183 research outputs found

    Recognition of partially occluded threat objects using the annealed Hopefield network

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    Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features

    Flight simulator for hypersonic vehicle and a study of NASP handling qualities

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    The research goal of the Human-Machine Systems Engineering Group was to study the existing handling quality studies in aircraft with sonic to supersonic speeds and power in order to understand information requirements needed for a hypersonic vehicle flight simulator. This goal falls within the NASA task statements: (1) develop flight simulator for hypersonic vehicle; (2) study NASP handling qualities; and (3) study effects of flexibility on handling qualities and on control system performance. Following the above statement of work, the group has developed three research strategies. These are: (1) to study existing handling quality studies and the associated aircraft and develop flight simulation data characterization; (2) to develop a profile for flight simulation data acquisition based on objective statement no. 1 above; and (3) to develop a simulator and an embedded expert system platform which can be used in handling quality experiments for hypersonic aircraft/flight simulation training

    Instantonic approach to triple well potential

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    By using a usual instanton method we obtain the energy splitting due to quantum tunneling through the triple well barrier. It is shown that the term related to the midpoint of the energy splitting in propagator is quite different from that of double well case, in that it is proportional to the algebraic average of the frequencies of the left and central wells.Comment: Revtex, 11 pages, Included one eps figur

    Sensemaking Approaches to Improving Processes of Emergency Medical Trauma Centers

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    Abstract Trauma Center (TC) operations at hospitals are conducted as part of very complex environments. TCs, which are focused on life-saving and resuscitative actions in the treatment of trauma patients, engage many individuals in the solving of multifaceted and on-going ill-structured problems. The solutions to these problems are comprised of multiple human experts" resources that must collaborate to be effective in reducing complexity and providing information for systemic approaches to acute injury care. Understanding an emergency situation requires interdisciplinary and collaborative judgments, surgical decision-making in complex scenarios, hands on practical experience, and insight into difficult trauma situations. This paper addressed the application of collaborative sensemaking (CS) techniques to improve the decision making processes in TCs complex environments

    Attack of Many Eavesdroppers via Optimal Strategy in Quantum Cryptography

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    We examine a situation that nn eavesdroppers attack the Bennett-Brassard cryptographic protocol via their own optimal and symmetric strategies. Information gain and mutual information with sender for each eavesdropper are explicitly derived. The receiver's error rate for the case of arbitrary nn eavesdroppers can be derived using a recursive relation. Although first eavesdropper can get mutual information without disturbance arising due to other eavesdroppers, subsequent eavesdropping generally increases the receiver's error rate. Other eavesdroppers cannot gain information on the input signal sufficiently. As a result, the information each eavesdropper gains becomes less than optimal one.Comment: 17 pages, 8 figure

    Deep learning computer-aided detection system for pneumonia in febrile neutropenia patients: a diagnostic cohort study

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    Abstract Background Diagnosis of pneumonia is critical in managing patients with febrile neutropenia (FN), however, chest X-ray (CXR) has limited performance in the detection of pneumonia. We aimed to evaluate the performance of a deep learning-based computer-aided detection (CAD) system in pneumonia detection in the CXRs of consecutive FN patients and investigated whether CAD could improve radiologists diagnostic performance when used as a second reader. Methods CXRs of patients with FN (a body temperature ≥ 38.3°C, or a sustained body temperature ≥ 38.0°C for an hour; absolute neutrophil count < 500/mm3) obtained between January and December 2017 were consecutively included, from a single tertiary referral hospital. Reference standards for the diagnosis of pneumonia were defined by consensus of two thoracic radiologists after reviewing medical records and CXRs. A commercialized, deep learning-based CAD system was retrospectively applied to detect pulmonary infiltrates on CXRs. For comparing performance, five radiologists independently interpreted CXRs initially without the CAD results (radiologist-alone interpretation), followed by the interpretation with CAD. The sensitivities and specificities for detection of pneumonia were compared between radiologist-alone interpretation and interpretation with CAD. The standalone performance of the CAD was also evaluated, using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Moreover, sensitivity and specificity of standalone CAD were compared with those of radiologist-alone interpretation. Results Among 525 CXRs from 413 patients (52.3% men; median age 59years), pneumonia was diagnosed in 128 (24.4%) CXRs. In the interpretation with CAD, average sensitivity of radiologists was significantly improved (75.4% to 79.4%, P = 0.003) while their specificity remained similar (75.4% to 76.8%, P = 0.101), compared to radiologist-alone interpretation. The CAD exhibited AUC, sensitivity, and specificity of 0.895, 88.3%, and 68.3%, respectively. The standalone CAD exhibited higher sensitivity (86.6% vs. 75.2%, P < 0.001) and lower specificity (64.8% vs. 75.4%, P < 0.001) compared to radiologist-alone interpretation. Conclusions In patients with FN, the deep learning-based CAD system exhibited radiologist-level performance in detecting pneumonia on CXRs and enhanced radiologists performance

    Homozygote CRIM1 variant is associated with thiopurine-induced neutropenia in leukemic patients with both wildtype NUDT15 and TPMT

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    Abstract Background NUDT15 and TPMT variants are strong genetic determinants of thiopurine-induced hematological toxicity that results in therapeutic failure in pediatric acute lymphoblastic leukemia (ALL). However, many patients with both wild-type (WT) NUDT15 and TPMT still suffer from thiopurine toxicity and therapeutic failure. Methods Whole-exome sequencing was done for discovery (N = 244) and replication (N = 76) cohorts. Age- and sex-adjusted multiple regression analyses of both WT patients were performed to identify (p < 0.01, N = 188 for discovery) and validate (p < 0.05, N = 52 for replication) candidate variants for the tolerated last-cycle 6-mercaptopurine (6-MP) dose intensity percentage (DIP). Both independent and additive effects of the candidate variants on well-known NUDT15 and TPMT were evaluated by multigene prediction models. Results Among the 12 candidate variants from the discovery phase, the rs3821169 variant of the gene encoding Cysteine-Rich Transmembrane BMP Regulator 1 (CRIM1) was successfully replicated (p < 0.05). It showed high interethnic variability with an impressively high allele frequency in East Asians (T = 0.255) compared to Africans (0.001), Americans (0.02), Europeans (0.009), and South Asians (0.05). Homozygote carriers of the CRIM1 rs3821169 variant (N = 12, 5%) showed significantly lower last-cycle 6-MP DIPs in the discovery, replication, and combined cohorts (p = 0.025, 0.013, and 0.001, respectively). The traditional two-gene model (NUDT15 and TPMT) for predicting 6-MP DIP < 25% was outperformed by the three-gene model that included CRIM1, in terms of the area under the receiver operating characteristic curve (0.734 vs. 0.665), prediction accuracy (0.759 vs. 0.756), sensitivity (0.636 vs. 0.523), positive predictive value (0.315 vs. 0.288), and negative predictive value (0.931 vs. 0.913). Conclusions The CRIM1 rs3821169 variant is suggested to be an independent and/or additive genetic determinant of thiopurine toxicity beyond NUDT15 and TPMT in pediatric ALL
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