Miracle-dz/UniFAR-FTUs huggingface dataset:
A Probabilistic Motion Model for Skid-Steer Wheeled Mobile Robot Navigation on Off-Road Terrains

Skid-Steer Wheeled Mobile Robots (SSWMRs) are increasingly being used for off-road autonomy applications. When turning at high speeds, these robots tend to undergo significant skidding and slipping. In this work, using Gaussian Process Regression (GPR) and Sigma-Point Transforms, we estimate the non-linear effects of tire-terrain interaction on robot velocities in a probabilistic fashion. Using the mean estimates from GPR, we propose a data-driven dynamic motion model that is more accurate at predicting future robot poses than conventional kinematic motion models. By efficiently solving a convex optimization problem based on the history of past robot motion, the GPR augmented motion model generalizes to previously unseen terrain conditions. The output distribution from the proposed motion model can be used for local motion planning approaches, such as stochastic model predictive control, leveraging model uncertainty to make safe decisions. We validate our work on a benchmark real-world multi-terrain SSWMR dataset. Our results show that the model generalizes to three different terrains while significantly reducing errors in linear and angular motion predictions. As shown in the attached video, we perform a separate set of experiments on a physical robot to demonstrate the robustness of the proposed algorithm.
Monitoring year-to-year variability in dry mixed-grass prairie yield using multi-sensor remote sensing

The Normalized Difference Vegetation Index (NDVI) and harvested biomass were compared to assess rangeland productivity (above-ground green biomass or “yield”) in southern Albertan dry mixed-grass prairie. Seasonal trends during the 2009 and 2010 growing seasons were investigated using harvested biomass and NDVI derived from ground spectrometry and the Aqua and Terra Moderate Resolution Imaging Spectroradiometers (MODIS) and Système Pour l'Observation de la Terre (SPOT) satellite platforms. Drought in 2009 and high precipitation in 2010 provided contrasting “treatments” that were captured with measurements of NDVI. NDVI showed a saturating response to green plant biomass, with the strongest correlation (R 2 =0.97) arising from mid-summer measurements. NDVI from satellite remote sensing can accurately estimate interannual variation in standing green biomass, and field spectrometry can provide validation for satellite data. These methods can be used to identify the effects of yearly precipitation variability on above-ground biomass in the dry mixed-grass prairie. Acknowledgments This project was primarily funded by the Agricultural Financial Services Corporation (AFSC) of Alberta, with additional funding from NSERC, iCORE and Earth Analytics Group to John Gamon. I thank the staff of AFSC, and David Hildebrand in particular, for their support and patience over the life of this project. I am grateful to have the guidance of Dr. Arturo Sanchez-Azofeifa, Associate Dean of Faculty of Science (International and Graduate Affairs) and the Director of the Centre for Earth Observation Science (CEOS) in his role as chair of my Master’s committee. I would also like to thank Dr. Edward Bork of the University of Alberta Agriculture, Food and Nutritional Sciences Department and the Director of the University of Alberta Rangeland Research Institute for participating on my committee and for providing the idea for differential NDVI measurements. I am also indebted to several hard-working field assistants, including Natasha Jmaeff, Chad Diederichs, Molly Patterson, and Chris Wong, whose long hours in the field and in the lab enabled successful completion of this project. Li Hitao assisted with analysis of the SPOT data. I am extremely grateful to Dr. John A. Gamon for providing me with this opportunity and his guidance – greatly appreciated!
Universal energy-speed-accuracy trade-offs in driven nonequilibrium systems

Physical systems driven away from equilibrium by an external controller dissipate heat to the environment; the excess entropy production in the thermal reservoir can be interpreted as a"cost"to transform the system in a finite time. The connection between measure theoretic optimal transport and dissipative nonequilibrium dynamics provides a language for quantifying this cost and has resulted in a collection of"thermodynamic speed limits", which argue that the minimum dissipation of a transformation between two probability distributions is directly proportional to the rate of driving. Thermodynamic speed limits rely on the assumption that the target probability distribution is perfectly realized, which is almost never the case in experiments or numerical simulations. Here, we address the ubiquitous situation in which the external controller is imperfect. As a consequence, we obtain a lower bound for the dissipated work in generic nonequilibrium control problems that 1) is asymptotically tight and 2) matches the thermodynamic speed limit in the case of optimal driving. We illustrate these bounds on analytically solvable examples and also develop a strategy for optimizing minimally dissipative protocols based on optimal transport flow matching, a generative machine learning technique. This latter approach ensures the scalability of both the theoretical and computational framework we put forth. Crucially, we demonstrate that we can compute the terms in our bound numerically using efficient algorithms from the computational optimal transport literature and that the protocols that we learn saturate the bound.
Vision Transformers with Natural Language Semantics

Tokens or patches within Vision Transformers (ViT) lack essential semantic information, unlike their counterparts in natural language processing (NLP). Typically, ViT tokens are associated with rectangular image patches that lack specific semantic context, making interpretation difficult and failing to effectively encapsulate information. We introduce a novel transformer model, Semantic Vision Transformers (sViT), which leverages recent progress on segmentation models to design novel tokenizer strategies. sViT effectively harnesses semantic information, creating an inductive bias reminiscent of convolutional neural networks while capturing global dependencies and contextual information within images that are characteristic of transformers. Through validation using real datasets, sViT demonstrates superiority over ViT, requiring less training data while maintaining similar or superior performance. Furthermore, sViT demonstrates significant superiority in out-of-distribution generalization and robustness to natural distribution shifts, attributed to its scale invariance semantic characteristic. Notably, the use of semantic tokens significantly enhances the model's interpretability. Lastly, the proposed paradigm facilitates the introduction of new and powerful augmentation techniques at the token (or segment) level, increasing training data diversity and generalization capabilities. Just as sentences are made of words, images are formed by semantic objects; our proposed methodology leverages recent progress in object segmentation and takes an important and natural step toward interpretable and robust vision transformers.
Loch Leven nutrient load and source apportionment study

Loch Leven is in unfavourable condition. Attaining good water quality is critical to sustaining its many designated conservation features (SSSI, SPA, Ramsar site and NNR) in order to meet European Union Habitats and Water Framework Directive targets. High inputs of nutrients to the loch from its catchment (especially of phosphorus) have been identified as a key driver of these water quality problems. Long-term monitoring of the water quality within Loch Leven has been undertaken by the Natural Environment Research Council (NERC) since the late 1960s. The overall objective of this project was to assess the current phosphorus and nitrogen inputs to Loch Leven and provide information on their likely sources.
Joint Beamforming Design and Stream Allocation for Non-Coherent Joint Transmission in Cell-Free MIMO Networks

We consider joint beamforming and stream allocation to maximize the weighted sum rate (WSR) for non-coherent joint transmission (NCJT) in user-centric cell-free MIMO networks, where distributed access points (APs) are organized in clusters to transmit different signals to serve each user equipment (UE). We for the first time consider the common limits of maximum number of receive streams at UEs in practical networks, and formulate a joint beamforming and transmit stream allocation problem for WSR maximization under per-AP transmit power constraints. Since the integer number of transmit streams determines the dimension of the beamformer, the joint optimization problem is mixed-integer and nonconvex with coupled decision variables that is inherently NP-hard. In this paper, we first propose a distributed low-interaction reduced weighted minimum mean square error (RWMMSE) beamforming algorithm for WSR maximization with fixed streams. Our proposed RWMMSE algorithm requires significantly less interaction across the network and has the current lowest computational complexity that scales linearly with the number of transmit antennas, without any compromise on WSR. We draw insights on the joint beamforming and stream allocation problem to decouple the decision variables and relax the mixed-integer constraints. We then propose a joint beamforming and linear stream allocation algorithm, termed as RWMMSE-LSA, which yields closed-form updates with linear stream allocation complexity and is guaranteed to converge to the stationary points of the original joint optimization problem. Simulation results demonstrate substantial performance gain of our proposed algorithms over the current best alternatives in both WSR performance and convergence time.
3DSFLabelling: Boosting 3D Scene Flow Estimation by Pseudo Auto-labelling

Learning 3D scene flow from LiDAR point clouds presents significant difficulties, including poor generalization from synthetic datasets to real scenes, scarcity of real-world 3D labels, and poor performance on real sparse LiDAR point clouds. We present a novel approach from the perspective of auto-labelling, aiming to generate a large number of 3D scene flow pseudo labels for real-world LiDAR point clouds. Specifically, we employ the assumption of rigid body motion to simulate potential object-level rigid movements in autonomous driving scenarios. By updating different motion attributes for multiple anchor boxes, the rigid motion decomposition is obtained for the whole scene. Furthermore, we developed a novel 3D scene flow data augmentation method for global and local motion. By perfectly synthesizing target point clouds based on augmented motion parameters, we easily obtain lots of 3D scene flow labels in point clouds highly consistent with real scenarios. On multiple real-world datasets including LiDAR KITTI, nuScenes, and Argoverse, our method outperforms all previous supervised and unsupervised methods without requiring manual labelling. Impressively, our method achieves a tenfold reduction in EPE3D metric on the LiDAR KITTI dataset, reducing it from $0.190m$ to a mere $0.008m$ error.
Multilingual Speech Models for Automatic Speech Recognition Exhibit Gender Performance Gaps

Current voice recognition approaches use multi-task, multilingual models for speech tasks like Automatic Speech Recognition (ASR) to make them applicable to many languages without substantial changes. However, broad language coverage can still mask performance gaps within languages, for example, across genders. We systematically evaluate multilingual ASR systems on gendered performance gaps. Using two popular models on three datasets in 19 languages across seven language families, we find clear gender disparities. However, the advantaged group varies between languages. While there are no significant differences across groups in phonetic variables (pitch, speaking rate, etc.), probing the model's internal states reveals a negative correlation between probe performance and the gendered performance gap. I.e., the easier to distinguish speaker gender in a language, the more the models favor female speakers. Our results show that group disparities remain unsolved despite great progress on multi-tasking and multilinguality. We provide first valuable insights for evaluating gender gaps in multilingual ASR systems. We release all code and artifacts at https://github.com/g8a9/multilingual-asr-gender-gap.
Nuclear magnetic resonance studies in a model transverse field Ising system

The suppression of ferroquadrupolar order in TmVO$_4$ in a magnetic field is well-described by the transverse field Ising model, enabling detailed studies of critical dynamics near the quantum phase transition. We describe nuclear magnetic resonance measurements in pure and Y-doped single crystals. The non-Kramers nature of the ground state doublet leads to a unique form of the hyperfine coupling that exclusively probes the transverse field susceptibility. Our results show that this quantity diverges at the critical field, in contrast to the mean-field prediction. Furthermore, we find evidence for quantum critical fluctuations present near Tm-rich regions in Y-doped crystals at levels beyond which long-range order is suppressed, suggesting the presence of quantum Griffiths phases.
All-heavy pentaquarks in a nonrelativistic potential quark model

In a nonrelativistic potential quark model framework, we carry out a serious calculation of the all-heavy pentaquarks by adopting the explicitly correlated Gaussian method.A complete mass spectrum for the $1S$ states is obtained. For the $cccc\bar{c}$, $cccc\bar{b}$, $bbbb\bar{c}$, $bbbb\bar{b}$, $ccbb\bar{c}$, and $bbcc\bar{b}$ systems, the obtained states are compact and lie far above the lowest dissociation baryon-meson threshold. While, in the $cccb\bar{c}$, $cccb\bar{b}$, $bbbc\bar{c}$, and $bbbc\bar{b}$ systems with $\{123\}4\bar{5}$ symmetry, the two low-lying configurations with $J^P=5/2^-$ and $3/2^-$ have a typical molecular structure due to the special role of the color-Coulomb interactions, they may be good candidates of stable states below the dissociation baryon-meson thresholds.
Privacy Policies and Consent Management Platforms: Growth and Users' Interactions over Time

In response to growing concerns about user privacy, legislators have introduced new regulations and laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) that force websites to obtain user consent before activating personal data collection, fundamental to providing targeted advertising. The cornerstone of this consent-seeking process involves the use of Privacy Banners, the technical mechanism to collect users' approval for data collection practices. Consent management platforms (CMPs) have emerged as practical solutions to make it easier for website administrators to properly manage consent, allowing them to outsource the complexities of managing user consent and activating advertising features. This paper presents a detailed and longitudinal analysis of the evolution of CMPs spanning nine years. We take a twofold perspective: Firstly, thanks to the HTTP Archive dataset, we provide insights into the growth, market share, and geographical spread of CMPs. Noteworthy observations include the substantial impact of GDPR on the proliferation of CMPs in Europe. Secondly, we analyse millions of user interactions with a medium-sized CMP present in thousands of websites worldwide. We observe how even small changes in the design of Privacy Banners have a critical impact on the user's giving or denying their consent to data collection. For instance, over 60% of users do not consent when offered a simple"one-click reject-all"option. Conversely, when opting out requires more than one click, about 90% of users prefer to simply give their consent. The main objective is in fact to eliminate the annoying privacy banner rather the make an informed decision. Curiously, we observe iOS users exhibit a higher tendency to accept cookies compared to Android users, possibly indicating greater confidence in the privacy offered by Apple devices.
Gate Operations for Superconducting Qubits and Non-Markovianity: Fidelities, Long-range Time Correlations, and Suppression of Decoherence

While the accuracy of qubit operations has been greatly improved in the last decade, further development is demanded to achieve the ultimate goal: a fault-tolerant quantum computer that can solve real-world problems more efficiently than classical computers. With growing fidelities even subtle effects of environmental noise such as qubit-reservoir correlations and non-Markovian dynamics turn into the focus for both circuit design and control. To guide progress, we disclose, in a numerically rigorous manner, a comprehensive picture of the single-qubit dynamics in presence of a broad class of noise sources and for entire sequences of gate operations. Thermal reservoirs ranging from Ohmic to deep $1/f^{\varepsilon}$-like sub-Ohmic behavior are considered to imitate realistic scenarios for superconducting qubits. Apart from dynamical features, two figures of merit are analyzed, namely, fidelities of the qubit performance over entire sequences and coherence times in presence of quantum control schemes such as the Hahn echo and dynamical decoupling. The relevance of retarded feedback and long-range qubit-reservoir correlations is demonstrated on a quantitative level, thus, providing a deeper understanding of the limitations of performances for current devices and guiding the design of future ones.
Learning to Deblur Polarized Images

A polarization camera can capture four polarized images with different polarizer angles in a single shot, which is useful in polarization-based vision applications since the degree of polarization (DoP) and the angle of polarization (AoP) can be directly computed from the captured polarized images. However, since the on-chip micro-polarizers block part of the light so that the sensor often requires a longer exposure time, the captured polarized images are prone to motion blur caused by camera shakes, leading to noticeable degradation in the computed DoP and AoP. Deblurring methods for conventional images often show degenerated performance when handling the polarized images since they only focus on deblurring without considering the polarization constrains. In this paper, we propose a polarized image deblurring pipeline to solve the problem in a polarization-aware manner by adopting a divide-and-conquer strategy to explicitly decompose the problem into two less ill-posed sub-problems, and design a two-stage neural network to handle the two sub-problems respectively. Experimental results show that our method achieves state-of-the-art performance on both synthetic and real-world images, and can improve the performance of polarization-based vision applications such as image dehazing and reflection removal.
Clique Decompositions in Random Graphs via Refined Absorption

We prove that if $p\ge n^{-\frac{1}{3}+\beta}$ for some $\beta>0$, then asymptotically almost surely the binomial random graph $G(n,p)$ has a $K_3$-packing containing all but at most $n + O(1)$ edges. Similarly, we prove that if $d \ge n^{\frac{2}{3}+\beta}$ for some $\beta>0$ and $d$ is even, then asymptotically almost surely the random $d$-regular graph $G_{n,d}$ has a triangle decomposition provided $3 \mid d \cdot n$. We also show that $G(n,p)$ admits a fractional $K_3$-decomposition for such a value of $p$. We prove analogous versions for a $K_q$-packing of $G(n,p)$ with $p\ge n^{-\frac{1}{q+0.5}+\beta}$ and leave of $(q-2)n+O(1)$ edges, for $K_q$-decompositions of $G_{n,d}$ with $(q-1)~|~d$ and $d\ge n^{1-\frac{1}{q+0.5}+\beta}$ provided $q\mid d\cdot n$, and for fractional $K_q$-decompositions.
Imprints of new physics operators in the semileptonic $B \to a_1 (1260) \ell^- \bar{\nu}_\ell$ process in SMEFT approach

At present, there are several measurements of $B$ decays that exhibit discrepancies with the predictions of the Standard Model, and suggest the presence of new physics in $b\to s$ and $b \to c(u)$ quark level transitions. Motivated by the prospects of the ongoing high-luminosity $B$ factories, we study the exclusive $B \to a_1 (1260) \ell^- \bar{\nu}_\ell$ process within the Standard Model Effective Field Theory (SMEFT) formalism, to understand the sensitivity of new physics. The new physics parameters are constrained by using the experimental branching fractions of the (semi)leptonic $B \to \ell \bar{\nu}$ and $B \to (\pi, \rho, \omega) \ell \bar{\nu}$ processes (where $\ell = e, \mu, \tau$) which undergo $b \to u \ell \bar{\nu}$ quark level transitions. We then perform a comprehensive angular analysis of the exclusive $B \to a_1 (1260) \ell^- \bar{\nu}_\ell$ process in the Standard Model and in the presence of various new physics operators. We also provide the predictions and comment on various observables, such as branching ratio, forward-backward asymmetry, and the test of lepton flavor non universality of the $B \to a_1 (1260) \ell^- \bar{\nu}_\ell$ channel.
LatentSwap: An Efficient Latent Code Mapping Framework for Face Swapping

We propose LatentSwap, a simple face swapping framework generating a face swap latent code of a given generator. Utilizing randomly sampled latent codes, our framework is light and does not require datasets besides employing the pre-trained models, with the training procedure also being fast and straightforward. The loss objective consists of only three terms, and can effectively control the face swap results between source and target images. By attaching a pre-trained GAN inversion model independent to the model and using the StyleGAN2 generator, our model produces photorealistic and high-resolution images comparable to other competitive face swap models. We show that our framework is applicable to other generators such as StyleNeRF, paving a way to 3D-aware face swapping and is also compatible with other downstream StyleGAN2 generator tasks. The source code and models can be found at \url{https://github.com/usingcolor/LatentSwap}.
Levelling Up Learning: Exploring the Impact of Gamification in Flipped Classrooms

In recent years, the integration of gamification into educational settings has garnered significant attention as a means to enhance student engagement and learning outcomes. By leveraging gamified elements such as points and leaderboards, educators aim to promote active participation, motivation, and deeper understanding among students. This study investigates the effects of gamification on student engagement in a flipped classroom environment. The findings suggest that gamification strategies, when effectively implemented, can have a positive impact on student motivation and engagement. This paper concludes with recommendations for educators, potential challenges such as superficial engagement and demotivation, and future directions for research to address these challenges and further explore the potential of gamification in fostering student success.
Quantum anomalous Hall crystal at fractional filling of moir\'e superlattices

We predict the emergence a state of matter with intertwined ferromagnetism, charge order and topology in fractionally filled moir\'e superlattice bands. Remarkably, these quantum anomalous Hall crystals exhibit a quantized integer Hall conductance that is different than expected from the filling and Chern number of the band. Microscopic calculations show that this phase is robustly favored at half-filling ($\nu=1/2$) at larger twist angles of the twisted semiconductor bilayer $t$MoTe$_2$