Data Provenance Inference in Logic Programming: Reducing Effort of Instance-driven Debugging

Data provenance allows scientists in different domains validating their models and algorithms to find out anomalies and unexpected behaviors. In previous works, we described on-the-fly interpretation of (Python) scripts to build workflow provenance graph automatically and then infer fine-grained provenance information based on the workflow provenance graph and the availability of data. To broaden the scope of our approach and demonstrate its viability, in this paper we extend it beyond procedural languages, to be used for purely declarative languages such as logic programming under the stable model semantics. For experiments and validation, we use the Answer Set Programming solver oClingo, which makes it possible to formulate and solve stream reasoning problems in a purely declarative fashion. We demonstrate how the benefits of the provenance inference over the explicit provenance still holds in a declarative setting, and we briefly discuss the potential impact for declarative programming, in particular for instance-driven debugging of the model in declarative problem solving

Fatores associados à ocorrência de cesáreas no municipio de Florianópolis em 2006.

Trabalho de Conclusão de Curso - Universidade Federal de Santa Catarina. Curso de Medicina. Departamento de Saúde Pública

Pseudoscalar top-Higgs coupling: Exploration of CP\mathrm{CP}-odd observables to resolve the sign ambiguity

We present a collection of $\mathrm{CP}$-odd observables for the process $pp\to t\,\left(\rightarrow b {\ell}^+ \nu_{\ell}\right) \bar{t}\,\left(\rightarrow \bar{b} {\ell}^-{\bar{\nu}}_{\ell}\right)\,H$ that are linearly dependent on the scalar ($\kappa_t$) and pseudoscalar ($\tilde{\kappa}_t$) top-Higgs coupling and hence sensitive to the corresponding relative sign. The proposed observables are based on triple product (TP) correlations that we extract from the expression for the differential cross section in terms of the spin vectors of the top and antitop quarks. In order to explore other possibilities, we progressively modify these TPs, first by combining them, and then by replacing the spin vectors by the lepton momenta or the $t$ and $\bar{t}$ momenta by their visible parts. We generate Monte Carlo data sets for several benchmark scenarios, including the Standard Model ($\kappa_t=1$, $\tilde{\kappa}_t=0$) and two scenarios with mixed $\mathrm{CP}$ properties ($\kappa_t=1$, $\tilde{\kappa}_t=\pm 1$). Assuming an integrated luminosity that is consistent with that envisioned for the High Luminosity Large Hadron Collider, using Monte Carlo-truth and taking into account only statistical uncertainties, we find that the most promising observable can disentangle the "$\mathrm{CP}$-mixed" scenarios with an effective separation of $\sim 19\sigma$. In the case of observables that do not require the reconstruction of the $t$ and $\bar{t}$ momenta, the power of discrimination is up to $\sim 13\sigma$ for the same number of events. We also show that the most promising observables can still disentangle the $\mathrm{CP}$-mixed scenarios when the number of events is reduced to values consistent with expectations for the Large Hadron Collider in the near term.Comment: 28 pages, 7 figures. Published versio

Injecting knowledge into deep neural networks

Much of the recent hype around artificial intelligence stems from recent advances in Neural Networks, currently the most widely used algorithm that succeeded where other approaches failed for decades. Neural Networks today can leverage large amounts of data to be trained to perform hard tasks such as recognising objects in an image or translating languages. The process they use to perform these tasks is equivalent to a complex pattern recognition procedure which uses some clever mathematics to expose the underlying structure in a body of data. Humans think in a more conceptual way. We build a mental model of our world. We have the ability to extract relationships such as causality between elements involved in learning to perform a task, and the ability to use background knowledge when learning. One of the key challenges in making more human-like artificial intelligence is incorporating these properties of natural learning into the neural network paradigm. Designing such a system which could utilise background knowledge in learning a new task would enable the networks to be trained on much less data, opening up a new world of opportunities for Neural Networks to be applied to tasks which were previously not feasible due to the scarce availability of data. In identifying these challenges, we have been inspired by recent seminal papers within the Deep Learning community, which call for new approaches to enhance deep representations with (common-sense) background knowledge. This is considered as a key enabler to significantly improve the ability of machines to learn new tasks faster and in a domain invariant way. The main practical challenges involved in this research are finding how best to extract and format relevant knowledge from a trained network, and finding how best to inject this knowledge into an untrained network

Alien Registration- Corey, Mileo W. (Wade, Aroostook County)

https://digitalmaine.com/alien_docs/32521/thumbnail.jp

When Change Is the Best Option: Method for the Evaluation of the Impact of Change of Use in Houses of Worship

Houses of worship constitute valuable landmarks in the built environment; they represent the power of faith and mankind, in the form of durable buildings designed to stand the test of time. Nevertheless, houses of worship are becoming redundant as a result of endogenous factors, such as maintenance or lack of funding, and exogenous factors, often related to suburbanization and demographic changes. As a consequence, many houses of worship are suffering a process of decay, which calls for adaptive reuse as a necessary response. While the adaptive reuse of houses of worship is becoming a common practice, current practices do not prioritize the comprehensive preservation of the character-defining features. Specifically, traditional preservation approaches do not take into consideration the relevance of the sensory perception of the space as a determinant in the preservation of the character and significance of the place. This thesis seeks to provide a useful tool for preservation and design professionals in the decision-making process of adaptive reuse of houses of worship. In order to do so, this thesis: (1) identifies the character-defining elements of houses of worship and their source, (2) analyzes current practices in adaptive reuse of houses of worship, and (3) proposes an evaluation method that, when applied in early stages of the reuse process, assesses the impact that the change of use may cause in the character of houses of worship from a physical and experiential points of view

Polyphenols as Modulator of Oxidative Stress in Cancer Disease: New Therapeutic Strategies

Cancer onset and progression have been linked to oxidative stress by increasing DNA mutations or inducing DNA damage, genome instability, and cell proliferation and therefore antioxidant agents could interfere with carcinogenesis. It is well known that conventional radio-/chemotherapies influence tumour outcome through ROS modulation. Since these antitumour treatments have important side effects, the challenge is to develop new anticancer therapeutic strategies more effective and less toxic for patients. To this purpose, many natural polyphenols have emerged as very promising anticancer bioactive compounds. Beside their well-known antioxidant activities, several polyphenols target epigenetic processes involved in cancer development through the modulation of oxidative stress. An alternative strategy to the cytotoxic treatment is an approach leading to cytostasis through the induction of therapy-induced senescence. Many anticancer polyphenols cause cellular growth arrest through the induction of a ROS-dependent premature senescence and are considered promising antitumour therapeutic tools. Furthermore, one of the most innovative and interesting topics is the evaluation of efficacy of prooxidant therapies on cancer stem cells (CSCs). Several ROS inducers-polyphenols can impact CSCs metabolisms and self-renewal related pathways. Natural polyphenol roles, mainly in chemoprevention and cancer therapies, are described and discussed in the light of the current literature data

Probing sensitivity to charged scalars through partial differential widths: τ→Kππντ\tau\rightarrow K\pi\pi\nu_{\tau} decays

We define and test $\mathrm{CP}$-even and $\mathrm{CP}$-odd partial differential widths for the process $\tau\rightarrow K\pi\pi\nu_{\tau}$ assuming that an intermediate heavy charged scalar contributes to the decay amplitude. Adopting a model-independent approach, we use a Monte Carlo simulation in order to study the number of events needed to recover information on the new physics from these observables. Our analysis of the $\mathrm{CP}$-odd observables indicates that the magnitude of $f_H \eta_P$, which is related to the new-physics contribution, can be recovered with an uncertainty smaller than $3$ for $3\times 10^6$ events. This number of events would also allow one to retrieve certain parameters appearing in the SM amplitude at the percent level. In addition, we discuss the possibility of using the proposed observables to study specific models involving two Higgs doublets, such as the aligned two-Higgs-doublet model (A2HDM). This analysis is undertaken within the context of the upcoming Super B-factories, which are expected to provide a considerably larger number of events than that which was supplied by the B-factories. Moreover, a similar set of observables could be employed to study other decay modes such as $\tau\rightarrow\pi\pi\pi\nu_{\tau},\,\tau\rightarrow KK\pi\nu_{\tau}$ and $\tau\rightarrow KKK\nu_{\tau}$.Comment: 29 pages, 4 figures, published versio