Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical frequency comb generator using phase modulation in amplified circulating loop

A method to generate a comb of precisely spaced optical frequencies over a terahertz span is presented. This scheme uses a sinusoidally driven phase modulator and an optical amplifier placed within an optical fiber loop, so that the modulation is enhanced by multiple passes through the loop. By maintaining the loop round-trip gain slightly below unity, a comb of tens to hundreds of frequencies can be generated. If the loop input is derived from a laser locked to an absolute frequency reference, then each of the output frequencies has an absolute accuracy approaching that of the input. <<ETX>>
Outcomes in young adulthood for very-low-birth-weight infants.

A BSTRACT Background Very-low-birth-weight infants (those weighing less than 1500 g) born during the initial years of neonatal intensive care have now reached young adulthood. Methods We compared a cohort of 242 survivors among very-low-birth-weight infants born between 1977 and 1979 (mean birth weight, 1179 g; mean gestational age at birth, 29.7 weeks) with 233 controls from the same population in Cleveland who had normal birth weights. We assessed the level of education, cognitive and academic achievement, and rates of chronic illness and risk-taking behavior at 20 years of age. Outcomes were adjusted for sex and sociodemographic status. Results Fewer very-low-birth-weight young adults than normal-birth-weight young adults had graduated from high school (74 percent vs. 83 percent, P=0.04). Very-low-birth-weight men, but not women, were significantly less likely than normal-birth-weight controls to be enrolled in postsecondary study (30 percent vs. 53 percent, P=0.002). Very-low-birth-weight participants had a lower mean IQ (87 vs. 92) and lower academic achievement scores (P<0.001 for both comparisons). They had higher rates of neurosensory impairments (10 percent vs. <1 percent, P<0.001) and subnormal height (10 percent vs. 5 percent, P=0.04). The very-low-birth-weight group reported less alcohol and drug use and had lower rates of pregnancy than normal-birth-weight controls; these differences persisted when comparisons were restricted to the participants without neurosensory impairment. Conclusions Educational disadvantage associated with very low birth weight persists into early adulthood. (N Engl J Med 2002;346:149-57.)
Climate change, body size evolution, and Cope's Rule in deep-sea ostracodes

Causes of macroevolutionary trends in body size, such as Cope's Rule, the tendency of body size to increase over time, remain poorly understood. We used size measurements from Cenozoic populations of the ostracode genus Poseidonamicus, in conjunction with phylogeny and paleotemperature estimates, to show that climatic cooling leads to significant increases in body size, both overall and within individual lineages. The magnitude of size increase due to Cenozoic cooling is consistent with temperature-size relationships in geographically separated modern populations (Bergmann's Rule). Thus population-level phenotypic evolution in response to climate change can be an important determinant of macroevolutionary trends in body size.
Complete manifolds with nonnegative curvature operator

In this short note, as a simple application of the strong result proved recently by Bohm and Wilking, we give a classification on closed manifolds with 2-nonnegative curvature operator. Moreover, by the new invariant cone constructions of Bohm and Wilking, we show that any complete Riemannian manifold (with dimension > 3) whose curvature operator is bounded and satisfies the pinching condition R > δtr(R)/2n(n-1) I > 0, for some δ > 0, must be compact. This provides an intrinsic analogue of a result of Hamilton on convex hypersurfaces.
Commentary: monetary policy at zero inflation

It is always a pleasure to read one of Lars Svensson’s papers on monetary policy. They are carefully reasoned, substantive, and address real world problems of monetary control. As the bibliography to his paper shows, he has done an impressive amount of theoretical and empirical work on the many facets of inflation control. The paper today summarizes a considerable part of his work. I recommend the paper to all central bankers. They will not agree with all of his conclusions, but his paper should encourage them to think about where and why they disagree, if they do.
Efficient Parsing-based Keyword Search over Databases

We study a parsing-based semantics for keyword search over databases that relies on parsing the search query using a grammar. The parsing-based semantics is often used to override the traditional “bag-of-words” semantics in web search and enterprise search scenarios. Compared to the “bag-of-words” semantics, the parsing based semantics is richer and more customizable. While a formalism for parsing-based semantics for keyword search has been proposed in prior work and ad-hoc implementations exist, the problem of designing efficient algorithms to support the semantics is largely unstudied. In this paper, we present a suite of efficient algorithms and auxiliary indexes for this problem. Our algorithms work for a broad classes of grammars used in practice, and cover a variety of database matching functions (setand substring-containment, approximate and exact equality) and scoring functions to filter and rank different parses. We formally analyze the running time complexity of our algorithms and provide a thorough empirical evaluation over real-world data to show that our algorithms scale well with the size of the database and grammar.
Robowell: An Automated Process for Monitoring Ground Water Quality Using Established Sampling Protocols

Robowell is an automated process for monitoring selected ground water quality properties and constituents by pumping a well or multilevel sampler. Robowell was developed and tested to provide a cost‐effective monitoring system that meets protocols expected for manual sampling. The process uses commercially available electronics, instrumentation, and hardware, so it can be configured to monitor ground water quality using the equipment, purge protocol, and monitoring well design most appropriate for the monitoring site and the contaminants of interest. A Robowell prototype was installed on a sewage‐treatment plant infiltration bed that overlies a well‐studied u neon fined sand and gravel aquifer at the Massachusetts Military Reservation, Cape Cod, Massachusetts, during a time when two distinct plumes of constituents were released. The prototype was operated from May 10 to November 13, 1996, and quality‐assurance/quality‐control measurements demonstrated that the data obtained by the automated method was equivalent to data obtained by manual sampling methods using the same sampling protocols. Water level, specific conductance, pH, water temperature, dissolved oxygen, and dissolved ammonium were monitored by the prototype as the wells were purged according to U.S. Geological Survey (LJSGS) ground water sampling protocols. Remote access to the data record, via phone modem communications, indicated the arrival of each plume over a few days and the subsequent geochemical reactions over the following weeks. Real‐time availability of the monitoring record provided the information needed to initiate manual sampling efforts in response to changes in measured ground water quality, which proved the method and characterized the screened portion of the plume in detail through time. The methods and the case study described are presented to document the process for future use.
Evaluating Information Visualizations : Issues and Opportunities

The evaluation of information visualization techniques and systems is important in order to understand how to improve the systems and create innovative, useful new techniques. This article describes some of the challenges and issues in information visualization evaluation, and it discusses potential opportunities to improve the process. By better identifying the tasks people perform using information visualization tools as well as the insights gained therein, one can conduct more rigorous and comprehensive assessments of the tools.
Measurement with some theory: using sign restrictions to evaluate business cycle models ∗

We propose a method to evaluate business cycle models which does not require knowledge of the DGP and is robust to the time series specification of the aggregate decision rules. We derive robust restrictions in a class of models; use some of them to identify structural shocks and others to evaluate the model. The approach has good size and excellent power properties even in small samples. The median of the distribution of the responses is a good estimator of the true responses. We examine the dynamics of hours in response to technology shocks and of consumption in response to goverment expenditure shocks.
forecasting exchange rates using artificial neural networks

In the dynamic global economy, the accuracy in fore casting the foreign currency exchange rates is of crucial importance for any future inves tment. The use of computational intelligence based techniques for forecasting has been proved ex tremely successful in recent times. The aim of this study is to identify a neural network m odel which has ability to predict the US Dollar against Sri Lankan Rupee (USD/LKR) with high er accuracy level. In this study both static and dynamic neural network architectures wer e considered. Three types of neural network models were employed: (I) Feedforward neura l network (FFNN; static neural networks) with the Backpropagation (BPR) algorithm; (II) FFNN with the Scaled Conjugate Gradient (SCG) algorithm; (III) Time Delay neural n etwork (TDNN; dynamic neural network). Best performed models were found from eac h approach and forecastability of these models were compared to come up with the best model to predict the USD/LKR. It was found that the FFNN trained with the SCG algorithm performs better than FFNN trained with the BPR algorithm. Therefore the best static neural network for predictions is the FFNN trained with the SCG algorithm. The best TDNN outpe rforms the best static neural network model and finally this model can be proposed as the best model to predict the USD/LKR. The best performed TDNN model contains two hidden layer s, three neurons in each layer and six time delays. This model has ability to forecast uns een data with 76% prediction accuracy.
Mapping the AAV Capsid Host Antibody Response toward the Development of Second Generation Gene Delivery Vectors

The recombinant adeno-associated virus (rAAV) gene delivery system is entering a crucial and exciting phase with the promise of more than 20 years of intense research now realized in a number of successful human clinical trials. However, as a natural host to AAV infection, anti-AAV antibodies are prevalent in the human population. For example, ~70% of human sera samples are positive for AAV serotype 2 (AAV2). Furthermore, low levels of pre-existing neutralizing antibodies in the circulation are detrimental to the efficacy of corrective therapeutic AAV gene delivery. A key component to overcoming this obstacle is the identification of regions of the AAV capsid that participate in interactions with host immunity, especially neutralizing antibodies, to be modified for neutralization escape. Three main approaches have been utilized to map antigenic epitopes on AAV capsids. The first is directed evolution in which AAV variants are selected in the presence of monoclonal antibodies (MAbs) or pooled human sera. This results in AAV variants with mutations on important neutralizing epitopes. The second is epitope searching, achieved by peptide scanning, peptide insertion, or site-directed mutagenesis. The third, a structure biology-based approach, utilizes cryo-electron microscopy and image reconstruction of AAV capsids complexed to fragment antibodies, which are generated from MAbs, to directly visualize the epitopes. In this review, the contribution of these three approaches to the current knowledge of AAV epitopes and success in their use to create second generation vectors will be discussed.
Beneficial Effects of the Activation of the Angiotensin-(1–7) Mas Receptor in a Murine Model of Adriamycin-Induced Nephropathy

Angiotensin-(1–7) [Ang-(1–7)] is a biologically active heptapeptide that may counterbalance the physiological actions of angiotensin II (Ang II) within the renin-angiotensin system (RAS). Here, we evaluated whether activation of the Mas receptor with the oral agonist, AVE 0991, would have renoprotective effects in a model of adriamycin (ADR)-induced nephropathy. We also evaluated whether the Mas receptor contributed for the protective effects of treatment with AT1 receptor blockers. ADR (10 mg/kg) induced significant renal injury and dysfunction that was maximal at day 14 after injection. Treatment with the Mas receptor agonist AVE 0991 improved renal function parameters, reduced urinary protein loss and attenuated histological changes. Renoprotection was associated with reduction in urinary levels of TGF-β. Similar renoprotection was observed after treatment with the AT1 receptor antagonist, Losartan. AT1 and Mas receptor mRNA levels dropped after ADR administration and treatment with losartan reestablished the expression of Mas receptor and increased the expression of ACE2. ADR-induced nephropathy was similar in wild type (Mas+/+) and Mas knockout (Mas −/−) mice, suggesting there was no endogenous role for Mas receptor activation. However, treatment with Losartan was able to reduce renal injury only in Mas+/+, but not in Mas −/− mice. Therefore, these findings suggest that exogenous activation of the Mas receptor protects from ADR-induced nephropathy and contributes to the beneficial effects of AT1 receptor blockade. Medications which target specifically the ACE2/Ang-(1–7)/Mas axis may offer new therapeutic opportunities to treat human nephropathies.
Shakespeare for Adult Education Deans: Lessons from Liberal Learning.

Administrators can acquire a broader range of material drawn from Shakespeare and, by implication, from other great authors as sources for a fuller cognizance of life. The vast field of Shakespearean scholarship and criticism overshadows the works themselves. Nevertheless, adults can rediscover Shakespeare through performances and recreational reading. They approach the plays with their own experiences and find new things to examine. Reading and studying the plays can be pleasurable and personally satisfying. To experience Shakespeare is to see language as art and to see the power of the spoken word. One's construction and interpretation of 'ae play are challenged by each performance. Other appealing aspects of Shakespeare are the complex characters, the play between illusion and reality, and one of the central issues in many Shakespearean plays--the importance of a good reputation. Shakespeare can help administrators realize the uncertainty of existence so they can place their vocations on a more reasonable level of importance. (9
Factors controlling seasonal variations in stable isotope composition of particulate organic matter in a softwater eutrophic lake

Weekly water samples were taken to measure stable isotope composition (δ13C and δ15N) of particulate organic matter (POM) in Lake Wauberg, Florida, from June 1994 to May 1995. The average δ13C of POM was ‐19.3%, consistent with an autochthonous origin from phytoplankton production, and exhibited a seasonal pattern that coincided with changes in water temperature, pH, CO2 concentration, and phytoplankton biomass in the surface water. The 13C enrichment in POM was attributed to reduced isotope fractionation due to carbon (C) limitation and the use of an isotopically heavy dissolved inorganic carbon pool supported mainly by atmospheric invasion and anaerobic respiration. Intermittent declines in δ13C of POM were related to the frequent collapses of phytoplankton blooms and increases in CO2 concentration resulting from both increased community respiration and terrestrial loading. Average δ15N of POM was 1.3% and varied within a narrow range (20.1% to 2.5%). No significant correlation between phytoplankton biovolume and the δ15N of POM was found. The low δ15N is indicative of strong N2 fixation, which is in line with the low concentration of dissolved inorganic nitrogen and the presence of high biovolume of N2‐fixing cyanobacteria in the surface water. This study suggests that stable C isotopes are good proxies for surface water CO2 concentration and primary production, while stable N isotopes can be used to indicate N2 fixation.
Collaborative product development and situated knowledge contexts

Purpose – The purpose of this paper is to develop a framework for understanding how problem formulation, information search and division of work in new product development (NPD) activities is shaped by mind sets vested in organizations with diverging positions in the value chain and correspondingly situated knowledge contexts. The authors aim to focus on how this influences the marketability of new product ideas.Design/methodology/approach – The empirical data are derived from exploratory observation studies of NPD meetings and interviews of involved managers and specialists in three business dyads. The cases involve ingredient and meat suppliers, retail chains and marketing agencies located in Denmark.Findings – The authors show that the scope and organization of NPD activities indeed are shaped by the combinations of situated knowledge contexts involved. An important intervening variable however concerns the atmosphere of the relationship, involving emotions and attitudes of the actors involved from for.
Dynamic Traffic Flow Prediction Based on GPS Data

This paper presents a solution for traffic flow prediction in a city area. GPS devices offer new opportunities for short-term traffic prediction, especially in arterial road networks where traditional fixed-location sensors are sparse or expensive to install. However, GPS data is often sparse both temporally and spatially. On its own, it is often insufficient for real-time traffic prediction. We consider the fusion of two types of data for the purpose of dynamic traffic prediction: GPS data that is provided as point speeds, rather than trajectories, as well as traffic data that is available from previous tracking. Inspired by the observation that a driver often has its own route selection behavior, we define a mobility pattern as a consecutive series of road segment/link selections that exhibit frequent appearance along all the itineraries of the vehicle. We predict the traffic flow using a hybrid method based on Variable-order Markov Model and adding on top of it the average speed of all the vehicles passing through each road segment. Our solution comes with a highly scalable traffic simulator application that can be used to predict, manage and optimize car traffic in cities. The prediction accuracy is estimated according to various criteria.
Channel length dependence of discrete dopant effects in narrow si nanowire transistors: A full 3D NEGF study

In this paper we study the variability induced by random discrete dopants in a gate-all-around silicon nanowire transistor. The electron transport is described using the Non-Equilibrium Green Function formalism. Coupled-mode-space representations are used. A silicon nanowire transistor with 4.2×4.2 nm2 cross-section and two different channel lengths (6 nm and 12 nm) has been considered. The mobility associated with discrete dopants can be estimated from the averaged current voltage characteristics. The threshold voltage and subthreshold slope variability are greatly reduced in the longer channel length transistor. At the same time the on current variability associated with the resistance variability of the access regions is virtually independent of the channel length.
A review of feature extraction techniques BTC, DCT, Walsh and PCA with FDM and BDM for face recognition

In the modern era the world comes nearer to every individual as an IT revolution where all the applications are computerized. As the level of security breaches and frauds of transaction increases, it requires highly secure identification and personal verification technologies. Though there are various biometric traits such as iris, fingerprint and palm print etc., we focused on face recognition as it is socially acceptable and reliable. Here user identity plays a very important role to uniquely verify or authenticate the individual person. Instead of designing more complex system, which is more expensive and which requires more software and hardware resources, it is essential to think about to bridge the gap which will create a system with simplicity, less costly and efficient, as well as socially acceptable. Utilizing biometrics for personal authentication is becoming convenient and considerably more accurate than current methods (such as the utilization of passwords or PINs). The factors which highly impact the face recognition system performance are illumination and pose variations. Feature extraction is the key to reach face recognition. In literature various feature extraction techniques in spatial and frequency domain are available. This paper gives overview of the existing feature extraction techniques PCA, DCT, Walsh and BTC for face recognition and comparative analysis.
Color Assessment and Transfer for Web Pages

Colors play a particularly important role in both designing and accessing Web pages. A well-designed color scheme improves Web pages' visual aesthetic and facilitates user interactions. As far as we know, existing color assessment studies focus on images; studies on color assessment and editing for Web pages are rare. This paper investigates color assessment for Web pages based on existing online color theme-rating data sets and applies this assessment to Web color edit. This study consists of three parts. First, we study the extraction of a Web page's color theme. Second, we construct color assessment models that score the color compatibility of a Web page by leveraging machine learning techniques. Third, we incorporate the learned color assessment model into a new application, namely, color transfer for Web pages. Our study combines techniques from computer graphics, Web mining, computer vision, and machine learning. Experimental results suggest that our constructed color assessment models are effective, and useful in the color transfer for Web pages, which has received little attention in both Web mining and computer graphics communities.
UNDERWATER ENVIRONMENT MODELLING BY FAST 3D MOSAICING

This paper describes a technique for fast three-dimensional reconstruction of underwater environments from multiple range views acquired by an acoustic camera. The final target of the work lies in improving the understanding of a human operator guiding an underwater remotely operated vehicle (ROV) equipped with an acoustic camera, which provides a sequence of 3D images. Since the field of view is narrow, we devise a technique for the reconstruction of relevant information of the image sequence up to build a mosaic of the surrounding scene in real time. Due to speckle noise and low resolution a robust approach based on statistics is proposed in order to increase the registration accuracy. In order to allow the real time registration we introduce a method for speed up the Iterative Closest Point (ICP) basing on a reverse calibration approach. Examples on real images have been presented to show the performances of the proposed algorithm in terms of both speed and accuracy.