Effects of clozapine and haloperidol on baseline levels of vacuous jaw movements in aged rats

BACKGROUND: IgE-mediated hypersensitivity to latex proteins has become a significant clinical problem over the last decade. Nursing and medical staff are at risk because of their occupational exposure to latex. AIMS: To determine the prevalence of type I hypersensitivity to latex allergens in the nursing staff of an Australian hospital. METHODS: A questionnaire which asked about symptoms associated with the use of latex gloves was completed by 140 nurses working in the Alfred Hospital (72 in general medical wards, 68 in intensive care units). Skin prick tests with eluates of five different types of latex glove as well as common aeroallergens (rye pollen and house dust mite) and banana extract were performed. RESULTS: Thirty-one nurses (22%) were skin prick test positive to at least one of the five latex glove eluates. All of these nurses were atopic, having positive skin prick tests to rye pollen or house dust mite. Symptoms of local dryness, itch and erythema associated with glove use were reported by more than half the study group, but not more frequently by those who were skin prick test positive to latex. Urticaria associated with glove use was reported more frequently by those with positive latex skin prick tests (13% vs 4%, p = 0.05). Eighty-seven per cent of the nurses who were latex skin test positive were also positive to banana extract. CONCLUSIONS: IgE-mediated hypersensitivity to latex is common in nurses working in an Australian hospital. Glove associated symptoms were frequently reported, but in most cases the symptoms were more typical of irritant or contact dermatitis rather than type I hypersensitivity reactions. However, the extent of subclinical sensitisation to latex found in this study suggests that symptomatic latex allergy is likely to emerge as an increasing problem for nursing staff in this country.     

Accelerating SPARQL queries by exploiting hash-based locality and adaptive partitioning

State-of-the-art distributed RDF systems partition data across multiple computer nodes (workers). Some systems perform cheap hash partitioning, which may result in expensive query evaluation. Others try to minimize inter-node communication, which requires an expensive data preprocessing phase, leading to a high startup cost. Apriori knowledge of the query workload has also been used to create partitions, which, however, are static and do not adapt to workload changes. In this paper, we propose AdPart, a distributed RDF system, which addresses the shortcomings of previous work. First, AdPart applies lightweight partitioning on the initial data, which distributes triples by hashing on their subjects; this renders its startup overhead low. At the same time, the locality-aware query optimizer of AdPart takes full advantage of the partitioning to (1) support the fully parallel processing of join patterns on subjects and (2) minimize data communication for general queries by applying hash distribution of intermediate results instead of broadcasting, wherever possible. Second, AdPart monitors the data access patterns and dynamically redistributes and replicates the instances of the most frequent ones among workers. As a result, the communication cost for future queries is drastically reduced or even eliminated. To control replication, AdPart implements an eviction policy for the redistributed patterns. Our experiments with synthetic and real data verify that AdPart: (1) starts faster than all existing systems; (2) processes thousands of queries before other systems become online; and (3) gracefully adapts to the query load, being able to evaluate queries on billion-scale RDF data in subseconds.     

Controllers with diagnostic capabilities. A neural network implementation

Designing controllers with diagnostic capabilities is important as in a feedback control system, detection and isolation of failures is generally affected by the particular control law used. Therefore, a common approach to control and failure diagnosis problems has significant merit. Controllers capable of performing failure diagnosis have additional diagnostic outputs to detect and isolate sensor and actuator faults. A linear such controller is usually called a four-parameter controller. Neural networks have proved to be a very powerful tool in the control systems area, where they have been used in the modelling and control of dynamical systems. In this paper, a neural network model of a controller with diagnostic capabilities (CDC) is presented for the first time. This nonlinear neural controller is trained to operate as a traditional controller, while at the same time it provides reproduction of the failure occurring either at the actuator or the sensor. The cases of actuator and sensor failure are studied independently. The validity of the results is verified by extensive simulations.A version of this paper under the title The Four-Parameter Controller. A Neural Network Implementation was presented at the IEEE Mediterranean Symposium on New Directions in Control Theory and Applications, Chania, Crete, Greece, June 21–23, 1993.     

Benchmarking and learning garbage collection delays for resource-restricted graphical user interfaces

Tablets, smartphones, and wearables have limited resources. Applications on these devices employ a graphical user interface (GUI) for interaction with users. Language runtimes for GUIs employ dynamic memory management using garbage collection (GC). However, GC policies and algorithms are designed for data centers and cloud computing, but they are not necessarily ideal for resource-constrained embedded devices. In this article, we present GUI GC, a JavaFX GUI benchmark, which we use to compare the performance of the four GC policies of the Eclipse OpenJ9 Java runtime on a resource-constrained environment. Overall, our experiments suggest that the default policy Gencon registered significantly lower execution times than its counterparts. The region-based policy, Balanced, did not fully utilize blocking times; thus, using GUI GC, we conducted experiments with explicit GC invocations that measured significant improvements of up to 13.22% when multiple CPUs were available. Furthermore, we created a second version of GUI GC that expands on the number of controllable load-stressing dimensions; we conducted a large number of randomly configured experiments to quantify the performance effect that each knob has. Finally, we analyzed our dataset to derive suitable knob configurations for desired runtime, GC, and hardware stress levels.     

SABRE: a Sensitive Attribute Bucketization and REdistribution framework for t-closeness

Today, the publication of microdata poses a privacy threat: anonymous personal records can be re-identified using third data sources. Past research has tried to develop a concept of privacy guarantee that an anonymized data set should satisfy before publication, culminating in the notion of t-closeness. To satisfy t-closeness, the records in a data set need to be grouped into Equivalence Classes (ECs), such that each EC contains records of indistinguishable quasi-identifier values, and its local distribution of sensitive attribute (SA) values conforms to the global table distribution of SA values. However, despite this progress, previous research has not offered an anonymization algorithm tailored for t-closeness. In this paper, we cover this gap with SABRE, a SA Bucketization and REdistribution framework for t-closeness. SABRE first greedily partitions a table into buckets of similar SA values and then redistributes the tuples of each bucket into dynamically determined ECs. This approach is facilitated by a property of the Earth Mover’s Distance (EMD) that we employ as a measure of distribution closeness: If the tuples in an EC are picked proportionally to the sizes of the buckets they hail from, then the EMD of that EC is tightly upper-bounded using localized upper bounds derived for each bucket. We prove that if the t-closeness constraint is properly obeyed during partitioning, then it is obeyed by the derived ECs too. We develop two instantiations of SABRE and extend it to a streaming environment. Our extensive experimental evaluation demonstrates that SABRE achieves information quality superior to schemes that merely applied algorithms tailored for other models to t-closeness, and can be much faster as well.     

Approximate and exact hybrid algorithms for private nearest-neighbor queries with database protection

Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. To protect user privacy, it is important not to disclose exact user coordinates to un-trusted entities that provide location-based services. Currently, there are two main approaches to protect the location privacy of users: (i) hiding locations inside cloaking regions (CRs) and (ii) encrypting location data using private information retrieval (PIR) protocols. Previous work focused on finding good trade-offs between privacy and performance of user protection techniques, but disregarded the important issue of protecting the POI dataset D. For instance, location cloaking requires large-sized CRs, leading to excessive disclosure of POIs (O(|D|) in the worst case). PIR, on the other hand, reduces this bound to \(O(\sqrt{|D|})\), but at the expense of high processing and communication overhead. We propose hybrid, two-step approaches for private location-based queries which provide protection for both the users and the database. In the first step, user locations are generalized to coarse-grained CRs which provide strong privacy. Next, a PIR protocol is applied with respect to the obtained query CR. To protect against excessive disclosure of POI locations, we devise two cryptographic protocols that privately evaluate whether a point is enclosed inside a rectangular region or a convex polygon. We also introduce algorithms to efficiently support PIR on dynamic POI sub-sets. We provide solutions for both approximate and exact NN queries. In the approximate case, our method discloses O(1) POI, orders of magnitude fewer than CR- or PIR-based techniques. For the exact case, we obtain optimal disclosure of a single POI, although with slightly higher computational overhead. Experimental results show that the hybrid approaches are scalable in practice, and outperform the pure-PIR approach in terms of computational and communication overhead.     

A market-based approach to managing the risk of peer-to-peer transactions

Market-based principles can be used to manage the risk of distributed peer-to-peer transactions. This is demonstrated by Ptrim, a system that builds a transaction default market on top of a main transaction processing system, within which peers offer to underwrite the transaction risk for a slight increase in the transaction cost. The insurance cost, determined through market-based mechanisms, is a way of identifying untrustworthy peers and perilous transactions. The risk of the transactions is contained, and at the same time members of the peer-to-peer network capitalise on their market knowledge by profiting as transaction insurers. We evaluated the approach through trials with the deployed Ptrim prototype, as well as composite experiments involving real online transaction data and real subjects participating in the transaction default market. We examine the efficacy of our approach both from a theoretical and an experimental perspective. Our findings suggest that the Ptrim market layer functions in an efficient manner, and is able to support the transaction processing system through the insurance offers it produces, thus acting as an effective means of reducing the risk of peer-to-peer transactions. In our conclusions we discuss how a system like Ptrim assimilates properties of real world markets, and its potential exposure and possible countermeasures to events such as those witnessed in the recent global financial turmoil.     

Correspondence of projected 3‐D points and lines using a continuous GRASP

The field of computer vision has experienced rapid growth over the past 50 years. Many computer vision problems have been solved using theory and ideas from algebraic projective geometry. In this paper, we look at a previously unsolved problem from object recognition, namely object recognition when the correspondences between the object and image data are not known a priori. We formulate this problem as a mixed‐integer non‐linear optimization problem in terms of the unknown projection relating the object and image, as well as the unknown assignments of object points and lines to those in the image. The global optimum of this problem recovers the relationship between the object points and lines with those in the image. When certain assumptions are enforced on the allowable projections mapping the object into the image, a proof is provided which permits one to solve the optimization problem via a simple decomposition. We illustrate this decomposition approach on some example scenarios.