Miracle-dz/UniFAR-FTUs huggingface dataset:
Document analysis applied to fragments: feature set for the reconstruction of torn documents

Document analysis is done to analyze entire forms (e.g. intelligent form analysis, table detection) or to describe the layout/structure of a document. In this paper document analysis is applied to snippets of torn documents to calculate features that can be used for reconstruction. The main intention is to handle snippets of varying size and different contents (e.g. handwritten or printed text). Documents can either be destroyed by the intention to make the printed content unavailable (e.g. business crime) or due to time induced degeneration of ancient documents (e.g. bad storage conditions). Current reconstruction methods for manually torn documents deal with the shape, or e.g. inpainting and texture synthesis techniques. In this paper the potential of document analysis techniques of snippets to support a reconstruction algorithm by considering additional features is shown. This implies a rotational analysis, a color analysis, a line detection, a paper type analysis (checked, lined, blank) and a classification of the text (printed or hand written). Preliminary results show that these features can be determined reliably on a real dataset consisting of 690 snippets.
Research Statement Online Optimization

My research so far has focussed on the area of Online Optimization which was the subject of my Ph.D. thesis. I have also done some related (and unrelated) work in Machine Learning, Game Theory and Economics. In the next few years, I hope to branch out into more areas within Machine Learning, which seems to be generating a lot of exciting new problems. In particular, I am interested in learning more about reinforcement learning and also applications of Machine Learning to Finance. Another direction I would like to go is in applying more sophisticated concentration-of-measure inequalities, such as Talagrand’s inequality, in the context of Machine Learning, an approach which has proved very fruitful in my earlier work on Online Optimization. I would also generally like to broaden my scope and work on new and interesting problems. I am also actively extending my previous work on Online Optimization, to deal with more complicated stochastic models and more kinds of loss functions, as discussed in the next section.
Automated stock price prediction and trading framework for Nifty intraday trading

Research on automated systems for Stock price prediction has gained much momentum in recent years owing to its potential to yield profits. In this paper, we present an automatic trading system for Nifty for deciding the buying and selling calls for intra-day trading that combines various methods to improve the quality and precision of the prediction. Historical data has been used to implement the various technical indicators and also to train the Neural Network that predicts movement for intra-day Nifty. Further, Sentiment Analysis techniques are applied to popular blog articles written by domain experts and to user comments to find sentiment orientation, so that analysis can be further improved and better prediction accuracy can be achieved. The system makes a prediction for every trading day with these methods to forecast if next day will be a positive day or negative. Further, buy and sell calls for intra-day trading are also decided by the system thus achieving full automation in stock trading.
TEXES Observations of Pure Rotational H2 Emission from AB Aurigae

We present observations of pure rotational molecular hydrogen emission from the Herbig Ae star, AB Aur. Our observations were made using the Texas Echelon Cross Echelle Spectrograph (TEXES) at the NASA Infrared Telescope Facility and the Gemini North Observatory. We searched for H2 emission in the S(1), S(2), and S(4) lines at high spectral resolution and detected all three. By fitting a simple model for the emission in the three transitions, we derive T = 670 ± 40 K and M = 0.52 ± 0.15 M⊕ for the emitting gas. On the basis of the 8.5 km s-1 FWHM of the S(2) line, assuming the emission comes from the circumstellar disk, and with an inclination estimate of the AB Aur system taken from the literature, we place the location for the emission near 18 AU. Comparison of our derived temperature to a disk structure model suggests that UV and X-ray heating are important in heating the disk atmosphere.
WIDE BAND LINEAR PRINTED ANTENNA ARRAY WITH LOW SIDELOBE COSECANT SQUARE-SHAPED BEAM PATTERN

A wide band, integrated linear printed antenna array with low sidelobe cosecant square-shaped beam pattern is presented. Array synthesis mainly about the wide shaped coverage, low ripple and sidelobe level (SLL) has been done using the modifled least square method by matrix inversion. A printed dipole integrated with wideband balun has been chosen as the array element for wide bandwidth and good integration with the feeding network. Simulated results have shown good agreement with the measured results of the antenna array, which has a VSWR • 1:3 bandwidth 15% and good pattern with cosecant square-shaped region beyond 30 - and SLL • i27dB in L-band.
A 3D Plane Recognition Algorithm Based on Image Matching

The three-dimensional(3D) plane-recognition technology is the basic research of 3D object matching, 3D scene modeling and scene segmentation. Traditional algorithms rely on specific devices and particular environment or the priori calibration parameters. The algorithm fails completely when lose those information. For the shortcomings of the traditional algorithms, the 3D multi-plane detection algorithm based on image matching has been found. It only requires two pictures from different perspective for a project to identify objects' different planes. The algorithm's operation is simple, adaptable and flexible. The algorithm is a new algorithm for 3D plane recognition, and the effectiveness of the algorithm has been verified by experiments.
A Matrix-Free Algorithm for Equality Constrained Optimization Problems with Rank-Deficient Jacobians

We present a line search algorithm for large-scale constrained optimization that is robust and efficient even for problems with (nearly) rank-deficient Jacobian matrices. The method is matrix-free (i.e., it does not require explicit storage or factorizations of derivative matrices), allows for inexact step computations, and is applicable for nonconvex problems. The main components of the approach are a trust region subproblem for handling ill-conditioned or inconsistent linear models of the constraints and a process for attaining a sufficient reduction in a local model of a penalty function. We show that the algorithm is globally convergent to first-order optimal points or to stationary points of an infeasibility measure. Numerical results are presented.
Reynolds-Averaged Navier-Stokes Simulation of a 2D Circulation Control Wind Tunnel Experiment

Numerical simulations are performed using a Reynolds-averaged Navier-Stokes (RANS) flow solver for a circulation control airfoil. 2D and 3D simulation results are compared to a circulation control wind tunnel test conducted at the NASA Langley Basic Aerodynamics Research Tunnel (BART). The RANS simulations are compared to a low blowing case with a jet momentum coefficient, Cμ ,o f 0.047 and a higher blowing case of 0.115. Three dimensional simulations of the model and tunnel walls show wall effects on the lift and airfoil surface pressures. These wall effects include a 4% decrease of the midspan sectional lift for the Cμ 0.115 blowing condition. Simulations comparing the performance of the Spalart Allmaras (SA) and Shear Stress Transport (SST) turbulence models are also made, showing the SST model compares best to the experimental data. A Rotational/Curvature Correction (RCC) to the turbulence model is also evaluated demonstrating an improvement in the CFD predictions.
Vertices of Spectrahedra arising from the Elliptope, the Theta Body, and Their Relatives

Utilizing dual descriptions of the normal cone of convex optimization problems in conic form, we characterize the vertices of semidefinite representations arising from Lov\'asz theta body, generalizations of the elliptope, and related convex sets. Our results generalize vertex characterizations due to Laurent and Poljak from the 1990's. Our approach also leads us to nice characterizations of strict complementarity and to connections with some of the related literature.
Completions of µ-algebras

We define the class of algebraic models of /spl mu/-calculi and study whether every such model can be embedded into a model which is a complete lattice. We show that this is false in the general case and focus then on free modal /spl mu/-algebras, i.e. Lindenbaum algebras of the propositional modal /spl mu/-calculus. We prove the following fact: the MacNeille-Dedekind completion of a free modal /spl mu/-algebra is a complete modal algebra, hence a modal /spl mu/-algebra (i.e. an algebraic model of the propositional modal /spl mu/-calculus). The canonical embedding of the free modal /spl mu/-algebra into its Dedekind-MacNeille completion preserves the interpretation of all the terms in the class Comp(/spl Sigma//sub 1//spl Pi//sub 1/) of the alternation-depth hierarchy. The proof uses algebraic techniques only and does not directly rely on previous work on the completeness of the modal /spl mu/-calculus.
Pre-fetching web pages through data mining based prediction.

The speed of fetching web pages to users is getting lower because the rapid expansion of Internet use, the inherited character of delay in the network and the Request/Response working mode of WWW, and this is becoming a serious concern for web surfers. In order to speed up fetching web pages, this paper presents an intelligent technique of web pre-fetching. We use a simplified WWW data model to represent the data in the cache of web browser to mine the association rules. We store these rules in a knowledge base so as to predict the user’s actions. Intelligent agents are responsible for mining the users’ interest and pre-fetching web pages, based on the interest association repository. In this way, browsing time has been reduced transparently.
The Recurrent Eclipse of an Unusual Pre-Main-Sequence Star in IC 348

The recurrence of a previously documented eclipse of a solar-like pre-main-sequence star in the young cluster IC 348 has been observed. The recurrence interval is 4.7 ± 0.1 yr, and portions of 4 cycles have now been seen. The duration of each eclipse is at least 3.5 yr, or ~75% of a cycle, verifying that this is not an eclipse by a stellar companion. The light curve is generally symmetric and approximately flat-bottomed. Brightness at maximum and minimum have been rather stable over the years, but the light curve is not perfectly repetitive or smooth, and small variations exist at all phases. We confirm that the star is redder when fainter. Models are discussed, and it is proposed that this could be a system similar to KH 15D in NGC 2264. Specifically, it may be an eccentric binary in which a portion of the orbit of one member is currently occulted during some binary phases by a circumbinary disk. The star deserves sustained observational attention for what it may reveal about the circumstellar environment of low-mass stars of planet-forming age.
A functional logic database library

Programmers need mechanisms to store application specific data that persists multiple program runs. To accomplish this task, they usually have to deal with storage specific code to access files or relational databases. Functional logic programming provides a natural framework to transparent persistent storage through persistent predicates, i.e., predicates with externally stored facts.We describe a functional logic database library, based on persistent predicates, for Curry. Our library supports functional logic programming with databases in the background, i.e., the programmer can access a database without storage specific code employing functional logic programming techniques.
Uncertainty Sampling for Constrained Cluster Ensemble

Constrained Clustering is a framework of improving clustering performance by using a set of constraints about data pairs. Since performance of constrained clustering depends on a set of constraints to use, we need a method to select good constraints that are expected to promote clustering performance. In this paper, we propose such a method, which actively selects data pairs to be constrained by using variance of clustering iteration. This method consists of a boosting based cluster ensemble algorithm that integrates a set of clusters produced by a constrained k-means with controlled data assignment order. Experimental results show that our method outperforms clustering with random sampling method.
Impact of a supported housing prioritization system using vulnerability and high service utilization

ABSTRACT For people experiencing chronic homelessness, supportive housing with intensive social, health, and behavioral health services reduces the likelihood of re-entering homelessness and the public costs of associated acute medical care, shelter use, and incarceration. Due to a limited supply of supportive housing, it must be allocated to those most in need. This paper examines findings from a unique, region-wide method for prioritizing individuals for supportive housing based on utilization of high-cost public services and vulnerability if left on the street. A sample of 196 individuals were prioritized for housing based on this method, while a comparison group of 102 were housed not using the method. Results showed that those housed under the prioritization method achieved greater reductions in utilization of high-cost public services, but were also less likely to have positive dispositions when exiting the housing programs, suggesting the need for a greater intensity of supports and/or multiple “doses” of supportive housing before stability can be expected. The method described in the paper can provide a starting point for developing regional, comprehensive systems of coordinated, prioritized entry into supportive housing, such as those now required by US Department of Housing and Urban Development.
Multi-Source Congestion Control for Content Centric Networks

Content Centric Networking (CCN) is one of the most promising content-oriented network architectures. In CCN, forwarding information base (FIB) is allowed to have multiple entries for a same content name prefix, which means CCN potentially supports multi-source download. When a content is obtained from multiple sources, the technical knowledge obtained for congestion control in the current Internet cannot be simply applied. This is because in the current Internet, FIB is restricted to have only one entry for each IP address prefix, which causes quite different path feature from CCN. This paper proposes a new congestion control for CCN with multi-source content retrieval. The proposed congestion control is composed of end-to-end window flow control and router assisted Interest forwarding control and enables transmission rate regulation only on a congested branch.
The Automorphic Membrane

We present a 1-loop toroidal membrane winding sum reproducing the conjectured M-theory, four-graviton, eight derivative, R4 amplitude. The U-duality and toroidal membrane world-volume modular groups appear as a Howe dual pair in a larger, exceptional, group. A detailed analysis is carried out for M-theory compactified on a 3-torus, where the target-space SL(3,) × SL(2,) U-duality and SL(3,) world-volume modular groups are embedded in E6(6)(). Unlike previous semi-classical expansions, U-duality is built in manifestly and realized at the quantum level thanks to Fourier invariance of cubic characters. In addition to winding modes, a pair of new discrete, flux-like, quantum numbers are necessary to ensure invariance under the larger group. The action for these modes is of Born-Infeld type, interpolating between standard Polyakov and Nambu-Goto membrane actions. After integration over the membrane moduli, we recover the known R4 amplitude, including membrane instantons. Divergences are disposed of by trading the non-compact volume integration for a compact integral over the two variables conjugate to the fluxes — a constant term computation in mathematical parlance. As byproducts, we suggest that, in line with membrane/fivebrane duality, the E6 theta series also describes five-branes wrapped on T6 in a manifestly U-duality invariant way. In addition we uncover a new action of E6 on ten dimensional pure spinors, which may have implications for ten dimensional super Yang-Mills theory. An extensive review of SL(3) automorphic forms is included in an appendix.
High Diversity of Anaerobic Alkane-Degrading Microbial Communities in Marine Seep Sediments Based on (1-methylalkyl)succinate Synthase Genes

Alkanes comprise a substantial fraction of crude oil and are prevalent at marine seeps. These environments are typically anoxic and host diverse microbial communities that grow on alkanes. The most widely distributed mechanism of anaerobic alkane activation is the addition of alkanes to fumarate by (1-methylalkyl)succinate synthase (Mas). Here we studied the diversity of MasD, the catalytic subunit of the enzyme, in 12 marine sediments sampled at seven seeps. We aimed to identify cosmopolitan species as well as to identify factors structuring the alkane-degrading community. Using next generation sequencing we obtained a total of 420 MasD species-level operational taxonomic units (OTU0.96) at 96% amino acid identity. Diversity analysis shows a high richness and evenness of alkane-degrading bacteria. Sites with similar hydrocarbon composition harbored similar alkane-degrading communities based on MasD genes; the MasD community structure is clearly driven by the hydrocarbon source available at the various seeps. Two of the detected OTU0.96 were cosmopolitan and abundant while 75% were locally restricted, suggesting the presence of few abundant and globally distributed alkane degraders as well as specialized variants that have developed under specific conditions at the diverse seep environments. Of the three MasD clades identified, the most diverse was affiliated with Deltaproteobacteria. A second clade was affiliated with both Deltaproteobacteria and Firmicutes likely indicating lateral gene transfer events. The third clade was only distantly related to known alkane-degrading organisms and comprises new divergent lineages of MasD homologs, which might belong to an overlooked phylum of alkane-degrading bacteria. In addition, masD geneFISH allowed for the in situ identification and quantification of the target guild in alkane-degrading enrichment cultures. Altogether, these findings suggest an unexpectedly high number of yet unknown groups of anaerobic alkane degraders and underline the need for comprehensive surveys of microbial diversity based on metabolic genes in addition to ribosomal genes.
Immune Reactions against Gene Gun Vaccines Are Differentially Modulated by Distinct Dendritic Cell Subsets in the Skin

The skin accommodates multiple dendritic cell (DC) subsets with remarkable functional diversity. Immune reactions are initiated and modulated by the triggering of DC by pathogen-associated or endogenous danger signals. In contrast to these processes, the influence of intrinsic features of protein antigens on the strength and type of immune responses is much less understood. Therefore, we investigated the involvement of distinct DC subsets in immune reactions against two structurally different model antigens, E. coli beta-galactosidase (betaGal) and chicken ovalbumin (OVA) under otherwise identical conditions. After epicutaneous administration of the respective DNA vaccines with a gene gun, wild type mice induced robust immune responses against both antigens. However, ablation of langerin+ DC almost abolished IgG1 and cytotoxic T lymphocytes against betaGal but enhanced T cell and antibody responses against OVA. We identified epidermal Langerhans cells (LC) as the subset responsible for the suppression of anti-OVA reactions and found regulatory T cells critically involved in this process. In contrast, reactions against betaGal were not affected by the selective elimination of LC, indicating that this antigen required a different langerin+ DC subset. The opposing findings obtained with OVA and betaGal vaccines were not due to immune-modulating activities of either the plasmid DNA or the antigen gene products, nor did the differential cellular localization, size or dose of the two proteins account for the opposite effects. Thus, skin-borne protein antigens may be differentially handled by distinct DC subsets, and, in this way, intrinsic features of the antigen can participate in immune modulation.
The Impact of Party-Switching on Legislative Behavior in Brazil

In this paper, I examine the impact of party-switching on legislator’s roll-call votes in Brazil. About one-third of deputies change party during each four year term; some change as many as seven times. Such volatility challenges basic concepts of representation if legislators change their policy positions to accommodate their new party, they violate the basic utility of party labels for electoral information cost reduction. This research has an additional utility. Legislative scholars agree that political parties are important parts of modern democracy, but roll-call based measures of party influence cannot separate out ∗I thank Argelina Figueiredo and Fernando Limongi for generously sharing their data. For excellent research assistance, I thank Moema Bonelli.
Mobile ecosystem driven application-specific low-power control microarchitecture

A well-established technique for reducing embedded processor power is to leverage rigorous static code analysis to enable application-specific hardware customizations. Energy can be saved by leveraging compile-time information to eliminate certain hardware operations. One such research area reduced branch prediction hardware power by statically extracting application control flow information. However, the applicability of these static techniques has stalled with respect to modern smartphones. These high-performance mobile processors inhabit a unique mobile ecosystem domain space. Rather than an application consisting of a monolithic instruction sequence compiled on the target processor, mobile applications are stored in a centralized marketplace and consist of high-level object-oriented code that dynamically binds with device-specific foundation libraries. While this model helps reduce development time and enables applications to be downloaded and run on a variety of device models and operating system versions, it significantly hinders whole application static analysis and application-specific hardware optimizations. This paper addresses the unique challenges of the mobile ecosystem by enabling on-device application analysis to guide reconfiguration of the branch target buffer. Software and hardware customizations are intelligently combined to greatly reduce power dissipation while maintaining or even improving performance.
A Linear Least Squares Fit Mapping Method for Information Retrieval From Natural Language Texts

This paper describes a unique method for mapping natural language texts to canonical terms that identify the contents of the texts. This method learns empirical associations between free-form texts and canonical terms from human-assigned matches and determines a Linear Least Squares Fit (LLSF) mapping function which represents weighted connections between words in the texts and the canonical terms. The mapping function enables us to project an arbitrary text to the canonical term space where the "transformed" text is compared with the terms, and similarity scores are obtained which quantify the relevance between the the text and the terms. This approach has superior power to discover synonyms or related terms and to preserve the context sensitivity of the mapping. We achieved a rate of 84% in both the recall and the precision with a testing set of 6,913 texts, outperforming other techniques including string matching (15%), morphological parsing (17%) and statistical weighting (21%).