Application of K-tree to document clustering

Digital collections are growing exponentially in size as the information age takes a firm grip on all aspects of society. As a result Information Retrieval (IR) has become an increasingly important area of research. It promises to provide new and more effective ways for users to find information relevant to their search intentions. Document clustering is one of the many tools in the IR toolbox and is far from being perfected. It groups documents that share common features. This grouping allows a user to quickly identify relevant information. If these groups are misleading then valuable information can accidentally be ignored. There- fore, the study and analysis of the quality of document clustering is important. With more and more digital information available, the performance of these algorithms is also of interest. An algorithm with a time complexity of O(n2) can quickly become impractical when clustering a corpus containing millions of documents. Therefore, the investigation of algorithms and data structures to perform clustering in an efficient manner is vital to its success as an IR tool. Document classification is another tool frequently used in the IR field. It predicts categories of new documents based on an existing database of (doc- ument, category) pairs. Support Vector Machines (SVM) have been found to be effective when classifying text documents. As the algorithms for classifica- tion are both efficient and of high quality, the largest gains can be made from improvements to representation. Document representations are vital for both clustering and classification. Representations exploit the content and structure of documents. Dimensionality reduction can improve the effectiveness of existing representations in terms of quality and run-time performance. Research into these areas is another way to improve the efficiency and quality of clustering and classification results. Evaluating document clustering is a difficult task. Intrinsic measures of quality such as distortion only indicate how well an algorithm minimised a sim- ilarity function in a particular vector space. Intrinsic comparisons are inherently limited by the given representation and are not comparable between different representations. Extrinsic measures of quality compare a clustering solution to a “ground truth” solution. This allows comparison between different approaches. As the “ground truth” is created by humans it can suffer from the fact that not every human interprets a topic in the same manner. Whether a document belongs to a particular topic or not can be subjective.

Plantar enthesopathy : thickening of the enthesis is correlated with energy dissipation of the plantar fat pad during walking

Background: The enthesis of the plantar fascia is thought to play an important role in stress dissipation. However, the potential link between entheseal thickening characteristic of enthesopathy and the stress-dissipating properties of the intervening plantar fat pad have not been investigated. Purpose: This study was conducted to identify whether plantar fat pad mechanics explain variance in the thickness of the fascial enthesis in individuals with and without plantar enthesopathy. Study Design: Case-control study; Level of evidence, 3. Methods: The study population consisted of 9 patients with unilateral plantar enthesopathy and 9 asymptomatic, individually matched controls. The thickness of the enthesis of the symptomatic, asymptomatic, and a matched control limb was acquired using high-resolution ultrasound. The compressive strain of the plantar fat pad during walking was estimated from dynamic lateral radiographs acquired with a multifunction fluoroscopy unit. Peak compressive stress was simultaneously acquired via a pressure platform. Principal viscoelastic parameters were estimated from subsequent stress-strain curves. Results: The symptomatic fascial enthesis (6.7 ± 2.0 mm) was significantly thicker than the asymptomatic enthesis (4.2 ± 0.4 mm), which in turn was thicker than the enthesis (3.3 ± 0.4 mm) of control limbs (P < .05). There was no significant difference in the mean thickness, peak stress, peak strain, or secant modulus of the plantar fat pad between limbs. However, the energy dissipated by the fat pad during loading and unloading was significantly lower in the symptomatic limb (0.55 ± 0.17) when compared with asymptomatic (0.69 ± 0.13) and control (0.70 ± 0.09) limbs (P < .05). The sonographic thickness of the enthesis was correlated with the energy dissipation ratio of the plantar fat pad (r = .72, P < .05), but only in the symptomatic limb. Conclusion: The energy-dissipating properties of the plantar fat pad are associated with the sonograpic appearance of the enthesis in symptomatic limbs, providing a previously unidentified link between the mechanical behavior of the plantar fat pad and enthesopathy.

Improving the performance of facial expression recognition using dynamic, subtle and regional features

Human facial expression is a complex process characterized of dynamic, subtle and regional emotional features. State-of-the-art approaches on facial expression recognition (FER) have not fully utilized this kind of features to improve the recognition performance. This paper proposes an approach to overcome this limitation using patch-based ‘salient’ Gabor features. A set of 3D patches are extracted to represent the subtle and regional features, and then inputted into patch matching operations for capturing the dynamic features. Experimental results show a significant performance improvement of the proposed approach due to the use of the dynamic features. Performance comparison with pervious work also confirms that the proposed approach achieves the highest CRR reported to date on the JAFFE database and a top-level performance on the Cohn-Kanade (CK) database.

Towards persistent localization and mapping with a continuous appearance-based topology

Appearance-based localization can provide loop closure detection at vast scales regardless of accumulated metric error. However, the computation time and memory requirements of current appearance-based methods scale not only with the size of the environment but also with the operation time of the platform. Additionally, repeated visits to locations will develop multiple competing representations, which will reduce recall performance over time. These properties impose severe restrictions on long-term autonomy for mobile robots, as loop closure performance will inevitably degrade with increased operation time. In this paper we present a graphical extension to CAT-SLAM, a particle filter-based algorithm for appearance-based localization and mapping, to provide constant computation and memory requirements over time and minimal degradation of recall performance during repeated visits to locations. We demonstrate loop closure detection in a large urban environment with capped computation time and memory requirements and performance exceeding previous appearance-based methods by a factor of 2. We discuss the limitations of the algorithm with respect to environment size, appearance change over time and applications in topological planning and navigation for long-term robot operation.

Fibroblast growth factor receptor inhibition synergizes with paclitaxel and doxorubicin in endometrial cancer cells

OBJECTIVE: The fibroblast growth factor (FGF) family of signaling molecules has been associated with chemoresistance and poor prognosis in a number of cancer types, including lung, breast, ovarian, prostate, and head and neck carcinomas. Given the identification of activating mutations in the FGF receptor 2 (FGFR2) receptor tyrosine kinase in a subset of endometrial tumors, agents with activity against FGFRs are currently being tested in clinical trials for recurrent and progressive endometrial cancer. Here, we evaluated the effect of FGFR inhibition on the in vitro efficacy of chemotherapy in endometrial cancer cell lines. METHODS: Human endometrial cancer cell lines with wild-type or activating FGFR2 mutations were used to determine any synergism with concurrent use of the pan-FGFR inhibitor, PD173074, and the chemotherapeutics, doxorubicin and paclitaxel, on cell proliferation and apoptosis. RESULTS: FGFR2 mutation status did not alter sensitivity to either chemotherapeutic agent alone. The combination of PD173074 with paclitaxel or doxorubicin showed synergistic activity in the 3 FGFR2 mutant cell lines evaluated. In addition, although nonmutant cell lines were resistant to FGFR inhibition alone, the addition of PD173074 potentiated the cytostatic effect of paclitaxel and doxorubicin in a subset of FGFR2 wild-type endometrial cancer cell lines. CONCLUSIONS: Together these data suggest a potential therapeutic benefit to combining an FGFR inhibitor with standard chemotherapeutic agents in endometrial cancer therapy particularly in patients with FGFR2 mutation positive tumors.

Correction of step artefact associated with MRI scanning of long bones

3D models of long bones are being utilised for a number of fields including orthopaedic implant design. Accurate reconstruction of 3D models is of utmost importance to design accurate implants to allow achieving a good alignment between two bone fragments. Thus for this purpose, CT scanners are employed to acquire accurate bone data exposing an individual to a high amount of ionising radiation. Magnetic resonance imaging (MRI) has been shown to be a potential alternative to computed tomography (CT) for scanning of volunteers for 3D reconstruction of long bones, essentially avoiding the high radiation dose from CT. In MRI imaging of long bones, the artefacts due to random movements of the skeletal system create challenges for researchers as they generate inaccuracies in the 3D models generated by using data sets containing such artefacts. One of the defects that have been observed during an initial study is the lateral shift artefact occurring in the reconstructed 3D models. This artefact is believed to result from volunteers moving the leg during two successive scanning stages (the lower limb has to be scanned in at least five stages due to the limited scanning length of the scanner). As this artefact creates inaccuracies in the implants designed using these models, it needs to be corrected before the application of 3D models to implant design. Therefore, this study aimed to correct the lateral shift artefact using 3D modelling techniques. The femora of five ovine hind limbs were scanned with a 3T MRI scanner using a 3D vibe based protocol. The scanning was conducted in two halves, while maintaining a good overlap between them. A lateral shift was generated by moving the limb several millimetres between two scanning stages. The 3D models were reconstructed using a multi threshold segmentation method. The correction of the artefact was achieved by aligning the two halves using the robust iterative closest point (ICP) algorithm, with the help of the overlapping region between the two. The models with the corrected artefact were compared with the reference model generated by CT scanning of the same sample. The results indicate that the correction of the artefact was achieved with an average deviation of 0.32 ± 0.02 mm between the corrected model and the reference model. In comparison, the model obtained from a single MRI scan generated an average error of 0.25 ± 0.02 mm when compared with the reference model. An average deviation of 0.34 ± 0.04 mm was seen when the models generated after the table was moved were compared to the reference models; thus, the movement of the table is also a contributing factor to the motion artefacts.

Dynamic capabilities and performance : strategy, structure and environment

Dynamic capabilities are widely considered to incorporate those processes that enable organizations to sustain superior performance over time. In this paper, we argue theoretically and demonstrate empirically that these effects are contingent on organizational structure and the competitive intensity in the market. Results from partial least square structural equation modeling (PLS-SEM) analyses indicate that organic organizational structures facilitate the impact of dynamic capabilities on organizational performance. Furthermore, we find that the performance effects of dynamic capabilities are contingent on the competitive intensity faced by firms. Our findings demonstrate the performance effects of internal alignment between organizational structure and dynamic capabilities, as well as the external fit of dynamic capabilities with competitive intensity. We outline the advantages of PLS-SEM for modeling latent constructs, such as dynamic capabilities, and conclude with managerial implications.

Back to basics: Informing the public of co-morbid physical health problems in those with mental illness

Abstract OBJECTIVE: Those with mental illness are at increased risk of physical health problems. The current study aimed to examine the information available online to the Australian public about the increased risk and consequences of physical illness in those with mental health problems and the services available to address these co-morbidities. METHODS: A structured online search was conducted with the search engine Google Australia (www.google.com.au) using generic search terms 'mental health information Australia', 'mental illness information Australia', 'depression', 'anxiety', and 'psychosis'. The direct content of websites was examined for information on the physical co-morbidities of mental illness. All external links on high-profile websites [the first five websites retrieved under each search term (n = 25)] were examined for information pertaining to physical health. RESULTS: Only 4.2% of websites informing the public about mental health contained direct content information about the increased risk of physical co-morbidities. The Australian Government's Department of Health and Ageing site did not contain any information. Of the high-profile websites, 62% had external links to resources about physical health and 55% had recommendations or resources for physical health. Most recommendations were generic. CONCLUSIONS: Relative to the seriousness of this problem, there is a paucity of information available to the public about the increased physical health risks associated with mental illness. Improved public awareness is the starting point of addressing this health inequity.

Rapid yet accurate speech indexing using dynamic match lattice spotting

The support for typically out-of-vocabulary query terms such as names, acronyms, and foreign words is an important requirement of many speech indexing applications. However, to date many unrestricted vocabulary indexing systems have struggled to provide a balance between good detection rate and fast query speeds. This paper presents a fast and accurate unrestricted vocabulary speech indexing technique named Dynamic Match Lattice Spotting (DMLS). The proposed method augments the conventional lattice spotting technique with dynamic sequence matching, together with a number of other novel algorithmic enhancements, to obtain a system that is capable of searching hours of speech in seconds while maintaining excellent detection performance

Short-term traffic volume forecasting : a k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm

To enhance the performance of the k-nearest neighbors approach in forecasting short-term traffic volume, this paper proposed and tested a two-step approach with the ability of forecasting multiple steps. In selecting k-nearest neighbors, a time constraint window is introduced, and then local minima of the distances between the state vectors are ranked to avoid overlappings among candidates. Moreover, to control extreme values’ undesirable impact, a novel algorithm with attractive analytical features is developed based on the principle component. The enhanced KNN method has been evaluated using the field data, and our comparison analysis shows that it outperformed the competing algorithms in most cases.

A more efficient and effective heuristic algorithm for the MapReduce placement problem in cloud computing

The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation in cloud computing. From the computational point of view, the mappers/reducers placement problem is a generalization of the classical bin packing problem, which is NP-complete. Thus, in this paper we propose a new heuristic algorithm for the mappers/reducers placement problem in cloud computing and evaluate it by comparing with other several heuristics on solution quality and computation time by solving a set of test problems with various characteristics. The computational results show that our heuristic algorithm is much more efficient than the other heuristics. Also, we verify the effectiveness of our heuristic algorithm by comparing the mapper/reducer placement for a benchmark problem generated by our heuristic algorithm with a conventional mapper/reducer placement. The comparison results show that the computation using our mapper/reducer placement is much cheaper while still satisfying the computation deadline.

Mirror neuron activation is associated with facial emotion processing

Theoretical accounts suggest that mirror neurons play a crucial role in social cognition. The current study used transcranial-magnetic stimulation (TMS) to investigate the association between mirror neuron activation and facialemotion processing, a fundamental aspect of social cognition, among healthy adults (n = 20). Facial emotion processing of static (but not dynamic) images correlated significantly with an enhanced motor response, proposed to reflect mirror neuron activation. These correlations did not appear to reflect general facial processing or pattern recognition, and provide support to current theoretical accounts linking the mirror neuron system to aspects of social cognition. We discuss the mechanism by which mirror neurons might facilitate facial emotion recognition.

A model predictive approach for community battery energy storage system optimization

This paper presents an efficient algorithm for optimizing the operation of battery storage in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses wavelet neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble load profile. A diurnal charging profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing forecasts in load.

Predictive motion planning for AUVs subject to strong time-varying currents and forecasting uncertainties

This paper presents a novel path planning method for minimizing the energy consumption of an autonomous underwater vehicle subjected to time varying ocean disturbances and forecast model uncertainty. The algorithm determines 4-Dimensional path candidates using Nonlinear Robust Model Predictive Control (NRMPC) and solutions optimised using A*-like algorithms. Vehicle performance limits are incorporated into the algorithm with disturbances represented as spatial and temporally varying ocean currents with a bounded uncertainty in their predictions. The proposed algorithm is demonstrated through simulations using a 4-Dimensional, spatially distributed time-series predictive ocean current model. Results show the combined NRMPC and A* approach is capable of generating energy-efficient paths which are resistant to both dynamic disturbances and ocean model uncertainty.

One practical algorithm for both stochastic and adversarial bandits

We present an algorithm for multiarmed bandits that achieves almost optimal performance in both stochastic and adversarial regimes without prior knowledge about the nature of the environment. Our algorithm is based on augmentation of the EXP3 algorithm with a new control lever in the form of exploration parameters that are tailored individually for each arm. The algorithm simultaneously applies the “old” control lever, the learning rate, to control the regret in the adversarial regime and the new control lever to detect and exploit gaps between the arm losses. This secures problem-dependent “logarithmic” regret when gaps are present without compromising on the worst-case performance guarantee in the adversarial regime. We show that the algorithm can exploit both the usual expected gaps between the arm losses in the stochastic regime and deterministic gaps between the arm losses in the adversarial regime. The algorithm retains “logarithmic” regret guarantee in the stochastic regime even when some observations are contaminated by an adversary, as long as on average the contamination does not reduce the gap by more than a half. Our results for the stochastic regime are supported by experimental validation.