Miracle-dz/UniFAR-FTUs huggingface dataset:
Observer-based Controller Design for Oscillation Damping of a Novel Suspended Underactuated Aerial Platform

In this work, we present a novel actuation strategy for a suspended aerial platform. By utilizing an underactuation approach, we demonstrate the successful oscillation damping of the proposed platform, modeled as a spherical double pendulum. A state estimator is designed in order to obtain the deflection angles of the platform, which uses only onboard IMU measurements. The state estimator is an extended Kalman filter (EKF) with intermittent measurements obtained at different frequencies. An optimal state feedback controller and a PD+ controller are designed in order to dampen the oscillations of the platform in the joint space and task space respectively. The proposed underactuated platform is found to be more energy-efficient than an omnidirectional platform and requires fewer actuators. The effectiveness of our proposed system is validated using both simulations and experimental studies.
Proximity QA: Unleashing the Power of Multi-Modal Large Language Models for Spatial Proximity Analysis

Multi-modal large language models (MLLMs) have demonstrated remarkable vision-language capabilities, primarily due to the exceptional in-context understanding and multi-task learning strengths of large language models (LLMs). The advent of visual instruction tuning has further enhanced MLLMs' performance in vision-language understanding. However, while existing MLLMs adeptly recognize \textit{what} objects are in an image, they still face challenges in effectively discerning \textit{where} these objects are, particularly along the distance (scene depth) axis. To overcome this limitation in MLLMs, we introduce Proximity Question Answering (Proximity QA), a novel framework designed to enable MLLMs to infer the proximity relationship between objects in images. The framework operates in two phases: the first phase focuses on guiding the models to understand the relative depth of objects, and the second phase further encourages the models to infer the proximity relationships between objects based on their depth perceptions. We also propose a VQA dataset called Proximity-110K, containing additional instructions that incorporate depth information and the proximity relationships of objects. We have conducted extensive experiments to validate Proximity QA's superior ability in depth perception and proximity analysis, outperforming other state-of-the-art MLLMs. Code and dataset will be released at \textcolor{magenta}{https://github.com/NorthSummer/ProximityQA.git}.
Design and Testbed Deployment of Frequency-Domain Equalization Full Duplex Radios

Full-duplex (FD) wireless can significantly enhance spectrum efficiency but requires effective self-interference (SI) cancellers. RF SI cancellation (SIC) via frequency-domain equalization (FDE), where bandpass filters channelize the SI, is suited for integrated circuits (ICs). In this paper, we explore the limits and higher layer challenges associated with using such cancellers. We evaluate the performance of a custom FDE-based canceller using two testbeds; one with mobile FD radios and the other with upgraded, static FD radios in the PAWR COSMOS testbed. The latter is a lasting artifact for the research community, alongside a dataset containing baseband waveforms captured on the COSMOS FD radios, facilitating FD-related experimentation at the higher networking layers. We evaluate the performance of the FDE-based FD radios in both testbeds, with experiments showing 95 dB overall achieved SIC (52 dB from RF SIC) across 20 MHz bandwidth, and an average link-level FD rate gain of 1.87x. We also conduct experiments in (i) uplink-downlink networks with inter-user interference, and (ii) heterogeneous networks with half-duplex and FD users. The experimental FD gains in the two types of networks depend on the users' SNR values and the number of FD users, and are 1.14x-1.25x and 1.25x-1.73x, respectively, confirming previous analytical results.
Force Push: Robust Single-Point Pushing with Force Feedback

We present the first controller for quasistatic robotic planar pushing with single-point contact using only force feedback. We consider an omnidirectional mobile robot pushing an object (the"slider") along a given path, where the robot is equipped with a force-torque sensor to measure the force at the contact point with the slider. The geometric, inertial, and frictional parameters of the slider are not known to the controller, nor are measurements of the slider's pose. We assume that the robot can be localized so that the global position of the contact point is always known and that the approximate initial position of the slider is provided. Simulations and real-world experiments show that our controller yields stable pushes that are robust to a wide range of slider parameters and state perturbations along both straight and curved paths. Furthermore, we use an admittance controller to adjust the pushing velocity based on the measured force when the slider contacts obstacles like walls.
Topological superconductivity in a magnetic-texture coupled Josephson junction

Topological superconductors are appealing building blocks for robust and reliable quantum information processing. Most platforms for engineering topological superconductivity rely on a combination of superconductors, materials with intrinsic strong spin-orbit coupling, and external magnetic fields, detrimental for superconductivity. We propose a setup where a conventional Josephson junction is linked via a magnetic-textured barrier. Antiferromagnetic and ferromagnetic insulators with periodically arranged domains are compatible with our proposal which does not require intrinsic spin-orbit or external magnetic fields. We find that the topological phase depends on the magnitude and period of the barrier magnetization. The superconducting phase controls the topological transition, which could be detected as a sharp suppression of the supercurrent across the junction.
Identification of graphite with perfect rhombohedral stacking by electronic Raman scattering

Rhombohedral graphite (RG) shows strong correlations in its topological flat band and is pivotal for exploring emergent, correlated electronic phenomena. Its key advantage is the enhancement of electronic interactions with the increase in the number of rhombohedrally stacked graphene layers. Increasing thickness also leads to an exponential increase in the number of stacking faults, necessitating a precise method to identify flawless rhombohedral stacking. Overcoming this challenge is difficult because the established technique for stacking sequence identification, based on the 2D peak, fails in thick RG samples. We demonstrate that the strong layer dependence of the band structure can be harnessed to identify RG without stacking faults, or alternatively, to detect their presence. For thicknesses ranging from 3 to 12 layers, we show that each perfect RG structure presents distinctive peak positions in electronic Raman scattering (ERS). This measurement can be carried out using a conventional confocal Raman spectrometer at room temperature, using visible excitation wavelengths. Consequently, this overcomes the identification challenge by employing a simple and fast optical measurement technique, thereby helping to establish RG as a platform for studying strong correlations in one of the simplest crystals possible.
Do Object Detection Localization Errors Affect Human Performance and Trust?

Bounding boxes are often used to communicate automatic object detection results to humans, aiding humans in a multitude of tasks. We investigate the relationship between bounding box localization errors and human task performance. We use observer performance studies on a visual multi-object counting task to measure both human trust and performance with different levels of bounding box accuracy. The results show that localization errors have no significant impact on human accuracy or trust in the system. Recall and precision errors impact both human performance and trust, suggesting that optimizing algorithms based on the F1 score is more beneficial in human-computer tasks. Lastly, the paper offers an improvement on bounding boxes in multi-object counting tasks with center dots, showing improved performance and better resilience to localization inaccuracy.
The GRAVITY young stellar object survey XIII. Tracing the time-variable asymmetric disk structure in the inner AU of the Herbig star HD98922

Temporal variability in the photometric and spectroscopic properties of protoplanetary disks is common in young stellar objects. However, evidence pointing toward changes in their morphology over short timescales has only been found for a few sources, mainly due to a lack of high-cadence observations at high angular resolution. Understanding this type of variation could be important for our understanding of phenomena related to disk evolution. We study the morphological variability of the innermost circumstellar environment of HD\,98922 focusing on its dust and gas content. Multi-epoch observations of HD\,98922 at milliarcsecond resolution with VLTI/GRAVITY in the K-band at low (R=20) and high (R=4000) spectral resolution are combined with VLTI/PIONIER archival data covering a total time span of 11 years. We interpret the interferometric visibilities and spectral energy distribution with geometrical models and through radiative transfer techniques using the code MCMax. We investigated high-spectral-resolution quantities (visibilities and differential phases) to obtain information on the properties of the HI Brackett-gamma (Brgamma )-line-emitting region. Comparing observations taken with similar (u,varv ) plane coverage, we find that the squared visibilities do not vary significantly, whereas we find strong variability in the closure phases, suggesting temporal variations in the asymmetric brightness distribution associated to the disk. Our observations are best fitted by a model of a crescent-like asymmetric dust feature located at sim 1\,au and accounting for sim 70\, of the near-infrared (NIR) emission. The feature has an almost constant magnitude and orbits the central star with a possible sub-Keplerian period of sim 12\,months, although a 9\,month period is another, albeit less probable, solution. The radiative transfer models show that the emission originates from a small amount of carbon-rich ($25$) silicates, or quantum-heated particles located in a low-density region. Among different possible scenarios, we favor hydrodynamical instabilities in the inner disk that can create a large vortex. The high spectral resolution differential phases in the Brgamma line show that the hot-gas compact component is offset from the star and in some cases is located between the star and the crescent feature. The scale of the emission does not favor magnetospheric accretion as a driving mechanism. The scenario of an asymmetric disk wind or a massive accreting substellar or planetary companion is discussed. With this unique observational data set for HD\,98922 we reveal morphological variability in the innermost 2\,au of its disk region. This property is possibly common to many other protoplanetary disks, but is not commonly observed due to a lack of high-cadence observation. It is therefore important to pursue this approach with other sources for which an extended dataset with PIONIER, GRAVITY, and possibly MATISSE is available.
Cross-positive linear maps, positive polynomials and sums of squares

A linear map $\Phi$ between matrix spaces is called cross-positive if it is positive on orthogonal pairs $(U,V)$ of positive semidefinite matrices in the sense that $\langle U,V\rangle:=\text{Tr}(UV)=0$ implies $\langle \Phi(U),V\rangle\geq0$, and is completely cross-positive if all its ampliations $I_n\otimes \Phi$ are cross-positive. (Completely) cross-positive maps arise in the theory of operator semigroups, where they are sometimes called exponentially-positive maps, and are also important in the theory of affine processes on symmetric cones in mathematical finance. To each $\Phi$ as above a bihomogeneous form is associated by $p_\Phi(x,y)=y^T\Phi(xx^T)y$. Then $\Phi$ is cross-positive if and only if $p_\Phi$ is nonnegative on the variety of pairs of orthogonal vectors $\{(x,y)\mid x^Ty=0\}$. Moreover, $\Phi$ is shown to be completely cross-positive if and only if $p_\Phi$ is a sum of squares modulo the principal ideal $(x^Ty)$. These observations bring the study of cross-positive maps into the powerful setting of real algebraic geometry. Here this interplay is exploited to prove quantitative bounds on the fraction of cross-positive maps that are completely cross-positive. Detailed results about cross-positive maps $\Phi$ mapping between $3\times 3$ matrices are given. Finally, an algorithm to produce cross-positive maps that are not completely cross-positive is presented.
Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data

Difficulty is one of the key drivers of player engagement and it is often one of the aspects that designers tweak most to optimise the player experience; operationalising it is, therefore, a crucial task for game development studios. A common practice consists of creating metrics out of data collected by player interactions with the content; however, this allows for estimation only after the content is released and does not consider the characteristics of potential future players. In this article, we present a number of potential solutions for the estimation of difficulty under such conditions, and we showcase the results of a comparative study intended to understand which method and which types of data perform better in different scenarios. The results reveal that models trained on a combination of cohort statistics and simulated data produce the most accurate estimations of difficulty in all scenarios. Furthermore, among these models, artificial neural networks show the most consistent results.
Nanomechanically-induced nonequilibrium quantum phase transition to a self-organized density wave of a Bose-Einstein condensate

We report on a nonequilibrium quantum phase transition (NQPT) in a hybrid quantum many-body system consisting of a vibrational mode of a damped nanomembrane interacting optomechanically with a cavity, whose output light couples to two internal states of an ultracold Bose gas held in an external quasi-one-dimensional box potential. For small effective membrane-atom couplings, the system is in a homogeneous Bose-Einstein condensate (BEC) steady state, with no membrane displacement. Depending on the transition frequency between the two internal atomic states, either one or both internal states are occupied. By increasing the atom-membrane couplings, the system transitions to a symmetry-broken self-organized BEC phase, which is characterized by a considerably displaced membrane steady-state and density wave-like BEC profiles. This NQPT can be both discontinuous and continuous for a certain interval of transition frequencies, and is purely discontinuous outside of it.
Propagation Dynamics of Rumor vs. Non-rumor across Multiple Social Media Platforms Driven by User Characteristics

Studying information propagation dynamics in social media can elucidate user behaviors and patterns. However, previous research often focuses on single platforms and fails to differentiate between the nuanced roles of source users and other participants in cascades. To address these limitations, we analyze propagation cascades on Twitter and Weibo combined with a crawled dataset of nearly one million users with authentic attributes. Our preliminary findings from multiple platforms robustly indicate that rumors tend to spread more deeply, while non-rumors distribute more broadly. Interestingly, we discover that the spread of rumors is slower, persists longer, and, in most cases, involves fewer participants than that of non-rumors. And an undiscovered highlight is that reputable active users, termed `onlookers', inadvertently or unwittingly spread rumors due to their extensive online interactions and the allure of sensational fake news. Conversely, celebrities exhibit caution, mindful of releasing unverified information. Additionally, we identify cascade features aligning with exponential patterns, highlight the Credibility Erosion Effect (CEE) phenomenon in the propagation process, and discover the different contents and policies between the two platforms. Our findings enhance current understanding and provide a valuable statistical analysis for future research.
Error-Tolerant E-Discovery Protocols

We consider the multi-party classification problem introduced by Dong, Hartline, and Vijayaraghavan (2022) in the context of electronic discovery (e-discovery). Based on a request for production from the requesting party, the responding party is required to provide documents that are responsive to the request except for those that are legally privileged. Our goal is to find a protocol that verifies that the responding party sends almost all responsive documents while minimizing the disclosure of non-responsive documents. We provide protocols in the challenging non-realizable setting, where the instance may not be perfectly separated by a linear classifier. We demonstrate empirically that our protocol successfully manages to find almost all relevant documents, while incurring only a small disclosure of non-responsive documents. We complement this with a theoretical analysis of our protocol in the single-dimensional setting, and other experiments on simulated data which suggest that the non-responsive disclosure incurred by our protocol may be unavoidable.
VR-based generation of photorealistic synthetic data for training hand-object tracking models

Supervised learning models for precise tracking of hand-object interactions (HOI) in 3D require large amounts of annotated data for training. Moreover, it is not intuitive for non-experts to label 3D ground truth (e.g. 6DoF object pose) on 2D images. To address these issues, we present"blender-hoisynth", an interactive synthetic data generator based on the Blender software. Blender-hoisynth can scalably generate and automatically annotate visual HOI training data. Other competing approaches usually generate synthetic HOI data compeletely without human input. While this may be beneficial in some scenarios, HOI applications inherently necessitate direct control over the HOIs as an expression of human intent. With blender-hoisynth, it is possible for users to interact with objects via virtual hands using standard Virtual Reality hardware. The synthetically generated data are characterized by a high degree of photorealism and contain visually plausible and physically realistic videos of hands grasping objects and moving them around in 3D. To demonstrate the efficacy of our data generation, we replace large parts of the training data in the well-known DexYCB dataset with hoisynth data and train a state-of-the-art HOI reconstruction model with it. We show that there is no significant degradation in the model performance despite the data replacement.
On The Power of Subtle Expressive Cues in the Perception of Human Affects

In this study, we introduce a sketch-based method for testing how subtle expressive cues influence the perception of affect in illustrations of human figures. We specifically study the impact of human posture and gaze direction, implicitly specified through nose orientation, on perceived emotions and mood. Through a series of user studies using sketchy illustrations of a running figure, where a professional illustrator manipulated gaze direction through adjustments on the nose orientation, we found that this simple change resulted in a diverse range of perceived affects, spanning from fear to concern and wonder. These findings shed light on the importance of fine details in defining context for context-aware system designs and underscore the importance of recognizing and expressing affect. Understanding minor expressive cues is crucial to developing emotionally intelligent systems capable of expressing affect.
Scavenging Hyena: Distilling Transformers into Long Convolution Models

The rapid evolution of Large Language Models (LLMs), epitomized by architectures like GPT-4, has reshaped the landscape of natural language processing. This paper introduces a pioneering approach to address the efficiency concerns associated with LLM pre-training, proposing the use of knowledge distillation for cross-architecture transfer. Leveraging insights from the efficient Hyena mechanism, our method replaces attention heads in transformer models by Hyena, offering a cost-effective alternative to traditional pre-training while confronting the challenge of processing long contextual information, inherent in quadratic attention mechanisms. Unlike conventional compression-focused methods, our technique not only enhances inference speed but also surpasses pre-training in terms of both accuracy and efficiency. In the era of evolving LLMs, our work contributes to the pursuit of sustainable AI solutions, striking a balance between computational power and environmental impact.
A Channel to Form Fast-spinning Black Hole-Neutron Star Binary Mergers as Multimessenger Sources. II. Accretion-induced Spin-up

In this work, we investigate an alternative channel for the formation of fast-spinning black hole-neutron star (BHNS) binaries, in which super-Eddington accretion is expected to occur in accreting BHs during the stable mass transfer phase within BH-stripped helium (BH--He-rich) star binary systems. We evolve intensive \texttt{MESA} grids of close-orbit BH--He-rich star systems to systematically explore the projected aligned spins of BHs in BHNS binaries, as well as the impact of different accretion limits on the tidal disruption probability and electromagnetic (EM) signature of BHNS mergers. Most of the BHs in BHNS mergers cannot be effectively spun up through accretion, if the accretion rate is limited to $\lesssim10\,\dot{M}_{\rm Edd}$, where $\dot{M}_{\rm Edd}$ is the standard Eddington accretion limit. In order to reach high spins (e.g., $\chi_{\rm BH} \gtrsim 0.5$), the BHs are required to be born less massive (e.g., $\lesssim3.0\,M_\odot$) in binary systems with initial periods of $\lesssim0.2-0.3\,{\rm days}$ and accrete material at $\sim100\,\dot{M}_{\rm Edd}$. However, even under this high accretion limit, $\gtrsim6\,M_\odot$ BHs are typically challenging to significantly spin up and generate detectable associated EM signals. Our population simulations suggest that different accretion limits have a slight impact on the ratio of tidal disruption events. However, as the accretion limit increases, the EM counterparts from the cosmological BHNS population can become bright overall.