Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic classification of business images

Digital cameras are becoming increasingly common for capturing information in business settings. In this paper, we describe a novel method for classifying images into the following semantic classes: document, whiteboard, business card, slide, and regular images. Our method is based on combining low-level image features, such as text color, layout, and handwriting features with high-level OCR output analysis. Several Support Vector Machine Classifiers are combined for multi-class classification of input images. The system yields 95% accuracy in classification.
Integer Precoding for FDD-Based MIMO Systems

How to achieve the closed-loop processing gain is an important issue for frequency division duplexing (FDD) based multi-input multi-output (MIMO) systems, since the channel state information (CSI) is not available by reciprocity as time division duplexing (TDD). Current technique mainly uses the codebook based precoding, where the receiver feeds back the precoding matrix index (PMI) of the codebooks to the transmitter for closedloop processing. However, due to the suboptimality of linear processing and the quantization error of the precoding matrix / CSI, its performance gain over the open-loop processing is limited. In this paper, we propose a new precoding architecture to improve the situation. Based on lattice reduction (LR) method, we choose to feedback an integer matrix to the transmitter and perform nonlinear precoding. By utilizing the special properties of the integer matrix, the required feedback amount can be greatly reduced. Although it needs somewhat more bits than the conventional scheme to feedback the CSI, significant performance gain is achieved and full diversity can be guaranteed.
Author ' s personal copy Self Controlling Tabu Search algorithm for the Quadratic Assignment Problem q

This paper addresses the application of the principles of feedback and self-controlling software to the tabu search algorithm. We introduce two new reaction strategies for the tabu search algorithm. The first strategy treats the tabu search algorithm as a target system to be controlled and uses a control-theoretic approach to adjust the algorithm parameters that affect search intensification. The second strategy is a flexible diversification strategy which can adjust the algorithm’s parameters based on the search history. These two strategies, combined with tabu search, form the Self Controlling Tabu Search (SC-Tabu) algorithm. The algorithm is implemented and tested on the Quadratic Assignment Problem (QAP). The results show that the self-controlling features of the algorithm make it possible to achieve good performance on different types of QAP instances. 2010 Elsevier Ltd. All rights reserved.
TOWARDS A SPINAL NEUROPROSTHESIS: RESTORING LOCOMOTION AFTER SPINAL CORD INJURY

The overall goal of this work is to develop the core framework for an implantable neuroprosthetic device that can restore locomotion after a severe spinal cord injury (SCI) causes paralysis. Our approach to this problem relies on a combination of the biological central pattern generator (CPG) for locomotion and an artificial silicon CPG. In particular, we propose that an artificial CPG can be used to generate on-line estimates of the motor output required to generate a desired gait, and that phasic electrical stimulation of spinal locomotor circuits can effectuate that output. To validate our proposed approach, we first prove that phasic electrical stimulation can reliably modulate the output of the biological CPG for locomotion. Application of discrete current pulses to a single spinal segment is shown to affect multiple parameters of an ongoing locomotor pattern in excised portions of the chemically-activated lamprey spinal cord. For any given stimulus, the effects on frequency, duration, and symmetry of locomotor output are demonstrated to be functions of the phase at which stimulation is applied within the CPG cycle. Based on these responses, we show that it is possible to choose a desired motor pattern and use precisely-timed stimuli to conform the CPG’s ii
The structure of bradyzoite-specific enolase from Toxoplasma gondii reveals insights into its dual cytoplasmic and nuclear functions

The second crystal structure of a parasite protein preferentially enriched in the brain cyst of T. gondii has been solved at 2.75 Å resolution. Bradyzoite enolase 1 is reported to have differential functions as a glycolytic enzyme and a transcriptional regulator in bradyzoites.
Advanced Dynamic Programming in Computational Linguistics: Theory, Algorithms and Applications Tutorial notes

Dynamic Programming (DP) is an important class of algorithms widely used in many areas of speech and language processing. Recently there have been a series of work trying to formalize many instances of DP algorithms under algebraic and graph-theoretic frameworks. This tutorial surveys two such frameworks, namely semirings and directed hypergraphs, and draws connections between them. We formalize two particular types of DP algorithms under each of these frameworks: the Viterbi-style topological algorithms and the Dijkstra-style best-first algorithms. Wherever relevant, we also discuss typical applications of these algorithms in Natural Language Processing.
Global Climate Change Adaptation Priorities for Biodiversity and Food Security

International policy is placing increasing emphasis on adaptation to climate change, including the allocation of new funds to assist adaptation efforts. Climate change adaptation funding may be most effective where it meets integrated goals, but global geographic priorities based on multiple development and ecological criteria are not well characterized. Here we show that human and natural adaptation needs related to maintaining agricultural productivity and ecosystem integrity intersect in ten major areas globally, providing a coherent set of international priorities for adaptation funding. An additional seven regional areas are identified as worthy of additional study. The priority areas are locations where changes in crop suitability affecting impoverished farmers intersect with changes in ranges of restricted-range species. Agreement among multiple climate models and emissions scenarios suggests that these priorities are robust. Adaptation funding directed to these areas could simultaneously address multiple international policy goals, including poverty reduction, protecting agricultural production and safeguarding ecosystem services.
Psychometric Evaluation of an Instrument for Measuring Organizational Climate for Quality

In recent years, there has been increased interest in measuring the climate for infection prevention; however, reliable and valid instruments are lacking. This study tested the psychometric properties of the Leading a Culture of Quality for Infection Prevention (LCQ-IP) instrument measuring the infection prevention climate in a sample of 972 infection preventionists from acute care hospitals. An exploratory principal component analysis showed that the instrument had structural validity and captured 4 factors related to the climate for infection prevention: Psychological Safety, Prioritization of Quality, Supportive Work Environment, and Improvement Orientation. LCQ-IP exhibited excellent internal consistency, with a Cronbach α of . 926. Criterion validity was supported with overall LCQ-IP scores, increasing with the number of evidence-based prevention policies in place (P = .047). This psychometrically sound instrument may be helpful to researchers and providers in assessing climate for quality related to infection prevention.
Age-based PCM wear leveling with nearly zero search cost

Improving the endurance of PCM is a fundamental issue when the technology is considered as an alternative to main memory usage. In the design of memory-based wear leveling approaches, a major challenge is how to efficiently determine the appropriate memory pages for allocation or swapping. In this paper, we present an efficient wear-leveling design that is compatible with existing virtual memory management. Two implementations, namely, bucket-based and array-based wear leveling, with nearly zero search cost are proposed to tradeoff time and space complexity. The results of experiments conducted based on popular benchmarks to evaluate the efficacy of the proposed design are very encouraging.
The DNA-binding Polycomb-group protein Pleiohomeotic maintains both active and repressed transcriptional states through a single site

Although epigenetic maintenance of either the active or repressed transcriptional state often involves overlapping regulatory elements, the underlying basis of this is not known. Epigenetic and pairing-sensitive silencing are related properties of Polycomb-group proteins, whereas their activities are generally opposed by the trithorax group. Both groups modify chromatin structure, but how their opposing activities are targeted to allow differential maintenance remains a mystery. Here, we identify a strong pairing-sensitive silencing (PSS) element at the 3′ border of the Drosophila even skipped (eve) locus. This element can maintain repression during embryonic as well as adult eye development. Transgenic dissection revealed that silencing activity depends on a binding site for the Polycomb-group protein Pleiohomeotic (Pho) and on pho gene function. Binding sites for the trithorax-group protein GAGA factor also contribute, whereas sites for the known Polycomb response element binding factors Zeste and Dsp1 are dispensible. Normally, eve expression in the nervous system is maintained throughout larval stages. An enhancer that functions fully in embryos does not maintain expression, but the adjacent PSS element confers maintenance. This positive activity also depends on pho gene activity and on Pho binding. Thus, a DNA-binding complex requiring Pho is differentially regulated to facilitate epigenetic transcriptional memory of both the active and the repressed state.
Egomotion using assorted features

We describe a novel and robust minimal solver for performing online visual odometry with a stereo rig. The proposed method can compute the underlying camera motion given any arbitrary, mixed combination of point and line correspondences across two stereo views. This facilitates a hybrid visual odometry pipeline that is enhanced by well-localized and reliably-tracked line features while retaining the well-known advantages of point features. Utilizing trifocal tensor geometry and quaternion representation of rotation matrices, we develop a polynomial system from which camera motion parameters can be robustly extracted in the presence of noise. We show how the more popular approach of using direct linear/subspace techniques fail in this regard and demonstrate improved performance using our formulation with extensive experiments and comparisons against the 3-point and line-sfm algorithms.
Covariate Measurement Error Adjustment for Matched Case–Control Studies

Summary. We propose a conditional scores procedure for obtaining bias‐corrected estimates of log odds ratios from matched case‐control data in which one or more covariates are subject to measurement error. The approach involves conditioning on sufficient statistics for the unobservable true covariates that are treated as fixed unknown parameters. For the case of Gaussian nondifferential measurement error, we derive a set of unbiased score equations that can then be solved to estimate the log odds ratio parameters of interest. The procedure successfully removes the bias in naive estimates, and standard error estimates are obtained by resampling methods. We present an example of the procedure applied to data from a matched case–control study of prostate cancer and serum hormone levels, and we compare its performance to that of regression calibration procedures.
Improving system reliability with automatic fault tree generation

Usually, fault tree analyses are performed manually. They are based on documents that describe the system. Considerable knowledge, system insight, and overview is necessary to consider many failure modes, and dependencies between system components and their functionality at a time. Often, the behavior is too complicated to fully comprehend all possible failure consequences. Manual fault tree analysis is error-prone, costly and not necessarily complete. Formal risk analysis, an approach for automatically generating a fault tree from finite state machine-based descriptions of a system, is presented. The generated fault tree is complete with respect to all failures assumed possible. It is the basis for subsequent improvements of the system design and quantitative analysis of safety and liveness requirements in the presence of failures. A case study of formal risk analysis, the automatic generation of a fault tree for all sensor failures of a production cell's elevating rotary table, is discussed.
The dialectics of identity and resistance among Dalits in Nepal

Based on two broad constituent samples, this dissertation investigates the dialectics -- content, modalities and processes -- of identity across and between two sites of Dalit life in Kathmandu, Nepal: everyday community and organized political advocacy. These samples comprised, respectively, (1) householders from three occupationally segregated Dalit neighborhoods, encompassing discrete communities of sweepers, metalworkers and tailors/musicians; (2) individual Dalit activists in the political sphere. Through 43 interviews with community members and 41 interviews with activists, the research investigated the modalities of identity across everyday and civil-society space and across class, caste, gender and generation. Research questions specifically sought to uncover constraints and possibilities of everyday identities and organized/activist political identities and further differences of gender, class and generation. The study revealed strong evidence of the continuing embeddedness of caste in Kathmandu. Their everyday experiences of discrimination force both community and political actors to strategically reveal or conceal their Dalit status depending on the situation. Evidence of resistance ranged from everyday individual acts to collective organized forms. The community ethnography revealed important differences across the sweeper, metal-worker and tailor-musician communities. The gender neutrality of the sweeper occupation allows sweeper women relatively more autonomy than that found in the two other occupational caste groups. The tailor/musician group showed all indicators of social mobility into the middle class and had adopted a caste-denying discourse that allowed them to embrace their musical traditions as an ethnic asset that was parlayed into commercial success. The political site revealed two important contradictions. First, Dalit activists based in political parties tend to privilege the nation-state and its bounded sovereignty as the strategic and ultimate terrain upon which the struggle for full Dalit inclusion is fought, while Dalit advocates based in non-governmental organizations appeal primarily to international human rights and the claims to universal human dignity. Second, there is a tension between the private lives of Dalit activists in which they negotiate everyday oppression and their public lives as proactive and empowered political actors. Finally, the important political moment of the People's Movement of April 2006 united Dali activists to fight locally for full citizenship rights.
Hormone Receptor and ERBB2 Status in Gene Expression Profiles of Human Breast Tumor Samples

The occurrence of large publically available repositories of human breast tumor gene expression profiles provides an important resource to discover new breast cancer biomarkers and therapeutic targets. For example, knowledge of the expression of the estrogen and progesterone hormone receptors (ER and PR), and that of the ERBB2 in breast tumor samples enables choice of therapies for the breast cancer patients that express these proteins. Identifying new biomarkers and therapeutic agents affecting the activity of signaling pathways regulated by the hormone receptors or ERBB2 might be accelerated by knowledge of their expression levels in large gene expression profiling data sets. Unfortunately, the status of these receptors is not invariably reported in public databases of breast tumor gene expression profiles. Attempts have been made to employ a single probe set to identify ER, PR and ERBB2 status, but the specificity or sensitivity of their prediction is low. We enquired whether estimation of ER, PR and ERBB2 status of profiled tumor samples could be improved by using multiple probe sets representing these three genes and others with related expression. We used 8 independent datasets of human breast tumor samples to define gene expression signatures comprising 24, 51 and 14 genes predictive of ER, PR and ERBB2 status respectively. These signatures, as demonstrated by sensitivity and specificity measures, reliably identified hormone receptor and ERBB2 expression in breast tumors that had been previously determined using protein and DNA based assays. Our findings demonstrate that gene signatures can be identified which reliably predict the expression status of the estrogen and progesterone hormone receptors and that of ERBB2 in publically available gene expression profiles of breast tumor samples. Using these signatures to query transcript profiles of breast tumor specimens may enable discovery of new biomarkers and therapeutic targets for particular subtypes of breast cancer.
Ensuring Data Security for Secure Cloud Hybrid Framework

With increasing technology demand for the cloud application is also increasing. Due to this security demands are also increasing. A user can access a cloud services from anywhere and at any time or almost instantly. These features make cloud computing so flexible and prone to risk. Therefore there are possibilities of lacking confidentiality, integrity and authentication among the cloud users. So the key purpose of this research is to investigate cloud securities and build a framework using encryption algorithms to provide data confidentiality, integrity and
Software Reliability Prediction using Neural Networks

Predicting the Software reliability is a pertinent issue and it is a major concern of software developers and engineers in changing environment considerations. Software reliability models are developed to estimate the probability of failure free operation of the software for a long time. Many Software Reliability Growth Models (SRGM) were developed to give the latent number of faults in the software product. However none of these models performing to the expectations of the developers of the software. In this paper, A research is made using artificial neural network models to monitor the performance of the software that leads to predict the software reliability. The MLP model outperforms SVR model, and based on the results, these models can be considered to be a reasonable alternative for software quality prediction.
Balance Ninja: Towards the Design of Digital Vertigo Games via Galvanic Vestibular Stimulation

Vertigo -- the momentary disruption of the stability of perception -- is an intriguing game element that underlies many unique play experiences, such as spinning in circles as children to rock climbing as adults, yet vertigo is relatively unexplored when it comes to digital play. In this paper we explore the potential of Galvanic Vestibular Stimulation (GVS) as a game design tool for digital vertigo games. We detail the design and evaluation of a novel two player GVS game, Balance Ninja. From study observations and analysis of Balance Ninja (N=20), we present three design themes and six design strategies that can be used to aid game designers of future digital vertigo games. With this work we aim to highlight that vertigo can be a valuable digital game element that helps to expand the range of games we play.
Blade-type crawler vehicle with gyro wheel for stably traversing uneven terrain at high speed

Unmanned rescue, observation, and/or research vehicles with high terrain adaptability, high speed, and high reliability are needed in difficult-to-reach locations. However, for most vehicles, high performance over rough terrain reduces the travel speed and/or requires complex mechanisms. We have developed a blade-type crawler robot with a very simple and reliable mechanism, which traverses uneven terrain at high speed. Moreover, the gyro wheel design stabilizes the success of this approach in improving the motion, ensuring robust traversal. The improvement in traveling speed and robustness over uneven terrain by our approach was confirmed by experiment.
Ehrlich-Schwoebel instability in molecular-beam epitaxy: A minimal model.

The instability of a growing crystal limited by a high-symmetry surface in molecular-beam epitaxy is studied in the limit where terrace size is very large compared to the atomic distance. In that case, everything is deterministic except the nucleation of new terraces. Moreover, exchange of atoms between steps is ignored. If the typical terrace size ${\mathcal{l}}_{\mathit{c}}$ is chosen as length unit, the model depends on a single parameter (${\mathcal{l}}_{\mathit{s}}$/${\mathcal{l}}_{\mathit{c}}$) which characterizes the strength of step-edge barriers (``Ehrlich-Schwoebel effect''). Numerical simulations are supported by nonlocal evolution equations relating the time and space derivatives of the surface height. The first mounds which appear have a radius ${\ensuremath{\lambda}}_{\mathit{c}}^{\mathrm{inf}}$ proportional to ${\mathcal{l}}_{\mathit{c}}$\ensuremath{\surd}${\mathcal{l}}_{\mathit{c}}$/${\mathcal{l}}_{\mathit{s}}$. In contrast with other authors who studied different models, coarsening is found to become extremely slow after the mounds have reached a radius ${\ensuremath{\lambda}}_{\mathit{c}}^{\mathrm{sup}}$ of order ${\mathcal{l}}_{\mathit{c}}^{2}$/${\mathcal{l}}_{\mathit{s}}$. \textcopyright{} 1996 The American Physical Society.
A Solution to Integrate Data for Phylogenetic Research

Integrating data for phylogenetic analysis from multivariate data is a known problem of data-intensive application. We present a solution to integrate both sequence data and taxonomy system, and give an implementation on Web. Two kinds of data model are described and the method to be synchronized with international database, to add own data in going projects, to keep quality of data both from public and users' own, and to build private taxonomy system in "My DarwinTree" effectively, is introduced. There are also some data screening rules and taxonomy system tree building algorithms to be introduced.
Correlation between Long-Term Aspirin Use and F-Fluorodeoxyglucose Uptake in Colorectal Cancer Measured by PET/CT

Purpose The aim of this study was to evaluate the relationship between long-term aspirin use with pretreatment 18 Fluorodeoxyglucose (FDG) uptake of primary lesions of Colorectal cancer (CRC) and evaluate their clinical significance. Materials and Methods We enrolled 84 patients with CRC who underwent 18F-FDG PET/CT scanning before surgery between 1st July 2008 and 1st March 2013 and followed up until 1st March 2014. Maximum standardized uptake value (SUVmax) of the primary tumor was measured by 18F-FDG PET/CT. The history of aspirin taken and other clinicopathogical factors were also obtained and their relationships were examined by Mann-Whitney or χ2 tests. Progression-free survival (PFS) was determined by standard Kaplan-Meier survival analysis. Cox proportional hazards regression was performed to determine whether history of aspirin taken, pretreatment SUVmax, age, gender, TNM stage, tumor sizes and differentiation influenced outcomes. Results CRC Patients with long-term history of aspirin use had lower SUVmax of primary lesions than control group (9.74±2.62 vs. 13.91±6.18) and showed a trend towards improved PFS after curative surgery. However, pretreatment of SUVmax showed no prognostic value in patients with CRC. Conclusions Long-term aspirin use is associated with lower pretreatment SUVmax of CRC and is a promising prognostic factor for predicting PFS in patients with CRC.