Learning-Based Data Storage [Vision] (Technical Report)

Deep neural network (DNN) and its variants have been extensively used for a wide spectrum of real applications such as image classification, face/speech recognition, fraud detection, and so on. In addition to many important machine learning tasks, as artificial networks emulating the way brain cells function, DNNs also show the capability of storing non-linear relationships between input and output data, which exhibits the potential of storing data via DNNs. We envision a new paradigm of data storage, "DNN-as-a-Database", where data are encoded in well-trained machine learning models. Compared with conventional data storage that directly records data in raw formats, learning-based structures (e.g., DNN) can implicitly encode data pairs of inputs and outputs and compute/materialize actual output data of different resolutions only if input data are provided. This new paradigm can greatly enhance the data security by allowing flexible data privacy settings on different levels, achieve low space consumption and fast computation with the acceleration of new hardware (e.g., Diffractive Neural Network and AI chips), and can be generalized to distributed DNN-based storage/computing. In this paper, we propose this novel concept of learning-based data storage, which utilizes a learning structure called learning-based memory unit (LMU), to store, organize, and retrieve data. As a case study, we use DNNs as the engine in the LMU, and study the data capacity and accuracy of the DNN-based data storage. Our preliminary experimental results show the feasibility of the learning-based data storage by achieving high (100%) accuracy of the DNN storage. We explore and design effective solutions to utilize the DNN-based data storage to manage and query relational tables. We discuss how to generalize our solutions to other data types (e.g., graphs) and environments such as distributed DNN storage/computing.Comment: 14 pages, 16 figure

Turbulent Flow over a Flexible Wall Undergoing a Streamwise Traveling Wavy Motion

Direct numerical simulation is used to study the turbulent flow over a smooth wavy wall undergoing transverse motion in the form of a streamwise travelling wave. The Reynolds number based on the mean velocity U of the external flow and wall motion wavelength λ is 10 170; the wave steepness is 2πa/λ = 0.25 where a is the travelling wave amplitude. A key parameter for this problem is the ratio of the wall motion phase speed c to U, and results are obtained for c/U in the range of â1.0 to 2.0 at 0.2 intervals. For negative c/U, we find that flow separation is enhanced and a large drag force is produced. For positive c/U, the results show that as c/U increases from zero, the separation bubble moves further upstream and away from the wall, and is reduced in strength. Above a threshold value of c/U ≈ 1, separation is eliminated; and, relative to small- c/U cases, turbulence intensity and turbulent shear stress are reduced significantly. The drag force decreases monotonically as c/U increases while the power required for the transverse motion generally increases for large c/U; the net power input is found to reach a minimum at c/U ≈ 1.2 (for fixed U). The results obtained in this study provide physical insight into the study of fish-like swimming mechanisms in terms of drag reduction and optimal propulsive efficiency

Quantitative seismic interpretation of rock brittleness based on statistical rock physics

Rock brittleness is one of the important properties for fracability evaluation, and it can be represented by different physical properties. The mineralogy-based brittleness index (BIM) builds a simple relationship between mineralogy and brittleness, but it may be ambiguous for rocks with a complex microstructure; whereas the elastic moduli-based brittleness index (BIE) is applicable in the field, but BIE interpretation needs to be constrained by lithofacies information. We have developed a new workflow for quantitative seismic interpretation of rock brittleness: Lithofacies are defined by a criterion combining BIM and BIE for comprehensive brittleness evaluation; statistical rock-physics methods are applied for quantitative interpretation by using inverted elastic parameters; acoustic impedance and elastic impedance are selected as the optimized pair of attributes for lithofacies classification. To improve the continuity and accuracy of the interpreted results, a Markov random field is applied in the Bayesian rule as the spatial constraint. A 2D synthetic test demonstrates the feasibility of the Bayesian classification with a Markov random field. This new interpretation framework is also applied to a shale reservoir formation from China. Comparison analysis indicates that brittle shale sections can be efficiently discriminated from ductile shale sections and tight sand sections by using the inverted elastic parameters

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models

Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters. However, it is ineffective or even detrimental when applied to reasoning tasks in Smaller Language Models (SLMs) with less than 10 billion parameters. To address this limitation, we introduce Dialogue-guided Chain-of-Thought (DialCoT) which employs a dialogue format to generate intermediate reasoning steps, guiding the model toward the final answer. Additionally, we optimize the model's reasoning path selection using the Proximal Policy Optimization (PPO) algorithm, further enhancing its reasoning capabilities. Our method offers several advantages compared to previous approaches. Firstly, we transform the process of solving complex reasoning questions by breaking them down into a series of simpler sub-questions, significantly reducing the task difficulty and making it more suitable for SLMs. Secondly, we optimize the model's reasoning path selection through the PPO algorithm. We conduct comprehensive experiments on four arithmetic reasoning datasets, demonstrating that our method achieves significant performance improvements compared to state-of-the-art competitors.Comment: Accepted to EMNLP 202

3-[(5-Methyl­furan-2-yl)methyl­ene]-1,5-dioxaspiro­[5.5]undecane-2,4-dione

There are two crystallographically independent mol­ecules in the asymmetric unit of the title compound, C15H16O5. In each, the 1,3-dioxane ring is in an envelope conformation with the C atom common to the cyclo­hexane ring forming the flap. The dihedral angles between the five essentially planar [maximum deviations from the least-squares planes of 0.049 (3) and 0.042 (3) Å] atoms of the 1,3-dioxane ring and the furan ring in the two mol­ecules are 7.15 (1) and 6.80 (1)°. The crystal structure is stabilized by weak inter­molecular C—H⋯O hydrogen bonds

Design and Mechanical Compatibility of Nylon Bionic Cancellous Bone Fabricated by Selective Laser Sintering

In order to avoid the stress shielding phenomenon in orthopedic bionic bone implantation, it is necessary to consider the design of mechanical compatible implants imitating the host bone. In this study, we developed a novel cancellous bone structure design method aimed at ensuring the mechanical compatibility between the bionic bone and human bone by means of computer-aided design (CAD) and finite element analysis technology (specifically, finite element modeling (FEM)). An orthogonal lattice model with volume porosity between 59% and 96% was developed by means of CAD. The effective equivalent elastic modulus of a honeycomb structure with square holes was studied by FEM simulation. With the purpose of verifying the validity of the cancellous bone structure design method, the honeycomb structure was fabricated by selective laser sintering (SLS) and the actual equivalent elastic modulus of the honeycomb structure was measured with a uniaxial compression test. The experimental results were compared with the FEM values and the predicted values. The results showed that the stiffness values of the designed structures were within the acceptable range of human cancellous bone of 50-500 MPa, which was similar to the stiffness values of human vertebrae L1 and L5. From the point of view of mechanical strength, the established cellular model can effectively match the elastic modulus of human vertebrae cancellous bone. The functional relationship between the volume porosity of the nylon square-pore honeycomb structure ranging from 59% to 96% and the effective elastic modulus was established. The effect of structural changes related to the manufacture of honeycomb structures on the equivalent elastic modulus of honeycomb structures was studied quantitatively by finite element modeling

The role of immunometabolism in macrophage polarization and its impact on acute lung injury/acute respiratory distress syndrome

Lung macrophages constitute the first line of defense against airborne particles and microbes and are key to maintaining pulmonary immune homeostasis. There is increasing evidence suggesting that macrophages also participate in the pathogenesis of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS), including the modulation of inflammatory responses and the repair of damaged lung tissues. The diversity of their functions may be attributed to their polarized states. Classically activated or inflammatory (M1) macrophages and alternatively activated or anti-inflammatory (M2) macrophages are the two main polarized macrophage phenotypes. The precise regulatory mechanism of macrophage polarization is a complex process that is not completely understood. A growing body of literature on immunometabolism has demonstrated the essential role of immunometabolism and its metabolic intermediates in macrophage polarization. In this review, we summarize macrophage polarization phenotypes, the role of immunometabolism, and its metabolic intermediates in macrophage polarization and ALI/ARDS, which may represent a new target and therapeutic direction