Miracle-dz/UniFAR-FTUs huggingface dataset:
Intertester reliability of shoulder complaints diagnoses in primary health care

Abstract Objective: Shoulder complaints are frequently encountered in general practice, but precise diagnosing is challenging. This study investigated agreement of shoulder complaints diagnoses between clinicians in a primary health care setting. Design: Cross-sectional study. Setting: Four primary health care clinicians used patients’ history and functional examination of the shoulder by selective tissue tension techniques (STTs), to diagnose shoulder complaints. Subjects: 62 patients, aged 18–75 years. Main outcome measure: Reliability of diagnoses was assessed by observed intertester agreement and Cohen’s kappa. A total of 372 diagnostic pairs were available for intertester comparisons. Results: Six diagnoses were assigned by all clinicians; supraspinatus-, infraspinatus-, subscapularis-tendinopathies; chronic subacromial bursitis; glenohumeral capsulitis, and acromioclavicular joint lesion. The observed agreement on these diagnoses ranged from 0.84 for glenohumeral capsulitis to 0.97 for acromioclavicular joint lesion. Kappa scores were 0.46 (95% CI 0.33, 0.58) for chronic subacromial bursitis; 0.53 (95% CI 0.34, 0.68), 0.59 (95% CI 0.47, 0.70), and 0.68 (95% CI 0.53, 0.82) for infraspinatus -, supraspinatus -, and subscapularis-tendinopathy, respectively. For glenohumeral capsulitis and acromioclavicular lesion kappa scores were 0.66 (95% CI 0.57, 0.73) and 0.78 (95% CI 0.61, 0.90). Kappa scores were higher for individual diagnoses than for individual tests, except for limitation in passive abduction (0.70, 95% CI 0.62, 0.78) and passive lateral rotation (0.66, 95% CI 0.57, 0.73). Conclusions: Although experienced clinicians showed substantial intertester agreement, precise diagnoses of shoulder complaints in primary health care remain a challenge. The present results call for further research on refined diagnoses of shoulder complaints. Key points Based on medical history and a systematic functional examination by selective tissue tension techniques (STTs), we investigated the agreement of shoulder complaints diagnoses across four primary health care clinicians and 62 patients. • Agreements on diagnoses were generally better than the agreement on individual tests. • Good kappa scores were obtained for the diagnoses glenohumeral capsulitis, rotator cuff tendinopathy, and acromioclavicular lesion. • Further research is necessary to investigate the diagnostic validity of functional shoulder examination by the STTs method.
Writing on a dirty paper in the presence of jamming

In this paper, the problem of writing on a dirty paper in the presence of jamming is examined. We consider an AWGN channel with an additive white Gaussian state and an additive adversarial jammer. The state is assumed to be known non-causally to the encoder and the jammer but not to the decoder. The capacity of the channel in the presence of a jammer is determined. A surprising result that this capacity is equal to the capacity of a relaxed version of the problem, where the state is also known non-causally to the decoder, is proved.
The puzzling origin of the autophagosomal membrane

Autophagy is one of the newest and fastest emerging research areas in biomedical life sciences. Autophagosomes, large double-membrane vesicles enclosing cytoplasmic components targeted for degradation, are the hallmark of this catabolic pathway. The origin of the lipid bilayers composing these transport carriers has been the central enigma of the field since the discovery of autophagy. A series of recent studies has implicated several cellular organelles as the possible source of the autophagosomal membranes, if anything further clouding our view. In this compendium, we will discuss these apparently contradictory results and briefly emphasize the relevance of determining the lipid source used for autophagy for future translational research, for example in drug discovery programs.
Stable and fast update rules for independent vector analysis based on auxiliary function technique

This paper presents stable and fast update rules for independent vector analysis (IVA) based on auxiliary function technique. The algorithm consists of two alternative updates: 1) weighted covariance matrix updates and 2) demixing matrix updates, which include no tuning parameters such as step size. The monotonic decrease of the objective function at each update is guaranteed. The experimental evaluation shows that the derived update rules yield faster convergence and better results than natural gradient updates.
Optimizing web search using social annotations

This paper explores the use of social annotations to improve websearch. Nowadays, many services, e.g. del.icio. us, have been developed for web users to organize and share their favorite webpages on line by using social annotations. We observe that the social annotations can benefit web search in two aspects: 1) the annotations are usually good summaries of corresponding webpages; 2) the count of annotations indicates the popularity of webpages. Two novel algorithms are proposed to incorporate the above information into page ranking: 1) SocialSimRank (SSR)calculates the similarity between social annotations and webqueries; 2) SocialPageRank (SPR) captures the popularity of webpages. Preliminary experimental results show that SSR can find the latent semantic association between queries and annotations, while SPR successfully measures the quality (popularity) of a webpage from the web users' perspective. We further evaluate the proposed methods empirically with 50 manually constructed queries and 3000 auto-generated queries on a dataset crawledfrom delicious. Experiments show that both SSR and SPRbenefit web search significantly.
A primer on social security systems and reforms

This article reviews the characteristics of different social security systems. Many configurations arise depending on the nature of a system’s funding and determination of benefits. Many reforms propose changing the U.S. Social Security system. The authors focus their analysis of the transition from a pay-as-you-go to a fully funded system. They argue that the key component of any reform is the treatment of the implicit liabilities of a country’s social security system. The welfare gains accruing to some cohorts as a result of such reforms usually stem from either a partial or complete default on the implicit debt of the system, and in that sense the gains imply only a redistribution of welfare across agents. In contrast, the elimination of existing distortions in social security financing can generate efficiency gains, allowing for welfare improvements for all agents. This result shifts the focus from the nature of the system itself and centers the debate on the distortions associated with social security financing.
Encoding under trust and distrust: the spontaneous activation of incongruent cognitions.

Past studies of strategic thinking have shown that the encoding of the message information becomes more complex under distrust. Receivers process the information as if they are trying to protect themselves from being misled by testing alternative potential interpretations. The present study investigates the possibility that when people are mistrustful they spontaneously activate associations that are incongruent with the given message. Findings from 3 experiments suggest that, even when the distrust is unrelated in any meaningful way to the message and even when receivers are unable to prepare a strategic response, the cognitive system reacts to distrust by automatically inducing the consideration of incongruent associations--it seems designed to ask, "and what if the information were false? " The theoretical implications of the results for theories of social perception and persuasion are discussed.
Round-Robin Duel Discriminative Language Models

Discriminative training has received a lot of attention from both the machine learning and speech recognition communities. The idea behind the discriminative approach is to construct a model that distinguishes correct samples from incorrect samples, while the conventional generative approach estimates the distributions of correct samples. We propose a novel discriminative training method and apply it to a language model for reranking speech recognition hypotheses. Our proposed method has round-robin duel discrimination (R2D2) criteria in which all the pairs of sentence hypotheses including pairs of incorrect sentences are distinguished from each other, taking their error rate into account. Since the objective function is convex, the global optimum can be found through a normal parameter estimation method such as the quasi-Newton method. Furthermore, the proposed method is an expansion of the global conditional log-linear model whose objective function corresponds to the conditional random fields. Our experimental results show that R2D2 outperforms conventional methods in many situations, including different languages, different feature constructions and different difficulties.
Road extraction from aerial images using a region competition algorithm

In this paper, we present a user-guided method based on the region competition algorithm to extract roads, and therefore we also provide some clues concerning the placement of the points required by the algorithm. The initial points are analyzed in order to find out whether it is necessary to add more initial points, and this process will be based on image information. Not only is the algorithm able to obtain the road centerline, but it also recovers the road sides. An initial simple model is deformed by using region growing techniques to obtain a rough road approximation. This model will be refined by region competition. The result of this approach is that it delivers the simplest output vector information, fully recovering the road details as they are on the image, without performing any kind of symbolization. Therefore, we tried to refine a general road model by using a reliable method to detect transitions between regions. This method is proposed in order to obtain information for feeding large-scale Geographic Information System.
Urbanopoly : Collection and Quality Assessment of Geospatial Linked Data via a Human Computation Game

The Urbanopoly app is a social, mobile and location-based Game with a Purpose designed around the idea of the “monopoly” board game. Urbanopoly consumes and generates geo-spatial linked and open data; through a Human Computation mechanisms, those data are validated and enriched; new information is published as linked data. Players are thus involved in the geo-spatial data collection according to the “citizens as sensors” approach and the Urbanopoly game is the entertaining expedient to assess and improve geo-spatial linked data quality.
Multi-class Support Vector Machine for human posture classification using a simplified shock graph

In this paper, we intend to present and discuss the validity and effectiveness of shock graph as feature vectors for human posture classification task. Basically, a shock graph is a shape abstraction that decomposed a shape into a set of hierarchically organized primitive parts. The shock graph that represents the silhouette of an object in terms of a set of qualitatively defined parts, organized in a hierarchical, directed acyclic graph, is used as a powerful representation of human shape in our work. To be exact, the paper aims to highlight the efficacy of the simplified shock graph (SSG) derived as a result of pruning from the original shock graph as revealed in [7]. We believe our SSG provides a compact, unique and simple way of representing human shape. In doing so, we have chosen to test the feature vectors of the SSG with three different multi-class Support Vector Machines (SVM) classifier models. In this study, there are three different method of SVM classifier will be used, namely the one-against-all (OAA), one-against-one (OAO) and the bias support vector machines (BSVM). In addition, Gaussian radial basis function (RBF) was opted as kernel for all three techniques with the Sequential Minimal Optimization (SMO) solver applied for training SVM of the two-class problem for both OAA and OAO methods along with Nearest Point Algorithm (NPA) solver used to train the multi class BSVM. The training time, regularization parameter, C values together with other parameters such as the number of support vectors, testing time and classification rate will be evaluated. Initial results demonstrated that perfect classification rate is attained for all three SVM models with the BSVM approach contributed the least number of support vectors. The best performance for training and testing time is owned by the OAO method. As such, the validity of SSG as feature vectors to represent human postures is confirmed based on the findings from all three methods of SVM applied.
Structured Scheduling of Recurrence Equations

We study scheduling of structured systems of recurrence equations. We first recall the formalism of structured systems of recurrence equations, then we explain how to implement a scheduling tool for non-structured systems of reccurence equations. We introduce the notion of structured schedule and propose criteria for determining whether a structured system of reccurence equations admits a structured schedule. We give an algorithm for its computation. Finaly we propose an algorithm for computing structured multi-dimensionnal schedule for systems which do not admit a structured linear schedule. With this last method, we derive a structured schedule for a structured system of recurrence equations computing the singular value decomposition of a matrix.
Impact of fine particles in ambient air on lung cancer

Recently, the International Agency for Research on Cancer (IARC) has classified outdoor air pollution and the particulate matter component of outdoor air pollution as class I carcinogen. Air pollution is consistently associated with lung cancer in epidemiologic and experimental studies. The IARC assessment is specifically designed as hazard identification, and it does not quantify the magnitude of the cancer risk. This article addresses the magnitude of the lung cancer risk in the population due to ambient air pollution exposure.
Fine grained indexing of software repositories to support impact analysis

Versioned and bug-tracked software systems provide a huge amount of historical data regarding source code changes and issues management. In this paper we deal with impact analysis of a change request and show that data stored in software repositories are a good descriptor on how past change requests have been resolved. A fine grained analysis method of software repositories is used to index code at different levels of granularity, such as lines of code and source files, with free text contained in software repositories. The method exploits information retrieval algorithms to link the change request description and code entities impacted by similar past change requests. We evaluate such approach on a set of three open-source projects.
Design and Implementation of Mobile Phone Management Software Based on Component Technology

In order to make use of the PC's ability of processing, management and storage, and implement the management of the mobile phone by the PC, this paper proposes the design method of mobile phone management software based on component technology. Using VC6.0 as the development platform, based on windows serial communication, AT commands, we implemented control to the mobile phone and synchronous transfer of information between the mobile phone and the PC. Based on PDU mode, ActiveX controls, Dynamic Link Library (DLL), ADO smart pointers, windows hooks, etc., we implemented management of phonebook, call records, SMS messages and planning tools such as notes, tasks, calendar and alarm clock The implementation process of the system has been presented in detail.
Fe3O4-based PLGA nanoparticles as MR contrast agents for the detection of thrombosis

Thrombotic disease is a great threat to human health, and early detection is particularly important. Magnetic resonance (MR) molecular imaging provides noninvasive imaging with the potential for early disease diagnosis. In this study, we developed Fe3O4-based poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) surface-modified with a cyclic Arg-Gly-Asp (cRGD) peptide as an MR contrast agent for the detection of thrombosis. The physical and chemical characteristics, biological toxicity, ability to target thrombi, and biodistribution of the NPs were studied. The Fe3O4-PLGA-cRGD NPs were constructed successfully, and hematologic and pathologic assays indicated no in vivo toxicity of the NPs. In a rat model of FeCl3-induced abdominal aorta thrombosis, the NPs readily and selectively accumulated on the surface of the thrombosis and under vascular endothelial cells ex vivo and in vivo. In the in vivo experiment, the biodistribution of the NPs suggested that the NPs might be internalized by the macrophages of the reticuloendothelial system in the liver and the spleen. The T2 signal decreased at the mural thrombus 10 min after injection and then gradually increased until 50 min. These results suggest that the NPs are suitable for in vivo molecular imaging of thrombosis under high shear stress conditions and represent a very promising MR contrast agent for sensitive and specific detection of thrombosis.
Intelligent Train Operation Algorithms for Subway by Expert System and Reinforcement Learning

Current research in automatic train operation concentrates on optimizing an energy-efficient speed profile and designing control algorithms to track the speed profile, which may reduce the comfort of passengers and impair the intelligence of train operation. Different from previous studies, this paper presents two intelligent train operation (ITO) algorithms without using precise train model information and offline optimized speed profiles. The first algorithm, i.e., ITOe, is based on an expert system that contains expert rules and a heuristic expert inference method. Then, in order to minimize the energy consumption of train operation online, an ITOr algorithm based on reinforcement learning (RL) is developed via designing an RL policy, reward, and value function. In addition, from the field data in the Yizhuang Line of the Beijing Subway, we choose the manual driving data with the best performance as ITOm. Finally, we present some numerical examples to test the ITO algorithms on the simulation platform established with actual data. The results indicate that, compared with ITOm, both ITOe and ITOr can improve punctuality and reduce energy consumption on the basis of ensuring passenger comfort. Moreover, ITOr can save about 10% energy consumption more than ITOe. In addition, ITOr is capable of adjusting the trip time dynamically, even in the case of accidents.
Rhetorical structure and Alzheimer’s disease

ABSTRACT Background: Language is one of the first faculties afflicted by Alzheimer’s disease (AD). A growing body of work has focussed on leveraging automated analysis of speech to accurately predict the onset of AD. Previous work, however, did not address the effects of AD on the structure of discourse in spontaneous speech and literature. Aims: Our goal is to identify the effects of AD on the structure of discourse, both in spontaneous speech and in literature. Methods & Procedures: We use two existing data sets, DementiaBank and the Carolina Conversations Collection, to explore how AD manifests itself in spontaneous speech. This is done by automatically extracting discourse relations according to Rhetorical Structure Theory. We also study written novels, comparing authors with and without dementia using the same tools. Outcomes & Results: Several discourse relations, especially those involving elaboration and attribution, are significant indicators of AD in speech. Indicators of the disease in written text, by contrast, involve relations of logical contingency. Conclusions: Our work highlights how AD can alter discourse structures in both spontaneous speech and written text. Future work should combine discourse analysis with previously studied lexico-syntactic features.
Perturbation realization, potentials, and sensitivity analysis of Markov processes

Two fundamental concepts and quantities, realization factors and performance potentials, are introduced for Markov processes. The relations among these two quantities and the group inverse of the infinitesimal generator are studied. It is shown that the sensitivity of the steady-state performance with respect to the change of the infinitesimal generator can be easily calculated by using either of these three quantities and that these quantities can be estimated by analyzing a single sample path of a Markov process. Based on these results, algorithms for estimating performance sensitivities on a single sample path of a Markov process can be proposed. The potentials in this paper are defined through realization factors and are shown to be the same as those defined by Poisson equations. The results provide a uniform framework of perturbation realization for infinitesimal perturbation analysis (IPA) and non-IPA approaches to the sensitivity analysis of steady-state performance; they also provide a theoretical background for the PA algorithms developed in recent years.
Aggregating Web Service matchmaking variants using web search engine and machine learning

Variety of Web Service discovery algorithms had been investigated for improvement of the retrieval quality. Combining the several algorithms according to their strong points, is proposed as enabling more refined discovery consequence. Now, many researches as OWL-Mx [6] are sited as examples, had already shown the method that join together and conclude for the specific domain. However, there are no way to conclude multi-algorithms results. Klusch [7] shows the brand-new way that leads the conclusion by using machine-learning algorithm Support Vector Machine (SVM) [1,4]. In this research, we attempted to apply the SVM aggregation and several new discovery algorithm using similarity based on search engine, shown on Trip Domain service discovery. And, 88 percent over score of precision, were gotten as the result from specifically prepared queries for Trip Domain. This experiment also had shown 10 percent missing which occurred by using web page count based similarity computation. In future work, we will conduct some comparison for getting more reliability of this proposed method.
MONEY , INTEREST , PRICES AND CAPITAL FLOWS

In this paper we develop the first search-theoretic monetary model of a two-country global economy. We show the connection between deep monetary frictions (i.e. search and matching frictions), capital holdup externalities, and the realization of an endogenous departure from the “excess smoothness” problem in the real exchange rate found in standard models. Using this alternative monetary model, we revisit the international monetary policy implications on the real exchange rate and output. JEL CODES: E31; E32; E43; E44
Learning semantic visual dictionaries: A new method for local feature encoding

In this paper, we develop a new method to learn semantic visual dictionaries for local image feature encoding. Conventional methods usually learn dictionaries from random local image patches. Different from them, we manually select a number of object classes whose visual patterns can be seen at local image patch level in complex images (Figure 1), and learn dictionaries from their thumbnail images. The benefit is that these thumbnail images have class labels, so we can cluster semantically similar images together to generate meaningful cluster centers. Some other contributions of this paper include developing an adaptation method to adapt the learned dictionaries to target datasets, and developing efficient algorithms to encode local patches with our semantic visual dictionaries. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed methods.