Miracle-dz/UniFAR-FTUs huggingface dataset:
Management of Infections Due to Antibiotic-Resistant Streptococcus pneumoniae

SUMMARY Antibiotic-resistant strains of Streptococcus pneumoniae are becoming more prevalent throughout the world; this has resulted in modifications of treatment approaches. Management of bacterial meningitis has the greatest consensus. Strategies for treating other systemic infections such as pneumonia, bacteremia, and musculoskeletal infections are evolving, in part related to the availability of new antibiotics which are active in vitro against isolates resistant to penicillin and the extended-spectrum cephalosporins. However, there are currently very limited data related to the clinical efficacy of these new agents. The studies upon which current recommendations are based are reviewed. Otitis media represents the single most common infection due to S. pneumoniae. Recommendations for treatment of acute otitis media due to drug-resistant strains and the rationale for these recommendations are discussed.
An Evaluation of KVM for Use in Cloud Computing

In this paper we describe a virtual cluster based on the Kernel-based Virtual Machine (KVM) as an alternative to VMWare and Xen. Speci(cid:28)cally we show how the virtual cluster is built and tailored to (cid:28)t virtual organizations. The technique presented in this paper, known as the Virtual Organization Cluster Model, shows great potential for cloud computing. In our implementation, we used a minimalist installation of Slackware Linux 12 on 14 compute nodes, ensuring minimal host overhead. Our prototype Virtual Organization Cluster is composed of 28 virtual computes nodes, each running CentOS 5.1 with Condor, and MPI. Results of testing the prototype were encouraging, with the exception of network performance. Categories and Subject Descriptors: C.4 [Computer Systems Organization]: Performance of Systems(cid:22)Design studies; Performance attributes; C.2. 4 [Computer-Communication Networks]: Distributed Systems
Optimizing query rewrites for keyword-based advertising

We consider the problem of query rewrites in the context of pay-per-click search advertising. Given a three-layer graph consisting of queries, query rewrites, and the corresponding ads that can be served for the rewrites, we formulate a family of graph covering problems whose goals are to suggest a subset of ads with the maximum benefit by suggesting rewrites for a given query. We obtain constant-factor approximation algorithms for these covering problems, under two versions of constraints and a realistic notion of ad benefit. We perform experiments on real data and show that our algorithms are capable of outperforming a competitive baseline algorithm in terms of the benefit of the rewrites.
The Utility of Display Space in Keeping Track of Rapidly Changing Information

The results of 4 experiments suggest that the information available in graphic displays can have considerable performance consequences and that the kind of display invariants provided can interact with variables such as task difficulty, working-memory load, and the organization of information that is to be remembered. In Experiment 1, participants benefited from the presence of invariant spatial correlates of values to be remembered and showed superior performance (fewer errors and faster responses) relative to participants using a spatially impoverished display, regardless of information organization. Experiment 2 showed that the presence of unique verbal labels for items to be remembered aids keeping track performance. Experiments 3 and 4 provided further evidence that participants kept track of changing information best in conditions with spatial display invariants. These data are relevant to practitioners faced with designing soft monitoring displays - that is, displays involving multiple attributes of a single object in some cases (e.g., the temperature and pressure in a boiler) and one attribute of many objects in others (e.g., the current flow through several valves feeding the same system). Actual or potential applications of this research include improving computer displays designed to support complex, memoryintensive monitoring performance
Supernode partitioning

Supercompilers must reschedule computations defined by nested DO-loops in order to make an efficient use of supercomputer features (vector units, multiple elementary processors, cache memory, etc…). Many rescheduling techniques like loop interchange, loop strip-mining or rectangular partitioning have been described to speedup program execution. We present here a class of partitionings that encompasses previous techniques and provides enough flexibility to adapt code to multiprocessors with two levels of parallelism and two levels of memory.
Seeing-is-believing: using camera phones for human-verifiable authentication

Current mechanisms for authenticating communication between devices that share no prior context are inconvenient for ordinary users, without the assistance of a trusted authority. We present and analyze seeing-is-believing, a system that utilizes 2D barcodes and camera-telephones to implement a visual channel for authentication and demonstrative identification of devices. We apply this visual channel to several problems in computer security, including authenticated key exchange between devices that share no prior context, establishment of a trusted path for configuration of a TCG-compliant computing platform, and secure device configuration in the context of a smart home.
Automatic graph drawing and readability of diagrams

The state of the art in automatic graph drawing is reviewed, with special attention to the readability of information system diagrams. Existing results in the literature are compared, and a comprehensive algorithmic approach to the problem is proposed. The algorithm presented draws graphs on a grid and is suitable for both undirected graphs and mixed graphs that contain as subgraphs hierarchic structures. Several applications of GIOTTO, a graphic tool that embodies the aforementioned facility, are shown. >
Understanding data quality: Ensuring data quality by design in the rail industry

The railways worldwide are increasingly looking to the integration of their data resources coupled with advanced analytics to enhance traffic management, to provide new insights on the health of infrastructure assets, to provide soft linkages to other transport modes, and ultimately to enable them to better serve their customers. As in many industrial sectors, over the past decade the rail industry has been investing heavily in sensing technologies that record every aspect of the operation of the railway network. However, as any data scientist knows, it does not matter how good an algorithm is, if you put rubbish in, you get rubbish out; and as the traditional industry model of working with data only within the system that it was collected by becomes increasingly fragile, the industry is discovering that it knows less than it thought about the data it is gathering. When coupled with legacy data resources of unknown accuracy, such as design diagrams for assets that in many cases are decades old, the rail industry now faces a crisis in which its data may become essentially worthless due to a poor understanding of the quality of its data. This paper reports the findings of the first phase of a three-phase systematic review of literature about how data quality can be managed and evaluated in the rail domain. It begins by discussing why data quality matters in a rail context, before going on to define the quality, introduce and expand the concept of a data quality schema.
Histories of host shifts and cospeciation among free‐living parasitoids of Rhagoletis flies

Host shifts by specialist insects can lead to reproductive isolation between insect populations that use different hosts, promoting diversification. When both a phytophagous insect and its ancestrally associated parasitoid shift to the same novel host plant, they may cospeciate. However, because adult parasitoids are free living, they can also colonize novel host insects and diversify independent of their ancestral host insect. Although shifts of parasitoids to new insect hosts have been documented in ecological time, the long‐term importance of such shifts to parasitoid diversity has not been evaluated. We used a genus of flies with a history of speciation via host shifting (Rhagoletis [Diptera: Tephritidae]) and three associated hymenopteran parasitoid genera (Diachasma, Coptera and Utetes) to examine cophylogenetic relationships between parasitoids and their host insects. We inferred phylogenies of Rhagoletis, Diachasma, Coptera and Utetes and used distance‐based cophylogenetic methods (ParaFit and PACo) to assess congruence between fly and parasitoid trees. We used an event‐based method with a free‐living parasitoid cost model to reconstruct cophylogenetic histories of each parasitoid genus and Rhagoletis. We found that the current species diversity and host–parasitoid associations between the Rhagoletis flies and parasitoids are the primary result of ancient cospeciation events. Parasitoid shifts to ancestrally unrelated hosts primarily occur near the branch tips, suggesting that host shifts contribute to recent parasitoid species diversity but that these lineages may not persist over longer time periods. Our analyses also stress the importance of biologically informed cost models when investigating the coevolutionary histories of hosts and free‐living parasitoids.
On construction of rate-compatible low-density parity-check codes

This paper deals with the problem of devising an efficient framework for constructing rate-compatible low-density parity-check (RC-LDPC) codes. We present a deterministic framework for constructing a family of linear-time encodable RC-LDPC codes from a mother code using puncturing and extending. Application of the proposed construction to a type-II hybrid ARQ scheme with information block length k=1024 and code rates 8/19 to 8/10, using an optimized irregular mother code of rate 8/13, results in a throughput which is only about 0.7dB away from Shannon limit. This outperforms existing similar schemes based on turbo codes and LDPC codes by up to 0.5dB.
A sketching interface for modeling the internal structures of 3D shapes

This paper presents a sketch-based modeling system for creating objects that have internal structures. The user input consists of hand-drawn sketches and the system automatically generates a volumetric model. The volumetric representation solves any self-intersection problems and enables the creation of models with a variety of topological structures, such as a torus or a hollow sphere. To specify internal structures, our system allows the user to cut the model temporarily and apply modeling operations to the exposed face. In addition, the user can draw multiple contours in the Create or Sweep stages. Our system also allows automatic rotation of the model so that the user does not need to perform frequent manual rotations. Our system is much simpler to implement than a surface-oriented system because no complicated mesh editing code is required. We observed that novice users could quickly create a variety of objects using our systém.
A Hierarchical Semiparametric Regression Model for Combining HIV‐1 Phylogenetic Analyses Using Iterative Reweighting Algorithms

Summary Phylogenetic modeling is computationally challenging and most phylogeny models fit a single phylogeny to a single set of molecular sequences. Individual phylogenetic analyses are typically performed independently using publicly available software that fits a computationally intensive Bayesian model using Markov chain Monte Carlo (MCMC) simulation.
Thrust Control for Multirotor Aerial Vehicles

This paper presents a novel control algorithm to regulate the aerodynamic thrust produced by fixed-pitch rotors commonly used on small-scale electrically powered multirotor aerial vehicles. The proposed controller significantly improves the disturbance rejection and gust tolerance of rotor thrust control compared to state-of-the-art RPM (revolutions per minute) rotor control schemes. The thrust modeling approach taken is based on a model of aerodynamic power generated by a fixed-pitch rotor and computed in real time on the embedded electronic speed controllers using measurements of electrical power and rotor angular velocity. Static and dynamic flight tests were carried out in downdrafts and updrafts of varying strengths to quantify the resulting improvement in maintaining a desired thrust setpoint. The performance of the proposed approach in flight conditions is demonstrated by a path tracking experiment, where a quadrotor was flown through an artificial wind gust and the trajectory tracking error was measured. The proposed approach for thrust control demonstrably reduced the tracking error compared to classical RPM rotor control.
Decision-Making with Cross-Entropy for Self-Adaptation

Approaches to decision-making in self-adaptive systems are increasingly becoming more effective at managing the target system by taking into account more elements of the decision problem that were previously ignored. These approaches have to solve complex optimization problems at run time, and even though they have been shown to be suitable for different kinds of systems, their time complexity can make them excessively slow for systems that have a large adaptation-relevant state space, or that require a tight control loop driven by fast decisions. In this paper we present an approach to speed up complex proactive latency-aware self-adaptation decisions, using the cross-entropy (CE) method for combinatorial optimization. The CE method is an any-time algorithm based on random sampling from the solution space, and is not guaranteed to find an optimal solution. Nevertheless, our experiments using two very different systems show that in practice it finds solutions that are close to optimum even when its running time is restricted to a fraction of a second, attaining speedups of up to 40 times over the previous fastest solution approach.
Robust nonnegative matrix factorization for nonlinear unmixing of hyperspectral images

This paper introduces a robust linear model to describe hyperspectral data arising from the mixture of several pure spectral signatures. This new model not only generalizes the commonly used linear mixing model but also allows for possible nonlinear effects to be handled, relying on mild assumptions regarding these nonlinearities. Based on this model, a nonlinear unmixing procedure is proposed. The standard nonnegativity and sum-to-one constraints inherent to spectral unmixing are coupled with a group-sparse constraint imposed on the nonlinearity component. The resulting objective function is minimized using a multiplicative algorithm. Simulation results obtained on synthetic and real data show that the proposed strategy competes with state-of-the-art linear and nonlinear unmixing methods.
UTFRC - Utility-driven TCP-Friendly Rate Control for Multimedia Streams

This paper describes UTFRC - Utility-driven TCP-Friendly Rate Control, a congestion control mechanism more suitable for streaming layered scalable video streams incest-effort networks than TFRC \cite{2}. UTFRC relies on the original TFRC for achieving a stable throughput, but uses the throughput outputted by TFRC only as a guideline and shapes this throughput, on a coarser granularity scale, according to media characteristics of the video stream. This way, UTFRC is TCP-friendly when computing available bandwidth, but it is also media-sensitive (i.e., media-friendly). We present two versions of UTFRC, a simple one based only on of the video stream and an advanced one which takes into account also the client prefetching buffer value, but other media characteristics of the stream can also be considered. UTFRC improves the perceived quality of video streams in best-effort network conditions.
Evaluation of Cerebral Perfusion Parameters Measured by Perfusion CT in Chronic Cerebral Ischemia: Comparison With Xenon CT

Purpose The purpose of this work was to evaluate the usefulness of perfusion CT in the evaluation of patients with chronic cerebral ischemia by comparing it with xenon CT (Xe-CT). Method Cerebral blood flow (CBF) of perfusion CT (CBFper) and time to peak (TTP) were compared with the CBF of Xe-CT (CBFxe) in 18 patients. Cerebral blood volume (CBV) was compared with cerebral vascular reserve (CVR) in 10 of 18 patients who underwent pre- and postacetazolamide Xe-CT. Results CBFper and TTP demonstrated a high correlation with CBFxe in relative values by side-to-side comparisons (r = 0.743, p < 0.0001 and r = −0.760, p < 0.0001, respectively). There was a negative correlation between relative CBV and relative CVR (r = −0.637, p = 0.0025). Visually, territories with delayed TTP corresponded well to those of decreased CBFxe, but these territories tended to be larger in TTP maps. Conclusion Perfusion CT is a useful tool to evaluate chronic hemodynamic disturbance and can be an alternative method for those using acetazolamide challenge.
ODISSEA: A Peer-to-Peer Architecture for Scalable Web Search and Information Retrieval

We consider the problem of building a P2P-based search engine for massive document collections. We describe a prototype system called ODISSEA (Open DIStributed Search Engine Architecture) that is currently under development in our group. ODISSEA provides a highly distributed global indexing and query execution service that can be used for content residing inside or outside of a P2P network. ODISSEA is different from many other approaches to P2P search in that it assumes a two-tier search engine architecture and a global index structure distributed over the nodes of the system. We give an overview of the proposed system and discuss the basic design choices. Our main focus is on efficient query execution, and we discuss how recent work on topqueries in the database community can be applied in a highly distributed environment. We also give some preliminary simulation results on a real search engine log and a terabytesize web page collection that indicate good scalability for our approach. Project homepage: http://cis.poly.edu/westlab/odissea/. A preliminary version of this paper appeared at the International Workshop on the Web and Databases, June 2003. Contact author. Email: suel@poly.edu
Lognormal Infection Times of Online Information Spread

The infection times of individuals in online information spread such as the inter-arrival time of Twitter messages or the propagation time of news stories on a social media site can be explained through a convolution of lognormally distributed observation and reaction times of the individual participants. Experimental measurements support the lognormal shape of the individual contributing processes, and have resemblance to previously reported lognormal distributions of human behavior and contagious processes.
Identification of Parametric Underspread Linear Systems and Super-Resolution Radar

Identification of time-varying linear systems, which introduce both time-shifts (delays) and frequency-shifts (Doppler-shifts), is a central task in many engineering applications. This paper studies the problem of identification of underspread linear systems (ULSs), whose responses lie within a unit-area region in the delay-Doppler space, by probing them with a known input signal. It is shown that sufficiently-underspread parametric linear systems, described by a finite set of delays and Doppler-shifts, are identifiable from a single observation as long as the time-bandwidth product of the input signal is proportional to the square of the total number of delay-Doppler pairs in the system. In addition, an algorithm is developed that enables identification of parametric ULSs from an input train of pulses in polynomial time by exploiting recent results on sub-Nyquist sampling for time delay estimation and classical results on recovery of frequencies from a sum of complex exponentials. Finally, application of these results to super-resolution target detection using radar is discussed. Specifically, it is shown that the proposed procedure allows to distinguish between multiple targets with very close proximity in the delay-Doppler space, resulting in a resolution that substantially exceeds that of standard matched-filtering based techniques without introducing leakage effects inherent in recently proposed compressed sensing-based radar methods.
Transductive Visual-Semantic Embedding for Zero-shot Learning

Zero-shot learning (ZSL) aims to bridge the knowledge transfer via available semantic representations (e.g., attributes) between labeled source instances of seen classes and unlabelled target instances of unseen classes. Most existing ZSL approaches achieve this by learning a projection from the visual feature space to the semantic representation space based on the source instances, and directly applying it to the target instances. However, the intrinsic manifold structures residing in both semantic representations and visual features are not effectively incorporated into the learned projection function. Moreover, these methods may suffer from the inherent projection shift problem, due to the disjointness between seen and unseen classes. To overcome these drawbacks, we propose a novel framework termed transductive visual-semantic embedding (TVSE) for ZSL. In specific, TVSE first learns a latent embedding space to incorporate the manifold structures in both labeled source instances and unlabeled target instances under the transductive setting. In the learned space, each instance is viewed as a mixture of seen class scores. TVSE then effectively constructs the relational mapping between seen and unseen classes using the available semantic representations, and applies it to map the seen class scores of the target instances to their predictions of unseen classes. Extensive experiments on four benchmark datasets demonstrate that the proposed TVSE achieves competitive performance compared with the state-of-the-arts for zero-shot recognition and retrieval tasks.
Incentivizing spectrum sensing in database-driven dynamic spectrum sharing

The legacy concept of exclusion zones (EZs) is inept at enabling efficient utilization of fallow spectrum by secondary users (SUs), since legacy EZs are static and overly-conservative. The notion of a static EZ implies that it has to protect incumbent users (IUs) from the union of likely interference scenarios, leading to a worst-case, conservative solution. In this paper, we propose the concept of dynamic, multi-tier EZs, which takes advantage of participatory spectrum sensing carried out by SUs to support efficient database-driven spectrum sharing while protecting IUs against SU-induced aggregate interference. Specifically, the database directly incentivizes SUs to participate in spectrum sensing, which augments geolocation database by defining smaller EZs with dynamic boundaries and creating additional spectrum access opportunities for SUs. We propose an incentive mechanism based on a two-level game-theoretic model, in which the database conducts dynamic pricing in a first-level Stackelberg game in the presence of SUs who strategically contribute to spectrum sensing in a second-level stochastic game. The existence of an equilibrium solution is proven. According to our findings, the proposed incentive mechanism for the concept of dynamic, multi-tier EZs is effective to improve spectrum utilization efficiency while guaranteeing incumbent protection.