Miracle-dz/UniFAR-FTUs huggingface dataset:
Residual-Recursion Autoencoder for Shape Illustration Images

Shape illustration images (SIIs) are common and important in describing the cross-sections of industrial products. Same as MNIST, the handwritten digit images, SIIs are gray or binary and containing shapes that are surrounded by large areas of blanks. In this work, Residual-Recursion Autoencoder (RRAE) has been proposed to extract low-dimensional features from SIIs while maintaining reconstruction accuracy as high as possible. RRAE will try to reconstruct the original image several times and recursively fill the latest residual image to the reserved channel of the encoder's input before the next trial of reconstruction. As a kind of neural network training framework, RRAE can wrap over other autoencoders and increase their performance. From experiment results, the reconstruction loss is decreased by 86.47% for convolutional autoencoder with high-resolution SIIs, 10.77% for variational autoencoder and 8.06% for conditional variational autoencoder with MNIST.
Magnetorotational instability in diamagnetic, misaligned protostellar discs

In the present study, we addressed the question of how the growth rate of the magnetorotational instability is modified when the radial component of the stellar dipole magnetic field is taken into account in addition to the vertical component. Considering a fiducial radius in the disc where diamagnetic currents are pronounced, we carried out a linear stability analysis to obtain the growth rates of the magnetorotational instability for various parameters such as the ratio of the radial-to-vertical component and the gradient of the magnetic field, the Alfvenic Mach number and the diamagnetization parameter. Our results show that the interaction between the diamagnetic current and the radial component of the magnetic field increases the growth rate of the magnetorotational instability and generates a force perpendicular to the disc plane which may induce a torque. It is also shown that considering the radial component of the magnetic field and taking into account a radial gradient in the vertical component of the magnetic field causes an increase in the magnitudes of the growth rates of both the axisymmetric ($m=0$) and the non-axisymmetric ($m=1$) modes.
Integrating Planning and Interpretable Goal Recognition for Autonomous Driving

The ability to predict the intentions and driving trajectories of other vehicles is a key problem for autonomous driving. We propose an integrated planning and prediction system which leverages the computational benefit of using a finite space of maneuvers, and extend the approach to planning and prediction of sequences (plans) of maneuvers via rational inverse planning to recognise the goals of other vehicles. Goal recognition informs a Monte Carlo Tree Search (MCTS) algorithm to plan optimal maneuvers for the ego vehicle. Inverse planning and MCTS utilise a shared set of defined maneuvers to construct plans which are explainable by means of rationality, i.e. plans are optimal in given metrics. Evaluation in simulations of four urban driving scenarios demonstrate the system's ability to robustly recognise the goals of other vehicles while generating near-optimal plans. In each scenario we extract intuitive explanations for the recognised goals and maneuver predictions which justify the system's decisions.
$\mathrm{G}_2$-instantons on the 7-sphere

We study the deformation theory of $\mathrm{G}_2$-instantons on the 7-sphere, specifically those obtained from ASD instantons on the 4-sphere via the quaternionic Hopf fibration. We find that the pullback of the standard instanton lies in a smooth, complete, 15-dimensional family of $\mathrm{G}_2$-instantons. In general, the space of $\mathrm{G}_2$-instanton deformations on $S^7$ is identified with three copies of the space of ASD deformations on $S^4. $
Direct evidence for flat bands in twisted bilayer graphene from nano-ARPES

Transport experiments in twisted bilayer graphene revealed multiple superconducting domes separated by correlated insulating states. These properties are generally associated with strongly correlated states in a flat mini-band of the hexagonal moir\'e superlattice as it was predicted by band structure calculations. Evidence for such a flat band comes from local tunneling spectroscopy and electronic compressibility measurements, reporting two or more sharp peaks in the density of states that may be associated with closely spaced van Hove singularities. Direct momentum resolved measurements proved difficult though. Here, we combine different imaging techniques and angle resolved photoemission with simultaneous real and momentum space resolution (nano-ARPES) to directly map the band dispersion in twisted bilayer graphene devices near charge neutrality. Our experiments reveal large areas with homogeneous twist angle that support a flat band with spectral weight that is highly localized in momentum space. The flat band is separated from the dispersive Dirac bands which show multiple moir\'e hybridization gaps. These data establish the salient features of the twisted bilayer graphene band structure.
Using generative adversarial networks to synthesize artificial financial datasets

Generative Adversarial Networks (GANs) became very popular for generation of realistically looking images. In this paper, we propose to use GANs to synthesize artificial financial data for research and benchmarking purposes. We test this approach on three American Express datasets, and show that properly trained GANs can replicate these datasets with high fidelity. For our experiments, we define a novel type of GAN, and suggest methods for data preprocessing that allow good training and testing performance of GANs. We also discuss methods for evaluating the quality of generated data, and their comparison with the original real data.
NOMENCLATURE AND SEMANTIC DESCRIPTIONS OF ULCERATIVE AND INFLAMMATORY LESIONS SEEN IN CROHN’S DISEASE IN SMALL BOWEL CAPSULE ENDOSCOPY: AN INTERNATIONAL DELPHI CONSENSUS STATEMENT

Background In the medical literature, the nomenclature and descriptions (ND) of small bowel (SB) ulcerative and inflammatory (U-I) lesions in capsule endoscopy (CE) are scarce and inconsistent. Inter-observer variability in interpreting these findings remains a major limitation in the assessment of the severity of mucosal lesions, which can impact negatively on clinical care, training and research on SB-CE. Objective Focusing on SB-CE in Crohn’s disease (CD), our aim is to establish a consensus on the ND of U-I lesions. Methods An international panel of experienced SB-CE readers was formed during the 2016 United European Gastroenterology Week meeting. A core group of five CE and inflammatory bowel disease (IBD) experts established an Internet-based, three-round Delphi consensus but did not participate in the voting process. The core group built illustrated questionnaires, including SB-CE still frames of U-I lesions from patients with documented CD. Twenty-seven other experts were asked to rate and comment on the different proposals for the ND of the most frequent SB U-I lesions. For each round, we used a 6-point rating scale (varying from ‘strongly disagree’ to ‘strongly agree’). The consensus was reached when at least 80 % of the voting members scored the statement within the ‘agree’ or ‘strongly agree’ categories. Results A 100% participation rate was obtained for all the rounds. Consensual ND were reached for the following seven U-I lesions: aphthoid erosion, deep ulceration, superficial ulceration, stenosis, edema, hyperemia and denudation. Conclusion Considering the most frequent SB U-I lesions seen in CE in CD, a consensual ND was reached by the international group of experts. These descriptions and names are useful not only for daily practice and medical education, but also for medical research.
Microscopic origin of spin-orbit torque in ferromagnetic heterostructures: A first-principles approach

We present an ab initio based theoretical framework which elucidates the origin of the spin-orbit torque (SOT) in normal-metal (NM)/ferromagnet (FM) heterostructures. The SOT is decomposed into two contributions, namely, spin-Hall and the spin-orbital components. We find that (i) the fieldlike (FL) SOT is dominated by the spin-orbital component and (ii) both components contribute to the dampinglike (DL) torque with comparable magnitude in the limit of thick Pt film. The contribution of the spin-orbital component to the DL-SOT is present only for NMs with strong SOC coupling strength. We demonstrate that the FL-SOT can be expressed in terms of the nonequilibrium spin-resolved orbital moment accumulation. The calculations reveal that the experimentally reported oxygen-induced sign reversal of the FL-SOT in Pt/Co bilayers is due to the significant reduction of the majority-spin orbital moment accumulation on the interfacial NM atoms.
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction

Autonomous agents must be able to safely interact with other vehicles to integrate into urban environments. The safety of these agents is dependent on their ability to predict collisions with other vehicles' future trajectories for replanning and collision avoidance. The information needed to predict collisions can be learned from previously observed vehicle trajectories in a specific environment, generating a traffic model. The learned traffic model can then be incorporated as prior knowledge into any trajectory estimation method being used in this environment. This work presents a Gaussian process based probabilistic traffic model that is used to quantify vehicle behaviors in an intersection. The Gaussian process model provides estimates for the average vehicle trajectory, while also capturing the variance between the different paths a vehicle may take in the intersection. The method is demonstrated on a set of time-series position trajectories. These trajectories are reconstructed by removing object recognition errors and missed frames that may occur due to data source processing. To create the intersection traffic model, the reconstructed trajectories are clustered based on their source and destination lanes. For each cluster, a Gaussian process model is created to capture the average behavior and the variance of the cluster. To show the applicability of the Gaussian model, the test trajectories are classified with only partial observations. Performance is quantified by the number of observations required to correctly classify the vehicle trajectory. Both the intersection traffic modeling computations and the classification procedure are timed. These times are presented as results and demonstrate that the model can be constructed in a reasonable amount of time and the classification procedure can be used for online applications.
An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models

Nowadays, autonomous driving has attracted much attention from both industry and academia. Convolutional neural network (CNN) is a key component in autonomous driving, which is also increasingly adopted in pervasive computing such as smartphones, wearable devices, and IoT networks. Prior work shows CNN-based classification models are vulnerable to adversarial attacks. However, it is uncertain to what extent regression models such as driving models are vulnerable to adversarial attacks, the effectiveness of existing defense techniques, and the defense implications for system and middleware builders.This paper presents an in-depth analysis of five adversarial attacks and four defense methods on three driving models. Experiments show that, similar to classification models, these models are still highly vulnerable to adversarial attacks. This poses a big security threat to autonomous driving and thus should be taken into account in practice. While these defense methods can effectively defend against different attacks, none of them are able to provide adequate protection against all five attacks. We derive several implications for system and middleware builders: (1) when adding a defense component against adversarial attacks, it is important to deploy multiple defense methods in tandem to achieve a good coverage of various attacks, (2) a black-box attack is much less effective compared with a white-box attack, implying that it is important to keep model details (e.g., model architecture, hyperparameters) confidential via model obfuscation, and (3) driving models with a complex architecture are preferred if computing resources permit as they are more resilient to adversarial attacks than simple models.
TRK Sum Rule for Interacting Photons

The Thomas-Reiche-Kuhn sum rule is a fundamental consequence of the position-momentum commutation relation for an atomic electron and it provides an important constraint on the transition matrix elements for an atom. Here we propose a TRK sum rule for electromagnetic fields which is valid even in the presence of very strong light-matter interactions and/or optical nonlinearities. While the standard TRK sum rule involves dipole matrix moments calculated between atomic energy levels (in the absence of interaction with the field), the sum rule here proposed involves expectation values of field operators calculated between general eigenstates of the interacting light-matter system. This sum rule provides constraints and guidance for the analysis of strongly interacting light-matter systems and can be used to test the validity of approximate effective Hamiltonians often used in quantum optics.
Data-based computation of stabilizing minimum dwell times for discrete-time switched linear systems

We present an algorithm to compute stabilizing minimum dwell times for discrete-time switched linear systems without the explicit knowledge of state-space models of their subsystems. Given a set of finite traces of state trajectories of the subsystems that satisfies certain properties, our algorithm involves the following tasks: first, multiple Lyapunov functions are designed from the given data; second, a set of relevant scalars is computed from these functions; and third, a stabilizing minimum dwell time is determined as a function of these scalars. A numerical example is presented to demonstrate the proposed algorithm.
RGB-based Semantic Segmentation Using Self-Supervised Depth Pre-Training

Although well-known large-scale datasets, such as ImageNet, have driven image understanding forward, most of these datasets require extensive manual annotation and are thus not easily scalable. This limits the advancement of image understanding techniques. The impact of these large-scale datasets can be observed in almost every vision task and technique in the form of pre-training for initialization. In this work, we propose an easily scalable and self-supervised technique that can be used to pre-train any semantic RGB segmentation method. In particular, our pre-training approach makes use of automatically generated labels that can be obtained using depth sensors. These labels, denoted by HN-labels, represent different height and normal patches, which allow mining of local semantic information that is useful in the task of semantic RGB segmentation. We show how our proposed self-supervised pre-training with HN-labels can be used to replace ImageNet pre-training, while using 25x less images and without requiring any manual labeling. We pre-train a semantic segmentation network with our HN-labels, which resembles our final task more than pre-training on a less related task, e.g. classification with ImageNet. We evaluate on two datasets (NYUv2 and CamVid), and we show how the similarity in tasks is advantageous not only in speeding up the pre-training process, but also in achieving better final semantic segmentation accuracy than ImageNet pre-training
Optimal Centralized Dynamic-Time-Division-Duplex

The study of optimal properties of centralized dynamic-time-division-duplex (D-TDD) employed at a wireless network consisting of multiple nodes is a highly challenging and partially understood problem in the literature. In this paper, we develop an optimal centralized D-TDD scheme for a wireless network comprised of K full-duplex nodes impaired by self-interference and additive white Gaussian noise. As a special case, we also propose the optimal centralized D-TDD scheme when part or all nodes in the wireless network are half-duplex. Thereby, we derive the optimal adaptive scheduling of the reception, transmission, simultaneous reception and transmission, and silence at every node in the network in each time slot such that the rate region of the network is maximized. The performance of the optimal centralized D-TDD can serve as an upper-bound to any other TDD scheme, which is useful in qualifying the relative performance of TDD schemes. The numerical results show that the proposed centralized D-TDD scheme achieves significant rate gains over existing centralized D-TDD schemes.
The utility of tactile force to autonomous learning of in-hand manipulation is task-dependent

Tactile sensors provide information that can be used to learn and execute manipulation tasks. Different tasks, however, might require different levels of sensory information; which in turn likely affect learning rates and performance. This paper evaluates the role of tactile information on autonomous learning of manipulation with a simulated 3-finger tendon-driven hand. We compare the ability of the same learning algorithm (Proximal Policy Optimization, PPO) to learn two manipulation tasks (rolling a ball about the horizontal axis with and without rotational stiffness) with three levels of tactile sensing: no sensing, 1D normal force, and 3D force vector. Surprisingly, and contrary to recent work on manipulation, adding 1D force-sensing did not always improve learning rates compared to no sensing---likely due to whether or not normal force is relevant to the task. Nonetheless, even though 3D force-sensing increases the dimensionality of the sensory input---which would in general hamper algorithm convergence---it resulted in faster learning rates and better performance. We conclude that, in general, sensory input is useful to learning only when it is relevant to the task---as is the case of 3D force-sensing for in-hand manipulation against gravity. Moreover, the utility of 3D force-sensing can even offset the added computational cost of learning with higher-dimensional sensory input.
Compensatory Microhabitat Selection by Northern Pacific Rattlesnakes (Crotalus oreganus oreganus) in a Cool and Wet Macroclimate

Abstract. Northern Pacific Rattlesnakes (Crotalus oreganus oreganus) have a range that extends from southern California into British Columbia. This subspecies is common in relatively warm and arid regions, but also occurs in habitats that are neither warm nor arid. We hypothesized that the presence of suitable microhabitat conditions can compensate for a less suitable macroclimate, allowing C. o. oreganus to exist in less suitable regions. We developed environmental niche models for C. o. oreganus at two spatial scales: (1) northern California and western Oregon, and (2) the northern California coast. These models explored macroclimatic suitability of northern, coastal California relative to other regions within the range of C. o. oreganus. The models revealed that the three most northern and coastal counties of California have a significantly less suitable macroclimate relative to the rest of each study area. Next, we used paired resource selection functions to determine microhabitat differences between rocky outcrops used as hibernacula and outcrops that are unoccupied by rattlesnakes despite similarities. Our analysis indicated selection for outcrops with more deep crevices, less vegetative cover, and slopes facing due south (180° from North). Additionally, we mapped landslides near hibernacula, which revealed that hibernacula commonly occurred within the head-scarps of landslides. We suggest that because landslide triggers (e.g., heavy rainfall and earthquakes) occur frequently along California's north coast, more rocky outcrops are created that are suitable as hibernacula. The relatively high abundance of these suitable outcrops compensates for the marginal macroclimate, allowing C. o. oreganus to occur in the region.
Low-dimensional statistical manifold embedding of directed graphs

We propose a novel node embedding of directed graphs to statistical manifolds, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-linear way. Each node is encoded with a probability density function over a measurable space. Furthermore, we analyze the connection between the geometrical properties of such embedding and their efficient learning procedure. Extensive experiments show that our proposed embedding is better in preserving the global geodesic information of graphs, as well as outperforming existing embedding models on directed graphs in a variety of evaluation metrics, in an unsupervised setting.
Phylogeographic Analysis of Mudpuppies (Necturus maculosus)

Abstract. The geology of the Pleistocene, and particularly the Last Glacial Maximum approximately 26.5 ka, is a critical driver of species present-day distributions and levels of genetic diversity in northern regions. Using mitochondrial DNA sequence data, we tested several predictions relating to the postglacial recolonization of the northern United States and southern Canada by Mudpuppies (Necturus maculosus). Our analyses revealed a significant split between western and eastern lineages, with the divide corresponding to the location of the Mississippi River. Our data support the presence of one or more Mississippian glacial refugia, with subsequent expansion and diversification of a western cluster into the upper Midwest and an eastern cluster into the eastern Great Lakes and New England. As predicted in cases of postglacial colonization, each of these clusters contains a single widespread and common haplotype along with numerous low-frequency, closely related haplotypes. Given recent conservation concerns about amphibians in general, and Mudpuppies specifically, we discuss our results in light of species conservation. Knowledge of a species' genetic diversity allows for informed management and facilitates decisions that preserve local adaptation and evolutionary potential.
Brucelose em uma égua doadora de embriões: Relato

The aim of this study was to report the occurrence of a case of brucellosis in a Quarter Horse Mare, used as donor embryos at a horse farm in Feira Nova - PE, focusing on major clinical manifestations, methods of diagnosis, treatment and prophylaxis and control actions. The Mare, who was 12 years old and was kept at Bay most of the day and a few hours in exclusive picket, presented swelling at the withers with presence of abscess, which presented healing a�耀 er local treatment, followed by recurrence of symptoms. In the historic abortamentos have been reported and birth of foals debilitated, with body weight below average and positive serology for Leptospira. The symptoms we opted for screening test Bu�耀 ered Acid Antigen (AAT) in an accredited laboratory. The complement fixation test (CFT) confirmed the positive result. By market value and use as donor embryos, the owner opted for treatment with Streptomycin and Oxytetracycline and then drain the abscess and local bandage. As the animal has not shown good response to treatment was sacrificed. It is concluded that the sacrifice of animals is recommended because it is not always a satisfactory response to treatment.