Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of object functions using deformable part model

In recent years, a tremendous research effort has been made in the area of generic object recognition. However, the most important thing is not the names but functions for robots to comprehend objects. Object functions refer to “the purpose that something has or the job that someone or something does”. Various elements (e.g., the physical information, material, appearance and human interaction) independently or mutually form object functions. There are many researches on object functions using human-object interaction, while there are few using appearance. However, it can be believed that object functions may be formed by appearance. In this paper, we propose a new method to estimate object functions from appearance on images. Our approach is to estimate object functions using DPM by dividing object appearance into parts. There are important parts and less important parts in the appearance for the functions. Therefore, we identify the important parts in the object appearance for the functions. Experimental results show that the important parts about specific functions can be extracted and object functions are related to the appearance.
Algebraic Algorithms for Matching and Matroid Problems

We present new algebraic approaches for two well-known combinatorial problems: nonbipartite matching and matroid intersection. Our work yields new randomized algorithms that exceed or match the efficiency of existing algorithms. For nonbipartite matching, we obtain a simple, purely algebraic algorithm with running time $O(n^\omega)$ where $n$ is the number of vertices and $\omega$ is the matrix multiplication exponent. This resolves the central open problem of Mucha and Sankowski (2004). For matroid intersection, our algorithm has running time $O(nr^{\omega-1})$ for matroids with $n$ elements and rank $r$ that satisfy some natural conditions.
Diagnostic accuracy of 128-row multidetector computed tomography coronary angiography in the diagnosis of significant coronary artery stenosis.

OBJECTIVE To evaluate the diagnostic accuracy of 128-multi detector row computed tomography coronary angiography (MDCTCA) with that of invasive conventional coronary angiography (CCA) in the diagnosis of coronary artery disease (CAD). MATERIAL AND METHOD Forty-two consecutive patients underwent both MDCTCA and CCA. All MDCTCA were evaluated for the presence of obstructive coronary stenosis by a blinded experts, and results were compared with quantitative CCA. RESULTS Three vessels and five segments were uninterpretable on MDCTCA. Therefore, 123 vessels and 558 segments from 42 patients were analyzed. Sensitivity, specificity and positive and negative predictive values of computed tomography for detecting detect > 50% luminal narrowing were 100%, 91%, 91%, and 100%, respectively, by patient, 98%, 98%, 96%, and 99%, respectively, by vessel, and 98%, 99%, 94%, and 99%, respectively, by segment. Moreover accuracy for detecting > 70% luminal narrowing were excellent by patient, vessel, and segment. CONCLUSION Noninvasive 128-detector row CT coronary angiography provides high diagnostic accuracy on per segment, vessel, and patient analysis.
Bioinformatics Applications Note Data and Text Mining the Ccpn Metabolomics Project: a Fast Protocol for Metabolite Identification by 2d-nmr

We present here the freely available Metabolomics Project resource specifically designed to work under the CcpNmr Analysis program produced by CCPN (Collaborative Computing Project for NMR) (Vranken et al., 2005, The CCPN data model for NMR spectroscopy: development of a software pipeline. Proteins, 59, 687–696). The project consists of a database of assigned 1D and 2D spectra of many common metabolites. The project aims to help the user to analyze and assign 1D and 2D NMR spectra of unknown metabolite mixtures. Spectra of unknown mixtures can be easily superimposed and compared with the database spectra, thus facilitating their assignment and identification. Availability: The CCPN Metabolomics Project, together with an annotated example dataset, is freely available via: (experiments & protocol); tjs23@cam.ac. uk (software).
Liver X Receptor Activation Stimulates Iron Export in Human Alternative Macrophages

Rationale: In atherosclerotic plaques, iron preferentially accumulates in macrophages where it can exert pro-oxidant activities. Objective: The objective of this study was, first, to better characterize the iron distribution and metabolism in macrophage subpopulations in human atherosclerotic plaques and, second, to determine whether iron homeostasis is under the control of nuclear receptors, such as the liver X receptors (LXRs). Methods and Results: Here we report that iron depots accumulate in human atherosclerotic plaque areas enriched in CD68 and mannose receptor (MR)-positive (CD68+MR+) alternative M2 macrophages. In vitro IL-4 polarization of human monocytes into M2 macrophages also resulted in a gene expression profile and phenotype favoring iron accumulation. However, M2 macrophages on iron exposure acquire a phenotype favoring iron release, through a strong increase in ferroportin expression, illustrated by a more avid oxidation of extracellular low-density lipoprotein by iron-loaded M2 macrophages. In line, in human atherosclerotic plaques, CD68+MR+ macrophages accumulate oxidized lipids, which activate LXR&agr; and LXR&bgr;, resulting in the induction of ABCA1, ABCG1, and apolipoprotein E expression. Moreover, in iron-loaded M2 macrophages, LXR activation induces nuclear factor erythroid 2-like 2 expression, thereby increasing ferroportin expression, which, together with a decrease of hepcidin mRNA levels, promotes iron export. Conclusions: These data identify a role for M2 macrophages in iron handling, a process regulated by LXR activation.
Low Power Multiplier Designs Based on Improved Column Bypassing Schemes

In this paper, we proposed two novel low power multiplier designs based on improved column bypassing schemes. The power saving comes from bypassing signals along those adder columns in the array multiplier corresponding to zero bits in the multiplicand. Spurious signal switching activities can then be eliminated when bypassing occurs. The proposed designs successfully resolve the adverse DC power consumption problem in previous research due to troublesome tri-state input buffers. Our designs also implement the bypassing logic cleverly via C2MOS circuitry and eliminate the costly (both circuit and power-wise) multiplexers. The circuit overheads of the proposed designs can be as low as 10% compared with 54% in M. C. Wen et al. (2005). Simulations results also indicate previous work may fail to gain any power saving (and actually deteriorate power consumption) when Vdd is higher than 1.6V. Our designs, nonetheless, achieve power saving consistently in different working conditions and the best saving can be as much as 29%
Mapping preclinical compensation in Parkinson's disease: An imaging genomics approach

Mutations in the Parkin (PARK2) and PINK1 gene (PARK 6) can cause recessively inherited Parkinson's disease (PD). The presence of a single Parkin or PINK1 mutation is associated with a dopaminergic nigrostriatal dysfunction and conveys an increased risk to develop PD throughout lifetime. Therefore neuroimaging of non‐manifesting individuals with a mutant Parkin or PINK1 allele opens up a window for the investigation of preclinical and very early phases of PD in vivo. Here we review how functional magnetic resonance imaging (fMRI) can be used to identify compensatory mechanisms that help to prevent development of overt disease. In two separate experiments, Parkin mutation carriers displayed stronger activation of rostral supplementary motor area (SMA) and right dorsal premotor cortex (PMd) during a simple motor sequence task and anterior cingulate motor area and left rostral PMd during internal movement selection as opposed to externally cued movements. The additional recruitment of the rostral SMA and right rostral PMd during the finger sequence task was also observed in a separate group of nonmanifesting mutation carriers with a single heterozygous PINK1 mutation. Because mutation carriers were not impaired at performing the task, the additional recruitment of motor cortical areas indicates a compensatory mechanism that effectively counteracts the nigrostriatal dysfunction. These first results warrant further studies that use these imaging genomics approach to tap into preclinical compensation of PD. Extensions of this line of research involve fMRI paradigms probing nonmotor brain functions. Additionally, the same fMRI paradigms should be applied to nonmanifesting mutation carriers in genes linked to autosomal dominant PD. This will help to determine how “generically” the human brain compensates for a preclinical dopaminergic dysfunction. © 2009 Movement Disorder Society
Combining Structure Equation Model with Bayesian Networks for predicting with high accuracy of recommending surgery for better survival in Benign prostatic hyperplasia patients

Abstract: Causal discovery is the key aspect of science. Inferring causality can be achieved in various ways. Typically, you start with your hypothesis (based on what you know so far) and based on the data you collect, you update your hypothesis. In a nutshell, causality can be inferred via your background knowledge and empirical data. Causal Both Bayesian networks (BN) and Structural equation model (SEM) are graphical models that are able to model causality both from background knowledge and empirical data. SEM heavily relies on your background knowledge and use data to justify your knowledge. On the other hand, BN can combine your background knowledge with a causal model that gives the maximum likelihood based on data. If the relationships turn out to be statistically significant, then expert’s knowledge is considered statistically valid and can be used to provide guidelines in practice using the model based on expert’s knowledge.
Top-k Query Processing on Encrypted Databases with Strong Security Guarantees

Concerns about privacy in outsourced cloud databases have grown recently and many efficient and scalable query processing methods over encrypted data have been proposed. However, there is very limited work on how to securely process top-k ranking queries over encrypted databases in the cloud. In this paper, we propose the first efficient and provably secure top-k query processing construction that achieves adaptive CQA security. We develop an encrypted data structure called EHL and describe several secure sub-protocols under our security model to answer top-k queries. Furthermore, we optimize our query algorithms for both space and time efficiency. Finally, we empirically evaluate our protocol using real world datasets and demonstrate that our construction is efficient and practical.
CommonTies: a context-aware nudge towards social interaction

Urbanization has created transient, ethnically-varied, and densely-populated communities where meaningful human contact is difficult. Urban social norms such as "civil inattention" --- a deliberate display of unwillingness to become more familiar with strangers --- discourage social interactions among strangers. While these norms help reduce anxiety or fear in overcrowded urban centers they hinder meaningful social interactions in public spaces (coffee shops, museums, and malls, etc.) and events (conferences, galas, etc.) where such interactions should occur. This paper describes CommonTies, a simple technological nudge that managers of interaction spaces and organizers of social events can use to leverage contextual information to encourage social interactions among strangers.
SMARTies: Sentiment Models for Arabic Target entities

We consider entity-level sentiment analysis in Arabic, a morphologically rich language with increasing resources. We present a system that is applied to complex posts written in response to Arabic newspaper articles. Our goal is to identify important entity “targets” within the post along with the polarity expressed about each target. We achieve significant improvements over multiple baselines, demonstrating that the use of specific morphological representations improves the performance of identifying both important targets and their sentiment, and that the use of distributional semantic clusters further boosts performances for these representations, especially when richer linguistic resources are not available.
Weighting schemes for audio-visual fusion in speech recognition

We demonstrate an improvement in the state-of-the-art large vocabulary continuous speech recognition (LVCSR) performance, under clean and noisy conditions, by the use of visual information, in addition to the traditional audio one. We take a decision fusion approach for the audio-visual information, where the single-modality (audio- and visual- only) HMM classifiers are combined to recognize audio-visual speech. More specifically, we tackle the problem of estimating the appropriate combination weights for each of the modalities. Two different techniques are described: the first uses an automatically extracted estimate of the audio stream reliability in order to modify the weights for each modality (both clean and noisy audio results are reported), while the second is a discriminative model combination approach where weights on pre-defined model classes are optimized to minimize WER (clean audio only results).
EMG-Based Model Predictive Control for Physical Human–Robot Interaction: Application for Assist-As-Needed Control

In this letter, we propose an electromyography (EMG)-based optimal control framework to design physical human–robot interaction for rehabilitation and develop a novel assist-as-needed (AAN) controller based on a model predictive control (MPC) approach. To enhance the recovery of motor functions, encouraging the voluntary movements of patients is necessary while a therapist is assisting them. Therefore, in an AAN control framework, the robot only assists the deficient torque to generate a target movement. In our study, we first estimate the joint torque of a patient from measured EMG signals and then derive the deficient joint torque to generate the target movements by considering the patient's estimated joint torque with an MPC method. Results showed that our proposed method adaptively derived the necessary torque to follow the target elbow joint trajectories based on the subject's voluntary movements.
Evolutionary generation of rule base in TSK fuzzy model for real estate appraisal

Takagi-Sugeno-Kang-type fuzzy model to assist with real estate appraisals is described and optimized using evolutionary algorithms Two approaches were compared in the paper. The first one consisted in learning the rule base and the second one in combining learning the rule base and tuning the membership functions in one process. Five TSK-type fuzzy models comprising 3 or 4 input variables referring to the attributes of a property were evaluated. The evolutionary algorithms were based on Pittsburgh approach with the real coded chromosomes of constant length comprising whole rule base or both the rule base and all parameters of all membership functions. The experiments were conducted using training and testing sets prepared on the basis of actual 134 sales transactions made in one of Polish cities and located in a residential section.
Parallel projections for stereo reconstruction

This paper proposes a novel technique to computing geometric information from images captured under parallel projections. Parallel images are desirable for stereo reconstruction because parallel projection significantly reduces foreshortening. As a result, correlation based matching becomes more effective. Since parallel projection cameras are not commonly available, we construct parallel images by rebinning a large sequence of perspective images. Epipolar geometry, depth recovery and projective invariant for both 1D and 2D parallel stereos are studied. From the uncertainty analysis of depth reconstruction, it is shown that parallel stereo is superior to both conventional perspective stereo and the recently developed multiperspective stereo for vision reconstruction, in that uniform reconstruction error is obtained in parallel stereo. Traditional stereo reconstruction techniques, e.g. multi-baseline stereo, can still be applicable to parallel stereo without any modifications because epipolar lines in a parallel stereo are perfectly straight. Experimental results further confirm the performance of our approach.
Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh, PA

Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools.
Analysis of Energy Detector Based Spectrum Sensing in Fading Cooperative Cognitive Network

Ever increasing wireless services and networks results in spectrum scarcity. But already allocated spectrum is in under utilization in the current static fixed spectrum allocation method . So dynamic spectrum access is only solution for this issue . Spectrum sensing is one of the key technique in cognitive radio for dynamic spectrum access. There are many algorithm proposed for the spectrum sensing. Among all the algorithm energy detector is the only one that is less computationally complex so it is adapted as one of the candidate in 802.22 standard of TV white space based RAN network. Many research work proposed on energy detector based spectrum sensing but all focus on AWN channel of non-fading environment. But real channel is not always non fading one . In case of the cooperative spectrum sensing with different cognitive radio different channel will be experienced, some will be fading . So here in this paper an analysis of energy detector performance on Rayleigh fading environment is going to analyze under cooperative spectrum sensing . Under this analysis the amplify and forward network and indoor cases also considered and performance is studied.
Comparison of monoclonal antibody and calcofluor white stains for the detection of Pneumocystis carinii from respiratory specimens

Three monoclonal antibody staining kits, from Genetic Systems, Disease Detection International, and Meridian Diagnostics, were compared with calcofluor white for the direct detection of Pneumocystis carinii in respiratory specimens. Of the 150 specimens tested, 23 were found positive for P. carinii by any of the four stains; 13 were bronchoalveolar lavage, 7 were induced sputum and 3 were expectorated sputum specimens. All stains detected the positive bronchoalveolar lavage specimens, the Genetic Systems stain detected six induced sputum specimens, and the other stains each detected four induced sputum specimens. The monoclonal antibody stains detected all three expectorated specimens, while the calcofluor stain detected only one. Overall, the sensitivities of the stains were 96% for Genetic Systems, 87% for both Disease Detection International and Meridian Diagnostics, and 78% for calcofluor.
A Comparison of QMR, CGS and TFQMR on a Distributed Memory Machine

Diese Arbeit ist zur Publikation eingereicht. Abstract For the solution of systems of linear equations with general non{ Hermitian nonsingular coeecient matrices, we propose an implementation of three diierent algorithms on a parallel machine with distributed memory. Each of the three algorithms, QMR, CGS and TFQMR, contains two matrix{vector products that dominate the execution time. While the matrix{vector products of CGS and TFQMR are dependent this is not valid for QMR. The two matrix{vector products of QMR can be computed simultaneously. We show how the performance of a parallel implementation is increased by exploiting this property. Timing results of all three algorithms on an Intel PARAGON XP/S 10 system are presented.
Accurate Closed-Form Approximation of BER for OSTBCs with Estimated CSI on Spatially Correlated Rayleigh Fading MIMO Channels

In this contribution, we present a novel accurate closed-form approximation for the bit error rate (BER) of orthogonal space-time block codes (OSTBCs) employing quadrature amplitude modulation with Gray-mapping on spatially correlated Rayleigh fading MIMO channels, in the presence of channel estimation errors. The presented BER expression allows to accurately examine the impact of spatial correlation and channel estimation errors on the BER for any OSTBC or signal-to-noise ratio, even in case of highly correlated channels.
An Efficient Multi Join Algorithm Utilizing a Lattice of Double Indices

(cid:3) Abstract — In this paper, a novel multi join algorithm to join multiple relations will be introduced. The novel algorithm is based on a hashed-based join algorithm of two relations to produce a double index. This is done by scanning the two relations once. But instead of moving the records into buckets, a double index will be built. This will eliminate the collision that can happen from a complete hash algorithm. The double index will be divided into join buckets of similar categories from the two relations. The algorithm then joins buckets with similar keys to produce joined buckets. This will lead at the end to a complete join index of the two relations. without actually joining the actual relations. The time complexity required to build the join index of two categories is Om log m where m is the size of each category. Totaling time complexity to O n log m for all buckets. The join index will be used to materialize the joined relation if required. Otherwise, it will be used along with other join indices of other relations to build a lattice to be used in multi-join operations with minimal I/O requirements. The lattice of the join indices can be fitted into the main memory to reduce time complexity of the multi join algorithm.
A kinematic simulator for motion planning of a mobile robot on a terrain

This paper deals with the problem of simulating the behavior of a mobile robot moving on a terrain along a precomputed nominal path. The nominal path is supposed to be provided by a planner. The purpose of the simulator to instantiate the nominal path into a complete trajectory, while verifying that a set of constraints is satisfied all along the motion. The simulator is based on an iterative algorithm to compute configurations for the robot at each time increment. Each iteration consists of two successive steps: determination of a stable configuration and of the associated contact points on the ground, and determination of the motion of all joints by minimization of the sliding at the contact points.