Bridging the gap between algorithmic and learned index structures

Index structures such as B-trees and bloom filters are the well-established petrol engines of database systems. However, these structures do not fully exploit patterns in data distribution. To address this, researchers have suggested using machine learning models as electric engines that can entirely replace index structures. Such a paradigm shift in data system design, however, opens many unsolved design challenges. More research is needed to understand the theoretical guarantees and design efficient support for insertion and deletion. In this thesis, we adopt a different position: index algorithms are good enough, and instead of going back to the drawing board to fit data systems with learned models, we should develop lightweight hybrid engines that build on the benefits of both algorithmic and learned index structures. The indexes that we suggest provide the theoretical performance guarantees and updatability of algorithmic indexes while using position prediction models to leverage the data distributions and thereby improve the performance of the index structure. We investigate the potential for minimal modifications to algorithmic indexes such that they can leverage data distribution similar to how learned indexes work. In this regard, we propose and explore the use of helping models that boost classical index performance using techniques from machine learning. Our suggested approach inherits performance guarantees from its algorithmic baseline index, but at the same time it considers the data distribution to improve performance considerably. We study single-dimensional range indexes, spatial indexes, and stream indexing, and show that the suggested approach results in range indexes that outperform the algorithmic indexes and have comparable performance to the read-only, fully learned indexes and hence can be reliably used as a default index structure in a database engine. Besides, we consider the updatability of the indexes and suggest solutions for updating the index, notably when the data distribution drastically changes over time (e.g., for indexing data streams). In particular, we propose a specific learning-augmented index for indexing a sliding window with timestamps in a data stream. Additionally, we highlight the limitations of learned indexes for low-latency lookup on real- world data distributions. To tackle this issue, we suggest adding an algorithmic enhancement layer to a learned model to correct the prediction error with a small memory latency. This approach enables efficient modelling of the data distribution and resolves the local biases of a learned model at the cost of roughly one memory lookup.Open Acces

Interconnecting outriggers system in tall building structure

Tall building are innovative structure that shall be studies their systems and connections to accept stability in related to certain lateral load such as seismic loads post-earthquake and wind loads. Central core are a major structural element that designed in combination to frames which interact through outriggers to transfer the loads to the foundation. The issue of rigidity is the cooperation between elements in tall building systems. Significantly, type of connections among components based on their stiffness can be defined in the form of percentage of safe distribution of loads in tall building system. The research proposes a design of interconnecting of tall building blocks distribution through tensile force and The effect of soil pressure at underground levels that creates a lateral forces that push the face of the frames below ground level is also another form of loads. In summary, this research is to understand the effect of interconnecting of blocks on overall formation stiffness based on different analysis Models. It figures out the effect of inter connecting at abutment in levels on retaining wall and self -stability of frame related to soil pressure

Relationship between bridging and dimensions of sella turcica with classification of craniofacial skeleton

Purpose: In orthodontics, it is essential to determine the craniofacial skeleton pattern (class I, II, III) for planning treatment. Sella turcica bridging that is seen on lateral cephalometric radiographs is considered as a normal finding. This study aimed to compare sella turcica bridging and its dimensions in patients with various craniofacial patterns. Material and methods: A total of 105 lateral cephalometric radiographs (53 men and 52 women), aged 14-26 years, were randomly and equally assigned to three groups of class I, II, and III, respectively. The length, diameter, and depth of the sella turcica as well as sella turcica bridging were determined on radiographs. The chi-squared test was used for assessing the relationship between sella turcica bridging and craniofacial skeleton classification. ANOVA was used for assessing the relationship between the dimensions of the sella turcica and craniofacial skeleton classification. The Pearson's correlation coefficient was used for assessing the relationship between age and the dimensions of the sella turcica. Results: The sella turcica had a normal shape in 64.76% of patients, whereas 35.33% of patients had sella turcica bridging. In total, 11.42% of patients belonged to class I, 34.28% to class II, and 66.62% to class III. The diameter of the sella turcica had a significant relationship with age; the diameter of the sella turcica increased with age (p < 0.001). Conclusions: There is a significant relationship between craniofacial skeleton patterns and sella turcica bridging, i.e., the incidence of sella turcica bridging is higher in class III patients. The sella turcica had a greater diameter in older patients

Interaction between articulatory gestures and inner speech in a counting task

International audienceInteraction between covert and overt orofacial gestures has been poorly studied apart from old and rather qualitative experiments. The question deserves special interest in the context of the debate between auditory and motor theories of speech perception, where dual tasks may be of great interest. It is shown here that dynamic mandible and lips movement produced by a participant result in strong and stable perturbations to an inner speech counting task that has to be realized at the same time, while static orofacial configurations and static or dynamic manual actions produce no perturbation. This enables the authors to discuss how such kinds of orofacial perturbations could be introduced in dual task paradigms to assess the role of motor processes in speech perception

The complexity of the connected graph access structure on seven participants

In this paper, we study an important problem in secret sharing that determines the exact value or bound for the complexity. First, we used induced subgraph complexity of the graph G with access structure, Gama, to obtain a lower bound on the complexity of the graph G. Secondly, by applying decomposition techniques we obtain an upper bound on the complexity of the graph G. We determine the exact values of the complexity for each of the ten graph access structures on seven participants. Also, we improve the value bound of the complexity of the six graph access structures with seven participants