Miracle-dz/UniFAR-FTUs huggingface dataset:
UpBit: Scalable In-Memory Updatable Bitmap Indexing

Bitmap indexes are widely used in both scientific and commercial databases. They bring fast read performance for specific types of queries, such as equality and selective range queries. A major drawback of bitmap indexes, however, is that supporting updates is particularly costly. Bitmap indexes are kept compressed to minimize storage footprint; as a result, updating a bitmap index requires the expensive step of decoding and then encoding a bitvector. Today, more and more applications need support for both reads and writes, blurring the boundaries between analytical processing and transaction processing. This requires new system designs and access methods that support general updates and, at the same time, offer competitive read performance. In this paper, we propose scalable in-memory Updatable Bitmap indexing (UpBit), which offers efficient updates, without hurting read performance. UpBit relies on two design points. First, in addition to the main bitvector for each domain value, UpBit maintains an update bitvector, to keep track of updated values. Effectively, every update can now be directed to a highly-compressible, easy-to-update bitvector. While update bitvectors double the amount of uncompressed data, they are sparse, and as a result their compressed size is small. Second, we introduce fence pointers in all update bitvectors which allow for efficient retrieval of a value at an arbitrary position. Using both synthetic and real-life data, we demonstrate that UpBit significantly outperforms state-of-the-art bitmap indexes for workloads that contain both reads and writes. In particular, compared to update-optimized bitmap index designs UpBit is 15-29x faster in terms of update time and 2.7x faster in terms of read performance. In addition, compared to read-optimized bitmap index designs UpBit achieves efficient and scalable updates (51-115x lower update latency), while allowing for comparable read performance, having up to 8% overhead.
Beyond medication: Life situation of older patients in opioid maintenance treatment

Abstract Background: The opioid maintenance population is rising and aging. Although this creates new challenges for society, little is known about the social issues older patients in this treatment face. The Ministry of Health and Care Services in Norway was revising the National Action Plan for Drug Treatment and Rehabilitation in 2015. In this context, the Ministry requested a report on health and social issues and possible challenges for the health care services. Aims of this article are to provide an understanding of the complex social issues associated with older patients in opioid maintenance treatment, and give implications for future work. a Method: Statistical data from national, regional and local reports, plans and projects were collected. A literature search was conducted and qualitative data from focus groups were included. A methodological triangulation was used in the analysis. Results: Patients experienced several social issues, especially: stigmatization, isolation and financial difficulties. Patients were stigmatized from society in general, but also from the social and mental health services. They had difficulties socializing and lacked the skills needed for doing so. Even though the majority of patients received benefit payments, self-reported poverty was emphasized. Conclusions: Raised awareness of issues faced by older patients in opioid maintenance treatment is needed. An improved collaboration between the municipality, specialist health care services, the local community, and voluntary organizations should be used to a greater extent. The patients have to be a part of this collaboration. Organizational challenges need to be approached and professional competence enhanced.
Molecular epidemiology of candidemia: evidence of clusters of smoldering nosocomial infections.

BACKGROUND Invasive fungal infections pose a serious threat to hospitalized patients worldwide. In particular, the prevalence of clusters of nosocomial infection among patients with candidemia remains unknown. The aim of this study was to investigate the molecular epidemiology of candidemia in a nationwide setting in Iceland during a 16-year period. METHODS The genotypes of all available fungal bloodstream isolates during 1991-2006 (n = 219) were determined by polymerase chain reaction fingerprinting with use of 4 separate primers. Clusters were defined as isolation of > or =2 strains with genotypes that had > or =90% relatedness in the same hospital within a period of 90 days. RESULTS Candida albicans represented 61.6% of isolates, followed by Candida glabrata (13.7%), Candida tropicalis (9.1%), and Candida parapsilosis (8.7%). Polymerase chain reaction fingerprinting revealed 35 clones of C. albicans, 10 clones of C. glabrata, 7 clones of C. tropicalis, 4 clones of C. parapsilosis, and 5 clones of Candida dubliniensis. Overall, 18.7%-39.9% of all infections were part of nosocomial clusters, most commonly caused by C. albicans, C. parapsilosis, and C. tropicalis. Most clusters involved 2 cases and disproportionately affected patients in adult and neonatal intensive care units (P = .045). The 7-day (16%) and 30-day (32%) case-fatality rates among cluster-associated cases did not differ statistically significantly from those for sporadic nosocomial infections. None of the clusters were identified by the hospital surveillance team. CONCLUSIONS In an unselected patient population, as many as one-third of all cases of candidemia may be attributable to nosocomial clusters. The risk is dependent on hospital wards and patient populations; it is highest in intensive care units. Small clusters are not identified by routine hospital surveillance.
A Binary Integer Programming Model for Optimal Object Distribution

Whether deploying new distributed software, redeploying existing distributed application components or distributing a standalone legacy application, a key decision that needs to be made is the location of each software component in the target distributed environment. This decision depends on the usage and interaction patterns of the distributable components. For a given pattern of interactions among the components, choosing the component locations involves a trade-o between the resulting local and remote communication of the deployed components. To assist in the process of deploying distributed software, we de ned an abstract model of distributed applications and we have developed a binary integer programming (BIP) model that takes into account an application's communication patterns and the target network characteristics. The goal is to minimize the overall remote communication bandwidth for the application, while achieving a meaningful distribution of components in the target network setting. The BIP model yields solutions (when they exist) that are optimal for systems that can be described in terms of our application model. Object-oriented software is especially well-suited for this methodology since it is inherently structured in terms of distributable components (e.g., classes, objects) and the major communication patterns can be determined by identifying the foreign method invocations. We include examples of optimal object deployment solutions using our methodology and sample object-oriented applications.
Skeletal muscle tissue engineering: strategies for volumetric constructs

Skeletal muscle tissue is characterized by high metabolic requirements, defined structure and high regenerative potential. As such, it constitutes an appealing platform for tissue engineering to address volumetric defects, as proven by recent works in this field. Several issues common to all engineered constructs constrain the variety of tissues that can be realized in vitro, principal among them the lack of a vascular system and the absence of reliable cell sources; as it is, the only successful tissue engineering constructs are not characterized by active function, present limited cellular survival at implantation and possess low metabolic requirements. Recently, functionally competent constructs have been engineered, with vascular structures supporting their metabolic requirements. In addition to the use of biochemical cues, physical means, mechanical stimulation and the application of electric tension have proven effective in stimulating the differentiation of cells and the maturation of the constructs; while the use of co-cultures provided fine control of cellular developments through paracrine activity. This review will provide a brief analysis of some of the most promising improvements in the field, with particular attention to the techniques that could prove easily transferable to other branches of tissue engineering.
Adaptive Decoding for Brain-Machine Interfaces Through Bayesian Parameter Updates

Brain-machine interfaces (BMIs) transform the activity of neurons recorded in motor areas of the brain into movements of external actuators. Representation of movements by neuronal populations varies over time, during both voluntary limb movements and movements controlled through BMIs, due to motor learning, neuronal plasticity, and instability in recordings. To ensure accurate BMI performance over long time spans, BMI decoders must adapt to these changes. We propose the Bayesian regression self-training method for updating the parameters of an unscented Kalman filter decoder. This novel paradigm uses the decoder's output to periodically update its neuronal tuning model in a Bayesian linear regression. We use two previously known statistical formulations of Bayesian linear regression: a joint formulation, which allows fast and exact inference, and a factorized formulation, which allows the addition and temporary omission of neurons from updates but requires approximate variational inference. To evaluate these methods, we performed offline reconstructions and closed-loop experiments with rhesus monkeys implanted cortically with microwire electrodes. Offline reconstructions used data recorded in areas M1, S1, PMd, SMA, and PP of three monkeys while they controlled a cursor using a handheld joystick. The Bayesian regression self-training updates significantly improved the accuracy of offline reconstructions compared to the same decoder without updates. We performed 11 sessions of real-time, closed-loop experiments with a monkey implanted in areas M1 and S1. These sessions spanned 29 days. The monkey controlled the cursor using the decoder with and without updates. The updates maintained control accuracy and did not require information about monkey hand movements, assumptions about desired movements, or knowledge of the intended movement goals as training signals. These results indicate that Bayesian regression self-training can maintain BMI control accuracy over long periods, making clinical neuroprosthetics more viable.
Small-World Overlay P 2 P Networks : Construction and Handling Dynamic Flash Crowd

In this paper, we consider how to “construct” and “maintain” an overlay structured P2P network based on the “smallworld paradigm”. Two main attractive properties of a smallworld network are (1) low average hop distance between any two randomly chosen nodes and, (2) high clustering coefficient. A network with a low average hop distance implies a small latency for object lookup. While a network with a high clustering coefficient implies the underlying P2P network has the “potential” to provide object lookup service even in the midst of heavy object traffic loading, for example, under a flash crowd scenario. In this paper, we present the SWOP protocol of constructing a small-world overlay P2P network. We compare our result with other structured P2P networks such as the Chord protocol. Although the Chord protocol can provide object lookup with latency of complexity, where is the number of nodes in a P2P network, we show that the SWOP protocol can further improve the object lookup performance. We also take advantage of the high clustering coefficient of a small-world P2P network and propose an object replication algorithm to handle the heavy object traffic loading situation, e.g. under the dynamic flash crowd scenario. We show that the SWOP network can quickly and efficiently deliver the “popular” and “dynamic” object to all requested nodes. Based on our knowledge, this is the first work that addresses how to handle the “dynamic” flash crowd scenario on a structured P2P
Multilevel cooperative search: application to the circuit/hypergraph partitioning problem

In this paper, we present an adaptation for hypergraph partitioning of the multilevel cooperative search paradigm rst introduced by Toulouse, Thulasiraman, and Glover [23]. We also introduce a new approach for coarsening hypergraphs, and describe a parallel implementation of this algorithm on the SGI O2000 system. Experiments on ISPD98 benchmark suite of circuits show, for 4-way and 8-way partitioning, a reduction of 3% to 15% in the size of hyperedge-cuts compared to hMETIS. Bisections of hypergraphs based on our algorithm also outperforms hMETIS, although more modestly.
Generalization of temporal filter and linear transformation for robust speech recognition

Temporal filtering of feature trajectories and linear transformation of feature vectors are two effective ways to compensate the speech features to achieve robust speech recognition in noisy and reverberant environments. In the previous studies, as the two methods are usually applied in sequence, the interaction between the two methods is not optimized. In this paper, we propose a generalized transform which integrates temporal filter and linear transformation into a single process. The new transform parameters are optimized to minimize an approximated Kullback-Leibler divergence between the distribution of the compensated features and the distribution represented by a clean reference model. The proposed method is evaluated on the Aurora-5 clean condition training task. The experiments show that the generalized transform significantly outperforms the simple cascade of temporal filtering and linear transformation. For example, the word accuracy is improved from 81.55% (cascade) to 83.99% (generalized) and from 72.09% (cascade) to 76.04% (generalized) for office and living room environments, respectively, in speaker based feature adaptation scheme.
Is there an association between altered baroreceptor sensitivity and obstructive sleep apnoea in the healthy elderly?

Obstructive sleep apnoea (OSA) is associated with a rise in cardiovascular risk in which increased sympathetic activity and depressed baroreceptor reflex sensitivity (BRS) have been proposed. We examined this association in a sample of healthy elderly subjects with unrecognised OSA. 801 healthy elderly (aged ≥65 years) subjects undergoing clinical, respiratory polygraphy and vascular assessment were examined. According to the apnoea–hypopnoea index (AHI), the subjects were stratified into no OSA, mild–moderate OSA and severe OSA cases. OSA was present in 62% of the sample, 62% being mild–moderate and 38% severe. No differences were found for BRS value according to sex and OSA severity. 54% of the group had normal BRS value, 36% mild impairment and 10% severe dysfunction. BRS was negatively associated with body mass index (p=0.006), 24-h systolic (p=0.001) and diastolic pressure (p=0.001), and oxygen desaturation index (ODI) (p=0.03). Regression analyses revealed that subjects with lower BRS were those with hypertension (OR 0.41, 95% CI 0.24–0.81; p=0.002) and overweight (OR 0.42, 95% CI 0.25–0.81; p=0.008), without the effect of AHI and ODI. In the healthy elderly, the presence of a severe BRS dysfunction affects a small amount of severe cases without effect on snorers and mild OSA. Hypertension and obesity seem to play a great role in BRS impairment. In the healthy elderly with unrecognised OSA, the risk of blunted baroreceptor sensitivity was low http://ow.ly/kofu302IZWS
Port Hamiltonian System in Descriptor Form for Balanced Reduction: Application to a Nanotweezer

Abstract This paper proposes a method of balanced model reduction for constrained linear port Hamiltonian systems. Constrained linear port Hamiltonian systems are first written in a canonical descriptor form such that the Hamiltonian structure is preserved. The computations of the controllability and observability Gramians are then used to derive the balanced port Hamiltonian representation of the system. The method of flow constraint is applied to reduce the system. Finally, numerical simulations for the reduction of a micro mechanical actuator model is given to illustrate the effectiveness of the proposed method.
GRBs: when do blackbody spectra look like non-thermal ones?

We argue that a nonthermally looking spectrum of a gamma-ray burst (GRB) can be formed as a superposition of a set of thermal blackbody spectra. This superposition may be done by time integration which is present even in `time resolved' GRB spectroscopy. A nonthermal spectrum can be obtained also by the space integration which should take place unless all the emission comes from a plane front moving exactly towards the observer. We propose a model of the gamma-ray burst spectrum formation based on this idea. This model allows the GRB radiation to be optically thick and to have higher values of baryon load. Thus the latter is limited by the energy considerations only, and not by the condition of a small optical depth.
Sensitivity to Interaural Time Delay in Stimulus Fine Structure and Onset/Offset of a Bilateral Cochlear Implant Listener

Published data on the sensitivity of bilateral CI listeners to interaural time delay (ITD) in electrical pulse sequences [e.g. 1] indicate generally higher just noticeable differences (JNDs) for lateralization discrimination than of normal hearing (NH) listeners. In addition, the JNDs appear to increase more rapidly with increasing stimulus (pulse) rate compared to NH listeners, although amplitude-modulation in higher-rate stimuli helps to preserve most of the sensitivity [1]. Up to now, it has not been directly proven that ITD in the temporal fine structure contributes to lateralization discrimination of CI listeners and how its contribution relates to that of ITD in the gating portions (onset and offset). This study directly measures the relative importance of ITD in the temporal fine structure and in the onset and offset of a four-pulse stimulus, attempting to exclude confounding effects of ILD in the onset/offset. Furthermore, conditions with delay only in the stimulus onset or offset are tested to examine if the particular importance of stimulus onset and offset, as known from normal hearing [2], can also be found in CI listeners. Since the relative contribution of fine structure and gating delay is expected to depend on the pulse rate, all types of ITD are tested as a function of pulse rate.
Iterative Filtering for a Dynamical Reputation System

The paper introduces a novel iterative method that assigns a rep- utation to n +m items: n raters and m objects. Each rater evaluates a subset of objects leading to a n m rating matrix with a certain sparsity pattern. From this rating matrix we give a nonlinear formula to dene the reputation of raters and objects. We also provide an iterative algorithm that superlin- early converges to the unique vector of reputations and this for any rating matrix. In contrast to classical outliers detection, no evaluation is discarded in this method but each one is taken into account with dierent weights for the reputation of the objects. The complexity of one iteration step is linear in the number of evaluations, making our algorithm ecient for large data set. Experiments show good robustness of the reputation of the objects against cheaters and spammers and good detection properties of cheaters and spam- mers.
Quality Control for Crowdsourced Hierarchical Classification

Repeated labeling is a widely adopted quality control method in crowdsourcing. This method is based on selecting one reliable label from multiple labels collected by workers because a single label from only one worker has a wide variance of accuracy. Hierarchical classification, where each class has a hierarchical relationship, is a typical task in crowdsourcing. However, direct applications of existing methods designed for multi-class classification have the disadvantage of discriminating among a large number of classes. In this paper, we propose a label aggregation method for hierarchical classification tasks. Our method takes the hierarchical structure into account to handle a large number of classes and estimate worker abilities more precisely. Our method is inspired by the steps model based on item response theory, which models responses of examinees to sequentially dependent questions. We considered hierarchical classification to be a question consisting of a sequence of subquestions and built a worker response model for hierarchical classification. We conducted experiments using real crowdsourced hierarchical classification tasks and demonstrated the benefit of incorporating a hierarchical structure to improve the label aggregation accuracy.
Non-Data-Aided Joint Carrier Frequency Offset and Channel Estimator for Uplink MC-CDMA Systems

This paper addresses the problem of joint estimation of carrier frequency offset (CFO) and channel impulse response (CIR) in the uplink transmission of multicarrier code-division multiple-access (MC-CDMA) systems. A subspace-based blind estimator is proposed, which only uses the received signal and the desired user's spreading code to estimate the CFO and CIR. The CFO is estimated using a one-dimensional linear search where the objective of the search is to minimize the determinant of a low dimensional matrix and the CIR is estimated as the unit-norm eigenvector corresponding to the minimum eigenvalue of this matrix. The identifiability of the CFO and CIR estimates is investigated by imposing asymptotic constraints on the spreading codes. Although the cost function used by the proposed estimator is nonlinear and nonconvex, it is locally convex in the neighborhood of the true CFO value. This property is used to formulate a simple two-stage CFO estimator. In the first stage, a coarse search is performed to lock into the local convexity region. Then, an adaptive algorithm is used in the second stage to finetune the CFO estimate. The ldquolinearizedrdquo proposed estimator is shown to be unbiased. The simulation results show that the proposed estimator's performance is close to the Cramer-Rao lower bound (CRLB).
Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies-Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study. Keywords Cluster analysis; Davies-Bouldin index; fuzzy cluster analysis; India; Kohonen artificial neural networks; meteorological stations