Miracle-dz/UniFAR-FTUs huggingface dataset:
A prototype Upper Atmospheric Research Collaboratory (UARC)

The National Collaboratory concept has great potential for enabling 'critical mass' working groups and highly interdisciplinary research projects. We report here on a new program to build a prototype collaboratory using the Sondrestrom Upper Atmospheric Research Facility in Kangerlussuaq, Greenland and a group of associated scientists. The Upper Atmospheric Research Collaboratory (UARC) is a joint venture of researchers in upper atmospheric and space science, computer science, and behavioral science to develop a testbed for collaborative remote research. We define the 'collaboratory' as an advanced information technology environment which enables teams to work together over distance and time on a wide variety of intellectual tasks. It provides: (1) human-to-human communications using shared computer tools and work spaces; (2) group access and use of a network of information, data, and knowledge sources; and (3) remote access and control of instruments for data acquisition. The UARC testbed is being implemented to support a distributed community of space scientists so that they have network access to the remote instrument facility in Kangerlussuaq and are able to interact among geographically distributed locations. The goal is to enable them to use the UARC rather than physical travel to Greenland to conduct team research campaigns. Even on short notice through the collaboratory from their home institutions, participants will be able to meet together to operate a battery of remote interactive observations and to acquire, process, and interpret the data.
‘Marker Swap’ Plasmids: Convenient Tools for Budding Yeast Molecular Genetics

One‐step gene disruption constructs for disruption of HIS3, LEU2, TRP1 or URA3 with each of the other three markers have been constructed. All of these constructs have been tested and found to effectively convert markers either in gene disruptions or on plasmids. The ‘swapped’ strains allow the unambiguous genetic analysis of synthetic phenotypes with multiple genes, even if the original gene disruptions were made with the same marker. They also allow introduction of multiple plasmids in a single transformant, even if the original plasmids had the same marker, and allow transformation of plasmids into strains containing gene disruptions made with the same marker that is on the plasmids. These ‘marker‐swap’ plasmids therefore eliminate the need for much subcloning to change markers. Marker‐swapped alleles are acceptably stable mitotically and meiotically for most applications. © 1997 John Wiley & Sons, Ltd.
Incorporating circuit-level information into the retiming process

The advances in CMOS technology over the past decades have created the need for the design of extremely complex Very Large Scale Integration (VLSI) Integrated Circuits (IC)s. The simultaneous progress in Computer-Aided Design (CAD) tools enable large design teams to work independently on sophisticated VLSI projects. The pipelining process is widely utilized in VLSI ICs as a performance enhancement tool. Efficient automated pipelining algorithms have been developed to permit the application of pipelining to large integrated circuits. The emerging technique of retiming, on the other hand, has not found its way into the VLSI circuit design process due to complex and non-practical algorithms. Therefore, algorithms and/or methodologies to help achieve retiming with simpler and practical algorithms can significantly improve the acceptance of retiming techniques in standard VLSI design methodologies. A retiming methodology is presented in this dissertation to model low-level circuit characteristics in VLSI ICs. This objective is achieved by modeling low level circuit parameters using the Register Electrical Characteristic (REC) model. The path delays in a VLSI IC are defined from register-to-register based on this REC model. The REC model forms the core of the retiming algorithms introduced in this dissertation. The REC model, for the first time, permits incorporating low-level circuit issues into the retiming process, thereby yielding significantly more accurate retiming results than the existing retiming algorithms currently described in the literature. Path monotonicity constraints have been developed to permit the application of standard Linear Programming (LP) based techniques to the general retiming process. These monotonicity constraints permit circuits to be retimed with low-level characteristics with significantly less CPU time complexity. Although the application of retiming to practical circuits has not as yet become common place, the research described in this dissertation is a significant improvement in making retiming into a practical and useful design methodology. The relationship between clock scheduling and retiming is also discussed in this dissertation where it is shown that the two processes are inextricably intertwined. The results of applying retiming to benchmark circuits have demonstrated performance improvements of up to 50%. When clock scheduling techniques are combined with retiming techniques while including low-level circuit characteristics, retiming can significantly improve the design efficiency and performance of the next generation VLSI circuits.
Hydraulic guidelines for the re-introduction and management of large woody debris in lowland rivers

The volume of large woody debris in most of the world's lowland rivers has been depleted, either through persistent de-snagging or clearance of the riparian vegetation from which it is naturally recruited. The now recognized important environmental role of debris in rivers, the established environmental value of vegetated riparian buffer strips and the movement towards rehabilitation of degraded riverine habitats demand more objective procedures for the management of woody debris in streams. In some instances it may be centuries before the process of natural recruitment of wood from rehabilitated riparian strips achieves an ecologically adequate volume and quality of instream debris. To accelerate this process, the re-introduction of debris is being considered. This paper presents the results of laboratory and field hydraulic investigations relevant to the problem of managing debris in lowland rivers. The laboratory experiments were used to develop a model, based on the momentum principle, of the effect of debris on afflux, or the increase in water surface elevation. Debris drag was found to be less affected by position and shape than by orientation to the flow and blockage ratio, or the proportion of the channel occupied by the debris. Debris aligned at 20–30° to the flow produced an afflux one-third of that produced by debris which was perpendicular. Significant loss of conveyance occurs only for debris which is large relative to the channel dimensions (greater than about 10% of the channel area blocked by debris). Wake interference acts to reduce the hydraulic effect of debris so that if spaced within two diameters, multiple in-line items of debris produce an afflux no greater than that of a single item. The models of debris hydraulics presented here can be used to predict the effect of removing, lopping, rotating or re-introducing debris to rivers.
Review: Production and functionality of active peptides from milk

In recent years, research on the production of active peptides obtained from milk and their potential functionality has grown, to a great extent. Bioactive peptides have been defined as specific protein fragments that have a positive impact on body functions or conditions, and they may ultimately have an influence on health. Individual proteins of casein or milk-derived products such as cheese and yogurt have been used as a protein source to study the isolation and activity of peptides with several applications. Currently, the milk whey waste obtained in the production of cheese also represents a protein source from which active peptides could be isolated with potential industrial applications. The active properties of milk peptides and the results found with regard to their physiological effects have led to the classification of peptides as belonging to the group of ingredients of protein nature, appropriate for use in functional foods or pharmaceutical formulations. In this study, the main peptides obtained from milk protein and the past research studies about its production and biological activities will be explained. Second, an analysis will be made on the methods to determinate the biological activities, the separation of bioactive peptides and its structure identification. All of these form the base required to obtain synthetic peptides. Finally, we explain the experimental animal and human trials done in the past years. Nevertheless, more research is required on the design and implementation of equipment for the industrial production and separation of peptides. In addition, different authors suggest that more emphasis should therefore be given to preclinical studies, proving that results are consistent and that effects are demonstrated repeatedly by several research human groups.
A review on speech enhancement algorithms and why to combine with environment classification

Speech enhancement has been an intensive research for several decades to enhance the noisy speech that is corrupted by additive noise, multiplicative noise or convolutional noise. Even after decades of research it is still the most challenging problem, because most papers rely on estimating the noise during the nonspeech activity assuming that the background noise is uncorrelated (statistically independent of speech signal), nonstationary and slowly varying, so that the noise characteristics estimated in the absence of speech can be used subsequently in the presence of speech, whereas in a real time environment such assumptions do not hold for all the time. In this paper, we discuss the historical development of approaches that starts from the year 1970 to, the recent, 2013 for enhancing the noisy speech corrupted by additive background noise. Seeing the history, there are algorithms that enhance the noisy speech very well as long as a specific application is concerned such as the In-car noisy environments. It has to be observed that a speech enhancement algorithm performs well with a good estimation of the noise Power Spectral Density (PSD) from the noisy speech. Our idea pops up based on this observation, for online speech enhancement (i.e. in a real time environment) such as mobile phone applications, instead of estimating the noise from the noisy speech alone, the system should be able to monitor an environment continuously and classify it. Based on the current environment of the user, the system should adapt the algorithm (i.e. enhancement or estimation algorithm) for the current environment to enhance the noisy speech.
Web Service Matchmaking Using Web Search Engine and Machine Learning

Web Services discovery that locates adequate services, has been studied very actively for better quality of service retrieval. Starting from conventional keyword matching, logic-based matching and combination of the methods with information retrieval approach have been proposed to enable better discovery performance. The combining method using term-similarity can overcome the decision failure when the keyword or the logic-based methods were applied, and it was shown that the methods outperform the existing methods. And researches to aggregate matchmaking variants by ma- chine learning has been attempted, and it also improves the discovery performance. The approaches still suffer from fixed corpus set for term similarity calculation. In this research, we attempted to calculate the similarity based on search engine to reflect the current Web context. Tokenized terms are used for the matchmaking degree. Variants for the matchmaking from ontology and term similarity are aggregated using Support Vector Machine (SVM) with non-linear kernel function. Matchmaking test on the trip domain service discovery was conducted. Experimental result based on the standard measure of precision and recall rate for the top 1-20 services of matched result on the trip domain test set are shown.
GABA plasma membrane transporters, GAT‐1 and GAT‐3, display different distributions in the rat hippocampus

This study evaluates the distribution of two high affinity gamma‐aminobutyric acid (GABA) transporters (GAT‐1 and GAT‐3) in the rat hippocampus using immunocytochemistry and affinity purified antibodies. GAT‐1 immunoreactivity was prominent in punctate structures and axons in all layers of the dentate gyrus. In Ammon's horn, immunoreactive processes were concentrated around the somata of pyramidal cells, particularly at their basal regions. The apical and basal dendritic fields of pyramidal cells also displayed numerous GAT‐1 immunoreactive punctate structures and axons. The zone of termination of the mossy fibers that includes both the hilus of the dentate gyrus and stratum lucidum of the CA3 area was the lightest immunolabeled region of the hippocampal complex. Electron microscopic preparations demonstrated that GAT‐1 immunoreactive axon terminals form symmetric synapses with somata, axon initial segments, and dendrites of granule and pyramidal cells in the dentate gyrus and Ammon's horn, respectively. Immunoreactivity was localized to the plasma membrane and the cytoplasm of axon terminals. The somata of previously described local circuit neurons in the dentate gyrus and Ammon's horn contained GAT‐1 immunoreactivity associated with the Golgi complex. Light, diffuse GAT‐3 immunoreactivity was present throughout the hippocampal formation. Thin, astrocytic glial processes displayed GAT‐1 and GAT‐3 immunoreactivity. This localization of GAT‐1 and GAT‐3 indicates that they are involved in the uptake of GABA from the extracellular space into GABAergic axon terminals and astrocytes. © 1996 Wiley‐Liss, Inc.
A Survey of Adaptive Sampling in Realistic Image Synthesis

An important problem in Computer Graphics is realistic image synthesis, used in numerous applications such as architecture or lm production. An integral part of image synthesis is light transport simulation using the Monte Carlo method, where an image is generated by accumulating the contributions of randomly generated light paths that simulate multiple reections of light. High computation requirements of this calculation negatively impact the practical use of image synthesis. A number of methods exist to accelerate this calculation. One important example of these methods is adaptive sampling, which uses an estimate of the pixel error to preferably generate light paths that decrease this error quickly. We present a survey of various adaptive sampling methods and point out their limitations, which will be addressed in our future research.
Detection of radiation-induced hydrocarbons in irradiated fish and prawns by means of on-line coupled liquid chromatography-gas chromatography

Radiation-induced hydrocarbons were analyzed in a fatty (halibut) and a lean fish (cod) as well as in a prawn species by on-line coupled liquid chromatography (LC)-gas chromatography (GC) combined with mass spectrometry. In irradiated halibut which is known to contain mainly saturated and monounsaturated fatty acids, all expected radiolytic alkanes, alkenes, and alkadienes could be detected. The yields of the C n-1 and C n-2:1 hydrocarbons were comparable to those found in irradiated lipids of terrestrial animals and plants. However, in cod and prawns which contain high levels of polyunsaturated fatty acids (PUFA), the C n-1 hydrocarbons were found in concentrations which were up to 10 times higher whereas the C n-2:1 products were again comparable to those of terrestrial animals and plants. The identification of radiation-induced hydrocarbons in fish lipids was achieved by transfer of the hydrocarbons from the LC column to the gas chromatographic column in fractions differing in their degree of unsaturation. For the first time, radiation-induced hydrocarbons with more than four double bonds generated from polyunsaturated fatty acids (20:4ω6 and 20:5ω3) could be identified.
Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm

Complex networks are a special type of graph that frequently appears in nature and in many different fields of science and engineering. Studying complex networks is the key to solve the problems in these fields. Complex networks have unique features which we cannot find in regular graphs, and the study of complex networks gives rise to many interesting research questions. An interesting feature to study in complex networks is community structure. Intuitively speaking, communities are group of vertices in a graph that are densely connected with each other in the same group, while sparsely connected with other nodes in the graph. The notion of community has practical significance. Many different concept and phenomenons in real world problems can be translated into communities in a graph, such as politicians with similar opinions in the political opinion network. In this thesis work, a distributed version of a popular community detection method-Louvain method-is developed using graph computation framework Apache Spark GraphX. Characteristics of this algorithm, such as convergence and quality of communities produced, are studied by both theoretical reasoning and experimental evaluation. The result shows that this algorithm can parallelize community detection effectively. This thesis also explores the possibility of using graph sampling to accelerate resolution parameter selection of a resolution-limit-free community detection method. Two sampling algorithms, random node selection and forest fire sampling algorithm, are compared. This comparison leads to suggestions of choice of sampling algorithm and parameter value of the chosen sampling algorithm.
Supervised Speech Separation Using Deep Neural Networks

Speech is crucial for human communication. However, speech communication for both humans and automatic devices can be negatively impacted by background noise, which is common in real environments. Due to numerous applications, such as hearing prostheses and automatic speech recognition, separation of target speech from sound mixtures is of great importance. Among many techniques, speech separation using a single microphone is most desirable from an application standpoint. The resulting monaural speech separation problem has been a central problem in speech processing for several decades. However, its success has been limited thus far. Time-frequency (T-F) masking is a proven way to suppress background noise. With T-F masking as the computational goal, speech separation reduces to a mask estimation problem, which can be cast as a supervised learning problem. This opens speech separation to a plethora of machine learning techniques. Deep neural networks (DNN) are particularly suitable to this problem due to their strong representational capacity. This dissertation presents a systematic effort to develop monaural speech separation systems using DNNs. We start by presenting a comparative study on acoustic features for supervised separation. In this relatively early work, we use support vector machine as classifier to predict the ideal binary mask (IBM), which is a primary goal in computational auditory scene analysis. We found that traditional speech and speaker recognition
Renal Biopsy: Use of Biomarkers as a Tool for the Diagnosis of Focal Segmental Glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS) is a glomerulopathy associated with nephrotic syndrome and podocyte injury. FSGS occurs both in children and adults and it is considered the main idiopathic nephrotic syndrome nowadays. It is extremely difficult to establish a morphological diagnosis, since some biopsies lack a considerable quantifiable number of sclerotic glomeruli, given their focal aspect and the fact that FSGS occurs in less than half of the glomeruli. Therefore, many biological molecules have been evaluated as potential markers that would enhance the diagnosis of FSGS. Some of these molecules and receptors are associated with the pathogenesis of FSGS and have potential use in diagnosis.
Abstract resource management in an unconstrained plan execution system

We describe the abstract resource management mechanism in ESL (execution support language). ESL is the implementation substrate for the New Millennium Remote Agent Smart Executive, part of a NASA program to demonstrate autonomous control of an unmanned spacecraft scheduled to launch in 1998. The executive is responsible for robust plan execution in the face of unexpected run-time contingencies. Part of this task requires run-time management of the spacecraft's configuration, whose component states are modeled as abstract resources. In this paper we describe the ESL constructs for managing these abstract resources. The resource management facilities in ESL are similar to the constraint management constructs in RAPs. The major contribution in this paper is the implementation of these facilities in an unconstrained execution substrate implemented as an extension to a standard programming language (in this case, Common Lisp) rather than within a constrained self-contained plan execution language. This turns out to significantly simplify complex programming tasks. The main technical problem in a resource management system is designing a representation that allows automatic determination of when conflicts exist. In its full generality this becomes a full-blown planning problem, and therefore an impractical strategy for a reactive executive. Instead, we model abstract resources as properties, logical assertions whose final values are guaranteed unique. When two properties are identical but for their final value then they are in conflict. This paper describes the ESL constructs and mechanisms for scheduling tasks so that they do not attempt to achieve conflicting properties simultaneously, and for invoking external recovery mechanisms for restoring properties to their desired states when forced away from those states by unexpected contingencies.
Field and Laboratory Tests of New Insecticides Against the Apple Maggot, Rhagoletis pomonella (Walsh) (Diptera: Tephritidae)

Abstract Laboratory bioassays and field tests were conducted to compare the effectiveness of the new insecticides, imidacloprid, indoxacarb, pyriproxyfen, spinosad, thiacloprid, and thiamethoxam, against apple maggot. The activity ranking of the compounds in reducing oviposition in laboratory bioassays was: imidacloprid, 95% reduction at 11 ppm > thiamethoxam, 91% and thiacloprid, 89% reduction at 100 ppm > spinosad, 98% reduction at 316 ppm > indoxacarb, 80% reduction at 1000 ppm > pyriproxyfen, 0% reduction at 38 ppm. In laboratory bioassays, the only insecticides that were toxic to flies at concentrations equal to or below the recommended field rates were imidacloprid, (50% of flies at 11 ppm), spinosad (90% of flies at rates >10 ppm), and thiamethoxam (≈50% of flies at 32 ppm). In field trials, thiacloprid was the only material that consistently controlled apple maggot fruit infestation that was comparable to standard treatments of organophosphate insecticides. Spinosad applied at weekly intervals, and indoxacarb applied as biweekly sprays provided adequate control of apple maggot damage when infestation levels in the field were low, but were not effective in preventing damage in small plots when apple maggot pressure was high.