Sensitivity of the Eisenberg-Noe clearing vector to individual interbank liabilities

We quantify the sensitivity of the Eisenberg-Noe clearing vector to estimation errors in the bilateral liabilities of a financial system in a stylized setting. The interbank liabilities matrix is a crucial input to the computation of the clearing vector. However, in practice central bankers and regulators must often estimate this matrix because complete information on bilateral liabilities is rarely available. As a result, the clearing vector may suffer from estimation errors in the liabilities matrix. We quantify the clearing vector's sensitivity to such estimation errors and show that its directional derivatives are, like the clearing vector itself, solutions of fixed point equations. We describe estimation errors utilizing a basis for the space of matrices representing permissible perturbations and derive analytical solutions to the maximal deviations of the Eisenberg-Noe clearing vector. This allows us to compute upper bounds for the worst case perturbations of the clearing vector in our simple setting. Moreover, we quantify the probability of observing clearing vector deviations of a certain magnitude, for uniformly or normally distributed errors in the relative liability matrix. Applying our methodology to a dataset of European banks, we find that perturbations to the relative liabilities can result in economically sizeable differences that could lead to an underestimation of the risk of contagion. Our results are a first step towards allowing regulators to quantify errors in their simulations.Comment: 37 page

Spin-resolved electron waiting times in a quantum dot spin valve

We study the electronic waiting time distributions (WTDs) in a non-interacting quantum dot spin valve by varying spin polarization and the noncollinear angle between the magnetizations of the leads using scattering matrix approach. Since the quantum dot spin valve involves two channels (spin up and down) in both the incoming and outgoing channels, we study three different kinds of WTDs, which are two-channel WTD, spin-resolved single-channel WTD and cross-channel WTD. We analyze the behaviors of WTDs in short times, correlated with the current behaviors for different spin polarizations and noncollinear angles. Cross-channel WTD reflects the correlation between two spin channels and can be used to characterize the spin transfer torque process. We study the influence of the earlier detection on the subsequent detection from the perspective of cross-channel WTD, and define the influence degree quantity as the cumulative absolute difference between cross-channel WTDs and first passage time distributions to quantitatively characterize the spin flip process. The influence degree shows a similar behavior with spin transfer torque and can be a new pathway to characterize spin correlation in spintronics system.Comment: 9 pages, 7 figure

SARS-related Perceptions in Hong Kong

To understand different aspects of community responses related to severe acute respiratory syndrome (SARS), 2 population-based, random telephone surveys were conducted in June 2003 and January 2004 in Hong Kong. More than 70% of respondents would avoid visiting hospitals or mainland China to avoid contracting SARS. Most respondents believed that SARS could be transmitted through droplets, fomites, sewage, and animals. More than 90% believed that public health measures were efficacious means of prevention; 40.4% believed that SARS would resurge in Hong Kong; and ≈70% would then wear masks in public places. High percentages of respondents felt helpless, horrified, and apprehensive because of SARS. Approximately 16% showed signs of posttraumatic symptoms, and ≈40% perceived increased stress in family or work settings. The general public in Hong Kong has been very vigilant about SARS but needs to be more psychologically prepared to face a resurgence of the epidemic

Multimodal Machine Learning for Automated ICD Coding

This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.Comment: Machine Learning for Healthcare 201

Perceptions on site-specific advanced practice roles for radiation therapists in Singapore : a single centre study

Perception of the radiation oncologists (ROs) and radiation therapists (RTTs) on site-specific advanced practice (SSAP) roles for RTTs, the establishment of SSAP in radiotherapy and the possible implication on current services in Singapore were assessed. Opinions of ROs and RTTs on management support, driving forces, restraints and implication upon successful establishment of SSAP were obtained. Main findings include strong RO’s support for SSAP development and RTTs' requisition for fair opportunities on role development. Other potential benefits include RTTs' career advancement, job satisfaction and retention. Enhancement of inter-professional relationship, service quality and patient satisfaction is anticipated with greater communication and collaboration

Comparison of Survival Patterns of Northern and Southern Genotypes of the North American Tick \u3cem\u3eIxodes scapularis\u3c/em\u3e (Acari: Ixodidae) under Northern and Southern Conditions

Background: Several investigators have reported genetic differences between northern and southern populations of Ixodes scapularis in North America, as well as differences in patterns of disease transmission. Ecological and behavioral correlates of these genetic differences, which might have implications for disease transmission, have not been reported. We compared survival of northern with that of southern genotypes under both northern and southern environmental conditions in laboratory trials. Methods: Subadult I. scapularis from laboratory colonies that originated from adults collected from deer from several sites in the northeastern, north central, and southern U.S. were exposed to controlled conditions in environmental chambers. Northern and southern genotypes were exposed to light:dark and temperature conditions of northern and southern sites with controlled relative humidities, and mortality through time was recorded. Results: Ticks from different geographical locations differed in survival patterns, with larvae from Wisconsin surviving longer than larvae from Massachusetts, South Carolina or Georgia, when held under the same conditions. In another experiment, larvae from Florida survived longer than larvae from Michigan. Therefore, survival patterns of regional genotypes did not follow a simple north–south gradient. The most consistent result was that larvae from all locations generally survived longer under northern conditions than under southern conditions. Conclusions: Our results suggest that conditions in southern North America are less hospitable than in the north to populations of I. scapularis. Southern conditions might have resulted in ecological or behavioral adaptations that contribute to the relative rarity of I. scapularis borne diseases, such as Lyme borreliosis, in the southern compared to the northern United States

Privacy Risks of Securing Machine Learning Models against Adversarial Examples

The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and the privacy domain have typically been considered separately. It is thus unclear whether the defense methods in one domain will have any unexpected impact on the other domain. In this paper, we take a step towards resolving this limitation by combining the two domains. In particular, we measure the success of membership inference attacks against six state-of-the-art defense methods that mitigate the risk of adversarial examples (i.e., evasion attacks). Membership inference attacks determine whether or not an individual data record has been part of a model's training set. The accuracy of such attacks reflects the information leakage of training algorithms about individual members of the training set. Adversarial defense methods against adversarial examples influence the model's decision boundaries such that model predictions remain unchanged for a small area around each input. However, this objective is optimized on training data. Thus, individual data records in the training set have a significant influence on robust models. This makes the models more vulnerable to inference attacks. To perform the membership inference attacks, we leverage the existing inference methods that exploit model predictions. We also propose two new inference methods that exploit structural properties of robust models on adversarially perturbed data. Our experimental evaluation demonstrates that compared with the natural training (undefended) approach, adversarial defense methods can indeed increase the target model's risk against membership inference attacks.Comment: ACM CCS 2019, code is available at https://github.com/inspire-group/privacy-vs-robustnes

Key Articles, Guidelines, and Consensus Papers Relative to the Treatment of Dyslipidemias—2005

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90074/1/phco.26.7.939.pd