Miracle-dz/UniFAR-FTUs huggingface dataset:
The Cross-Section of Output and Inflation in a Dynamic Stochastic General Equilibrium Model with Sticky Prices

In a standard dynamic stochastic general equilibrium framework, with sticky prices, the cross sectional distribution of output and inflation across a population of firms is studied. The only form of heterogeneity is confined to the probability that the ith changes its prices in response to a shock. In this Calvo setup the moments of the cross sectional distribution of output and inflation depend crucially on the proportion of firms that are allowed to change their prices. We test this model empirically using German balance sheet data on a very large population of firms. We find a significant counter-cyclical correlation between the skewness of inflation and aggregates, but the relation with output is less sure. Our results can be interpreted as indirect evidence of the importance of price stickiness in macroeconomic adjustments.
Orographic Effects of Geomorphology on Precipitation in a Pluvial Basin of the Eastern Tibetan Plateau

The eastern Tibetan Plateau is subjected to strong spatial variations in precipitation, but the underlying reasons are still not well understood due to sparse in-situ meteorological observations. In this study, streamflow observations were adopted to investigate the orographic controls on precipitation in the Qingyijiang (QYJ) Basin of the eastern Tibetan Plateau. The method of multi-year annual water balance was used to estimate the basin-level precipitation using in-situ streamflow and flux-based evapotranspiration. In addition, elevation transect was designed to examine the possible links between precipitation and geomorphology. The results showed the severe under-estimation of regional precipitation by weather sites (~1150 mm yr−1) in the QYJ Basin, where the runoff depth was as high as ~1450 mm yr−1. The water balance revealed a much higher level of precipitation (~2000 mm yr−1) in the QYJ Basin, but precipitation in the two adjacent basins was contrastingly low (<1000 mm yr−1). The spatial pattern of precipitation was well consistent with the local horn-mouth geomorphology, with more precipitation occurring in the geomorphologically converging and elevating region. Furthermore, within the the QYJ Basin, annual precipitation was larger in the sub-basins (>2200 mm) on or near the bottom of the horn-mouth geomorphology than the others (<1800 mm). With these results, we concluded that the high precipitation level in the QYJ Basin could be attributed to the combined converging and lifting effects of geomorphology on the westward atmospheric vapor. Therefore, flooding risk should be carefully accounted for in the basins with similar geomorphology in the eastern Tibetan Plateau.
Joint Demosaicing and Denoising via Learned Nonparametric Random Fields

We introduce a machine learning approach to demosaicing, the reconstruction of color images from incomplete color filter array samples. There are two challenges to overcome by a demosaicing method: 1) it needs to model and respect the statistics of natural images in order to reconstruct natural looking images and 2) it should be able to perform well in the presence of noise. To facilitate an objective assessment of current methods, we introduce a public ground truth data set of natural images suitable for research in image demosaicing and denoising. We then use this large data set to develop a machine learning approach to demosaicing. Our proposed method addresses both demosaicing challenges by learning a statistical model of images and noise from hundreds of natural images. The resulting model performs simultaneous demosaicing and denoising. We show that the machine learning approach has a number of benefits: 1) the model is trained to directly optimize a user-specified performance measure such as peak signal-to-noise ratio (PSNR) or structural similarity; 2) we can handle novel color filter array layouts by retraining the model on such layouts; and 3) it outperforms the previous state-of-the-art, in some setups by 0.7-dB PSNR, faithfully reconstructing edges, textures, and smooth areas. Our results demonstrate that in demosaicing and related imaging applications, discriminatively trained machine learning models have the potential for peak performance at comparatively low engineering effort.
Crowdsourcing the evaluation of a domain-adapted named entity recognition system

Named entity recognition systems sometimes have difficulty when applied to data from domains that do not closely match the training data. We first use a simple rule-based technique for domain adaptation. Data for robust validation of the technique is then generated, and we use crowdsourcing techniques to show that this strategy produces reliable results even on data not seen by the rule designers. We show that it is possible to extract large improvements on the target data rapidly at low cost using these techniques.
Antiretroviral Monocyte Efficacy Score Linked to Cognitive Impairment in Hiv

Background Monocytes transmigrating to the brain play a central role in HIV neuropathology. We hypothesized that the continued existence of neurocognitive impairment (NCI) despite potent antiretroviral (ARV) therapy is mediated by the inability of such therapy to control this monocyte/macrophage reservoir. Methods Cross-sectional and longitudinal analyses were conducted within a prospectively enrolled cohort. We devised a monocyte efficacy (ME) score based on the anticipated effectiveness of ARV medications against monocytes/macrophages using published macrophage in vitro drug efficacy data. We examined, within an HIV neurocognitive database, its association with composite neuropsychological test scores (NPZ8) and clinical cognitive diagnoses among subjects on stable ARV medications unchanged for >6 months prior to assessment. Results Among 139 subjects on ARV therapy, higher ME score correlated with better NPZ8 performance (r=0.23, P<0.01), whereas a score devised to quantify expected penetration effectiveness of ARVs into the brain (CPE score) did not (r=0.12, P=0.15). In an adjusted model (adjusted r2=0.12), ME score (β=0.003, P=0.02), CD4+ T-cell nadir (β=0.001, P<0.01) and gender (β=-0.456, P=0.02) were associated with NPZ8, whereas CPE score was not (β=0.003, P=0.94). A higher ME score was associated with better clinical cognitive status (P<0.01). With a range of 12.5–433.0 units, a 100-unit increase in ME score resulted in a 10.6-fold decrease in the odds of a dementia diagnosis compared with normal cognition (P=0.01). Conclusions ARV efficacy against monocytes/macrophages correlates with cognitive function in HIV-infected individuals on ARV therapy within this cohort. If validated, efficacy against monocytes/macrophages may provide a new target to improve HIV NCI.
Molecular Profiling of Hard-to-Treat Childhood and Adolescent Cancers

Key Points Question Can genome sequencing facilitate the molecular profiling of the patient's tumor to identify actionable and targetable alterations? Findings In this diagnostic study of 62 consecutive pediatric patients with hard-to-treat cancer who were enrolled in the TRICEPS study, incorporating multimodal genomic sequencing, including RNA sequencing, into the management of refractory or relapsed childhood and adolescent cancers identified potentially actionable alterations in 54 (87%) of patients. Meaning Molecular profiling may enable the identification of potentially actionable alterations with clinical implications for most patients tested, including targeted therapy and clinically relevant information of diagnostic, prognostic, and monitoring significance.
Complexity of Inference in Graphical Models

It is well-known that inference in graphical models is hard in the worst case, but tractable for models with bounded treewidth. We ask whether treewidth is the only structural criterion of the underlying graph that enables tractable inference. In other words, is there some class of structures with unbounded treewidth in which inference is tractable? Subject to a combinatorial hypothesis due to Robertson et al. (1994), we show that low treewidth is indeed the only structural restriction that can ensure tractability. Thus, even for the "best case" graph structure, there is no inference algorithm with complexity polynomial in the treewidth.
Solving parity games through fictitious play

The thesis aims to find an efficient algorithm for solving parity games. Parity games are graph-based, 0-sum, 2-person games with infinite plays. It is known that these games are determined: all nodes in these games are won by exactly one player. Solving parity games is equivalent to the model checking problem of modal mu-calculus; an efficient solution has important implications to program verification and controller synthesis. Although the decision problem of which player wins a given node is generally believed to be in PTIME, all known algorithms so far have been shown to run in (sub)exponential time. The design of existing algorithms either derives from the determinacy proof of parity games or from a purely graph theoretical perspective, using certain rank functions to iteratively search for an optimal solution. Since parity games are 2-person, 0-sum games, in this thesis I borrow ideas of game theory and investigate the viability of using fictitious play to solve them. Fictitious play is a method where two players choose strategies in strict alternation, and where these choices are “best responses” against the last k (so called bounded recall length) or against all strategies (unbounded recall length) of the other player chosen so far. I use this method to design an algorithm that can solve partity games and study its theoretical and experimental properties. For example, I prove that the basic algorithm solves fully connected games in polynomial time through a number of iterations that is bounded by a small constant. Although the proof is not extended to the general cases in the thesis, the basic algorithm performs demonstrably well against existing solvers in experiments over a large number and variety of games. In particular, the empirically obtained number of iterations that our basic algorithm requires appears to increase polynomially against the game sizes for all the games tested. Furthermore, the algorithm is conjectured to have a run time complexity bounded by O(n4 log(n)) and I provide a discussion of strategy graphs and their emperically observed
Statistics on the space of trajectories for longitudinal data analysis

Statistical analysis of longitudinal data is a significant problem in Biomedical imaging applications. In the recent past, several researchers have developed mathematically rigorous methods based on differential geometry and statistics to tackle the problem of statistical analysis of longitudinal neuroimaging data. In this paper, we present a novel formulation of the longitudinal data analysis problem by identifying the structural changes over time (describing the trajectory of change) to a product Riemannian manifold endowed with a Riemannian metric and a probability measure. We present theoretical results showing that the maximum likelihood estimate of the mean and median of a Gaussian and Laplace distribution respectively on the product manifold yield the Fréchet mean and median respectively. We then present efficient recursive estimators for these intrinsic parameters and use them in conjunction with a nearest neighbor (NN) classifier to classify MR brain scans (acquired from the publicly available OASIS database) of patients with and without dementia.
Automatic product feature extraction from online product reviews using maximum entropy with lexical and syntactic features

The task of product feature extraction is to find product features that customers refer to their topic reviews. It would be useful to characterize the opinions about the products. We propose an approach for product feature extraction by combining lexical and syntactic features with a maximum entropy model. For the underlying principle of maximum entropy, it prefers the uniform distributions if there is no external knowledge. Using a maximum entropy approach, firstly we extract the learning features from the annotated corpus, secondly we train the maximum entropy model, thirdly we use trained model to extract product features, and finally we apply a natural language processing technique in postprocessing step to discover the remaining product features. Our experimental results show that this approach is suitable for automatic product feature extraction.
Semi-Automatic Recognition of Noun Modifier Relationships

Semantic relationships among words and phrases are often marked by explicit syntactic or lexical clues that help recognize such relationships in texts. Within complex nominals, however, few overt clues are available. Systems that analyze such nominals must compensate for the lack of surface clues with other information. One way is to load the system with lexical semantics for nouns or adjectives. This merely shifts the problem elsewhere: how do we define the lexical semantics and build large semantic lexicons? Another way is to find constructions similar to a given complex nominal, for which the relationships are already known. This is the way we chose, but it too has drawbacks. Similarity is not easily assessed, similar analyzed constructions may not exist, and if they do exist, their analysis may not be appropriate for the current nominal.We present a semi-automatic system that identifies semantic relationships in noun phrases without using precoded noun or adjective semantics. Instead, partial matching on previously analyzed noun phrases leads to a tentative interpretation of a new input. Processing can start without prior analyses, but the early stage requires user interaction. As more noun phrases are analyzed, the system learns to find better interpretations and reduces its reliance on the user. In experiments on English technical texts the system correctly identified 60--70% of relationships automatically.
Virtual Mirror: Real-Time Tracking of Shoes in Augmented Reality Environments

In this paper, we present a system that enhances the visualization of customized sports shoes using augmented reality techniques. Instead of viewing yourself in a real mirror, sophisticated 3D image processing techniques are used to verify the appearance of new shoe models. A single camera captures the person and outputs the mirrored images onto a large display which replaces the real mirror. The 3D motion of both feet are tracked in real-time with a new motion tracking algorithm. Computer graphics models of the shoes are augmented into the video such that the person seems to wear the virtual shoes.
Animation: from cartoons to the user interface

User interfaces are often based on static presentations, a model ill suited for conveying change. Consequently, events on the screen frequently startle and confuse users. Cartoon animation, in contrast, is exceedingly successful at engaging its audience; even the most bizarre events are easily comprehended. The Self user interface has served as a testbed for the application of cartoon animation techniques as a means of making the interface easier to understand and more pleasant to use. Attention to timing and transient detail allows Self objects to move solidly. Use of cartoon-style motion blur allows Self objects to move quickly and still maintain their comprehensibility. Self objects arrive and depart smoothly, without sudden materializations and disappearances, and they rise to the front of overlapping objects smoothly through the use of dissolve. Anticipating motion with a small contrary motion and pacing the middle of transitions faster than the endpoints results in smoother and clearer movements. Despite the differences between user interfaces and cartoons --cartoons are frivolous, passive entertainment and user interfaces are serious, interactive tools -- cartoon animation has much to lend to user interfaces to realize both affective and cognitive benefits. *This work was originally supported by Sun Microsystems Laboratories, an NSF Graduate Fellowship, National Science Foundation Presidential Young Investigator Grant #CCR-8657631, IBM Powell Foundation, Apple Computer, Inc., Cray Laboratories, Tandem, NCR Corporation, Texas Instruments, Inc., and Digital Equipment Corporation.
STANDARDIZED CPUE OF BLUEFIN TUNA (THUNNUS THYNNUS) CAUGHT BY SPANISH TRAPS FOR THE PERIOD 1981-2007

Catch rates from the Spanish traps in the Atlantic have been traditionally standardized using a GLM approach with a lognormal error assumption. Nevertheless, some concerns have been expressed regarding the current unit of effort used for standardization purposes as well as the model error assumption. This document explores the adequacy of current effort unit and proposes alternative model error assumptions for the standardization of the CPUE index.
Camera View-Based American Football Video Analysis

We present a top-down statistical modeling approach to explore the semantic structure in American football video. First, a semantic space is defined where the video semantic structure is characterized by semantic units, a dynamic model over semantic units, and an observation model for mapping the semantic units with the visual features. Then, a new hidden Markov model (HMM)-based video generative model is proposed for American football video analysis, where semantic units are defined as latent or hidden states corresponding to four different camera views in the football field. A set of relevant visual features are selected based on the information gain for HMM training and two kinds of state emission function, Gaussian or the Gaussian mixture model (GMM), which characterize the observation density function associated with each latent state and are tested in the proposed HMM for camera view-based video analysis. Experimental results on several real football videos manifest the effectiveness of the proposed algorithm. It is shown that the HMM with GMM emission shows advantages over the Gaussian-based one in terms of the classification accuracy of video shots
Nuclear polyglutamine-containing protein aggregates as active proteolytic centers

Protein aggregates and nuclear inclusions (NIs) containing components of the ubiquitin–proteasome system (UPS), expanded polyglutamine (polyQ) proteins, and transcriptional coactivators characterize cellular responses to stress and are hallmarks of neurodegenerative diseases. The biological function of polyQ-containing aggregates is unknown. To analyze proteasomal activity within such aggregates, we present a nanoparticle (NP)-based method that enables controlled induction of sodium dodecyl sulfate–resistant inclusions of endogenous nuclear proteins while normal regulatory mechanisms remain in place. Consistent with the idea that the UPS maintains quality control, inhibition of proteasomal proteolysis promotes extra large protein aggregates (1.4–2 μm), whereas formation of NP-induced NIs is found to be inversely correlated to proteasome activation. We show that global proteasomal proteolysis increases in NP-treated nuclei and, on the local level, a subpopulation of NIs overlaps with focal domains of proteasome-dependent protein degradation. These results suggest that inclusions in the nucleus constitute active proteolysis modules that may serve to concentrate and decompose damaged, malfolded, or misplaced proteins.
Methicillin-Susceptible Staphylococcus aureus as a Predominantly Healthcare-Associated Pathogen: A Possible Reversal of Roles?

Background Methicillin-resistant Staphylococcus aureus (MRSA) strains have become common causes of skin and soft tissue infections (SSTI) among previously healthy people, a role of methicillin-susceptible (MSSA) isolates before the mid-1990s. We hypothesized that, as MRSA infections became more common among S. aureus infections in the community, perhaps MSSA infections had become more important as a cause of healthcare-associated infection. Methods We compared patients, including children and adults, with MRSA and MSSA infections at the University of Chicago Medical Center (UCMC) from all clinical units from July 1, 2004-June 30, 2005; we also compared the genotypes of the MRSA and MSSA infecting bacterial strains. Results Compared with MRSA patients, MSSA patients were more likely on bivariate analysis to have bacteremia, endocarditis, or sepsis (p = 0.03), to be an adult (p = 0.005), to be in the intensive care unit (21.9% vs. 15.6%) or another inpatient unit (45.6% vs. 40.7%) at the time of culture. MRSA (346/545) and MSSA (76/114) patients did not differ significantly in the proportion classified as HA-S. aureus by the CDC CA-MRSA definition (p = 0.5). The genetic backgrounds of MRSA and MSSA multilocus sequence type (ST) 1, ST5, ST8, ST30, and ST59 comprised in combination 94.5% of MRSA isolates and 50.9% of MSSA isolates. By logistic regression, being cared for in the Emergency Department (OR 4.6, CI 1.5-14.0, p = 0.008) was associated with MRSA infection. Conclusion Patients with MSSA at UCMC have characteristics consistent with a health-care-associated infection more often than do patients with MRSA; a possible role reversal has occurred for MSSA and MRSA strains. Clinical MSSA and MRSA strains shared genotype backgrounds.