Stochastic dynamics of model proteins on a directed graph

A method for reconstructing the energy landscape of simple polypeptidic chains is described. We show that we can construct an equivalent representation of the energy landscape by a suitable directed graph. Its topological and dynamical features are shown to yield an effective estimate of the time scales associated with the folding and with the equilibration processes. This conclusion is drawn by comparing molecular dynamics simulations at constant temperature with the dynamics on the graph, defined by a temperature dependent Markov process. The main advantage of the graph representation is that its dynamics can be naturally renormalized by collecting nodes into "hubs", while redefining their connectivity. We show that both topological and dynamical properties are preserved by the renormalization procedure. Moreover, we obtain clear indications that the heteropolymers exhibit common topological properties, at variance with the homopolymer, whose peculiar graph structure stems from its spatial homogeneity. In order to obtain a clear distinction between a "fast folder" and a "slow folder" in the heteropolymers one has to look at kinetic features of the directed graph. We find that the average time needed to the fast folder for reaching its native configuration is two orders of magnitude smaller than its equilibration time, while for the bad folder these time scales are comparable. Accordingly, we can conclude that the strategy described in this paper can be successfully applied also to more realistic models, by studying their renormalized dynamics on the directed graph, rather than performing lengthy molecular dynamics simulations.Comment: 15 pages, 12 figure

Awareness of the Importance of and Adherence to Patients’ Rights Among Physicians and Nurses in Oman: An analytical cross-sectional study across different levels of healthcare

Objectives: This study aimed to determine the extent to which physicians and nurses in Oman were aware of the importance of and adhere to patients’ rights and whether this differed according to role, nationality, position and institutional healthcare level. Methods: This analytical cross-sectional study was carried out between December 2015 and March 2016 at various governmental healthcare institutions in Oman. A self-administered questionnaire was distributed to 1,385 practitioners at all healthcare levels. Results: A total of 1,213 healthcare practitioners (response rate: 87.58%) completed the survey, of which 685 (56.47%) were nurses and 528 (43.53%) were physicians. Overall, awareness of the importance of patients’ rights was high (91.51%), although adherence to these rights in practice was low (63.81%). The right of the patient to be informed was considered least important and was least adhered to (81.2% and 56.39%). Nationality, role and institutional level were significantly associated with awareness (P = 0.002, 0.024 and 0.034, respectively). Non-Omani staff were significantly more likely than Omani staff to be aware of (odds ratio [OR] = 1.696; P = 0.032) and adhere to (OR = 2.769; P <0.001) patient rights. Furthermore, tertiary care staff were twice as likely as primary care staff to perceive the importance of patient rights (OR = 2.076; P = 0.019). While physicians were more likely than nurses to be aware of the importance of patient rights, this difference was not significant (OR = 1.516; P = 0.126). Conclusion: These findings may help inform measures to enhance awareness of and adherence to patients’ rights in Oman.Keywords: Medical Ethics; Patient Rights; Awareness; Adherence; Physicians; Nurses; Oman

Deterministic seismic hazard assessment for Sultanate of Oman

The Sultanate of Oman forms the southeastern part of the Arabian plate, which is surrounded by relatively high active tectonic zones. Studies of seismic risk assessment in Oman have been an important on-going socioeconomic concern. Using the results of the seismic hazard assessment to improve building design and construction is an effective way to reduce the seismic risk. In the current study, seismic hazard assessment for the Sultanate of Oman is performed through the deterministic approach with particular attention on the uncertainty analysis applying a recently developed method. The input data set contains a defined seismotectonic model consisting of 26 seismic zones, maximum magnitudes, and 6 alternative ground motion prediction equations that were used in four different tectonic environments: obduction zone earthquake (Zagros fold thrust belt), subduction zone earthquakes (Makran subduction zones), normal and strike-slip transform earthquakes (Owen and Gulf of Aden zones), and stable craton seismicity (Arabian stable craton). This input data set yielded a total of 76 scenarios at each point of interest. A 10 % probability that any of the 76 scenarios may exceed the largest median ground acceleration is selected. The deterministic seismic hazards in terms of PGA, 5 % damped spectral acceleration at 0.1, 0.2, 1.0 and 2.0 s are performed at 254 selected points. The ground motion was calculated at the 50th and 84th percentile levels for selected probability of exceeding the median value. The largest ground motion in the Sultanate of Oman is observed in the northeastern part of the country.Oman Ministerial Cabinet (Project 22409017

Visual Reasoning with Multi-hop Feature Modulation

Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue. For such tasks, one successful approach is to condition image-based convolutional network computation on language via Feature-wise Linear Modulation (FiLM) layers, i.e., per-channel scaling and shifting. We propose to generate the parameters of FiLM layers going up the hierarchy of a convolutional network in a multi-hop fashion rather than all at once, as in prior work. By alternating between attending to the language input and generating FiLM layer parameters, this approach is better able to scale to settings with longer input sequences such as dialogue. We demonstrate that multi-hop FiLM generation achieves state-of-the-art for the short input sequence task ReferIt --- on-par with single-hop FiLM generation --- while also significantly outperforming prior state-of-the-art and single-hop FiLM generation on the GuessWhat?! visual dialogue task.Comment: In Proc of ECCV 201

Probabilistic seismic hazard maps for the sultanate of Oman

This study presents the results of the first probabilistic seismic hazard assessment (PSHA) in the framework of logic tree for Oman. The earthquake catalogue was homogenized, declustered, and used to define seismotectonic source model that characterizes the seismicity of Oman. Two seismic source models were used in the current study; the first consists of 26 seismic source zones, while the second is expressing the alternative view that seismicity is uniform along the entire Makran and Zagros zones. The recurrence parameters for all the seismogenic zones were determined using the doubly bounded exponential distribution except the zones of Makran, which were modelled using the characteristic distribution. Maximum earthquakes were determined and the horizontal ground accelerations in terms of geometric mean were calculated using ground-motion prediction relationships developed based upon seismic data obtained from active tectonic environments similar to those surrounding Oman. The alternative seismotectonic source models, maximum magnitude, and ground-motion prediction relationships were weighted and used to account for the epistemic uncertainty. Hazard maps at rock sites were produced for 5 % damped spectral acceleration (SA) values at 0.1, 0.2, 0.3, 1.0 and 2.0 s spectral periods as well as peak ground acceleration (PGA) for return periods of 475 and 2,475 years. The highest hazard is found in Khasab City with maximum SA at 0.2 s spectral period reaching 243 and 397 cm/s[superscript 2] for return periods 475 and 2,475 years, respectively. The sensitivity analysis reveals that the choice of seismic source model and the ground-motion prediction equation influences the results most.Oman Ministerial Cabinet (project number 22409017

Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization

Adversarial self-play in two-player games has delivered impressive results when used with reinforcement learning algorithms that combine deep neural networks and tree search. Algorithms like AlphaZero and Expert Iteration learn tabula-rasa, producing highly informative training data on the fly. However, the self-play training strategy is not directly applicable to single-player games. Recently, several practically important combinatorial optimisation problems, such as the travelling salesman problem and the bin packing problem, have been reformulated as reinforcement learning problems, increasing the importance of enabling the benefits of self-play beyond two-player games. We present the Ranked Reward (R2) algorithm which accomplishes this by ranking the rewards obtained by a single agent over multiple games to create a relative performance metric. Results from applying the R2 algorithm to instances of a two-dimensional and three-dimensional bin packing problems show that it outperforms generic Monte Carlo tree search, heuristic algorithms and integer programming solvers. We also present an analysis of the ranked reward mechanism, in particular, the effects of problem instances with varying difficulty and different ranking thresholds

On non-local variational problems with lack of compactness related to non-linear optics

We give a simple proof of existence of solutions of the dispersion manage- ment and diffraction management equations for zero average dispersion, respectively diffraction. These solutions are found as maximizers of non-linear and non-local vari- ational problems which are invariant under a large non-compact group. Our proof of existence of maximizer is rather direct and avoids the use of Lions' concentration compactness argument or Ekeland's variational principle.Comment: 30 page