Region-DH: Region-based Deep Hashing for Multi-Instance Aware Image Retrieval

This paper introduces an instance-aware hashing approach Region-DH for large-scale multi-label image retrieval. The accurate object bounds can significantly increase the hashing performance of instance features. We design a unified deep neural network that simultaneously localizes and recognizes objects while learning the hash functions for binary codes. Region-DH focuses on recognizing objects and building compact binary codes that represent more foreground patterns. Region-DH can flexibly be used with existing deep neural networks or more complex object detectors for image hashing. Extensive experiments are performed on benchmark datasets and show the efficacy and robustness of the proposed Region-DH model

Reiterated periodic homogenization of integral functionals with convex and nonstandard growth integrands

Multiscale periodic homogenization is extended to an Orlicz-Sobolev setting. It is shown by the reiteraded periodic two-scale convergence method that the sequence of minimizers of a class of highly oscillatory minimizations problems involving convex functionals, converges to the minimizers of a homogenized problem with a suitable convex function

Emergence of Major Pandemics: Examining the Use of AI for the Fight Against Covid-19

Covid-19 is an infectious disease caused by the SARS-CoV-2 virus and which is considered today as a global health emergency. Long before this pandemic, several others such as the plague of Athens, the plague of Antonine, the black plague, the Spanish flu, cholera, the Asian flu, AIDS raged, with consequences as fatal, even more serious than covid-19. The emergence of AI over the past ten years has brought it to the forefront of the response to this disease. The objective of this work is to present the significant contribution of AI in the fight against the new coronavirus, comparing it to previous large pandemics. A preliminary search of information related to past pandemics and covid-19 has been carried out. Next, the contribution of AI following the WHO framework for combating pandemics was presented. Finally, the discussion part resulted in the conclusion that if AI had already been fundamentally implemented during the time of the other major pandemics, the damage to human losses would have been less

Graph based management of temporal data

In recent decades, there has been a significant increase in the use of smart devices and sensors that led to high-volume temporal data generation. Temporal modeling and querying of this huge data have been essential for effective querying and retrieval. However, custom temporal models have the problem of generalizability, whereas the extended temporal models require users to adapt to new querying languages. In this thesis, we propose a method to improve the modeling and retrieval of temporal data using an existing graph database system (i.e., Neo4j) without extending with additional operators. Our work focuses on temporal data represented as intervals (event with a start and end time). We propose a novel way of storing temporal interval as cartesian points where the start time and the end time are stored as the x and y axis of the cartesian coordinate. We present how queries based on Allen’s interval relationships can be represented using our model on a cartesian coordinate system by visualizing these queries. Temporal queries based on Allen’s temporal intervals are then used to validate our model and compare with the traditional way of storing temporal intervals (i.e., as attributes of nodes). Our experimental results on a soccer graph database with around 4000 games show that the spatial representation of temporal interval can provide significant performance (up to 3.5 times speedup) gains compared to a traditional model

Comparative evaluation of enzyme activities and phenol content of Irish potato (Solanum tuberosum) grown under EM and IMO manures Bokashi

Irish potato (Solanum tuberosum) is one of the world’s most consumed staple. In order to develop natural fertilizers to increase Irish potato yield, we assessed the use of manures (EM Bokashi and IMO Bokashi, which are cocktail of beneficial bacteria; used as a soil remediation and health measure in many organic farms) on potato tubers in Bambili village in the North West Region of Cameroon, through evaluating biochemical parameters such as the phenol content, and the activities of peroxidase (POX), polyphenol oxidase (PPO) and pectinmethyl esterase (PME) enzymes. In this respect, a land of 18 x 8 m2 with six plots of 18 beds each was used to cultivate the plant. The plant length and weight of tubers were quantified in the field. Relative to controls (55.96 ± 25.83 cm), both EM and IMO Bokashi produced longer plants (73.85 ± 27.74 cm and 65.25 ± 23.45 cm respectively) but between experimental plants, EM Bokashi led to heavier tuber weights (234 ± 132 g) compared to IMO Bokashi. Interestingly, biochemical analyses showed the highest phenolic content and PME activity in plants treated with EM Bokashi. All treatments significantly increased POX activity while they decreased PPO activity. In addition, significant and positive correlations (P < 0.01) were observed between stem length and PME activity independent of treatment. Plant treated with IMO Bokashi had significant and positive correlations between stem length and weight on the one hand and between stem length and biochemical parameters on the other hand. These findings showed that EM and IMO Bokashi treatments increased the phenol content, PME, PPO and POX activities during the growth of S. tuberosum and can thus be used to improve its growth and productivity.Keywords: Solanum tuberosum, phenol content, biochemical markers, Bokashi manures, productivit

Two-particle level diagrammatic approaches for strongly correlated systems

This thesis presents the development of new numerical methods for the treatment of strongly correlated electron systems based on self-consistent approaches at both the one and the two-particle level such as the parquet formalism. The parquet formalism was solved for the first time on a two-dimensional cluster. When the fully irreducible vertex is approximated by the bare vertex, we obtain the parquet approximation. Its validity was investigated by comparing results that it produces to those of other conserving approximations such as the FLuctuation EXchange (FLEX) approximation or the Second Order Perturbation Theory (SOPT). We found that it provides a significant improvement of FLEX or SOPT and a satisfactory agreement with Quantum Monte Carlo results despite instabilities in the self-consistency at low temperatures and for strong Coulomb interaction. We use the parquet formalism to study the Quantum Critical Point at finite doping in the Hubbard model by decomposing the vertex into its contributions from different channels. We apply this decomposition to the pairing channel and we find that the dominant contribution to the vertex originates in the spin channel even at the quantum critical doping. Furthermore, we explore the divergence of the two parts of the pairing matrix at optimal doping and observe that the irreducible vertex decreases monotonically as the doping is increased while the bare susceptibility exhibits an algebraic divergence at the quantum critical doping supporting the Quantum Critical BCS scenario proposed by She and Zaanen. To circumvent the instabilities in the iteration of the parquet formalism, we explored the dual fermion approach introduced by Rubtsov et al. Here, we extended the formalism to the Dynamical Cluster Approximation, in the process introducing a small parameter in the dual fermions perturbation theory. We demonstrate the quality of the resulting Dual Fermion DCA through a systematic study of the cluster size dependence and of the different perturbative approximations. These efforts represent the initial steps in the development of the Multi-Scale Many Body approach that appropriately treats correlations at different length scales

Malawi's Future Human Capital: Is the Country on Track to Meeting the MDGs on Education?

This study uses demographic and multi-state population projections to estimate the future population structure by age, sex and educational attainment in Malawi, and importantly, to assess the likelihood of meeting the Millennium Development Goals (MDG) related to universal primary education and gender disparity no later than 2015. Data from the 1998 and 1987 censuses, and from the 1992 and 2000 Malawi Demographic and Health Surveys (DHS) are used. First, we examine school enrollment ratios, repetition and dropout rates, and educational attainment, as well as differentials in infant and child mortality by level of the mother's education. Second, we estimate fertility, mortality, and educational transition rates from the DHS. Third, we estimate the population structure in 2000 and then perform forward projections to 2015. Finally, we examine the percentage distribution of the projected population by level of education. Malawi is one of the poorest countries in sub-Saharan Africa, with a population close to 10 million as of 2000. Less than 80 percent of the 6-14 year old children are still in school, and only 15 percent of those aged 14-17 have completed primary school