A simulated study of implicit feedback models

In this paper we report on a study of implicit feedback models for unobtrusively tracking the information needs of searchers. Such models use relevance information gathered from searcher interaction and can be a potential substitute for explicit relevance feedback. We introduce a variety of implicit feedback models designed to enhance an Information Retrieval (IR) system's representation of searchers' information needs. To benchmark their performance we use a simulation-centric evaluation methodology that measures how well each model learns relevance and improves search effectiveness. The results show that a heuristic-based binary voting model and one based on Jeffrey's rule of conditioning [5] outperform the other models under investigation

Writer Identification Using Inexpensive Signal Processing Techniques

We propose to use novel and classical audio and text signal-processing and otherwise techniques for "inexpensive" fast writer identification tasks of scanned hand-written documents "visually". The "inexpensive" refers to the efficiency of the identification process in terms of CPU cycles while preserving decent accuracy for preliminary identification. This is a comparative study of multiple algorithm combinations in a pattern recognition pipeline implemented in Java around an open-source Modular Audio Recognition Framework (MARF) that can do a lot more beyond audio. We present our preliminary experimental findings in such an identification task. We simulate "visual" identification by "looking" at the hand-written document as a whole rather than trying to extract fine-grained features out of it prior classification.Comment: 9 pages; 1 figure; presented at CISSE'09 at http://conference.cisse2009.org/proceedings.aspx ; includes the the application source code; based on MARF described in arXiv:0905.123

Handwritten digit recognition by bio-inspired hierarchical networks

The human brain processes information showing learning and prediction abilities but the underlying neuronal mechanisms still remain unknown. Recently, many studies prove that neuronal networks are able of both generalizations and associations of sensory inputs. In this paper, following a set of neurophysiological evidences, we propose a learning framework with a strong biological plausibility that mimics prominent functions of cortical circuitries. We developed the Inductive Conceptual Network (ICN), that is a hierarchical bio-inspired network, able to learn invariant patterns by Variable-order Markov Models implemented in its nodes. The outputs of the top-most node of ICN hierarchy, representing the highest input generalization, allow for automatic classification of inputs. We found that the ICN clusterized MNIST images with an error of 5.73% and USPS images with an error of 12.56%

Quantum computing for pattern classification

It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of pattern classification. We introduce a quantum pattern classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database.Comment: 14 pages, 3 figures, presented at the 13th Pacific Rim International Conference on Artificial Intelligenc

Pulse Shape Analysis and Identification of Multipoint Events in a Large-Volume Proportional Counter in an Experimental Search for 2K Capture Kr-78

A pulse shape analysis algorithm and a method for suppressing the noise component of signals from a large copper proportional counter in the experiment aimed at searching for 2K capture of Kr-78 are described. These signals correspond to a compound event with different numbers of charge clusters due to from primary ionization is formed by these signals. A technique for separating single- and multipoint events and determining the charge in individual clusters is presented. Using the Daubechies wavelets in multiresolutional signal analysis, it is possible to increase the sensitivity and the resolution in extraction of multipoint events in the detector by a factor of 3-4.Comment: 10 pages, 8 figures. submitted to Instruments and Experimental Techniques; ISSN 0020/441

Functional performance after complex endovascular aortic repair: a single-center retrospective cohort study

Purpose Complex endovascular aortic repair (EVAR) procedures provide a treatment option for patients with aortic aneurysms involving visceral branches. Good technical results and short-term outcomes have been reported. Whether complex EVAR provides acceptable functional outcomes is not clear. The current study aims to describe postoperative functional outcomes in complex EVAR patients-an older and relatively frail patient group. Materials and Methods A single-center retrospective cohort study was performed, using data from a computerized database of consecutive patients who underwent complex EVAR in the Leiden University Medical Center (LUMC, The Netherlands) between July 2013 and September 2020. As of May 2017, patients scheduled for complex EVAR were referred to a geriatric care pathway to determine (Instrumental) Activities of Daily Living ((I)ADL) scores at baseline and, if informed consent was given, after 12 months. For the total patient group, adverse functional performance outcomes were: discharge to a nursing home and 12-month mortality. For the patients included in geriatric follow-up, the additional outcome was the incidence of functional decline (defined by a >= 2 point increase in (I)ADL-score) at 12-month follow-up Results Eighty-two patients underwent complex EVAR, of which 68 (82.9%) were male. Mean age was 73.3 years (SD=6.3). Within 30 days postsurgery, 6 patients (7.3%) died. Mortality within 12 months for the total patient group was 14.6% (n=12). After surgery, no patients had to be discharged to a nursing home. Fifteen patients (18.3%) were discharged to a rehabilitation center. Twenty-three patients gave informed consent and were included in geriatric follow-up. Five patients (21.7%) presented functional decline 12 months postsurgery and 4 patients had died (17.4%) by that time. This means that 39.1% of the patients in the care pathway suffered an adverse outcome. Conclusion To our knowledge, this is the only study that examined functional performance after complex EVAR, using a prospectively maintained database. No patients were newly discharged to a nursing home and functional performance results at 12 months are promising. Future multidisciplinary research should focus on determining which patients are most prone to deterioration of function, so that efforts can be directed toward preventing postoperative functional decline.Cardiovascular Aspects of Radiolog