Experimental study of soil anisotropy using hollow cylinder testing

Most sedimentary deposits are inherently anisotropic due to their natural deposition in horizontal layers. This inherent anisotropy highlights the fact that the response of soils to loading is depending on both stress magnitude and its direction. Most of the field problems in geotechnical engineering are three-dimensional, and a soil is more likely to subject an anisotropic stress state (σ1 ≠ σ2 ≠ σ3), together with rotation of the principal axes. It is therefore essential that the soil behaviour under such realistic and general loading conditions is to be well understood, so that engineers can devise appropriate geotechnical design and analysis in practical situations. The Small-Strain Hollow Cylinder Apparatus (SS-HCA), developed by GDS Instruments Ltd. has been used to study drained anisotropic behaviour of sand under generalized stress conditions. In particular, the anisotropic stress-strain-strength characteristics, volume change behaviour, non-coaxiality and combined effects of α and b are studied. Three testing programs composed of two main types of stress paths (e.g. monotonic loading with different inclinations of the major principal stress and cyclic rotation of principal stress axes) were conducted. Inherently anisotropic behaviour of sands is clearly illustrated by deformation response that is strongly dependent on the loading direction in the monotonic shear tests. For a given loading direction, the mechanical response of sands is affected by the material density, the particle properties and the loading history. Non-coincidence of principal directions of stress and strain increment is observed and shear band inclinations in hollow cylindrical specimens follow the theoretical predictions. Results also clearly show the effects of intermediate principal stress on the deformation response of sand. This is seen in variation of stress-strain response and peak friction angle with differing b-values. A significant plastic deformation is induced during rotational shear despite the magnitudes of principal stresses remaining constant. Volumetric strain during rotational shear is mainly contractive and the amount of the volumetric strain increases with the increase in the stress ratio. Most of the contractive volumetric strain occurred during the first 20 cycles and its accumulation rate tended to decrease as the number of cycles increases. When principal stress rotation continues, the sand samples appear to be stabilized and the strain trajectory in the deviatoric plane approaches an ellipse. The test results also demonstrate that the mechanical behaviour of sand under rotational shear is generally non-coaxial, and the stress ratio has a significant effect on the non-coaxiality. The larger the stress ratio, the lower degree of non-coaxiality is induced. It was also observed that parameter b is not a negligible factor for the sand deformation during rotational shear, but has significant impact. The larger the b-value, the more the volumetric strain is accumulated

Experimental study of soil anisotropy using hollow cylinder testing

Most sedimentary deposits are inherently anisotropic due to their natural deposition in horizontal layers. This inherent anisotropy highlights the fact that the response of soils to loading is depending on both stress magnitude and its direction. Most of the field problems in geotechnical engineering are three-dimensional, and a soil is more likely to subject an anisotropic stress state (σ1 ≠ σ2 ≠ σ3), together with rotation of the principal axes. It is therefore essential that the soil behaviour under such realistic and general loading conditions is to be well understood, so that engineers can devise appropriate geotechnical design and analysis in practical situations. The Small-Strain Hollow Cylinder Apparatus (SS-HCA), developed by GDS Instruments Ltd. has been used to study drained anisotropic behaviour of sand under generalized stress conditions. In particular, the anisotropic stress-strain-strength characteristics, volume change behaviour, non-coaxiality and combined effects of α and b are studied. Three testing programs composed of two main types of stress paths (e.g. monotonic loading with different inclinations of the major principal stress and cyclic rotation of principal stress axes) were conducted. Inherently anisotropic behaviour of sands is clearly illustrated by deformation response that is strongly dependent on the loading direction in the monotonic shear tests. For a given loading direction, the mechanical response of sands is affected by the material density, the particle properties and the loading history. Non-coincidence of principal directions of stress and strain increment is observed and shear band inclinations in hollow cylindrical specimens follow the theoretical predictions. Results also clearly show the effects of intermediate principal stress on the deformation response of sand. This is seen in variation of stress-strain response and peak friction angle with differing b-values. A significant plastic deformation is induced during rotational shear despite the magnitudes of principal stresses remaining constant. Volumetric strain during rotational shear is mainly contractive and the amount of the volumetric strain increases with the increase in the stress ratio. Most of the contractive volumetric strain occurred during the first 20 cycles and its accumulation rate tended to decrease as the number of cycles increases. When principal stress rotation continues, the sand samples appear to be stabilized and the strain trajectory in the deviatoric plane approaches an ellipse. The test results also demonstrate that the mechanical behaviour of sand under rotational shear is generally non-coaxial, and the stress ratio has a significant effect on the non-coaxiality. The larger the stress ratio, the lower degree of non-coaxiality is induced. It was also observed that parameter b is not a negligible factor for the sand deformation during rotational shear, but has significant impact. The larger the b-value, the more the volumetric strain is accumulated

DiffusionTrack: Diffusion Model For Multi-Object Tracking

Multi-object tracking (MOT) is a challenging vision task that aims to detect individual objects within a single frame and associate them across multiple frames. Recent MOT approaches can be categorized into two-stage tracking-by-detection (TBD) methods and one-stage joint detection and tracking (JDT) methods. Despite the success of these approaches, they also suffer from common problems, such as harmful global or local inconsistency, poor trade-off between robustness and model complexity, and lack of flexibility in different scenes within the same video. In this paper we propose a simple but robust framework that formulates object detection and association jointly as a consistent denoising diffusion process from paired noise boxes to paired ground-truth boxes. This novel progressive denoising diffusion strategy substantially augments the tracker's effectiveness, enabling it to discriminate between various objects. During the training stage, paired object boxes diffuse from paired ground-truth boxes to random distribution, and the model learns detection and tracking simultaneously by reversing this noising process. In inference, the model refines a set of paired randomly generated boxes to the detection and tracking results in a flexible one-step or multi-step denoising diffusion process. Extensive experiments on three widely used MOT benchmarks, including MOT17, MOT20, and Dancetrack, demonstrate that our approach achieves competitive performance compared to the current state-of-the-art methods

Link prediction using discrete-time quantum walk

Predviđanje veze jedno je od ključnih pitanja složenih mreža koje trenutačno privlači pozor mnogih istraživača. Do sada su predložene mnoge metode predviđanja veze. Klasično slučajno gibanje predstavlja učinkoviti alat koji se uvelike rabi u proučavanju problema predviđanja veze. Kvantno gibanje je kvantni analog klasičnog slučajnog gibanja. Rezultati mnogih istraživanja pokazuju da kvantni algoritmi koji rabe kvantno gibanje nadmašujuju svoje klasične kopije u mnogim primjenama, kao što su, na primjer, usklađivanje i istraživanje grafikona. Međutim, malo je istraživanja o predviđanju veze na temelju kvantnog gibanja, a posebice kvantnog gibanja u diskretnom vremenu. U ovom se radu predlaže nova metoda predviđanja veze zasnovana na kvantnom gibanju u diskretnom vremenu. Rezultati eksperimenta pokazuju da je točnost predviđanja našom metodom bolja nego tipičnim metodama. Vremenska složenost naše metode koja se izvodi na klasičnim računalima, u usporedbi s metodama baziranim na klasičnom slučajnom gibanju, malo je bolja. No, naša se metoda može znatno ubrzati izvođenjem na kvantnim računalima.Link prediction is one of the key issues of complex networks, which attracts much research interest currently. Many link prediction methods have been proposed so far. The classical random walk as an effective tool has been widely used to study the link prediction problems. Quantum walk is the quantum analogue of classical random walk. Numerous research results show that quantum algorithms using quantum walk outperform their classical counterparts in many applications, such as graph matching and searching. But there have been few studies of the link prediction based on quantum walk, especially on discrete-time quantum walk. This paper proposes a new link prediction method based on discrete-time quantum walk. Experiment results show that prediction accuracy of our method is better than the typical methods. The time complexity of our method running on classical computers, compared with the methods based on classical random walk, is slightly higher. But our method can be greatly accelerated by executing on quantum computers

Large-scale single-photon imaging

Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing. However, large-scale high-fidelity single-photon imaging remains a big challenge, due to the complex hardware manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging over an order of magnitude, with significant enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 $\times$ 32 pixels, 90 scenes, 10 different bit depth, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this real-world physical noise model, we for the first time synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depth, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique on a series of experiments including macroscopic and microscopic imaging, microfluidic inspection, and Fourier ptychography. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance, with more than 5 dB superiority on PSNR compared to the existing methods

SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction

With the growing model size, deep neural networks (DNN) are increasingly trained over massive GPU accelerators, which demands a proper parallelization plan that transforms a DNN model into fine-grained tasks and then schedules them to GPUs for execution. Due to the large search space, the contemporary parallelization plan generators often rely on empirical rules that couple transformation and scheduling, and fall short in exploring more flexible schedules that yield better memory usage and compute efficiency. This tension can be exacerbated by the emerging models with increasing complexity in their structure and model size. SuperScaler is a system that facilitates the design and generation of highly flexible parallelization plans. It formulates the plan design and generation into three sequential phases explicitly: model transformation, space-time scheduling, and data dependency preserving. Such a principled approach decouples multiple seemingly intertwined factors and enables the composition of highly flexible parallelization plans. As a result, SuperScaler can not only generate empirical parallelization plans, but also construct new plans that achieve up to 3.5X speedup compared to state-of-the-art solutions like DeepSpeed, Megatron and Alpa, for emerging DNN models like Swin-Transformer and AlphaFold2, as well as well-optimized models like GPT-3