Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Tolerant Nanoscale Structures and Related Delay

Redundancy is a method in the system for designing fault tolerant structure with the Nanoscale gate in electronic systems. Until now, many ways are represented for this purpose, which increases complexity or decreases the system's reliability. In this paper, we compare two methods for designing a fault-tolerant structure with Nanoscale gates. These ways are NAND Multiplexing (NM) and Averaging Cells (AC). The results of simulation that evaluate the area cost and reliability of the gates indicates that NM based gates are more reliable than AC gates when the error probabilities of the circuit parts are lower than 0.003. However, when this value is exceeded (which is expected for electronic nanotechnologies), AC gates are more reliable at a lower area cost. In this paper, we show comparing the NM and the AC in the aspect of the delay parameter. It indicates that the AC method has a constant delay, but the NM system's delay will rise with increasing redundancy. As a result, the AC method's overhead for designing a fault-tolerant system with the Nanoscale gate is lower than the NM method in the aspect of the area and delay time with better reliability.
Self-Localization of Unmanned Aerial Vehicles Using Visual Inertial Odometry- Acronym

This thesis is concerned with visual-inertial odometry (VIO) algorithms and their usability and suitability for integration into the feedback loop of the unmanned aerial vehicle (UAV) control system. The main part of the thesis is focused on the comparison of chosen VIO algorithms in terms of pose estimation precision for chosen camera mounting orientations, camera frame rate, UAV velocity and the feedback suitability. According to prior survey of VIO algorithms precision, availability and fitness on UAV deployment, three VIO algorithms are chosen for this thesis, namely S-MSCKF, SVO, and VINS-Fusion. The VIO algorithms performance is evaluated in both the simulation environment and the real environment to prove the suitability for feedback loop integration. The trajectory-shaping filter was implemented to smooth the trajectory by constraining the accelerations according to the UAV dynamics. Such filter improves not only the precision of VIO pose estimation but also the similarity of the control reference generated from the tracked trajectory. Lastly, the feedback integration for tested algorithms is presented for all used VIO algorithms in the simulator and partially in the real deployment.
A comparative computational study on the synthesis prescriptions, structures and acid properties of B-, Al- and G-incorporated MTW-type zeolites

The B-, Al- and Ga-incorporated H-, NH4-, Li-, Na- and K-forms MTW zeolites were comparatively studied to understand the crystallization thermochemistry, structure stabilities and acid properties using dispersion corrected density functional theory. Substitution energies were introduced to evaluate the synthesis prescriptions for MTW zeolites. The calculated results show that the trivalent and alkaline metal ions for the synthesis of the MTW zeolites are favored in the following order: Al > Ga > B and Na > K > Li > NH4 > H. The B–O, Si–O and O–H bonds have a covalent character while Al–O bonds show an obvious ionic character. However, the character of Ga–O bonds fall in between that of Al–O and B–O bonds. Introducing alkaline metal ions significantly changes the cell volumes. As a result, the cell volumes of the H-form B-, Al- and Ga-incorporated MTW zeolites are larger than Li-form, and smaller than K-form MTW zeolites. Further investigation of their properties show that the Bronsted acid sites of the zeolites follow the order of HAl-MTW > HGa-MTW > HB-MTW. The Lewis acidity of HB-MTW is similar to that of HAl-MTW, and weaker than that of HGa-MTW. These results are in good agreement with the previous experiments and provide new insights for the MTW zeolites.
IFT 6085 - Lecture 4 Black-box Models and Lower bounds

In the previous lectures, we derived upper bounds on the convergence rate of gradient descent for minimizing different types of objective functions. Specifically, we found that depending on the properties we have for the objective function (Lipschitz-continuity, convexity, strong convexity, and smoothness), we can obtain different upper bounds on the convergence rate of gradient descent minimizing this objective function. We also saw that these different bounds are obtained with different algorithms: e.g. considering the last term of the sequence we construct, or the mean of all the points.
Interreﬂections in Computer Vision A Survey & An Introduction To Spectral Inﬁnite-Bounce Model

Interreﬂections are observed on concave objects or when multiple objects are located closely. In a vision system, interreﬂections can largely aﬀect color values captured by the camera. Due to this fact, modeling interreﬂections is important for many vision applications. In this paper, we consider the problem of treating and modeling interreﬂections in the domain of computer vision. First, a survey of existing approaches in the state of the art is given. These approaches are detailed, discussed and compared. Most of the state of the art models take into consideration only two bounces of light between surface elements. We, afterwards, introduce a new interreﬂection model based on radiometric deﬁnitions. This model is the ﬁrst one that takes into consideration an inﬁnite number of light bounces between surface elements while providing image RGB values as a result. The accuracy of our model is studied by comparing it to real camera outputs. Thanks to our new model, the importance of using inﬁnite bounces of light while studying interreﬂection, instead of only two bounces is demonstrated.
X-ray diffraction of bacteriorhodopsin photocycle intermediates (Review)

Recent advances in the crystallography of bacteriorhodopsin, the light-driven proton pump, have yielded structural models for all intermediates of the photochemical cycle. For seven of the species, X-ray diffraction data were collected from trapped photostationary states in crystals, and for the two remaining ones the structures of selected mutants are available. The changes of the retinal chromophore, protein and bound water describe, at an atomic level, how accommodation of the twisted photoisomerized retinal to its binding site causes de-protonation of the retinal Schiff base and initiates cascades of gradual conformational rearrangements of the protein. One cascade propagates in the extracellular direction and results in proton release, and the other in the cytoplasmic direction and results in side-chain and main-chain rearrangements, formation of a chain of hydrogen-bonded water, and proton uptake from the bulk. Such local-global conformational coupling, with gradual spreading of a local perturbation over the rest of the protein, might be the uniting principle of transporters and receptors.
Optical Excitations with Electron Beams: Challenges and Opportunities

Free electron beams such as those employed in electron microscopes have evolved into powerful tools to investigate photonic nanostructures with an unrivaled combination of spatial and spectral precision through the analysis of electron energy losses and cathodoluminescence light emission. In combination with ultrafast optics, the emerging field of ultrafast electron microscopy utilizes synchronized femtosecond electron and light pulses that are aimed at the sampled structures, holding the promise to bring simultaneous sub-Å–sub-fs–sub-meV space–time–energy resolution to the study of material and optical-field dynamics. In addition, these advances enable the manipulation of the wave function of individual free electrons in unprecedented ways, opening sound prospects to probe and control quantum excitations at the nanoscale. Here, we provide an overview of photonics research based on free electrons, supplemented by original theoretical insights and discussion of several stimulating challenges and opportunities. In particular, we show that the excitation probability by a single electron is independent of its wave function, apart from a classical average over the transverse beam density profile, whereas the probability for two or more modulated electrons depends on their relative spatial arrangement, thus reflecting the quantum nature of their interactions. We derive first-principles analytical expressions that embody these results and have general validity for arbitrarily shaped electrons and any type of electron–sample interaction. We conclude with some perspectives on various exciting directions that include disruptive approaches to noninvasive spectroscopy and microscopy, the possibility of sampling the nonlinear optical response at the nanoscale, the manipulation of the density matrices associated with free electrons and optical sample modes, and appealing applications in optical modulation of electron beams, all of which could potentially revolutionize the use of free electrons in photonics.
The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions

Recently, there has been a significant interest in performing convolution over irregularly sampled point clouds. Since point clouds are very different from regular raster images, it is imperative to study the generalization of the convolution networks more closely, especially their robustness under variations in scale and rotations of the input data. This paper investigates different variants of PointConv, a convolution network on point clouds, to examine their robustness to input scale and rotation changes. Of the variants we explored, two are novel and generated significant improvements. The first is replacing the multilayer perceptron based weight function with much simpler third degree polynomials, together with a Sobolev norm regularization. Secondly, for 3D datasets, we derive a novel viewpoint-invariant descriptor by utilizing 3D geometric properties as the input to PointConv, in addition to the regular 3D coordinates. We have also explored choices of activation functions, neighborhood, and subsampling methods. Experiments are conducted on the 2D MNIST&CIFAR-10 datasets as well as the 3D SemanticKITTI&ScanNet datasets. Results reveal that on 2D, using third degree polynomials greatly improves PointConv's robustness to scale changes and rotations, even surpassing traditional 2D CNNs for the MNIST dataset. On 3D datasets, the novel viewpoint-invariant descriptor significantly improves the performance as well as robustness of PointConv. We achieve the state-of-the-art semantic segmentation performance on the SemanticKITTI dataset, as well as comparable performance with the current highest framework on the ScanNet dataset among point-based approaches.
Flying Robots for Safe and Efficient Parcel Delivery Within the COVID-19 Pandemic

The integration of small-scale Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITSs) will empower novel smart-city applications and services. After the unforeseen outbreak of the COVID-19 pandemic, the public demand for delivery services has increased. Mobile robotic systems inherently offer the potential for minimizing the amount of direct human-to-human interactions within the parcel delivery process. In this paper, we apply a system-level perspective for identifying key challenges and promising solution approaches for modeling, analysis, and optimization of UAV-aided parcel delivery. We present a system-of-systems model for UAV-enhanced parcel delivery to cope with higher capacity requirements induced by the COVID-19. The proposed system-of-systems consists of various complex aspects such as assigning and distributing delivery jobs, establishing and maintaining reliable communication links between the vehicles, as well as path planning and mobility control. In a proof of concept evaluation, we simulate the logistics performance and network communications of a hybrid delivery fleet composed of ground and aerial vehicles operating under pandemic induced constraints, such as the prioritization of time-critical deliveries like medical goods. Results indicate that network requirements for reliable communications within the delivery fleet can be met by existing network communications approaches. Furthermore, gains in delivery capacity induced by drone usage and a trade-off caused by prioritizing time-critical goods, therefore, resulting in a penalty for regular deliveries, are observed.
Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS

Camera-based Deep Learning algorithms are increasingly needed for perception in Automated Driving systems. However, constraints from the automotive industry challenge the deployment of CNNs by imposing embedded systems with limited computational resources. In this paper, we propose an approach to embed a multi-task CNN network under such conditions on a commercial prototype platform, i.e. a low power System on Chip (SoC) processing four surround-view fisheye cameras at 10 FPS. The first focus is on designing an efficient and compact multi-task network architecture. Secondly, a pruning method is applied to compress the CNN, helping to reduce the runtime and memory usage by a factor of 2 without lowering the performances significantly. Finally, several embedded optimization techniques such as mixed-quantization format usage and efficient data transfers between different memory areas are proposed to ensure real-time execution and avoid bandwidth bottlenecks. The approach is evaluated on the hardware platform, considering embedded detection performances, runtime and memory bandwidth. Unlike most works from the literature that focus on classification task, we aim here to study the effect of pruning and quantization on a compact multi-task network with object detection, semantic segmentation and soiling detection tasks.
Disorder-induced topology in quench dynamics

We study the effect of strong disorder on topology and entanglement in quench dynamics. Although disorderinduced topological phases have been well studied in equilibrium, the disorder-induced topology in quench dynamics has not been explored. In this work, we predict a disorder-induced topology of post-quench states characterized by the quantized dynamical Chern number and the crossings in the entanglement spectrum in (1+ 1) dimensions. The dynamical Chern number undergoes transitions from zero to unity, and back to zero when increasing the disorder strength. The boundaries between different dynamical Chern numbers are determined by delocalized critical points in the post-quench Hamiltonian with the strong disorder. An experimental realization in quantum walks is discussed.
5G D2D Transmission Mode Selection Performance & Cluster Limits Evaluation of Distributed Artificial Intelligence and Machine Learning Techniques

5G D2D Communication promises improvements in energy and spectral efficiency, overall system capacity, and higher data rates. However, to achieve optimum results it is important to select wisely the Transmission mode of the D2D Device to form clusters in the most fruitful positions in terms of Sum Rate and Power Consumption. Towards this end, this paper investigates the use of Distributed Artificial Intelligence (DAI) and innovative to D2D, Machine Learning (ML) approaches to achieve satisfactory results in terms of Spectral Efficiency (SE), Power Consumption (PC) and execution time, with the creation of clusters and backhauling D2D network under existing Base Station/Small Cell. Additionally, one of the major factors that affect the creation of high-quality clusters under a D2D network is the number of the Devices. Therefore, this paper focuses on a small (<=200) number of Devices, with the purpose to identify the limits of each approach in terms of number of devices. Specifically, to identify where it is beneficial to form a cluster, investigate the critical point that gains increases rapidly and at the end examine the applicability of 5G requirements. Additionally, prior work presented a Distributed Artificial Intelligence (DAI) Solution/Framework in D2D and a DAIS Transmission Mode Selection (TMS) plan was proposed. In this paper DAIS is further examined, improved in terms of thresholds evaluation, evaluated, and compared with other approaches (AI/ML). The results obtained demonstrate the exceptional performance of DAIS, compared to all other related approaches in terms of SE, PC, execution time and cluster formation efficiency. Also, results show that the investigated AI/ML approaches are also beneficial for Transmission Mode Selection (TMS) in 5G D2D communication, even with a smaller (>=5) number of devices as a lower limit.
A Visual Analytics Approach to Facilitate the Proctoring of Online Exams

Online exams have become widely used to evaluate students’ performance in mastering knowledge in recent years, especially during the pandemic of COVID-19. However, it is challenging to conduct proctoring for online exams due to the lack of face-to-face interaction. Also, prior research has shown that online exams are more vulnerable to various cheating behaviors, which can damage their credibility. This paper presents a novel visual analytics approach to facilitate the proctoring of online exams by analyzing the exam video records and mouse movement data of each student. Specifically, we detect and visualize suspected head and mouse movements of students in three levels of detail, which provides course instructors and teachers with convenient, efficient and reliable proctoring for online exams. Our extensive evaluations, including usage scenarios, a carefully-designed user study and expert interviews, demonstrate the effectiveness and usability of our approach.
PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses

With the increasing adoption of graph neural networks (GNNs) in the machine learning community, GPUs have become an essential tool to accelerate GNN training. However, training GNNs on very large graphs that do not fit in GPU memory is still a challenging task. Unlike conventional neural networks, mini-batching input samples in GNNs requires complicated tasks such as traversing neighboring nodes and gathering their feature values. While this process accounts for a significant portion of the training time, we find existing GNN implementations using popular deep neural network (DNN) libraries such as PyTorch are limited to a CPU-centric approach for the entire data preparation step. This "all-in-CPU" approach has negative impact on the overall GNN training performance as it over-utilizes CPU resources and hinders GPU acceleration of GNN training. To overcome such limitations, we introduce PyTorch-Direct, which enables a GPU-centric data accessing paradigm for GNN training. In PyTorch-Direct, GPUs are capable of efficiently accessing complicated data structures in host memory directly without CPU intervention. Our microbenchmark and end-to-end GNN training results show that PyTorch-Direct reduces data transfer time by 47.1% on average and speeds up GNN training by up to 1.6x. Furthermore, by reducing CPU utilization, PyTorch-Direct also saves system power by 12.4% to 17.5% during training. To minimize programmer effort, we introduce a new "unified tensor" type along with necessary changes to the PyTorch memory allocator, dispatch logic, and placement rules. As a result, users need to change at most two lines of their PyTorch GNN training code for each tensor object to take advantage of PyTorch-Direct.
Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting

We assess the value of machine learning as an accelerator for the parameterization schemes of operational weather forecasting systems, specifically the parameterization of nonorographic gravity wave drag. Emulators of this scheme can be trained to produce stable and accurate results up to seasonal forecasting timescales. Generally, networks that are more complex produce emulators that are more accurate. By training on an increased complexity version of the existing parameterization scheme, we build emulators that produce more accurate forecasts. For medium range forecasting, we have found evidence that our emulators are more accurate than the version of the parametrization scheme that is used for operational predictions. Using the current operational CPU hardware, our emulators have a similar computational cost to the existing scheme, but are heavily limited by data movement. On GPU hardware, our emulators perform 10 times faster than the existing scheme on a CPU.
Non-Parametric Adaptive Network Pruning

—Popular network pruning algorithms reduce redundant information by optimizing hand-crafted parametric models, and may cause suboptimal performance and long time in selecting ﬁlters. We innovatively introduce non-parametric modeling to simplify the algorithm design, resulting in an automatic and efﬁcient pruning approach called EPruner. Inspired by the face recognition community, we use a message passing algorithm Afﬁnity Propagation on the weight matrices to obtain an adaptive number of exemplars, which then act as the preserved ﬁlters. EPruner breaks the dependency on the training data in determining the “important” ﬁlters and allows the CPU implementation in seconds, an order of magnitude faster than GPU based SOTAs. Moreover, we show that the weights of exemplars provide a better initialization for the ﬁne-tuning. On VGGNet-16, EPruner achieves a 76.34%-FLOPs reduction by removing 88.80% parameters, with 0.06% accuracy improvement on CIFAR-10. In ResNet-152, EPruner achieves a 65.12%-FLOPs reduction by removing 64.18% parameters, with only 0.71% top-5 accuracy loss on ILSVRC-2012. Code can be available at https://github.com/lmbxmu/EPruner.
The Quantum Field Theory on Which the Everyday World Supervenes

Effective Field Theory (EFT) is the successful paradigm underlying modern theoretical physics, including the"Core Theory"of the Standard Model of particle physics plus Einstein's general relativity. I will argue that EFT grants us a unique insight: each EFT model comes with a built-in specification of its domain of applicability. Hence, once a model is tested within some domain (of energies and interaction strengths), we can be confident that it will continue to be accurate within that domain. Currently, the Core Theory has been tested in regimes that include all of the energy scales relevant to the physics of everyday life (biology, chemistry, technology, etc.). Therefore, we have reason to be confident that the laws of physics underlying the phenomena of everyday life are completely known.
Diffuse-interface blended method for the imposition of physical boundaries in two-fluid flows

Multiphase ﬂows are commonly found in chemical engineering processes such as distilla-tion columns, bubble columns, ﬂuidized beds and heat exchangers. Physical boundaries in numerical simulations of multiphase ﬂows are generally deﬁned by a mesh that con-forms to the physical boundaries of the system. Depending on the complexity of the physical system, generating the conformal mesh can be time-consuming and the resulting mesh could potentially contain a large number of skewed elements, which is undesirable. The diﬀuse-interface approach allows for a structured mesh to be used while still capturing the desired solid-ﬂuid boundaries. In this work, a diﬀuse-interface method for the imposition of physical boundaries is developed for two-ﬂuid incompressible ﬂow systems. The diﬀuse-interface is used to deﬁne the physical boundaries and the boundary conditions are imposed by blending the conservation equations from the two-ﬂuid model with that of the solid. The results from the diﬀuse-interface method and mesh-deﬁned boundaries are found to be in good agreement at small diﬀuse-interface widths.