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Register a new userOutside Computation with Superior Functions
حساب خارجي مع وظائف متفوقة
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نظرا لأن خوارزمية عامة لحساب فعال للقيم الخارجية بموجب إطار الوظائف الفائقة التي يقترحها نوث (1977) من شأنها أن تسفر عن خوارزمية زمنية فرعية لسعة، تنتهك فرضية الوقت الأسي القوي (Seth).
We show that a general algorithm for efficient computation of outside values under the minimum of superior functions framework proposed by Knuth (1977) would yield a sub-exponential time algorithm for SAT, violating the Strong Exponential Time Hypothesis (SETH).
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Abstract We give a general framework for inference in spanning tree models. We propose unified algorithms for the important cases of first-order expectations and second-order expectations in edge-factored, non-projective spanning-tree models. Our alg
orithms exploit a fundamental connection between gradients and expectations, which allows us to derive efficient algorithms. These algorithms are easy to implement with or without automatic differentiation software. We motivate the development of our framework with several cautionary tales of previous research, which has developed numerous inefficient algorithms for computing expectations and their gradients. We demonstrate how our framework efficiently computes several quantities with known algorithms, including the expected attachment score, entropy, and generalized expectation criteria. As a bonus, we give algorithms for quantities that are missing in the literature, including the KL divergence. In all cases, our approach matches the efficiency of existing algorithms and, in several cases, reduces the runtime complexity by a factor of the sentence length. We validate the implementation of our framework through runtime experiments. We find our algorithms are up to 15 and 9 times faster than previous algorithms for computing the Shannon entropy and the gradient of the generalized expectation objective, respectively.
The effective differential cross-section of the studied interaction was calculated within and outside the standard model. radiative corrections resulting from the introduction of scalar (S), pseudo scalar (P) and tensor (T) components into amplitude
of interaction were calculated in two deferent ways. It show that it is only related with square of coupling constants, . The study discussed the durability of the standard model to expand the amplitude of elastic scattering of neutrino on the electron, using the experimental values of the newly obtained coupling constants from the TEXONO, LSND experiments.
The objectives of this study were to assess the changes in pulmonary function after surgical correction of deviated nasal septum for nasal obstruction. Methods: A total of 81 patients with DNS with the complaints of nasal obstruction underwent septop
lasty were involved in this before-after prospective study. Demographic data, clinical and physical examination including anterior and posterior rhinoscopy was performed. Pre- and post-surgery (after 1 and 3 month) spirometry was done on these patients. FVC, FEV1, PEFR, FEF25-75% and the ratio of FEV1 to FVC were the parameters measured. And the results were analyzed using various statistical tools for difference in spirometry findings depending on age group, gender, smoking and duration of disease. Results: Postoperative all PFT values, except for FEV1/FVC% value, were significantly higher than the preoperative values. Postoperative FEV1/FVC% value was significantly lower than preoperative value. The improvement was more in younger age groups, and it was more in nasal obstruction of shorter duration. There was no significant difference in improvement among males and females or among smoker and non-smokers. Conclusion: These findings suggest that septoplasty has a positive effect on pulmonary functions improvement in patients with nasal obstruction due to deviated nasal septum. And this improvement may be affected by age and the duration of obstruction, but not related to gender or smoking.
Biases and artifacts in training data can cause unwelcome behavior in text classifiers (such as shallow pattern matching), leading to lack of generalizability. One solution to this problem is to include users in the loop and leverage their feedback t
o improve models. We propose a novel explanatory debugging pipeline called HILDIF, enabling humans to improve deep text classifiers using influence functions as an explanation method. We experiment on the Natural Language Inference (NLI) task, showing that HILDIF can effectively alleviate artifact problems in fine-tuned BERT models and result in increased model generalizability.
The aim of this study was to study the plant species diversity at Al-Kahaf protected
area in A-Sheikh Badr region, Tartous governorate, in terms of functional
characteristics of the recorded plant species, its uses. The results can contribute
in u
nderstanding, and determining the role of those plant species for ecosystem
function and local community and help in the management of this protected area.
Plant species diversity was studied on three versants; using the intercepted line
method; and in the watercourses surrounding Al Kahaf castle. Additional surveys
have also been conducted over the whole site. Life forms, dispersal types, and the
uses of these species were recorded. The recorded species belong to 53 plant family.
Fabaceae was the most represented family (17 species), followed by Asteraceae
(12 species), and Lamiaceae (11 species).
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