Miracle-dz/UniFAR-FTUs huggingface dataset:
Cystatin C as a marker of acute kidney injury in the emergency department.

BACKGROUND AND OBJECTIVES The diagnosis of acute kidney injury (AKI) is usually based on changes in serum creatinine, which is a poor marker of early renal dysfunction. The discriminative and predictive abilities of serum and urinary cystatin C were examined for the prediction of AKI. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS In this prospective cohort study, serum and urinary cystatin C were serially measured in a heterogeneous group of patients (n = 616) presenting to a tertiary care emergency department. The primary outcome was AKI, classified according to RIFLE and AKIN criteria. The secondary outcome was an adjudication based on clinical criteria to AKI, prerenal azotemia, chronic kidney disease (CKD), and normal kidney function. RESULTS Patients were adjudicated to have AKI in 21.1%, prerenal azotemia in 25.8%, CKD in 2.4%, and normal kidney function in 50.7%. For the diagnosis of AKI, the discriminatory ability of urinary creatinine and cystatin C was marginal. Both serum cystatin C and serum creatinine (at presentation and 6 hours later) showed high discriminatory ability for the diagnosis of AKI. However, only serum cystatin C attained a significant early predictive power (Hosmer-Lemeshow P value > 0.05). Serum cystatin C could differentiate between AKI and prerenal azotemia, but not between AKI and CKD. CONCLUSIONS Serum cystatin C is an early, predictive biomarker of AKI, which outperforms serum creatinine in the heterogeneous emergency department setting. However, neither biomarker discriminated between AKI and CKD. Additional biomarkers continue to be needed for improved specificity in the diagnosis of community-acquired AKI.
Urban Distinctions: Class, Culture and Sociability in the City of Porto

This essay employs Pierre Bourdieu's theory of practice and the methodology of Multiple Correspondence Analysis (MCA) to extend the mapping of the dynamic relations between class and culture presented in Bourdieu's Distinction to encompass urban space, drawing on data from a multiâ€ method research project on the city of Porto, Portugal. We present a detailed analysis of the formation and structure of local social space and show its relevance for the study of the (re)production of urban lifestyles. Differences in the volume and composition of the capital of city residents are identified and shown to underpin the relations between social positions, dispositions and position takings in various realms of cultural consumption. Meaningful configurations of â€˜lifestyle modalitiesâ€™ have clear roots in the city's social space, which in Portugal, as in France, can be interpreted in terms of distinction, pretension and necessity.
Warum kontrolliert die Polizei (nicht)

Given the recurring unrest in French suburbs and their absence in Germany, this paper examines the question of to what extent the collective action repertoire differs between German and French police forces. Police powers of stop and search demon-strate the relevance of occupational norms and values, which point in different directions depending on the context. In the studied German city, particularly in the most deprived areas identity checks are recognized as an additional workload by the po-lice, and therefore used less often. In contrast, French police officers perceive identi-ty checks as belonging to their professional identity.
Inter-session network coding for transmitting multiple layered streams over single-hop wireless networks

This paper studies the problem of transmitting multiple independent layered video streams over single-hop wireless networks using network coding (NC). We combine feedback-free random linear NC (RLNC) with unequal error protection (UEP) and our goal is to investigate the benefits of coding across streams, i.e. inter session NC. To this end, we present a transmission scheme that in addition to mixing packets of different layers of each stream (intra-session NC), mixes packets of different streams as well. Then, we propose the analytical formulation of the layer decoding probabilities for each user and utilize it to define a theoretical performance metric. Assessing this performance metric under various scenarios, it is observed that inter-session NC improves the trade-off among the performances of users. Furthermore, the analytical results show that the throughput gain of inter-session NC over intra-session NC increases with the number of independent streams and also by increasing packet error rate, but degrades as network becomes more heterogeneous.
Diferulate Cross-Links Impede the Enz y matic Degradation of Non-Ligniüed Maize Walls

We assessed the e†ect of ferulate substitution and diferulate crosslinking of xylans on the degradation of cell walls by two fungal enzyme mixtures, one of which contained feruloyl esterase and high xylanase activities. NonligniÐed cell suspensions of maize (Zea mays) were grown with 0 or 40 lM 2aminoindan-2-phosphonic acid to produce walls with normal (17É2 mg g~1) or reduced (5É1 mg g~1) ferulate concentrations. Walls were incubated with mercaptoethanol to inhibit diferulate formation or with hydrogen peroxide to stimulate diferulate formation by wall bound peroxidases. Varying the ferulate substitution of xylans did not a†ect cell wall hydrolysis. In contrast, increasing ferulate dimerisation from 18 to 40% reduced carbohydrate release by 94È122 mg g~1 after 3 h and by 0È48 mg g~1 after 54 h of enzymatic hydrolysis. Diferulate crosslinks impeded the release of xylans, cellulose and pectins from walls. These results provide compelling evidence that diferulate cross-links reduce the rate and, to a lesser degree, the extent of wall hydrolysis by fungal enzymes. Our results also suggest that enzyme mixtures containing high xylanase activity but not feruloyl esterase activity can partially overcome the inhibitory e†ects of diferulate cross-linking on wall hydrolysis. 1998 SCI. ( J Sci Food Agric 77, 193È200 (1998)
A Novel Probabilistic Approach for Efficient Information Retrieval

Information on the World Wide Web is increasing tremendously. To get the relevant information from very large data sets is essential. In traditional retrieval systems, the query is given to large corpus to retrieve the relevant documents. The traditional models for information retrieval are just one subclass of retrieval techniques that have been studied in many years. Although many techniques share common characteristics in the information retrieval hierarchy, they all share a core set of similarities that justify their own class and these algorithms are design for isolated datasets. But in most of cases, relationships among different datasets are always existed. A new probabilistic Hidden Markov model is proposed and based on this model new information retrieval (IR) technique is presented. Hidden Markov models (HMMs) are widely used in science, engineering and many other areas. In a HMM, there are two types of states like hidden states and observable states. HMM is powerful modeling of context as well as the current observations. Hidden Markov model is finite state machine which offer a good balance between simplicity and expressiveness of context. IR is performed by determining the sequence of states that was most likely to have generated the entire document, and retrieving the information that were associated with certain designated target states. Determining this sequence is efficiently performed by dynamic programming with the Viterbi algorithm.
Working Paper Artificial Neural Network Represent at ions for Hierarchical Preference Structures

In this paper, we introduce two artificial neural network formulations that can be used to predict the preference ratings from the pairwise comparison matrices of the Analytic Hierarchy Process (AHP). First, we introduce a modified Hopfield network that can be used to exactly determine the vector of preference ratings associated with a positive reciprocal comparison matrix. The dynamics of this network are mathematically equivalent to the power method, a widely used numerical method for computing the principal eigenvectors of square matrices. However, we show that the Hopfield network representation is incapable of generalizing the preference patterns, and consequently is not suitable for approximating the preference ratings if the preference information is imprecise. Then we present a feed-forward neural network formulation that does have the ability to accurately approximate the preference ratings. A simulation experiment is used to verify the robustness of the feed-forward neural network formulation with respect to imprecise pairwise judgments. From the results of this experiment, we conclude that the feed-forward neural network formulation appears to be a powerful tool for analyzing discrete alternative multicriteria decision problems with imprecise or fuzzy ratio-scale preference judgments.
Learning invariant region descriptor operators with genetic programming and the F-measure

Recognizing and localizing objects is a classical problem in computer vision that is an important stage for many automated systems. In order to perform object recognition many researchers have focused on local features as the basis of their proposed methodologies. This work is devoted to the task of learning invariant region descriptor operators with genetic programming. The idea is to find a set of expressions that could be equal or better than the weighted gradient magnitude that is normally applied on the SIFT descriptor. This magnitude corresponds to the operator that we would like to improve through genetic programming (GP). The key for a successful problem statement was achieved with the F-measure. After a bibliographical study we have found a criterion that is simple, reliable, and useful in the estimation of such a metric. The measure that we propose here is based on the harmonic mean which is normally used by the information retrieval community. Experimental results show that the evolved descriptor¿s operator can enhance significantly the overall performance of the SIFT descriptor and surpass other state-of-the-art algorithms.
Named entity recognition from spontaneous open-domain speech

This paper presents an analysis of named entity recognition and classiﬁcation in spontaneous speech transcripts. We annotated a signiﬁcant fraction of the Switchboard corpus with six named entity classes and investigated a battery of machine learning models that include lexical, syntactic, and semantic attributes. The best recognition and classiﬁcation model obtains promising results, approaching within 5% a system evaluated on clean textual data.
Organizing ad hoc agents for human-agent service matching

Ambient intelligence, distributed agent systems, smart buildings and other emerging architectures leave the user lost between large amounts of autonomous entities or agents. With agents in cars, personal devices and other mobile systems, the set of agents available to the user can change rapidly, adding to the problems. We propose a set of agent relations to organize agents in an ad hoc situation. There are dynamic task-, location- and user relations, creating multiple relation hierarchies in agent space. These hierarchies allow robust, space- and task limited, context-sensitive search through agent space. We show how to use this structure to keep user interfaces near the user, and how it can be used to support the user in finding the agent he needs in a context-sensitive way.
Temporal Correlation between Social Tags and Emerging Long-Term Trend Detection

Social annotation has become a popular manner for web users to manage and share their information and interests. While users' interests vary with time, tag correlation also changes from users' perspectives. In this work, we explore four methods for estimating temporal correlation between social tags and detect if a long-term trend emerges from the history of temporal correlation between two tags. Three types of trends are specified: steadily-shifting, stabilizing, and cyclic. To compare the results of the four estimation methods, an indirect evaluation is realized by applying detected trends to tag recommendation.
Selecting Effective Strategies for Tailoring Persuasive Health Games to Gamer Types

games can be effective tools for motivating healthy behaviors and/or attitudes, and so recent years have witnessed an increasing number of persuasive games. However, most games adopt a one-size-fits-all approach to persuasion in their design. Studies on gameplay and player motivation have shown that treating gamers as a monolithic group is a bad design approach because a motivational approach that works for one individual may actually demotivate the desired behavior in others. To correct this problem, we conducted a large-scale study on 1108 gamers, which examined the persuasiveness of ten Persuasive Technology (PT) strategies, and the receptiveness of seven gamer types identified by BrianHex to the strategies most commonly used in PT design. We developed models showing the receptiveness of the gamer types to the ten strategies and created persuasive profiles, which are lists of strategies that can be employed to motivate behavior for each gamer type. Although we studied and created our models using ten strategies, in this paper, we report results of five strategies.
Intrinsic flexibility exploiting for scalable video streaming over multi-channel wireless networks

Scalable video has natural advantages in adapting to the multi-channel wireless networks. And some existing works tried to further optimize the scalable video transmission by combining the crude layer-importance mapping with some extrinsic techniques, such as Forward Error Correction (FEC) and Adaptive Modulation and Coding (AMC). However, the intrinsic flexibility of scalable video streaming over the multichannel wireless networks was neglected. In this paper, we try to exploit the intrinsic flexibility by firstly analyzing the priorities of H.264/SVC video data at the network abstraction layer unit (NALU) level, and then designing the priority-validity delivery scheme for the scalable video streaming. With this strategy, the sub-stream extraction is intelligently adjusted according to the delivery history, and the more important data in a group of pictures (GOP) will be delivered through the more reliable channels. Experimental results also validate the strategy's effectiveness in improving the objective quality and perceptual experience of the received video.
An Isolated Sign Language Recognition System Using RGB-D Sensor with Sparse Coding

An isolated sign language recognition system is presented in this paper. A RGB-D sensor, Microsoft Kinect, is used for obtaining color stream and skeleton points from the depth stream. For a particular sign we extract a representative feature vector composed by hand trajectories and hand shapes. A sparse dictionary learning algorithm, Label Consistent K-SVD (LC-KSVD), is applied to obtain a discriminative dictionary. Based on that, we further develop a new classification approach to get better result. Our system is evaluated on 34 isolated Chinese sign words including one-handed signs and two-handed signs. Experimental results show the proposed system gets high recognition accuracy, of the reported 96.75%, and obtain an average accuracy of 92.36% for signer independent recognition.
Movement as well-formedness conditions

We introduce a new formulation of dependency grammar recently suggested in (Duchier and Debusmann 2001) (henceforth DD). DD shares with GB (Chomsky 1986) a notion of movement. In GB, movement is carried out by tree transformations. In DD, it is the effect of well-formedness conditions on dependency trees and does not involve transformations. We illustrate both kinds of movement by showing how both theories analyze German verb-second clauses. Then, we point out the similarities between GB and DD, and raise the question whether GB’s transformational notion of movement could be replaced by DD’s constraint-based account of movement.
Wind-Tunnel Simulation of Pedestrian-Level Wind in Los Angeles

This summary describes the methodology developed to address pedestrian-level winds within the South Coast Air Basin, a 6,000-square mile area that includes Orange County, most of Los Angeles and Riverside Counties. Working with the city of Los Angeles officials guidelines for wind-testing procedures, analysis, and report-data presentation were established. The assessment is accomplished through wind-tunnel testing that couples fullscale meteorological data to physical modeling data. The primary application of this evaluation process is in the environmental impact assessment of proposed buildings, industrial complexes, and other construction projects that may substantially alter pedestrianlevel winds in public areas. To date, since 1990, fifteen different wind-tunnel studies have been carried out by the author using this method, and it has been well received by the city of Los Angeles planning officials.
Privacy-preserving regression algorithms

Regression is arguably the most applied data analysis method. Today there are many scenarios where data for attributes that correspond to predictor variables and the response variable itself are distributed among several parties that do not trust each other. Privacy-preserving data mining has grown rapidly studying the scenarios where data is vertically partitioned. While algorithms have been developed for many tasks (like clustering, association-rule mining and classification), for regression, the case of only two parties remains open. Also open is the most interesting case when the response variable is to be kept private. This paper provides the first set of algorithms that solves these cases. Our algorithms are practical and only require a commodity server (a supplier of random values) that does not collude with the parties. Our protocols are secure in the spirit of the the semi-honest model.
Set Overlap in Mining of Frequent Itemsets

An important module of soft computing methods is the set overlap operation. If a query set is tested with a large pool of source sets, the signature-based or the inverted-file methods are used to reduce the cost of operation. The paper introduces a modified version of the inverted-file approach, which yields in lowest costs for sparse input samples, i.e. where the number of records containing an element is relatively low.
A causal multicast protocol for mobile distributed systems

Causal message ordering in the context of group communication ensures that all the message receivers observe consistent ordering of events affecting a group as a whole. This paper presents a scalable causal multicast protocol for mobile distributed computing systems. In our protocol, only a part of the mobility agents in the system is involved in group computations and the resulting size of control information in messages can be kept small. Our protocol can outperform qualitatively the counterparts in terms of communication overhead and handoff complexity. An analytical model is also developed to evaluate our proposal. The performance results show that the proposed protocol is promising. key words: causal ordering, multicast, mobile computing
Automatic identification of load testing problems

Many software applications must provide services to hundreds or thousands of users concurrently. These applications must be load tested to ensure that they can function correctly under high load. Problems in load testing are due to problems in the load environment, the load generators, and the application under test. It is important to identify and address these problems to ensure that load testing results are correct and these problems are resolved. It is difficult to detect problems in a load test due to the large amount of data which must be examined. Current industrial practice mainly involves time-consuming manual checks which, for example, grep the logs of the application for error messages. In this paper, we present an approach which mines the execution logs of an application to uncover the dominant behavior (i.e., execution sequences) for the application and flags anomalies (i.e., deviations) from the dominant behavior. Using a case study of two open source and two large enterprise software applications, we show that our approach can automatically identify problems in a load test. Our approach flags < 0.01% of the log lines for closer analysis by domain experts. The flagged lines indicate load testing problems with a relatively small number of false alarms. Our approach scales well for large applications and is currently used daily in practice.
ITERATIVE SOLUTION OF NONLINEAR EQUATIONS FOR SPARK METHODS APPLIED TO DAES

We consider a broad class of systems of implicit differential-algebraic equations (DAEs) including the equations of mechanical systems with holonomic and nonholonomic constraints. We approximate numerically the solution to these DAEs by applying a class of super partitioned additive Runge-Kutta (SPARK) methods. Several properties of the SPARK coefficients, satisfied by the combination of Lobatto IIIA-B-C-C∗-D methods, are crucial to deal properly with the presence of constraints and algebraic variables. A major difficulty for an efficient implementation of these methods lies in the numerical solution of the resulting systems of nonlinear equations. To solve these systems we make use of inexact modified Newton iterations. Linear systems of the modified Newton method are solved approximately with a preconditioned linear iterative method. The preconditioner is based in part on the W-transformation of the SPARK coefficients and also on some specific linear transformations to the original system of nonlinear equations. These linear transformations rely heavily on certain properties of the SPARK coefficients. For nonstiff DAEs the preconditioner requires only one matrix decomposition of the same dimension as the DAEs.
UnSync-CMP: Multicore CMP Architecture for Energy-Efficient Soft-Error Reliability

Reducing device dimensions, increasing transistor densities, and smaller timing windows, expose the vulnerability of processors to soft errors induced by charge carrying particles. Since these factors are only consequences of the inevitable advancement in processor technology, the industry has been forced to improve reliability on general purpose chip multiprocessors (CMPs). With the availability of increased hardware resources, redundancy-based techniques are the most promising methods to eradicate soft-error failures in CMP systems. In this work, we propose a novel customizable and redundant CMP architecture (UnSync) that utilizes hardware-based detection mechanisms (most of which are readily available in the processor), to reduce overheads during error-free executions. In the presence of errors (which are infrequent), the always forward execution enabled recovery mechanism provides for resilience in the system. The inherent nature of our architecture framework supports customization of the redundancy, and thereby provides means to achieve possible performance-reliability tradeoffs in many-core systems. We provide a redundancy-based soft-error resilient CMP architecture for both write-through and write-back cache configurations. We design a detailed RTL model of our UnSync architecture and perform hardware synthesis to compare the hardware (power/area) overheads incurred. We compare the same with those of the Reunion technique, a state-of-the-art redundant multicore architecture. We also perform cycle-accurate simulations over a wide range of SPEC2000, and MiBench benchmarks to evaluate the performance efficiency achieved over that of the Reunion architecture. Experimental results show that, our UnSync architecture reduces power consumption by 34.5 percent and improves performance by up to 20 percent with 13.3 percent less area overhead, when compared to the Reunion architecture for the same level of reliability achieved.