However, for any the iterative procedure defined by converges (weakly) to a fixed point of N whenever such points exist. This is the Krasnoselskii–Mann (KM) approach to finding fixed points of ne operators.

Over the past two decades the ideology of shareholder value has become entrenched as a principle of corporate governance among companies based in the United States and Britain. Over the past two or three years, the rhetoric of shareholder value has become prominent in the corporate governance debates in European nations such as Germany, France and Sweden. Within the past year, the arguments for ‘maximizing shareholder value’ have even achieved prominence in Japan. In 1999 the OECD issued a document, The OECD Principles of Corporate Governance, that emphasizes that corporations should be run, first and foremost, in the interests of shareholders (OECD, 1999)

Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias. Fine-tuning deep models in a new domain can require a significant amount of labeled data, which for many applications is simply not available. We propose a new CNN architecture to exploit unlabeled and sparsely labeled target domain data. Our approach simultaneously optimizes for domain invariance to facilitate domain transfer and uses a soft label distribution matching loss to transfer information between tasks. Our proposed adaptation method offers empirical performance which exceeds previously published results on two standard benchmark visual domain adaptation tasks, evaluated across supervised and semi-supervised adaptation settings.

We report observation of holographic surface relief gratings with relatively large amplitude on a second order nonlinear optical polymeric material. Surface relief gratings on these polymer films were created upon exposure to polarized Ar+ laser beams at 488 nm without any subsequent processing steps. The surface structure of the relief gratings was investigated by atomic force microscopy. The depth of the surface relief in a typical case was 120 nm which is approximately 20% of the original film thickness. The diffraction efficiency of gold-coated gratings was investigated as a function of wavelength and capability of recording orthogonal gratings on the same film was demonstrated.

Metasurfaces have enabled a plethora of emerging functions within an ultrathin dimension, paving way towards flat and highly integrated photonic devices. Despite the rapid progress in this area, simultaneous realization of reconfigurability, high efficiency, and full control over the phase and amplitude of scattered light is posing a great challenge. Here, we try to tackle this challenge by introducing the concept of a reprogrammable hologram based on 1-bit coding metasurfaces. The state of each unit cell of the coding metasurface can be switched between '1' and '0' by electrically controlling the loaded diodes. Our proof-of-concept experiments show that multiple desired holographic images can be realized in real time with only a single coding metasurface. The proposed reprogrammable hologram may be a key in enabling future intelligent devices with reconfigurable and programmable functionalities that may lead to advances in a variety of applications such as microscopy, display, security, data storage, and information processing.Realizing metasurfaces with reconfigurability, high efficiency, and control over phase and amplitude is a challenge. Here, Li et al. introduce a reprogrammable hologram based on a 1-bit coding metasurface, where the state of each unit cell of the coding metasurface can be switched electrically.

The quartz crystal microbalance (QCM) is a simple, cost effective, high-resolution mass sensing technique, based upon the piezoelectric effect. As a methodology, the QCM evolved a solution measurement capability in largely analytical chemistry and electrochemistry applications due to its sensitive solution-surface interface measurement capability. The technique possesses a wide detection range. At the low mass end, it can detect monolayer surface coverage by small molecules or polymer films. At the upper end, it is capable of detecting much larger masses bound to the surface. These can be complex arrays of biopolymers and biomacromolecules, even whole cells. In addition, the QCM can provide information about the energy dissipating properties of the bound surface mass. Another important and unique feature of the technique is the ability to measure mass and energy dissipation properties of films while simultaneously carrying out electrochemistry on solution species or upon film systems bound to the upper electrode on the oscillating quartz crystal surface. These measurements can describe the course of electropolymerization of a film or can reveal ion or solute transport within a film during changes in the film environment or state, including the oxidation state for an electroactive film driven by the underlying surface potential. The past decade has witnessed an explosive growth in the application of the QCM technique to the study of a wide range of molecular systems at the solution-surface interface, in particular, biopolymer and biochemical systems. In this report, we start with a brief historical and technical overview. Then we discuss the application of the QCM technique to measurements involving micellar systems, self-assembling monolayers and their phase transition behavior, molecularly imprinted polymers, chemical sensors, films formed using the layer-by-layer assembly technique, and biopolymer films and point out the utility of the electrochemical capabilities of the technique to characterizing film properties, especially electroactive polymer films. We also describe the wide range of surface chemistries and attachment strategies used by investigators to bring about surface attachment and multi-layer interactions of these thin film systems. Next we review the wide range of recent applications of the technique to: studies of complex biochemical and biomimetic systems, the creation of protein and nucleic acid biosensors, studies of attached living cells and whole cell biosensor applications. Finally, we discuss future technical directions and applications of the QCM technique to areas such as drug discovery.