Miracle-dz/UniFAR-FTUs huggingface dataset:
Flight Control Design Using Hierarchical Dynamic Inversion and Quasi-steady States

Design of kernel control laws is an important step in autonomous ∞ight control design for UAVs, because the function of kernel control laws is to guarantee asymptotical innerloop stability of aircraft motion relative to the surrounding air. The design can be further complicated by nonlinear ∞ight dynamics. In this paper, we propose a hierarchical dynamic inversion (HDI) method for design of the kernel control law for a flxed-wing UAV based on quasi-steady states. HDI is more generally applicable to some non-a‐ne nonlinear systems. The designed kernel control law is incorporated into an autonomous ∞ight controller and verifled in simulation based on the high-fldelity 6-DOF nonlinear model of a UAV. The simulation results demonstrate that the UAV with the designed kernel control law successfully execute super maneuver and is robust against actuator gain variation.
Coercive Sexual Experiences that Include Orgasm Predict Negative Psychological, Relationship, and Sexual Outcomes

Psychological sexual coercion is known to negatively impact those who experience it, yet sexual encounters where orgasm is present are often presumed to be positive and absent of coercion. In the present study, we conducted an online survey with women (n = 179) and men (n = 251) to test associations between sexually coercive experiences that include orgasm and negative psychological, sexual, and relationship outcomes. To do so, we focused on three experiences: having an orgasm during coerced sex (CS), having a coerced orgasm during desired sex (CO), and having a coerced orgasm during coerced sex (COS). Using structural equation modeling, we found that ever having any of these coercion-plus-orgasm experiences with a current partner predicted significantly higher avoidance motivations (i.e., engaging in sex to avoid conflict with one’s partner), which in turn predicted significantly worse psychological distress, sexual satisfaction, relationship satisfaction, and sexual functioning (but not dyadic sexual desire). We also found that CS, CO, and COS predicted negative outcomes to a similar degree. However, testing gender/sex as a moderator clarified that CS predicted significantly lower sexual satisfaction, sexual functioning, and sexual desire for women but not men. Furthermore, CO predicted faking orgasms in women, but COS predicted faking orgasms in men. Together, results demonstrate that experiencing psychological sexual coercion and/or orgasm coercion is significantly associated with negative outcomes even if the coerced person’s orgasm occurs.
Wasserstein Graph Neural Networks for Graphs with Missing Attributes

Missing node attributes is a common problem in real-world graphs. Graph neural networks have been demonstrated power in graph representation learning while their performance is affected by the completeness of graph information. Most of them are not specified for missing-attribute graphs and fail to leverage incomplete attribute information effectively. In this paper, we propose an innovative node representation learning framework, Wasserstein Graph Neural Network (WGNN), to mitigate the problem. To make the most of limited observed attribute information and capture the uncertainty caused by missing values, we express nodes as low-dimensional distributions derived from the decomposition of the attribute matrix. Furthermore, we strengthen the expressiveness of representations by developing a novel message passing schema that aggregates distributional information from neighbors in the Wasserstein space. We test WGNN in node classification tasks under two missing-attribute cases on both synthetic and real-world datasets. In addition, we find WGNN suitable to recover missing values and adapt them to tackle matrix completion problems with graphs of users and items. Experimental results on both tasks demonstrate the superiority of our method.
Recoloring Unit Interval Graphs with Logarithmic Recourse Budget

In this paper we study the problem of coloring a unit interval graph which changes dynamically. In our model the unit intervals are added or removed one at the time, and have to be colored immediately, so that no two overlapping intervals share the same color. After each update only a limited number of intervals is allowed to be recolored. The limit on the number of recolorings per update is called the recourse budget. In this paper we show, that if the graph remains k-colorable at all times, and the updates consist of insertions only, then we can achieve the amortized recourse budget of O(k7 log n) while maintaining a proper coloring with k colors. This is an exponential improvement over the result in [9] in terms of both k and n. We complement this result by showing the lower bound of Ω(n) on the amortized recourse budget in the fully dynamic setting. Our incremental algorithm can be efficiently implemented. As a byproduct of independent interest we include a new result on coloring proper circular arc graphs. Let L be the maximum number of arcs intersecting in one point for some set of unit circular arcs A. We show that if there is a set A′ of non-intersecting unit arcs of size L2− 1 such that A ∪ A′ does not contain L + 1 arcs intersecting in one point, then it is possible to color A with L colors. This complements the work on unit circular arc coloring [2, 28, 29], which specifies sufficient conditions needed to color A with L + 1 colors or more.
Network Robustness Revisited

The robustness of complex networks was one of the first phenomena studied after the inception of network science. However, many contemporary presentations of this theory do not go beyond the original papers. Here we revisit this topic with the aim of providing a deep but didactic introduction. We pay attention to some complications in the computation of giant component sizes that are commonly ignored. Following an intuitive procedure, we derive simple formulas that capture the effect of common attack scenarios on arbitrary (configuration model) networks. We hope that this easy introduction will help new researchers discover this beautiful area of network science.
Knowledge Management for Cloud Computing Field

Migration legacy systems to cloud platforms is a knowledge intensive process. There is an ever increasing body of knowledge reporting empirical scenarios of successful and problematic cloud migration. Reusing this body of knowledge, dispersed and fragmented over the academic/multi-vocal literature, has practical values to mitigate costly risks and pitfalls in further projects of legacy to-cloud and cloud-to-cloud migration. In line with this, knowledge management systems/platforms pertinent to cloud migration are a prime prerequisite and a strategic imperative for an organization. We have conducted a qualitative exploratory study to understand the benefits and challenges of developing Knowledge Management Systems (KMS) for cloud migration in real trials. Whilst our prototype system demonstration supported the importance and bene-fits of developing Cloud Migration KMS (CM-KMS), our semi-structured industry interview study with 11 participants highlighted challenging impediments against developing this class of KMS. As a result, this study proposes nine significant challenges that cause the abandon of the design and maintenance of CM-KMS, including continuous changes and updates, integration of knowledge, knowledge granularity, preservation of context, automation, deconstruction of traditional knowledge, dependency on experts, hybrid knowledge of both vendor-specific and vendor-neutral cloud platforms, and parsimony. Our results inform cloud architects to pay attention to adopt CM-KMS for the legacy-to-cloud migration in their organizations.
The quantale of order-preserving maps

Abstract. In this paper, two new composition operations are defined among the order-preserving maps. They can act on order-preserving maps like the usual composition operation. They are coincide with the usual composition operation when the order-preserving maps are sup-preserving maps or meetpreserving maps. The usual composition operation can’t endow the set L of order preserving maps on a complete lattices L with quantale or co-quantale structures. Luckily, two new operations can endow L with a quantale and a co-quantale structures respectively, whenever L is a completely distributive lattice.
Finite- and Fixed-Time Nonovershooting Stabilizers and Safety Filters by Homogeneous Feedback

Nonovershooting stabilization is a form of safe control where the setpoint chosen by the user is at the boundary of the safe set. In this article, we develop homogeneous feedback laws for fixed-time nonovershooting stabilization for nonlinear systems that are input–output linearizable with a full relative degree, i.e., for systems that are diffeomorphically equivalent to the chain of integrators. These homogeneous feedback laws can also assume the secondary role of “fixed-time safety filters,” which keep the system within the closed safe set for all time but, in the case where the user's nominal control commands approach to the unsafe set, allow the system to reach the boundary of the safe set no later than a desired time that is independent of nominal control and independent of the value of the state at the time the nominal control begins to be overridden.
DWDM Transmission of 128Gb/s PM-16QAM Signal over 1815km of 7-core MCF Using Shared Carrier Reception for Improving the Received Signal Quality

We demonstrate SCR method with 1815km transmission of 128Gb/s PM-16QAM over 7-core MCF using ECL. Frequency offset compensation of SCR enables demodulation and tolerates more than 10% path difference. Phase noise compensation of SCR enables to extend the transmitted distance, when using DFB, up to 1650km provided precise skew adjustment.
On Loss Functions and Evaluation Metrics for Music Source Separation

We investigate which loss functions provide better separations via benchmarking an extensive set of those for music source separation. To that end, we first survey the most representative audio source separation losses we identified, to later consistently benchmark them in a controlled experimental setup. We also explore using such losses as evaluation metrics, via cross-correlating them with the results of a subjective test. Based on the observation that the standard signal-to-distortion ratio metric can be misleading in some scenarios, we study alternative evaluation metrics based on the considered losses.
Ditto: Building Digital Twins of Articulated Objects from Interaction

Digitizing physical objects into the virtual world has the potential to unlock new research and applications in embodied AI and mixed reality. This work focuses on recreating interactive digital twins of real-world articulated objects, which can be directly imported into virtual environments. We introduce Ditto to learn articulation model estimation and 3D geometry reconstruction of an articulated object through interactive perception. Given a pair of visual observations of an articulated object before and after interaction, Ditto reconstructs part-level geometry and estimates the articulation model of the object. We employ implicit neural representations for joint geometry and articulation modeling. Our experiments show that Ditto effectively builds digital twins of articulated objects in a category-agnostic way. We also apply Ditto to real-world objects and deploy the recreated digital twins in physical simulation. Code and additional results are available at https://ut-austin-rpl.github. io/Ditto/
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis

A plethora of attribution methods have recently been developed to explain deep neural networks. These methods use different classes of perturbations (e.g, occlusion, blurring, masking, etc) to estimate the importance of individual image pixels to drive a model's decision. Nevertheless, the space of possible perturbations is vast and current attribution methods typically require significant computation time to accurately sample the space in order to achieve high-quality explanations. In this work, we introduce EVA (Explaining using Verified Perturbation Analysis) - the first explainability method which comes with guarantees that an entire set of possible perturbations has been exhaustively searched. We leverage recent progress in verified perturbation analysis methods to directly propagate bounds through a neural network to exhaustively probe a - potentially infinite-size - set of perturbations in a single forward pass. Our approach takes advantage of the beneficial properties of verified perturbation analysis, i.e., time efficiency and guaranteed complete - sampling agnostic - coverage of the perturbation space - to identify image pixels that drive a model's decision. We evaluate EVA systematically and demonstrate state-of-the-art results on multiple benchmarks. Our code isfreely available: github.com/deel-ai/formal-explainability
Classifying Galaxy Morphologies with Few-shot Learning

The taxonomy of galaxy morphology is critical in astrophysics as the morphological properties are powerful tracers of galaxy evolution. With the upcoming Large-scale Imaging Surveys, billions of galaxy images challenge astronomers to accomplish the classification task by applying traditional methods or human inspection. Consequently, machine learning, in particular supervised deep learning, has been widely employed to classify galaxy morphologies recently due to its exceptional automation, efficiency, and accuracy. However, supervised deep learning requires extensive training sets, which causes considerable workloads; also, the results are strongly dependent on the characteristics of training sets, which leads to biased outcomes potentially. In this study, we attempt Few-shot Learning to bypass the two issues. Our research adopts the data set from the Galaxy Zoo Challenge Project on Kaggle, and we divide it into five categories according to the corresponding truth table. By classifying the above data set utilizing few-shot learning based on Siamese Networks and supervised deep learning based on AlexNet, VGG_16, and ResNet_50 trained with different volumes of training sets separately, we find that few-shot learning achieves the highest accuracy in most cases, and the most significant improvement is 21% compared to AlexNet when the training sets contain 1000 images. In addition, to guarantee the accuracy is no less than 90%, few-shot learning needs ∼6300 images for training, while ResNet_50 requires ∼13,000 images. Considering the advantages stated above, foreseeably, few-shot learning is suitable for the taxonomy of galaxy morphology and even for identifying rare astrophysical objects, despite limited training sets consisting of observational data only.
Learning Model Predictive Control for Quadrotors

Aerial robots can enhance their safe and agile navigation in complex and cluttered environments by efficiently exploiting the information collected during a given task. In this paper, we address the learning model predictive control problem for quadrotors. We design a learning receding-horizon nonlinear control strategy directly formulated on the system nonlinear manifold configuration space SO(3)×R3. The proposed approach exploits past successful task iterations to improve the system performance over time while respecting system dynamics and actuator constraints. We further relax its computational complexity making it compatible with real-time quadrotor control requirements. We show the effectiveness of the proposed approach in learning a minimum time control task, respecting dynamics, actuators, and environment constraints. Several experiments in simulation and real-world set-up validate the proposed approach.
Comparison on gait characteristics between controlled and free-living conditions in old adults

Gait is an important biomarker of functional conditions and gait characteristics can help us assessing health conditions and managing progression of diseases. Most of the existing research study the gait in controlled condition, such as clinical tests. In this paper, we study the gait characteristics in free-living conditions in old adults and compare them with that in controlled conditions, i.e., Timed Up and Go (TUG) test. 65 subjects (12 patients with mobility impairment and 53 healthy controls) are recruited from elderly nursing institutions. The video data are collected from them in TUG test and free-living conditions and the 9 gait characteristics, including gait speed, are extracted from the data. Two-sample tests and independence test based on copula entropy are conducted on the extracted data to compare the characteristics in two conditions. Comparison results show that gait characteristics, such as gait speed, pace, speed variability, etc., in daily life are diﬀerent from that of in TUG test. In daily life, people tend to have slow gait speed, smaller pace and speed variability, more frequent stride, and smaller acceleration range than in TUG test. We also found that gait speed, pace, and speed variability have stronger dependence with TUG score in the 3 conditions (TUG, daily life, and both) and that other 5 characteristics have stronger dependence with TUG score in both condition than in each condition. The comparison in this study suggests that TUG and daily life conditions are complementary with each other, and that TUG test can be considered as intervention on the movement state of human.
Finite-amplitude elastic waves in viscoelastic channel flow from large to zero Reynolds number

Abstract Using branch continuation in the FENE-P model, we show that finite-amplitude travelling waves borne out of the recently discovered linear instability of viscoelastic channel flow (Khalid et al., J. Fluid Mech., vol. 915, 2021, A43) are substantially subcritical reaching much lower Weissenberg ($Wi$) numbers than on the neutral curve at a given Reynolds ($Re$) number over $Re \in [0,3000]$. The travelling waves on the lower branch are surprisingly weak indicating that viscoelastic channel flow is susceptible to (nonlinear) instability triggered by small finite-amplitude disturbances for $Wi$ and $Re$ well below the neutral curve. The critical $Wi$ for these waves to appear in a saddle node bifurcation decreases monotonically from, for example, $\approx 37$ at $Re=3000$ down to $\approx 7.5$ at $Re=0$ at the solvent-to-total-viscosity ratio $\beta =0.9$. In this latter creeping flow limit, we also show that these waves exist at $Wi \lesssim 50$ for higher polymer concentrations, $\beta \in [0.5,0.97)$, where there is no known linear instability. Our results therefore indicate that these travelling waves, found in simulations and named ‘arrowheads’ by Dubief et al. (Phys. Rev. Fluids, vol. 7, 2022, 073301), exist much more generally in $(Wi,Re, \beta )$ parameter space than their spawning neutral curve and, hence, can either directly, or indirectly through their instabilities, influence the dynamics seen far away from where the flow is linearly unstable. Possible connections to elastic and elasto-inertial turbulence are discussed.
Nonverbal Sound Detection for Disordered Speech

Voice assistants have become an essential tool for people with various disabilities because they enable complex phone-or tablet-based interactions without the need for fine-grained motor control, such as with touchscreens. However, these systems are not tuned for the unique characteristics of individuals with speech disorders, including many of those who have a motor-speech disorder, are deaf or hard of hearing, have a severe stutter, or are minimally verbal. We introduce an alternative voice-based input system which relies on sound event detection using fifteen nonverbal mouth sounds like "pop", "click", or "eh. " This system was designed to work regardless of ones’ speech abilities and allows full access to existing technology. In this paper, we describe the design of a dataset, model considerations for real-world deployment, and efforts towards model personalization. Our fully-supervised model achieves segment-level precision and recall of 88.6% and 88.4% on an internal dataset of 710 adults, while achieving 0.31 false positives per hour on aggressors such as speech. Five-shot personalization enables satisfactory performance in 84.5% of cases where the generic model fails.
Optimal market completion through financial derivatives with applications to volatility risk

This paper investigates the optimal choices of financial derivatives to complete a financial market in the framework of stochastic volatility (SV) models. We introduce an efficient and accurate simulation-based method, applicable to generalized diffusion models, to approximate the optimal derivatives-based portfolio strategy. We build upon the double optimization approach (i.e. expected utility maximization and risk exposure minimization) proposed in Escobar-Anel et al. (2022); demonstrating that strangle options are the best choices for market completion within equity options. Furthermore, we explore the benefit of using volatility index derivatives and conclude that they could be more convenient substitutes when only longterm maturity equity options are available.
Simulating the Space Weather in the AU Mic System: Stellar Winds and Extreme Coronal Mass Ejections

Two close-in planets have been recently found around the M-dwarf flare star AU Microscopii (AU Mic). These Neptune-sized planets (AU Mic b and c) seem to be located very close to the so-called “evaporation valley” in the exoplanet population, making this system an important target for studying atmospheric loss on exoplanets. This process, while mainly driven by high-energy stellar radiation, will be strongly mediated by the space environment surrounding the planets. Here we present an investigation of this last area, performing 3D numerical modeling of the quiescent stellar wind from AU Mic, as well as time-dependent simulations describing the evolution of a highly energetic coronal mass ejection (CME) event in this system. Observational constraints on the stellar magnetic field and properties of the eruption are incorporated in our models. We carry out qualitative and quantitative characterizations of the stellar wind, the emerging CMEs, as well as the expected steady and transient conditions along the orbit of both exoplanets. Our results predict extreme space weather for AU Mic and its planets. This includes sub-Alfvénic regions for the large majority of the exoplanet orbits, very high dynamic and magnetic pressure values in quiescence (varying within 102–105 times the dynamic pressure experienced by Earth), and an even harsher environment during the passage of any escaping CME associated with the frequent flaring observed in AU Mic. These space weather conditions alone pose an immense challenge for the survival of exoplanetary atmospheres (if any) in this system.