Miracle-dz/UniFAR-FTUs huggingface dataset:
Path Following of Underactuated Marine Underwater Vehicles in the Presence of Unknown Ocean Currents

The use of autonomous marine vehicles, and especially autonomous underwater vehicles, is rapidly increasing within several fields of study. In particular, such vehicles can be applied for sea floor mapping, oceanography, environmental monitoring, inspection and maintenance of underwater structures (for instance within the oil and gas industry) and military purposes. They are also highly suitable for operations below ice-covered areas in the Arctic. However, there are still many challenges related to making such underwater vehicles autonomous. A fundamental task of an autonomous underwater vehicle vessel is to follow a general path in the presence of unknown ocean currents. There exist several results for underwater vehicles to follow a general path when no ocean currents are present [1] and to follow a geometrically simple path such as a straight line when ocean currents affect the vehicle [2, 3], but the problem of general path following in the presence of unknown ocean currents has not been solved yet. This paper presents a method to achieve this. The results are an extension of the results in [1], and introduce a virtual Serret-Frenet reference frame that is anchored in and propagates along the desired path. The closed-loop system consists of an ocean current observer, a guidance law, a controller and an update law to drive the Serret-Frenet frame along the path, and is shown to be asymptotically stable given that certain assumptions are fulfilled. This guarantees that the autonomous underwater vehicle will converge to the desired path and move along it with the desired velocity. Simulation results are presented to verify and illustrate the theoretical results.Copyright © 2014 by ASME
Rewiring with Positional Encodings for Graph Neural Networks

Several recent works use positional encodings to extend the receptive fields of graph neural network (GNN) layers equipped with attention mechanisms. These techniques, however, extend receptive fields to the complete graph, at substantial computational cost and risking a change in the inductive biases of conventional GNNs, or require complex architecture adjustments. As a conservative alternative, we use positional encodings to expand receptive fields to $r$-hop neighborhoods. More specifically, our method augments the input graph with additional nodes/edges and uses positional encodings as node and/or edge features. We thus modify graphs before inputting them to a downstream GNN model, instead of modifying the model itself. This makes our method model-agnostic, i.e., compatible with any of the existing GNN architectures. We also provide examples of positional encodings that are lossless with a one-to-one map between the original and the modified graphs. We demonstrate that extending receptive fields via positional encodings and a virtual fully-connected node significantly improves GNN performance and alleviates over-squashing using small $r$. We obtain improvements on a variety of models and datasets and reach competitive performance using traditional GNNs or graph Transformers.
Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback

Field of view (FoV) prediction is critical in 360-degree video multicast, which is a key component of the emerging Virtual Reality (VR) and Augmented Reality (AR) applications. Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the weight sharing of traditional convolutional networks, nor do they adequately consider the difficulty of obtaining complete multi-user FoV information, which degrades the prediction performance. This paper proposes a spherical convolution-empowered FoV prediction method, which is a multi-source prediction framework combining salient features extracted from 360-degree video with limited FoV feedback information. A spherical convolution neural network (CNN) is used instead of a traditional two-dimensional CNN to eliminate the problem of weight sharing failure caused by video projection distortion. Specifically, salient spatial-temporal features are extracted through a spherical convolution-based saliency detection model, after which the limited feedback FoV information is represented as a time-series model based on a spherical convolution-empowered gated recurrent unit network. Finally, the extracted salient video features are combined to predict future user FoVs. The experimental results show that the performance of the proposed method is better than other prediction methods.
Detecting multi-sensor fusion errors in advanced driver-assistance systems

Advanced Driver-Assistance Systems (ADAS) have been thriving and widely deployed in recent years. In general, these systems receive sensor data, compute driving decisions, and output control signals to the vehicles. To smooth out the uncertainties brought by sensor outputs, they usually leverage multi-sensor fusion (MSF) to fuse the sensor outputs and produce a more reliable understanding of the surroundings. However, MSF cannot completely eliminate the uncertainties since it lacks the knowledge about which sensor provides the most accurate data and how to optimally integrate the data provided by the sensors. As a result, critical consequences might happen unexpectedly. In this work, we observed that the popular MSF methods in an industry-grade ADAS can mislead the car control and result in serious safety hazards. We define the failures (e.g., car crashes) caused by the faulty MSF as fusion errors and develop a novel evolutionary-based domain-specific search framework, FusED, for the efficient detection of fusion errors. We further apply causality analysis to show that the found fusion errors are indeed caused by the MSF method. We evaluate our framework on two widely used MSF methods in two driving environments. Experimental results show that FusED identifies more than 150 fusion errors. Finally, we provide several suggestions to improve the MSF methods we study.
ClassSPLOM - A Scatterplot Matrix to Visualize Separation of Multiclass Multidimensional Data

In multiclass classification of multidimensional data, the user wants to build a model of the classes to predict the label of unseen data. The model is trained on the data and tested on unseen data with known labels to evaluate its quality. The results are visualized as a confusion matrix which shows how many data labels have been predicted correctly or confused with other classes. The multidimensional nature of the data prevents the direct visualization of the classes so we design ClassSPLOM to give more perceptual insights about the classification results. It uses the Scatterplot Matrix (SPLOM) metaphor to visualize a Linear Discriminant Analysis projection of the data for each pair of classes and a set of Receiving Operating Curves to evaluate their trustworthiness. We illustrate ClassSPLOM on a use case in Arabic dialects identification.
Gradient estimate for complex Monge-Ampere equation with continuous right hand side

(1) The deep work of Kolodziej which states that if e ∈ L(M) for some p > 1, then φ ∈ C for some α ∈ (0, 1). (2) C2,α estimate when the right hand side F ∈ C. For real Monge Ampère equation, this goes back to Caffarelli[2]. For complex Monge-Ampère equation, following the theory of Evans-Krylov(see [15] for details on extension to complex setting), we know [φ]C2,α′ (M,g) is uniformly bounded, for each α ′ < α, if ∆φ is uniformly bounded. (3) The important theorem of Chen-He [5] which proves that if F ∈W 1,p for p > 2n, then we have n+∆φ < C. All the above results either estimate the C0 bound(or continuity bound of φ), or estimate the complex Hessian of φ. In this note, we will obtain estimates which, in a sense, are in between of these results. Namely we show that it is possible to obtain estimate for the gradient of φ, when the right hand side F is only assumed to be have certain continuity conditions. Let ω(r) be the modulus of continuity of F . That is, for r > 0, define ω(r) = supx,y∈M,d(x,y)<r |F (x)−F (y)|. Here d(x, y) is the distance function
Optimal Support for Distressed Subsidiaries -- a Systemic Risk Perspective

We consider a network of bank holdings, where every holding has two subsidiaries of different types. A subsidiary can trade with another holding's subsidiary of the same type. Holdings support their subsidiaries up to a certain level when they would otherwise fail to honor their financial obligations. We investigate the spread of contagion in this banking network when the number of bank holdings is large, and find the final number of defaulted subsidiaries under different rules for the holding support. We also consider resilience of this multilayered network to small shocks. Our work sheds light onto the role that holding structures can play in the amplification of financial stress. We find that depending on the capitalization of the network, a holding structure can be beneficial as compared to smaller separated entities. In other instances, it can be harmful and actually increase contagion. We illustrate our results in a numerical case study and also determine the optimal level of holding support from a regulator perspective.
Stratifying Online Field Experiments Using the Pigeonhole Design

Practitioners and academics have long appreciated the beneﬁts that experimentation brings to ﬁrms. For online web-facing ﬁrms, however, it still remains challenging in handling heterogeneity when experimental units arrive sequentially in online ﬁeld experiments. In this paper, we study a novel online experimental design problem, which we refer to as the “Online Stratiﬁcation Problem. ” In this problem, experimental units with heterogeneous covariate information arrive sequentially and must be immediately assigned into either the control or the treatment group, with an objective of minimizing the total discrepancy, which is deﬁned as the minimum weight perfect matching between the two groups. To solve this problem, we propose a novel experimental design approach, which we refer to as the “Pigeonhole Design. ” The pigeonhole design ﬁrst partitions the covariate space into smaller spaces, which we refer to as pigeonholes, and then, when the experimental units arrive at each pigeonhole, balances the number of control and treatment units for each pigeonhole. We analyze the theoretical performance of the pigeonhole design and show its eﬀectiveness by comparing against two well-known benchmark designs: the match-pair design and the completely randomized design. We conduct extensive simulations to study the numerical performance of the diﬀerent designs and conclude with practical suggestions.
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization

Gaussian process hyperparameter optimization requires linear solves with, and log -determinants of, large kernel matrices. Iterative numerical tech-niques are becoming popular to scale to larger datasets, relying on the conjugate gradient method (CG) for the linear solves and stochastic trace estimation for the log -determinant. This work introduces new algorithmic and theoretical in-sights for preconditioning these computations. While preconditioning is well understood in the context of CG, we demonstrate that it can also accelerate convergence and reduce variance of the estimates for the log -determinant and its derivative. We prove general probabilistic error bounds for the preconditioned computation of the log -determinant, log -marginal likelihood and its derivatives. Additionally, we derive speciﬁc rates for a range of kernel-preconditioner combinations, showing that up to exponential convergence can be achieved. Our theoretical results enable prov-ably efﬁcient optimization of kernel hyperparameters, which we validate empirically on large-scale benchmark problems. There our approach accelerates training by up to an order of magnitude.
Spatiotemporal Impact Assessment of Hurricanes on Electric Power Systems

Severe windstorms such as hurricanes are the primary cause of extensive power grid damages resulting in widespread customer outages and expensive recovery. This paper aims to develop a probabilistic impact assessment framework to model and quantify the spatiotemporal impacts of windstorms such as hurricanes on the bulk power grid. The variations in hurricane trajectory and wind speed are modeled using historical data from past events in the US. The impacts of individual power grid components are modeled using fragility curves typically obtained using historical outage data. Finally, the system losses are modeled using a loss metric quantifying the total load disconnected due to the impact of the hurricane as it travels inland. The simulation is performed on the 2000-bus synthetic Texas grid mapped on the geographical footprint of Texas. The simulation results show that the loss increases significantly for a few time steps when the hurricane's wind field is intense and saturates gradually when the hurricane's intensity decays while traversing further inland.
BiRD: Race Detection in Software Binaries under Relaxed Memory Models

Instruction reordering and interleavings in program execution under relaxed memory semantics result in non-intuitive behaviors, making it difficult to provide assurances about program correctness. Studies have shown that up to 90% of the concurrency bugs reported by state-of-the-art static analyzers are false alarms. As a result, filtering false alarms and detecting real concurrency bugs is a challenging problem. Unsurprisingly, this problem has attracted the interest of the research community over the past few decades. Nonetheless, many of the existing techniques rely on analyzing source code, rarely consider the effects introduced by compilers, and assume a sequentially consistent memory model. In a practical setting, however, developers often do not have access to the source code, and even commodity architectures such as x86 and ARM are not sequentially consistent. In this work, we present Bird, a prototype tool, to dynamically detect harmful data races in x86 binaries under relaxed memory models, TSO and PSO. Bird employs source-DPOR to explore all distinct feasible interleavings for a multithreaded application. Our evaluation of Bird on 42 publicly available benchmarks and its comparison with the state-of-the-art tools indicate Bird’s potential in effectively detecting data races in software binaries.
The relationships between physical fitness attributes and match demands in rugby union referees officiating the 2019 Rugby World Cup

ABSTRACT This study examined the relationships between physical fitness attributes and match demands in lead rugby union referees. Eleven referees underwent anthropometric and fitness assessments (40-m sprint, Yo-Yo Intermittent Recovery Test, 1.2-km shuttle run) prior to the 2019 Rugby World Cup. Match activities were assessed via global positioning system devices (total distance, high-speed running distance [>5 m·s−1], average speed, and peak intensities over 1-min, 5-min, and 10-min epochs) and heart rate variables were measured using chest-worn monitors (HRmean, summated-heart-rate-zones, time above 90% HRmax). Forty-metre sprint time was significantly related to maximum speed (P = 0.004; r = −0.79) and high-speed running distance (P = 0.037, r = −0.63) during matches. Likewise, ∑7 skinfold thickness was significantly correlated with high-speed running distance (P = 0.01, r = −0.72). Yo-Yo Intermittent Recovery Test, 1.2-km Shuttle Run Test, age, and body mass index exhibited non-significant correlations (P > 0.05, r = −0.58 to 0.53) with match demand variables. Results suggest it may be pertinent for referees to optimise sprint capacity and body fat composition to execute greater high-speed running volumes and reach higher peak speeds during matches. Yo-Yo Intermittent Recovery Test and 1.2-km shuttle run tests are not correlated to specific match activity variables and thus may not be relevant for monitoring of rugby union referees.
Experimental Investigations of the NASA Common Research Model (Invited)

An experimental aerodynamic investigation of the NASA Common Research Model has been conducted in the NASA NTF (National Transonic Facility). Data have been obtained at chord Reynolds numbers of 5, 19.8 and 30 million for the WB and WBT0 configurations. Data have also been obtained at a chord Reynolds number of 5 million for the WBNP, WBT+2 and WBT-2 configurations. Force and moment, surface pressure and surface flow visualization data were obtained but only the force and moment data are presented herein. Model deformation measurements, aeroelastic, nacelle/pylon, Reynolds number and tail effects have been assessed. The model deformation measurements showed more twist as you go out the wing span, with a break in the high q∞ data close to CL = 0.6 which is consistent with separation near the tip. Increases in dynamic pressure give an increase in pitching moment and drag and a decrease in lift for the WB and WBT0 configuration at Mach = 0.7, 0.85 and 0.87. The addition of a nacelle/pylon gave an increase in drag, decrease in lift and a less nose down pitching moment around the design lift condition of 0.5. Increases in chord Reynolds number have been found to follow the normal Reynolds number trends except at the 19.8 million low q∞ cases. The abnormality of the 19.8 million low q∞ cases is still under investigation. The tail effects also follow the expected trends. Finally, all of the data shown fall within the 2-sigma limits for repeatability.
A nucleation barrier spring-loads the CBM signalosome for binary activation

Immune cells activate in a binary, switch-like fashion that involves proteins polymerizing into large complexes known as signalosomes. The switch-like nature of signalosome formation has been proposed to result from large energy barriers to polymer nucleation. Whether such nucleation barriers indeed drive binary immune responses has not yet been shown. Here, we employed an in-cell biophysical approach to dissect the assembly mechanism of the CARD-BCL10-MALT1 (CBM) signalosome, a key determinant of transcription factor NF-κB activation in both innate and adaptive immunity. We found that the adaptor protein BCL10 encodes an intrinsic nucleation barrier, and that this barrier has been conserved from cnidaria to humans. Using optogenetic tools and a single-cell transcriptional reporter of NF-κB activity, we further revealed that endogenous human BCL10 is supersaturated even in unstimulated cells, indicating that the nucleation barrier operationally stores energy for subsequent activation. We found that upon stimulation, BCL10 nucleation by CARD9 multimers triggers self-templated polymerization that saturates NF-κB activation to produce a binary response. Pathogenic mutants of CARD9 that cause human immunodeficiencies eliminated nucleating activity. Conversely, a hyperactive cancer-causing mutation in BCL10 increased its spontaneous nucleation. Our results indicate that unassembled CBM signalosome components function analogously to a spring-loaded mousetrap, constitutively poised to activate NF-κB through irrevocable polymerization. This finding may inform our understanding of the root causes and progressive nature of pathogenic and age-associated inflammation.
Sodium-Glucose Co-Transporter 2 Inhibitors Use Improves the Satisfaction With Anti-diabetic Agent Treatment: A Questionnaire-based Propensity Score-matched Study

Background: Specific safety issues with sodium-glucose co-transporter-2 (SGLT2) inhibitors such as infection, fractures, worsening of renal function and euglycemic ketoacidosis have been raised. Concerns about adverse events might limit the use of this drug class. The satisfaction with SGLT2 inhibitors treatment in Chinese patients with type 2 diabetes mellitus (T2DM) is unknown. Material and Methods: Patients with T2DM who visited the hospital between October 2019 and June 2020 were included in this retrospective analysis. Patients were divided into SGLT2 inhibitors used group or not. The Satisfaction with Oral Anti-Diabetic Agent Scale (SOADAS) questionnaire and self-reported AEs were obtained at 3 months of follow-up. Propensity score matching (PSM) was performed to adjust for confounding factors. Univariate and multivariable linear regression models were used to explore potential risk factors associated with overall satisfaction. Results: A total of 145 T2DM patients were included, with 76 SGLT2 inhibitors users and 69 non-users. Patients administered with SGLT2 inhibitors presented with increased overall satisfaction (mean [SE]: 22.8 [0.67] vs. 20.6 [0.64], p = 0.016) and overall satisfaction rate (n [%]: 40 [52.6%] vs 21 [30.4%], p = 0.007) when compared to other anti-diabetic agents. The use of SGLT2 inhibitors significantly improved satisfaction of glycemic control ability (mean [SE]:3.9 [0.12] vs. 3.5 [0.12], p = 0.027), diabetic symptom’s control ability (3.5 [0.15] vs. 3.0 [0.15], p = 0.027), glycemic control speed (3.9 [0.11] vs. 3.4 [0.12], p = 0.011), medication tolerability (3.9 [0.10] vs. 3.5 [0.12], p = 0.012), and overall satisfaction (4.0 [0.11] vs. 3.6 [0.12], p = 0.037), but it did not improve satisfaction of medication effect on bodyweight (3.8 [0.11] vs. 3.4 [0.11], p = 0.166). After adjusting confounding factors (47 patients for each group), consistent results were obtained. No significant differences of self-reported clinical AEs were observed between SGLT2 inhibitors users and non-users. Multivariable regression analyses verified that the use of SGLT2 inhibitors was associated with increased levels of satisfaction. Conclusions: The use of SGLT2 inhibitors was associated with increased levels of satisfaction in T2DM patients, but not associated with overall clinical safety. Self-reported AEs were not related to the satisfaction with the use of anti-diabetic agents.