Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrast Insensitivity: The Critical Immaturity in Infant Visual Performance

This is a targeted review of the critical immaturities limiting psychophysical luminance contrast detection in human infants. Three-month-old infants are 50 times less sensitive to contrast than adults are. Rod experiments suggest that early-stage immaturities, like the short length of infant rod outer segments, have only a modest direct effect on infant visual performance. Infant contrast sensitivity may resemble adult extrafoveal sensitivity, because the foveal cones of the neonate are immature and may not generate strong enough responses to mediate visual performance. This use of the extrafoveal retina reduces the high-spatial frequency end of the infant contrast sensitivity function (CSF), contributing to poor infant resolution acuity. The remaining difference between infant and adult CSFs may be a simple overall reduction in infant sensitivity. The maximum of the infant CSF increases proportionately with age, and may be numerically near the infant’s age in weeks. Contrast discrimination experiments indicate that the critical immaturity that limits infant contrast sensitivity is a mid-level phenomenon, occurring before the site of the contrast gain control. For example, the infant ascending visual pathway might be limited by large amounts of intrinsic noise. These results suggest that there is little effect of inattentiveness to the psychophysical task by ostensibly alert infant patients or subjects. The clinician or researcher can interpret behavioral measurements of infant visual performance with confidence.
Sustainable Development by Design: Review of Life Cycle Design and Related Approaches

The environmental profile of goods and services that satisfy our individual and societal needs is shaped by design activities. Substantial evidence suggests that current patterns of human activity on a global scale are not following a sustainable path. Necessary changes to achieve a more sustainable system will require that environmental issues be more effectively addressed in design. But at present much confusion surrounds the incorporation of environmental objectives into the design process. Although not yet fully embraced by industry, the product life cycle system is becoming widely recognized as a useful design framework for understanding the links between societal needs, economic systems and their environmental consequences. The product life cycle encompasses all activities from raw material extraction, manufacturing, and use to final disposal of all residuals. Life cycle design (LCD), Design for Environment (DFE), and related initiatives based on this product life cycle are emerging as systematic ap.
Effectively Leveraging Visual Context to Detect Texts in Natural Scenes

Detecting texts in natural scenes is challenging because of large variation in size and layout of texts and strong distractions from background clutters. Leveraging contextual information is crucial in boosting the detection accuracy. In this paper, we construct a conditional random field (CRF) to utilize visual context that helps enhance true detections and suppress false alarms. Unlike previous works, the pairwise potentials in our model encode three different compatibility/repulsion relationships among character candidates under two different layout scenarios, and the unary potentials are obtained from multi-class recognition confidence of individual character candidates. In addition, we use easy texts to help recover difficult ones in an iterative manner. Due to these efforts, our method outperforms state-of-the-art text detection algorithms on the challenging ICDAR dataset.
Nontumor Cell Lines Subtractive Panning of a Phage Library Using Tumor and Identification of a Human Anti-CD 55 Single-Chain Fv by Updated Version

A large naı̈ve human single-chain (sc) Fv phage library was used to search for tumor-associated antigens by panning with a lung adenocarcinoma cell line, 1264, and counter-selecting with a nontumor bronchial epithelial cell line, BEAS-2B. After three rounds of subtractive panning, 239 of 673 clones analyzed bound selectively to 1264 tumor cells in a phage ELISA. Diversity analysis of these tumor-selective clones by BstNI fingerprinting and nucleotide sequencing revealed 14 distinct scFv fragments. Four clones bound selectively to 1264 over BEAS-2B cells when analyzed by a more discriminating flow cytometric assay using scFv. Moreover, these clones showed only limited cross-reactivity to several primary human cell lines. One clone, LU30, also cross-reacted strongly with the lung adenocarcinoma line, A549. The LU30 antigen was identified as decayaccelerating factor (CD55) by expression cloning from a 1264 cDNA library. The mean number of decay-accelerating factor molecules on the surface of 1264 and BEAS cells used for panning and counter-selection was estimated as 75,000 6 5,000 and 13,0006 10,000, respectively. Thus, phage library panning combined with expression cloning permits identification of antibodies and their cognate antigens for proteins that are differentially expressed on the surface of distinct cell populations.
Labor Selection, Turnover Costs and Optimal Monetary Policy

We study optimal monetary policy and welfare properties of a dynamic stochastic general equilibrium (DSGE) model with a labor selection process, labor turnover costs, and Nash bargained wages. We show that our model implies inefficiencies that cannot be offset in a standard wage bargaining regime. We also show that the inefficiencies rise with the magnitude of firing costs. As a result, in the optimal Ramsey plan, the optimal inflation volatility deviates from zero and is an increasing function of firing costs.
Identification of Morphologically Similar Seeds Using Multi-kernel Learning

Use of digital image analysis for the identification of seeds has not been recognized as a validated method. Image analysis for seed identification has been previously studied, and good recognition rates have been achieved. However, the data sets used in these experiments either contain very few groups of non-verified specimens or little representation of intra-species variations. This study considered a data set containing seed specimens that were verified to represent the species and a typical population variation, as well as look-alike species that share the same morphological appearance, in particular, seeds from species in the same genus, which can be particularly difficult for even trained professionals to visually distinguish. With representative specimens, the image features and machine learning algorithms described herein can achieve a high recognition rate: >97%. Three different types of features from seed images: colour, shape, and texture were extracted, and a multi-kernel support vector machine was used as the classifier. We compared our features to the previous state-of-the-art features and the results showed that the features we selected performed better on our data set.
Visualization of Stock Market Charts

With this work in progress we propose a visualization system for stock market charts. Insight into stock charts is important in technical stock market analysis where exclusively the chart shape is considered in decision making. We focus on clustering existing chart shapes. Clustering delivers representative charts representing a set of similar charts. In our work, we generate a 2D map of these representative charts and implement tools like zooming, levels of details and selection. Thus, we present a new approach of automatically generating the whole picture of the stock market dynamics.
Scheduling Decisions in Stream Processing on Heterogeneous Clusters

Stream processing is a paradigm evolving in response to well-known limitations of widely adopted MapReduce paradigm for big data processing, a hot topic of today's computer world. Moreover, in the field of computation facilities, heterogeneity of data processing clusters, intended or unintended, is starting to be relatively common. This paper deals with scheduling problems and decisions in stream processing on heterogeneous clusters. It brings an overview of current state of the art of stream processing on heterogeneous clusters with focus on resource allocation and scheduling. Basic scheduling decisions are discussed and demonstrated on naive scheduling of a sample application. The paper presents a proposal of a novel scheduler for stream processing frameworks on heterogeneous clusters, which employs design-time knowledge as well as benchmarking techniques to achieve optimal resource-aware deployment of applications over the clusters and eventually better overall utilization of the cluster.
Perceived severity of cyberbullying behaviour: differences between genders, grades and participant roles

Abstract This study aimed to develop a new scale to examine primary and secondary school students’ perceptions of the severity of cyberbullying behaviours, and to explore further whether differences exist in the means of gender, grade and participant role. A total of 707 primary and secondary school students (M = 14.7) in Taiwan participated in this study. Two Olweus-like global items were used to identify students’ participant roles. A self-reported cyberbullying severity scale (CSS) was developed and validated by Rasch measurement. Results of this study supported the reliability and validity of the 16-item CSS. Impersonation was rated as the most serious type of cyberbullying. Cyberbullying behaviours that occurred in private were rated as less severe than were those that occurred in public. A Rasch latent regression analysis revealed that some gender and involvement effects were found, but no statistically significant difference was found among means of four participant roles. The behavioural hierarchy of cyberbullying severity, mean differences among personal attributions and cyberbullying intervention are discussed at the end of the article.
Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p

Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein–protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental 13C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional 13C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.
The Initial Mass Function of the Galactic Bulge down to ~0.15 M☉

We present a luminosity function (LF) for lower main-sequence stars in the Galactic bulge near (l,b) = (0°, -6°) to J = 24, corresponding to MJ ~ 9.3. This LF is derived from Hubble Space Telescope (HST) near infrared camera and multiobject spectrometer (NICMOS) observations of a region of 22. ″5 × 22. ″5, with the F110W and F160W filters. The main-sequence locus in the infrared shows a strong change in slope at J ~ 20.5 (MJ ~ 5.75) that is well fitted by new low-mass models that include water and molecular hydrogen opacity. Our derived mass function (which is not corrected for binary companions) is the deepest measured to date in the bulge and extends to 0.15 M☉, with a power-law slope of α = -1.33 ± 0.07; a Salpeter mass function would have α = -2.35. We also combine our J-band LF with previously published data for the evolved stars to produce a bulge LF spanning ~15 mag. We show that this mass function has negligible dependence on the adopted bulge metallicity and distance modulus. Although shallower than the Salpeter slope, the slope of the bulge initial mass function (IMF) is steeper than that recently found for the Galactic disk (α = -0.8 and α = -0.54 from the data of Reid & Gizis and Gould et al., respectively, in the same mass interval) but is virtually identical to the disk IMF derived by Kroupa and coworkers. The bulge IMF is also quite similar to the mass functions derived for those globular clusters that are believed to have experienced little or no dynamical evolution. Finally, we derive the M/LJ ratio of the bulge to be ~0.9 ± 0.1 and briefly discuss the implications of this bulge IMF for the interpretation of the microlensing events observed in the direction of the Galactic bulge.
Consumer Heterogeneity and Medical Care Price Responsiveness : Evidence and Implications for Optimal Insurance Design ∗

Measuring consumer responsiveness to medical care prices is a central issue in health economics and a key ingredient in the optimal design and regulation of health insurance markets. We study consumer price responsiveness to medical care prices leveraging a natural experiment that occurred at a large self-insured firm that forced all of its employees to switch from an insurance plan that provided free health care to a non-linear high deductible insurance plan with markedly higher cost sharing. We find that the switch caused a 16.45% reduction in total yearly medical expenditures at the firm, leading to approximately $123 million in savings per year. We use this plan switch to study heterogeneity in price responsiveness as a function of (i) consumer demographics (ii) consumer health status and (iii) types of medical services consumed. We use both regression and machine learning techniques to estimate heterogeneous demand elasticities for medical care, and use our results to study the welfare implications of the plan switch as well as counterfactual analyses of (i) optimal menu design and (ii) optimal value-based contract design. We find that the plan switch led to a welfare increase between $604 and $794 per individual, with the benefits from more efficient medical spending outweighing the incremental loss from reduced risk protection. ∗We thank Microsoft Research for their support of this work. We also thank seminar participants at Microsoft Research. Finally, we thank Ishita Chordia for excellent research assistance. All errors are our own.
Exploiting object dynamics for recognition and control

This thesis explores a new approach to object recognition for active vision systems that integrates information across multiple observations of an object. The approach exploits the order relationship between successive frames to derive a classifier based on the characteristic motion of local features across visual sweeps. This motion model reveals structural information about the object that can be exploited for recognition. The first contribution of this thesis is a recognition system that extends invariant local features (shape contexts) into the time domain by integration of the motion model. Second, an entropy-based view selection scheme is presented that allows the vision system to “skip ahead” to highly discriminative views. Evaluations on two datasets demonstrate that both the motion model and active view selection yield higher-quality hypotheses about object categories quicker than a baseline system that treats object views as unordered streams of images. Thesis Supervisor: Deb Roy Title: Associate Professor of Media Arts and Sciences, Program in Media Arts and Sciences
A Peptide That Antagonizes TCR-Mediated Reactions with Both Syngeneic and Allogeneic Agonists: Functional and Structural Aspects1

We identify and consider some characteristics of a peptide antagonist for the Ag-specific receptor on 2C cells (the 2C TCR). The peptide, GNYSFYAL (called GNY), binds to H-2Kb, and a very high-resolution crystal structure of the GNY-Kb complex at 1.35 Å is described. Although the GNY peptide does not bind to Ld, the potency of GNY-Kb as an antagonist is evident from its ability to specifically inhibit 2C TCR-mediated reactions to an allogenic agonist complex (QLSPFPFDL-Ld), as well as to a syngeneic agonist complex (SIYRYYGL-Kb). The crystal structure and the activities of alanine-substituted peptide variants point to the properties of the peptide P4 side chain and the conformation of the Tyr-P6 side chain as the structural determinants of GNYSFYAL antagonist activity.
Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora

We introduce an agreement-based approach to learning parallel lexicons and phrases from non-parallel corpora. The basic idea is to encourage two asymmetric latent-variable translation models (i.e., source-to-target and target-to-source) to agree on identifying latent phrase and word alignments. The agreement is defined at both word and phrase levels. We develop a Viterbi EM algorithm for jointly training the two unidirectional models efficiently. Experiments on the Chinese-English dataset show that agreement-based learning significantly improves both alignment and translation performance.
Profiling Based Estimation of Communication for System Partitioning

The purpose of this paper is to facilitate hardware design. One of the first design decisions is the partitioning of the system in smaller subsystems, which can be implemented on separate hardware and/or software components. Internal communication within components and external communication between different components can be distinguished. Especially external communication is cumbersome, because it determines the requirements for the communication channels in the system. Distinction between internal and external communication is defined by the functional partitioning step which thus has a major impact on the quality of the final design. By estimating communication between functions in advance, we enable a better functional partitioning and therefore improve the communication requirements in the system. As in [4], Java is used in this paper as a specification language for the system, because this language is ideally fit for communication analysis. We present a profiler which measures communication between methods in Java. Our profiler counts the so called consumer-producer relations for each pair of methods. Each consumer-producer relation is defined as a pair of a read operation from and the corresponding write operation to the Java heap memory. The profiling requires a huge increase in execution time. To reduce this overhead, we implement reservoir sampling [11]. This sampling method randomly and uniformly selects a smaller set of read operations to be profiled. The size of this set, i.e. the reservoir, specifies the sampling accuracy. Our approach is evaluated on the SPECjvm98 benchmark suite. The sampling technique reduces the overhead by a factor of 15. Yet accuracy remains within pre-specified limits. Keywords— profiling, communication estimation, sampling and system partitioning
Whistler waves observed during an in-situ solar type III radio burst

On Dec. 24, 1996, the WIND spacecraft encoun- tered a solar type III source region at 1 AU, as indicated by a characteristic radio signature down to less than the second harmonic of the local electron plasma frequency, the enhance- ment of the electron flux at high energies and the presence of electrostatic noise at the local plasma frequency. During this interval, electromagnetic wave activity was recorded below the electron cyclotron frequency, by the Time Domain Sampler of the WAVES instrument. Analysis suggests that the observed waves are whistler waves, propagating, approximately parallel or antiparallel to the interplanetary magnetic field, in the plasma frame.
When are inferences too fragile to be believed?

The use of sensitivity analysis is routine in some fields of empirical econometrics, although econometric theorists have generally taken a critical attitude towards it. This paper presents a framework in which arguments for and against such analysis can be evaluated. It appears that sensitivity is not necessarily a bad, nor sturdiness necessarily a good.
C++ software environment for speech signal processing

Here we present the C++ library SPC (Speech Signal Processing Classes) as development tool for assembling of speech processing applications. SPC offers real-time processing, batch processing of large databases, visualization, and analysis of signals between processing steps. In SPC the data stream occurring in speech processing is partitioned in three different information flows: signal data, control information and visualization data. Because hardware dependent program code is limited exclusively to some special methods, SPC can be adapted to different hardware environments easily. System specific code is encapsulated in low level parts of SPC and SPC user programs can be compiled on various platforms without any changes in source code. Up to now SPC supports Windows 95/98/NT, IBM AIX and LINUX.
The growth in size and mass of cluster galaxies since z = 2

We study the formation and evolution of Brightest Cluster Galaxies starting from a z = 2 population of quiescent ellipticals and following them to z = 0. To this end, we use a suite of nine high-resolution dark matter-only simulations of galaxy clusters in aCDM universe. We develop a scheme in which simulation particles are weighted to generate realistic and dynamically stable stellar density profiles at z = 2. Our ini- tial conditions assign a stellar mass to every identified dark halo as expected from abundance matching; assuming there exists a one-to-one relation between the visible properties of galaxies and their host haloes. We set the sizes of the luminous com- ponents according to the observed relations for z � 2 massive quiescent galaxies. We study the evolution of the mass-size relation, the fate of satellite galaxies and the mass aggregation of the cluster central. From z = 2, these galaxies grow on average in size by a factor 5 to 10 of and in mass by 2 to 3. The stellar mass growth rate of the simulated BCGs in our sample is of 1.9 in the range 0.3 < z < 1.0 consistent with observations, and of 1.5 in the range 0.0 < z < 0.3. Furthermore the satellite galaxies evolve to the present day mass-size relation by z = 0. Assuming passively evolving stellar populations, we present surface brightness profiles for our cluster cen- trals which resemble those observed for the cDs in similar mass clusters both at z = 0 and at z = 1. This demonstrates that theCDM cosmology does indeed predict mi- nor and major mergers to occur in galaxy clusters with the frequency and mass ratio distribution required to explain the observed growth in size of passive galaxies since z = 2. Our experiment shows that Brightest Cluster Galaxies can form through dissi- pationless mergers of quiescent massive z = 2 galaxies, without substantial additional star formation.