Towards Robust and Reproducible Active Learning Using Neural Networks

Active learning (AL) is a promising ML paradigm that has the potential to parse through large unlabeled data and help reduce annotation cost in domains where labeling entire data can be prohibitive. Recently proposed neural network based AL methods use different heuristics to accomplish this goal. In this study, we show that recent AL methods offer a gain over random baseline under a brittle combination of experimental conditions. We demonstrate that such marginal gains vanish when experimental factors are changed, leading to reproducibility issues and suggesting that AL methods lack robustness. We also observe that with a properly tuned model, which employs recently proposed regularization techniques, the performance significantly improves for all AL methods including the random sampling baseline, and performance differences among the AL methods become negligible. Based on these observations, we suggest a set of experiments that are critical to assess the true effectiveness of an AL method. To facilitate these experiments we also present an open source toolkit. We believe our findings and recommendations will help advance reproducible research in robust AL using neural networks

Synthese angulär cyclisierter Chinon-Antibiotika vom Typ der Angucycline und Pradimicine

von Nasir HayatPaderborn, Univ.-GH, Diss., 199

Housing Associations and Relationship Marketing: Customers, Communication and Relationships

This dissertation focuses on relationships within the Housing Association market sector. It tries to answer the questions who are the real customers in this market and do key stakeholders in the business take priority over these customers. Also what sort of communication and relationship management do Housing Associations carry out and are there any issues that need to be taken into account in this area. The dissertation uses research carried out on a number of Housing Associations to draw its conclusions

Gamma Radiolytic Degradation of 4-Chlorophenol Determination of Degraded Products with HPLC and GC-MS

Contamination by chlorophenols of surface water and groundwater is an emerging issue in environmental science and engineering. After their usage as pesticide, herbicide and disinfectant, these organic compounds subsequently enter the aquatic environment through a number of routes. Some of the chlorophenols are slightly biodegradable, while others are more persistent and mobile in the aquatic environment especially chlorophenols. Gamma radiolytic degradation is one of advance oxidation process that has been thought to be one of the promising treatments to deal with this problem. This radiolytic study was carried out in methanolic 4-CP (4-chlorophenol) samples. Among several factors effecting radiolytic degradation of 4-CP, dose and concentration are important that were evaluated under atmospheric conditions. A degradation yield (G –value) for 4-CP of 0.38 and 1.35 was achieved in 20 and 100mg/dm3 solution. It was observed that degradation yield decreases with increasing 4-CP concentration. Gamma radiolysis produce free radicals in solvent which further react with 4-CP molecules to generate different products. The identification of degradation products was proposed using HPLC and GC-MS

MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images

Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of "paired" modalities, data in healthcare is often collected asynchronously. Hence, requiring the presence of all modalities for a given sample is not realistic for clinical tasks and significantly limits the size of the dataset during training. In this paper, we propose MedFuse, a conceptually simple yet promising LSTM-based fusion module that can accommodate uni-modal as well as multi-modal input. We evaluate the fusion method and introduce new benchmark results for in-hospital mortality prediction and phenotype classification, using clinical time-series data in the MIMIC-IV dataset and corresponding chest X-ray images in MIMIC-CXR. Compared to more complex multi-modal fusion strategies, MedFuse provides a performance improvement by a large margin on the fully paired test set. It also remains robust across the partially paired test set containing samples with missing chest X-ray images. We release our code for reproducibility and to enable the evaluation of competing models in the future

Comparison of percutaneous nephrostomy and double j stent in early normalization of renal function tests in patients with obstructive uropathy due to urolithiasis

Objective:  To compare the mean normalization period of serum levels of urea and creatinine after placement of PCN tube or a DJ stent as emergency management for obstructive uropathy due to urolithiasis. Methodology: A randomized controlled trial study is conducted in the Institute of Kidney Diseases, Hayatabad Medical Complex Peshawar from March 2018 - March 2019. The total sample of 416 was divided into two groups by the lottery method. Group A comprising of 208 patients who underwent Percutaneous Nephrostomy (PCN) and Group B Comprising of 208 in who underwent Double J Stenting for the relief of the obstructive uropathy respectively.  Serum levels of urea and creatinine were recorded at 24, 96 and 144 hours post-operatively. Results: The mean age of Group A 35.6 ± 8.4 years and the Mean age in Group B was 38.2± 10.4 years. The majority (76.6%) participants were male, including 70% were from PCN group and 79% were from DJ group. The time taken for normalization of serum urea level was 97.654 hours (4.068 days) and 106.453 hours (4.435 days) in the PCN and DJ stenting groups respectively. The normalization time of serum creatinine level was 95.4375 hours (3.98 days) and 104.8125 hours (4.36 days) in the patients undergoing PCN and DJ stenting respectively. The differences of normalization time in both groups were not statistically significant with p-values of 0.064 and 0.061 for normalization of serum urea and creatinine levels respectively. Conclusion: Both the PCN and DJ stenting methods were equally effective in stone management in obstructive uropathy patients for normalization of elevated serum urea and creatinine levels

Stochastic Analysis of Cascading-Failure Dynamics in Power Grids

A scalable and analytically tractable probabilistic model for the cascading failure dynamics in power grids is constructed while retaining key physical attributes and operating characteristics of the power grid. The approach is based upon extracting a reduced abstraction of large-scale power grids using a small number of aggregate state variables while modeling the system dynamics using a continuous-time Markov chain. The aggregate state variables represent critical power-grid attributes, which have been shown, from prior simulation-based and historical-data-based analysis, to strongly influence the cascading behavior. The transition rates among states are formulated in terms of certain parameters that capture grid\u27s operating characteristics comprising loading level, error in transmission-capacity estimation, and constraints in performing load shedding. The model allows the prediction of the evolution of blackout probability in time. Moreover, the asymptotic analysis of the blackout probability enables the calculation of the probability mass function of the blackout size. A key benefit of the model is that it enables the characterization of the severity of cascading failures in terms of the operating characteristics of the power grid.

SDN Testbed for Evaluation of Large Exo-Atmospheric EMP Attacks

Large-scale nuclear electromagnetic pulse (EMP) attacks and natural disasters can cause extensive network failures across wide geographic regions. Although operational networks are designed to handle most single or dual faults, recent efforts have also focused on more capable multi-failure disaster recovery schemes. Concurrently, advances in software-defined networking (SDN) technologies have delivered highly-adaptable frameworks for implementing new and improved service provisioning and recovery paradigms in real-world settings. Hence this study leverages these new innovations to develop a robust disaster recovery (counter-EMP) framework for large backbone networks. Detailed findings from an experimental testbed study are also presented

Efficient Interconnectivity Among Networks Under Security Constraint

Interconnectivity among networks is essential for enhancing communication capabilities of networks such as the expansion of geographical range, higher data rate, etc. However, interconnections may initiate vulnerability (e.g., cyber attacks) to a secure network due to introducing gateways and opportunities for security attacks such as malware, which may propagate from the less secure network. In this paper, the interconnectivity among subnetworks is maximized under the constraint of security risk. The dynamics of propagation of security risk is modeled by the evil-rain influence model and the SIR (Susceptible-Infected-Recovered) epidemic model. Through extensive numerical simulations using different network topologies and interconnection patterns, it is shown that the efficiency of interconnectivity increases nonlinearly and vulnerability increases linearly with the number of interconnections among subnetworks. Finally, parametric models are proposed to find the number of interconnections for any given efficiency of interconnectivity and vulnerability of the secure network