Miracle-dz/UniFAR-FTUs huggingface dataset:
An SVR­based and Location­aware Method for Mobile QoS Prediction

: With the rapid development of intelligent mobile communication technology, the num­ ber of mobile services and the number of mobile users are both continuously increasing. So, the services used by a user can only account for a very small proportion of the existing services, which results in a sparse user­service quality of service (QoS) matrix. However, QoS is critical for service selection and service recommendation. Therefore, predicting the unknown values of the sparse QoS matrix is essential. However, due to the sparsity of QoS data, the QoS predic­ tion accuracy is difficult to improve. Faced with the problem, this paper intends to utilize the outstanding generalization ability and only support vectors dependent property of support vector regression (SVR) to overcome the difficulty brought by the sparsity of data and predict the un­ known QoS more accurately. Moreover, it is evident that in the mobile environment, QoS values are closely related to the locations of the invoking users. Therefore, this paper intends to improve the accuracy of QoS prediction by incorporating not only the information of similar users but also the information of nearby users into feature vectors. On the other hand, the known QoS values of nearby users can be used to roughly estimate the unknown QoS values of the cold­start user, so as to alleviate the cold­start problem to some extent. Thus, a location­aware SVR­based method for QoS prediction (SVR4QP) is proposed. Compared with some classical QoS prediction algorithms, the experimental results show that in 1/3 of the cases, SVR4QP is moderate; in 1/6 of the cases, SVR4QP is suboptimal; and in half of the cases, SVR4QP is optimal. Compared with some novel mobile QoS prediction methods, the experimental results show that in 1/4 of the cases, SVR4QP is moderate; in half of the cases, SVR4QP is suboptimal; and in 1/4 of the cases, SVR4QP is op­ timal. All these indicate that SVR4QP has comparatively more accurate mobile QoS prediction.
Efficient conflict driven learning in a Boolean satisfiability solver

One of the most important features of current state-of-the-art SAT solvers is the use of conflict based backtracking and learning techniques. In this paper, we generalize various conflict driven learning strategies in terms of different partitioning schemes of the implication graph. We re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes. Extensive experiments with real world examples show that the best performing new learning scheme has at least a 2/spl times/ speedup compared with learning schemes employed in state-of-the-art SAT solvers.
SECOND MOMENT/ORDER APPROXIMATIONS BY KERNEL SMOOTHERS WITH APPLICATION TO VOLATILITY ESTIMATION

Volatility estimation and quantile regression are relevant active research areas in statistics, machine learning and econometrics. In this work, we propose two procedures to estimate local variances in generic regression problems by using of kernel smoothers. The proposed schemes can be applied in multidimesional scenarios (not just for time series analysis) and easily in a multi-output framework, as well. Moreover, they allow the possibility of providing uncertainty estimation using a generic kernel smoother technique. Several numerical experiments show the beneﬁts of the proposed methods, even comparing with benchmark techniques. One of these experiment involves a real dataset analysis.
Current and Future Challenges for Rehabilitation for Inflammatory Arthritis

This narrative review discusses the importance of rehabilitation in rheumatic and musculoskeletal diseases (RMDs), ultimately aiming to reduce their impact on individuals and society. It specifically emphasizes the need for rehabilitation in inflammatory arthritis (IA), particularly in cases where medical management is insufficient. It acknowledges that the complexity of rehabilitation demands a flexible approach. Thereby, it touches on the various models of rehabilitation, which may include multidisciplinary team care, extended practice models, shared care, remote care, and work rehabilitation. It discusses the challenges in research, practice, and policy implementation. In research, the need for innovative research designs is highlighted, whereas regarding clinical practice the importance of early detection of disability and patient engagement is underlined, as well as the role of telehealth and AI in reshaping the rehabilitation landscape. Financial barriers and work force shortages are identified as challenges that hinder the effective delivery of rehabilitative care. On the policy level, this paper suggests that the allocation of healthcare resources often prioritizes acute conditions over chronic diseases, leading to disparities in care. This paper concludes by emphasizing the critical role of evidence-based rehabilitation in improving the quality of life for people with RMDs, in particular for those with IA, and promoting their healthy aging. It also calls for tailored rehabilitation models and the early identification of persons with rehabilitation needs as future challenges in this field.
[Guidelines for the diagnosis and treatment of non-ST-segment elevation acute coronary syndromes].

Guidelines and Expert Consensus Documents summarize and evaluate all currently available evidence on a particular issue with the aim to assist physicians in selecting the best management strategies for a typical patient, suffering from a given condition, taking into account the impact on outcome, as well as the risk–benefit ratio of particular diagnostic or therapeutic means. Guidelines are no substitutes for textbooks. The legal implications of medical guidelines have been discussed previously. A great number of Guidelines and Expert Consensus Documents have been issued in recent years by the European Society of Cardiology (ESC) as well as by other societies and organizations. Because of the impact on clinical practice, quality criteria for development of guidelines have been established in order to make all decisions transparent to the user. The recommendations for formulating and issuing ESC Guidelines and Expert Consensus Documents can be found on the ESC website (http://www.escardio.org/knowledge/guidelines/rules). In brief, experts in the field are selected and undertake a comprehensive review of the published evidence for management and/or prevention of a given condition. A critical evaluation of diagnostic and therapeutic procedures is performed including assessment of the risk–benefit ratio. Estimates of expected health outcomes for larger societies are included, where data exist. The level of evidence and the strength of recommendation of particular treatment options are weighed and graded according to pre-defined scales, as outlined in Tables 1 and 2 . View this table: Table 1 Classes of recommendations View this table: Table 2 Levels of evidence The experts of the writing panels have provided disclosure statements of all relationships they may have which might be perceived as real or potential sources of conflicts of interest. These disclosure forms are kept on file at the European Heart House, headquarters of the ESC. Any changes in conflict of interest that arise during the writing period must be notified to the …
Eﬃcient Version Space Algorithms for “Human-in-the-Loop” Model Development

When active learning (AL) is applied to help the user develop a model on a large dataset through interactively presenting data instances for labeling, existing AL techniques can suﬀer from two main drawbacks: ﬁrst, they may require hundreds of labeled data instances in order to reach high accuracy; second, retrieving the next instance to label can be time consuming, making it incompatible with the interactive nature of the human exploration process. To address these issues, we introduce a novel version space based AL algorithm for kernel classiﬁers, which not only has strong theoretical guarantees on performance, but also allows for an eﬃcient implementation in time and space. In addition, by leveraging additional insights obtained in the user labeling process, we are able to factorize the version space to perform active learning in a set of subspaces, which further reduces the user labeling eﬀort. Evaluation results show that our algorithms signiﬁcantly outperform state-of-the-art version space algorithms, as well as a recent factorization-aware algorithm, for model development over large data sets.
A Network-Centric View on DASH in Wireless Multihop Networks

Video streaming in wireless multihop networks is a challenge due to different capabilities of end-user devices and changing network conditions. This challenge is addressed at the application layer with adaptive video streaming schemes like dynamic adaptive streaming over HTTP (DASH), which is widely applied by content providers. DASH copes with diverse end-user device capabilities by storing several representations of the same video such that DASH can offer a video in multiple qualities to users. Nevertheless, adjustments in DASH are solely taking place at the application layer. Especially in wireless multihop networks, adaptions on the lower layers are of particular importance. Therefore, we propose a novel application-aware cross-layer framework which adapts network support structures at the network layer, performs resource allocation at the medium access layer, switches between communication types at the physical layer and takes into account the properties and requirements of DASH at the application layer. Furthermore, we present a unified graph model, which takes into account the application layer, the network layer, the medium access layer and the physical layer jointly. We formulate a binary linear problem which chooses the optimal video representation for each user and finds the best combination of mechanisms on the lower three layers to optimally distribute the video content through the wireless multihop network. We show that our application- aware cross-layer framework which utilizes transitions leads to gains between 15-83 % compared to conventional approaches that do not switch between different mechanisms.
In-packet Bloom filter based data center networking with distributed OpenFlow controllers

This paper discusses a novel data center architecture based on load-balanced forwarding with in-packet Bloom filters enabled by two support services that distribute the directory and topology state of OpenFlow controller applications. By deploying an army of Rack Managers acting as OpenFlow controllers, the proposed architecture promises scalability, performance and fault-tolerance. We conjecture that packet forwarding itself may become a cloud internal service implemented by leveraging cloud application best practices such as low-latency, highly-available, key-value storage systems. Moreover, we contribute to demystifying the argument that the centralized controller model of OpenFlow networks is prone to a single point of failure and show that direct network controllers can be physically distributed, yielding a “sweet spot” between fully distributed and centralized networking paradigms.
Continuing medical education: Learning and change: implications for continuing medical education

Medical education, particularly continuing medical education (CME), has been greatly influenced by studies of adult learning. The observation that it is not teaching but learning that leads doctors to change their practice has resulted in a shift in perspective: rather than education being regarded as instruction, it is regarded as facilitation of learning. This paradigm shift has been based on research into how and why doctors change their practice and into the role of learning in that process. The direction of continuing medical education in North America and elsewhere has changed in response to the new perspective that has emerged from contemporary studies of learning and change. The nature of this new perspective is evident from a comparison of the common elements of CME in the 1980s with the approach that is now being used. Traditionally a CME programme was an educational event that applied appropriate resources and methods to fulfill set instructional objectives. Such programmes were often considered to be good if the information was valuable, the lecturer skilful, and the setting comfortable. Too often, however, there was little or no actual effect on medical practice, even though all three conditions were met. The critical difference in the 1990s is that it has increasingly been accepted that CME programmes are based—or should be—on the principle of teaching and education as a means of facilitating learning. This new approach has been adopted in response to studies on how and why doctors change their performance in clinical practice and the role of learning in that process. This article describes some of these models and sets out the key principles that have emerged for continuing medical education in the past decade. #### Summary points The purpose of continuing medical education is to facilitate change in clinical practice CME should be based on the natural processes …
Combining real and virtual components for visualizing ecological barriers

In this paper we organize the level of abstraction for reality using a model classification for displays on the Reality - Virtuality continuum. We illustrate our organization by a case study on three-dimensional models that represent ecological barriers to animal movement in the city of J(cid:228)rvenp(cid:228)(cid:228), Finland. Visualizing ecological barriers as a combination of real and virtual representations contributes to the research how these maps can be used as visualization tools for exploring digital geographic information. In addition to exploring they can be a valuable support in making decisions and solving spatial problems. As user groups for this kind of visualizations we identified the general public, the decision makers in land use planning, and the land use planners. We preliminary identify their specific preferences on spatial representations according to our classification.
Structural Gamification for Adaptation based on Learning Analytics

Gamification is widely used to increase learner motivation when using a learning environment. Recent research has shown that gamification should be adapted to learner profile, as learners do not respond at the same manner to game elements, and in some cases, learners can feel demotivated by certain game elements. We propose a structured approach for gamification, named structural gamification that allows adapting game elements without changing the educational content or altering the functioning of the learning environment. This approach relies on three levels of abstraction: motivational strategies (high-level tactics or techniques to encourage learners to participate in the learning process), game elements (specific implementations of motivational strategies), and game element instances (contextualised instantiations of game elements). Using this approach, adaptation can occur on any of the three levels, either by changing motivational strategy, or game element, or by modifying the game element instance directly. This allows for a more fine-grained adaptation that is based on three components: the learner profile, interaction traces with the learning environment, and contextual information. Our future work will be focused on the analysis of learner engagement during the learning activity through their interaction traces to allow for dynamic adaptive gamification.
Ras2 and Ras1 Protein Phosphorylation in Saccharomyces cerevisiae*

This work describes the phosphorylation ofSaccharomyces cerevisiae Ras proteins and explores the physiological role of the phosphorylation of Ras2 protein. Proteins expressed from activated alleles of RAS were less stable and less phosphorylated than proteins from cells expressing wild-type alleles of RAS. This difference in phosphorylation level did not result from increased signaling through the Ras-cAMP pathway or reflect the primarily GTP-bound nature of activated forms of Ras protein per se. In addition, phosphorylation of Ras protein was not dependent on proper localization of the Ras2 protein to the plasma membrane nor on the interaction of Ras2p with its exchange factor, Cdc25p. The preferred phosphorylation site on Ras2 protein was identified as serine 214. This site, when mutated to alanine, led to promiscuous phosphorylation of Ras2 protein on nearby serine residues. A decrease in phosphorylation may lead to a decrease in signaling through the Ras-cAMP pathway.
Isolating relevant component interactions with JINSI

When a component in a large system fails, developers encounter two problems: (1) reproducing the failure, and (2) investigating the causes of such a failure. Our JINSI tool lets developers capture and replay the interactions between a component and its environment, thus allowing for reproducing the failure at will. In addition, JINSI uses delta debugging to automatically isolate the subset of the interactions that is relevant for the failure. In a first study, JINSI has successfully isolated the relevant interaction of a JAVA component: "Out of the 32 interactions with the <VendingMachine> (BOB - wasn't sure about this one)component, seven interactions suffice to produce the failure.
Distributed Antenna Channels with Regenerative Relaying: Relay Selection and Asymptotic Capacity

Multiple-input-multiple-output (MIMO) techniques have been widely proposed as a means to improve capacity and reliability of wireless channels, and have become the most promising technology for next generation networks. However, their practical deployment in current wireless devices is severely affected by antenna correlation, which reduces their impact on performance. One approach to solve this limitation is relaying diversity. In relay channels, a set of wireless nodes aids a source-destination communication by relaying the source data, thus creating a distributed antenna array with uncorrelated path gains. In this paper, we study this multiple relay channel (MRC) following a decode-and-forward (D&F) strategy (i.e., regenerative forwarding), and derive its achievable rate under AWGN. A half-duplex constraint on relays is assumed, as well as distributed channel knowledge at both transmitter and receiver sides of the communication. For this channel, we obtain the optimum relay selection algorithm and the optimum power allocation within the network so that the transmission rate is maximized. Likewise, we bound the ergodic performance of the achievable rate and derive its asymptotic behavior in the number of relays. Results show that the achievable rate of regenerative MRC grows as the logarithm of the Lambert W function of the total number of relays, that is,. Therefore, D&F relaying, cannot achieve the capacity of actual MISO channels.
Measuring isolation across space and over time with new tools: Evidence from Californian metropolitan regions

Studies of segregation continue to explore analytic tools to engage with patterns of separation within cities. In recent work, scale has emerged as an important dimension of understanding segregation – simply put, separation is strongly affected by the scale which is used in the measurement process. Levels of segregation are also influenced by the time in which the analysis takes place. We outline an approach to separation which has four dimensions – (1) using bespoke neighborhoods – who do you meet at varying scales, (2) measuring the size of the change in separation over time, (3) estimating the rate of change in separation across space and time and (4) visualizing the change, mapping changing levels of contact. The themes are explored using data from the diverse, multi ethnic neighborhoods in Californian metropolitan areas. The result of a bespoke neighborhood approach to segregation provides a more complete demonstration of the pattern of ethnic segregation. We know that there are declining overall levels of segregation, but while levels are decreasing for Whites they are increasing for Hispanics and Asians but at different rates depending on local contexts. Viewing assimilation in a multi-scalar visual context expands our understanding of segregation and assimilation.
Journal of Graph Algorithms and Applications Computing and Drawing Isomorphic Subgraphs

The isomorphic subgraph problem is finding two disjoint subgraphs of a graph which coincide on at least k edges. The graph is partitioned into a subgraph, its copy, and a remainder. The problem resembles the NP-hard largest common subgraph problem, which searches copies of a graph in a pair of graphs. In this paper we show that the isomorphic sub-graph problem is NP-hard, even for restricted instances such as connected outerplanar graphs. Then we present two different heuristics for the computation of maximal connected isomorphic subgraphs. Both heuristics use weighting functions and have been tested on four independent test suites. Finally, we introduce a spring algorithm which preserves isomorphic sub-graphs and displays them as copies of each other. The drawing algorithm yields nice drawings and emphasizes the isomorphic subgraphs.
Sleep spindle deficits in antipsychotic-naïve early course schizophrenia and in non-psychotic first-degree relatives

Introduction: Chronic medicated patients with schizophrenia have marked reductions in sleep spindle activity and a correlated deficit in sleep-dependent memory consolidation. Using archival data, we investigated whether antipsychotic-naïve early course patients with schizophrenia and young non-psychotic first-degree relatives of patients with schizophrenia also show reduced sleep spindle activity and whether spindle activity correlates with cognitive function and symptoms. Method: Sleep spindles during Stage 2 sleep were compared in antipsychotic-naïve adults newly diagnosed with psychosis, young non-psychotic first-degree relatives of schizophrenia patients and two samples of healthy controls matched to the patients and relatives. The relations of spindle parameters with cognitive measures and symptom ratings were examined. Results: Early course schizophrenia patients showed significantly reduced spindle activity relative to healthy controls and to early course patients with other psychotic disorders. Relatives of schizophrenia patients also showed reduced spindle activity compared with controls. Reduced spindle activity correlated with measures of executive function in early course patients, positive symptoms in schizophrenia and IQ estimates across groups. Conclusions: Like chronic medicated schizophrenia patients, antipsychotic-naïve early course schizophrenia patients and young non-psychotic relatives of individuals with schizophrenia have reduced sleep spindle activity. These findings indicate that the spindle deficit is not an antipsychotic side-effect or a general feature of psychosis. Instead, the spindle deficit may predate the onset of schizophrenia, persist throughout its course and be an endophenotype that contributes to cognitive dysfunction.