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Transformers are set to become ubiquitous with applications ranging from chatbots and educational assistants to visual recognition and remote sensing. However, their increasing computational and memory demands is resulting in growing energy consumption. Building models with fast and energy-efficient inference is imperative to enable a variety of transformer-based applications. Look Up Table (LUT) based Weightless Neural Networks are faster than the conventional neural networks as their inference only involves a few lookup operations. Recently, an approach for learning LUT networks directly via an Extended Finite Difference method was proposed. We build on this idea, extending it for performing the functions of the Multi Layer Perceptron (MLP) layers in transformer models and integrating them with transformers to propose Quasi Weightless Transformers (QuWeiT). This allows for a computational and energy-efficient inference solution for transformer-based models. On I-ViT-T, we achieve a comparable accuracy of 95.64% on CIFAR-10 dataset while replacing approximately 55% of all the multiplications in the entire model and achieving a 2.2x energy efficiency. We also observe similar savings on experiments with the nanoGPT framework.

In this paper, a heuristic for a heterogeneous min-max multi-vehicle multi-depot traveling salesman problem is proposed, wherein heterogeneous vehicles start from given depot locations and need to cover a given set of targets. In the considered problem, vehicles can be structurally heterogeneous due to different vehicle speeds and/or functionally heterogeneous due to different vehicle-target assignments originating from different sensing capabilities of vehicles. The proposed heuristic for the considered problem has three stages: an initialization stage to generate an initial feasible solution, a local search stage to improve the incumbent solution by searching through different neighborhoods, and a perturbation/shaking stage, wherein the incumbent solution is perturbed to break from a local minimum. In this study, three types of neighborhood searches are employed. Furthermore, two different methods for constructing the initial feasible solution are considered, and multiple variations in the neighborhoods considered are explored in this study. The considered variations and construction methods are evaluated on a total of 128 instances generated with varying vehicle-to-target ratios, distribution for generating the targets, and vehicle-target assignment and are benchmarked against the best-known heuristic for this problem. Two heuristics were finally proposed based on the importance provided to objective value or computation time through extensive computational studies.

Surface electromyography (sEMG) non-invasively measures signals generated by muscle activity with sufficient sensitivity to detect individual spinal neurons and richness to identify dozens of gestures and their nuances. Wearable wrist-based sEMG sensors have the potential to offer low friction, subtle, information rich, always available human-computer inputs. To this end, we introduce emg2qwerty, a large-scale dataset of non-invasive electromyographic signals recorded at the wrists while touch typing on a QWERTY keyboard, together with ground-truth annotations and reproducible baselines. With 1,135 sessions spanning 108 users and 346 hours of recording, this is the largest such public dataset to date. These data demonstrate non-trivial, but well defined hierarchical relationships both in terms of the generative process, from neurons to muscles and muscle combinations, as well as in terms of domain shift across users and user sessions. Applying standard modeling techniques from the closely related field of Automatic Speech Recognition (ASR), we show strong baseline performance on predicting key-presses using sEMG signals alone. We believe the richness of this task and dataset will facilitate progress in several problems of interest to both the machine learning and neuroscientific communities. Dataset and code can be accessed at https://github.com/facebookresearch/emg2qwerty., Comment: Submitted to NeurIPS 2024 Datasets and Benchmarks Track

We construct black hole saddles dual to real-time/Schwinger-Keldysh (SK) path integrals with arbitrary splits of the thermal density matrix generalizing the holographic SK prescription in \cite{Glorioso:2018mmw}. Using a scalar probe on the AdS Schwarzschild black brane as an example, we demonstrate how KMS properties of the boundary correlators naturally derive from these geometries. As deforming the boundary time contour is equivalent to the action of half sided modular transformation, these saddles can be used to compute higher point modular transformed correlators using a well controlled bulk perturbation theory. An interesting relation between these saddles and the more familiar eternal geometries, and the respective generators of time translation on them is described. Inspired from recent discussions on algebras of observables in gravity, we motivate that classical ensembles of such geometries with different amount of modular transformations should be promoted to genuine configurations of the bulk geometry. In particular, this is argued to imply the existence of additional classical moduli in the boundary open EFT dual to the exterior dynamics, and via fluid gravity correspondence, in the fluctuating hydrodynamics of the boundary. Finally, a Lorentzian version of the Kontsevich-Segal conditions is verified for all these geometries., Comment: 40 pages

In this paper, we consider a transfer reinforcement learning problem involving agents with different action spaces. Specifically, for any new unseen task, the goal is to use a successful demonstration of this task by an expert agent in its action space to enable a learner agent learn an optimal policy in its own different action space with fewer samples than those required if the learner was learning on its own. Existing transfer learning methods across different action spaces either require handcrafted mappings between those action spaces provided by human experts, which can induce bias in the learning procedure, or require the expert agent to share its policy parameters with the learner agent, which does not generalize well to unseen tasks. In this work, we propose a method that learns a subgoal mapping between the expert agent policy and the learner agent policy. Since the expert agent and the learner agent have different action spaces, their optimal policies can have different subgoal trajectories. We learn this subgoal mapping by training a Long Short Term Memory (LSTM) network for a distribution of tasks and then use this mapping to predict the learner subgoal sequence for unseen tasks, thereby improving the speed of learning by biasing the agent's policy towards the predicted learner subgoal sequence. Through numerical experiments, we demonstrate that the proposed learning scheme can effectively find the subgoal mapping underlying the given distribution of tasks. Moreover, letting the learner agent imitate the expert agent's policy with the learnt subgoal mapping can significantly improve the sample efficiency and training time of the learner agent in unseen new tasks.

In this paper, we study an inverse reinforcement learning problem that involves learning the reward function of a learning agent using trajectory data collected while this agent is learning its optimal policy. To address this problem, we propose an inverse reinforcement learning method that allows us to estimate the policy parameters of the learning agent which can then be used to estimate its reward function. Our method relies on a new variant of the behavior cloning algorithm, which we call bundle behavior cloning, and uses a small number of trajectories generated by the learning agent's policy at different points in time to learn a set of policies that match the distribution of actions observed in the sampled trajectories. We then use the cloned policies to train a neural network model that estimates the reward function of the learning agent. We provide a theoretical analysis to show a complexity result on bound guarantees for our method that beats standard behavior cloning as well as numerical experiments for a reinforcement learning problem that validate the proposed method.

We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of personalized videos based on a user's image. Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation. Our largest video generation model is a 30B parameter transformer trained with a maximum context length of 73K video tokens, corresponding to a generated video of 16 seconds at 16 frames-per-second. We show multiple technical innovations and simplifications on the architecture, latent spaces, training objectives and recipes, data curation, evaluation protocols, parallelization techniques, and inference optimizations that allow us to reap the benefits of scaling pre-training data, model size, and training compute for training large scale media generation models. We hope this paper helps the research community to accelerate progress and innovation in media generation models. All videos from this paper are available at https://go.fb.me/MovieGenResearchVideos.

Under-canopy agricultural robots can enable various applications like precise monitoring, spraying, weeding, and plant manipulation tasks throughout the growing season. Autonomous navigation under the canopy is challenging due to the degradation in accuracy of RTK-GPS and the large variability in the visual appearance of the scene over time. In prior work, we developed a supervised learning-based perception system with semantic keypoint representation and deployed this in various field conditions. A large number of failures of this system can be attributed to the inability of the perception model to adapt to the domain shift encountered during deployment. In this paper, we propose a self-supervised online adaptation method for adapting the semantic keypoint representation using a visual foundational model, geometric prior, and pseudo labeling. Our preliminary experiments show that with minimal data and fine-tuning of parameters, the keypoint prediction model trained with labels on the source domain can be adapted in a self-supervised manner to various challenging target domains onboard the robot computer using our method. This can enable fully autonomous row-following capability in under-canopy robots across fields and crops without requiring human intervention.

This paper introduces Llettuce, an open-source tool designed to address the complexities of converting medical terms into OMOP standard concepts. Unlike existing solutions such as the Athena database search and Usagi, which struggle with semantic nuances and require substantial manual input, Llettuce leverages advanced natural language processing, including large language models and fuzzy matching, to automate and enhance the mapping process. Developed with a focus on GDPR compliance, Llettuce can be deployed locally, ensuring data protection while maintaining high performance in converting informal medical terms to standardised concepts.

The transition from a chaotic to a periodic oscillatory state can be smooth or abrupt in real-world turbulent systems. Although there have been several mathematical studies, the occurrence of abrupt transitions in real-world systems such as turbulent reactive flow systems is not well understood. A turbulent reactive flow system consists of the flame, the acoustic field, and the hydrodynamic field interacting nonlinearly. Generally, as the Reynolds number is increased, a laminar flow becomes turbulent, and the range of time scales associated with the flow broadens. Yet, as the Reynolds number is increased in a turbulent reactive flow system, a single dominant time scale emerges in the acoustic pressure oscillations, indicated by its loss of multifractality. For such smooth and abrupt transitions from chaos to order, we study the evolution of correlated and uncorrelated dynamics between the acoustic pressure and the heat release rate oscillations in the spatiotemporal domain of the turbulent reactive system. The correlated dynamics that add or remove energy from the acoustic field are defined as conformists and contrarians, respectively. The uncorrelated dynamics, neither adds nor removes energy is defined as disorder. Conformist dynamics dominate the contrarian dynamics as order emerges from chaos. We discover that the spatial extent of the disordered dynamics plays a critical role in deciding the nature of the transition. During the smooth transition, we observe a significant presence of disordered dynamics in the spatial domain. In contrast, abrupt transitions are accompanied by the disappearance of disordered dynamics from the spatial domain., Comment: 22 pages, 7 figures

The emergence of periodic oscillations is observed in various complex systems in nature and engineering. Thermoacoustic oscillations in systems comprising turbulent reactive flow exemplify such complexity in the engineering context, where the emergence of oscillatory dynamics is often undesirable. In this work, we experimentally study the transition to periodic oscillations within a turbulent flow reactive system, with varying fuel-to-air ratio, represented by equivalence ratio as a bifurcation parameter. Further, we explore the change in the nature of the transition by varying a secondary parameter. In our system, we vary the thermal power input and the location of the flame stabilizer position individually as a secondary parameter. Our findings reveal five qualitatively distinct types of transitions to periodic oscillations. Two types of these transitions exhibit a continuous nature. Another two types of transitions involve multiple shifts in the dynamical states consisting of both continuous and discontinuous bifurcations. The last type of transition is characterized by an abrupt bifurcation to high-amplitude periodic oscillations. Understanding this metamorphosis of the transition - from continuous to discontinuous nature - is critical for advancing our comprehension of the dynamic behavior in turbulent reactive flow systems. The insights gained from this study have the potential to inform the design and control of similar engineering systems where managing oscillatory behavior is crucial., Comment: 32 pages, 12 figures

Existing vision-language understanding benchmarks largely consist of images of objects in their usual contexts. As a consequence, recent multimodal large language models can perform well with only a shallow visual understanding by relying on background language biases. Thus, strong performance on these benchmarks does not necessarily correlate with strong visual understanding. In this paper, we release JourneyBench, a comprehensive human-annotated benchmark of generated images designed to assess the model's fine-grained multimodal reasoning abilities across five tasks: complementary multimodal chain of thought, multi-image VQA, imaginary image captioning, VQA with hallucination triggers, and fine-grained retrieval with sample-specific distractors. Unlike existing benchmarks, JourneyBench explicitly requires fine-grained multimodal reasoning in unusual imaginary scenarios where language bias and holistic image gist are insufficient. We benchmark state-of-the-art models on JourneyBench and analyze performance along a number of fine-grained dimensions. Results across all five tasks show that JourneyBench is exceptionally challenging for even the best models, indicating that models' visual reasoning abilities are not as strong as they first appear. We discuss the implications of our findings and propose avenues for further research.

The moving target traveling salesman problem with obstacles (MT-TSP-O) is a generalization of the traveling salesman problem (TSP) where, as its name suggests, the targets are moving. A solution to the MT-TSP-O is a trajectory that visits each moving target during a certain time window(s), and this trajectory avoids stationary obstacles. We assume each target moves at a constant velocity during each of its time windows. The agent has a speed limit, and this speed limit is no smaller than any target's speed. This paper presents the first complete algorithm for finding feasible solutions to the MT-TSP-O. Our algorithm builds a tree where the nodes are agent trajectories intercepting a unique sequence of targets within a unique sequence of time windows. We generate each of a parent node's children by extending the parent's trajectory to intercept one additional target, each child corresponding to a different choice of target and time window. This extension consists of planning a trajectory from the parent trajectory's final point in space-time to a moving target. To solve this point-to-moving-target subproblem, we define a novel generalization of a visibility graph called a moving target visibility graph (MTVG). Our overall algorithm is called MTVG-TSP. To validate MTVG-TSP, we test it on 570 instances with up to 30 targets. We implement a baseline method that samples trajectories of targets into points, based on prior work on special cases of the MT-TSP-O. MTVG-TSP finds feasible solutions in all cases where the baseline does, and when the sum of the targets' time window lengths enters a critical range, MTVG-TSP finds a feasible solution with up to 38 times less computation time., Comment: Accepted to WAFR 2024

Robots too small to see by eye have rapidly evolved in recent years thanks to the incorporation of on-board microelectronics. Semiconductor circuits have been used in microrobots capable of executing controlled wireless steering, prescribed legged gait patterns, and user-triggered transitions between digital states. Yet these promising new capabilities have come at the steep price of complicated fabrication. Even though circuit components can be reliably built by semiconductor foundries, currently available actuators for electronically integrated microrobots are built with intricate multi-step cleanroom protocols and use mechanisms like articulated legs or bubble generators that are hard to design and control. Here, we present a propulsion system for electronically integrated microrobots that can be built with a single step of lithographic processing, readily integrates with microelectronics thanks to low current/low voltage operation (1V, 10nA), and yields robots that swim at speeds over one body length per second. Inspired by work on micromotors, these robots generate electric fields in a surrounding fluid, and by extension propulsive electrokinetic flows. The underlying physics is captured by a model in which robot speed is proportional to applied current, making design and control straightforward. As proof, we build basic robots that use on-board circuits and a closed-loop optical control scheme to navigate waypoints and move in coordinated swarms. Broadly, solid-state propulsion clears the way for robust, easy to manufacture, electronically controlled microrobots that operate reliably over months to years.

This paper introduces a novel formulation aimed at determining the optimal schedule for recharging a fleet of $n$ heterogeneous robots, with the primary objective of minimizing resource utilization. This study provides a foundational framework applicable to Multi-Robot Mission Planning, particularly in scenarios demanding Long-Duration Autonomy (LDA) or other contexts that necessitate periodic recharging of multiple robots. A novel Integer Linear Programming (ILP) model is proposed to calculate the optimal initial conditions (partial charge) for individual robots, leading to the minimal utilization of charging stations. This formulation was further generalized to maximize the servicing time for robots given adequate charging stations. The efficacy of the proposed formulation is evaluated through a comparative analysis, measuring its performance against the thrift price scheduling algorithm documented in the existing literature. The findings not only validate the effectiveness of the proposed approach but also underscore its potential as a valuable tool in optimizing resource allocation for a range of robotic and engineering applications.

Accurate lane detection is essential for effective path planning and lane following in autonomous driving, especially in scenarios with significant occlusion from vehicles and pedestrians. Existing models often struggle under such conditions, leading to unreliable navigation and safety risks. We propose two innovative approaches to enhance lane detection in these challenging environments, each showing notable improvements over current methods. The first approach aug-Segment improves conventional lane detection models by augmenting the training dataset of CULanes with simulated occlusions and training a segmentation model. This method achieves a 12% improvement over a number of SOTA models on the CULanes dataset, demonstrating that enriched training data can better handle occlusions, however, since this model lacked robustness to certain settings, our main contribution is the second approach, LOID Lane Occlusion Inpainting and Detection. LOID introduces an advanced lane detection network that uses an image processing pipeline to identify and mask occlusions. It then employs inpainting models to reconstruct the road environment in the occluded areas. The enhanced image is processed by a lane detection algorithm, resulting in a 20% & 24% improvement over several SOTA models on the BDDK100 and CULanes datasets respectively, highlighting the effectiveness of this novel technique., Comment: 8 pages, 6 figures and 4 tables

In this paper, we address the challenge of generating novel views of real-world objects with limited multi-view images through our proposed approach, FewShotNeRF. Our method utilizes meta-learning to acquire optimal initialization, facilitating rapid adaptation of a Neural Radiance Field (NeRF) to specific scenes. The focus of our meta-learning process is on capturing shared geometry and textures within a category, embedded in the weight initialization. This approach expedites the learning process of NeRFs and leverages recent advancements in positional encodings to reduce the time required for fitting a NeRF to a scene, thereby accelerating the inner loop optimization of meta-learning. Notably, our method enables meta-learning on a large number of 3D scenes to establish a robust 3D prior for various categories. Through extensive evaluations on the Common Objects in 3D open source dataset, we empirically demonstrate the efficacy and potential of meta-learning in generating high-quality novel views of objects.

Under-canopy agricultural robots require robust navigation capabilities to enable full autonomy but struggle with tight row turning between crop rows due to degraded GPS reception, visual aliasing, occlusion, and complex vehicle dynamics. We propose an imitation learning approach using diffusion policies to learn row turning behaviors from demonstrations provided by human operators or privileged controllers. Simulation experiments in a corn field environment show potential in learning this task with only visual observations and velocity states. However, challenges remain in maintaining control within rows and handling varied initial conditions, highlighting areas for future improvement., Comment: Accepted as Extended Abstract to the IEEE ICRA@40 2024

Superconducting QUantum Interference Devices (SQUIDs) are extremely sensitive magnetic flux sensors which render them useful in a wide array of instrumentation. SQUIDs are often paired with other detectors as a readout mechanism to obtain quantitative insight. SQUIDs have impacted many fields but much less addressed is its impact on the field of fundamental physics, particularly in the search for dark matter. Dark matter is believed to make up around 27% of all mass-energy content of the universe and will provide critical insight into understanding large-scale structures of the universe. Axions and WIMPs are the prominent two dark matter candidates whose search has been fueled by the usage of SQUID read-outs.

We present a novel algorithm that fuses the existing convex-programming based approach with heuristic information to find optimality guarantees and near-optimal paths for the Shortest Path Problem in the Graph of Convex Sets (SPP-GCS). Our method, inspired by $A^*$, initiates a best-first-like procedure from a designated subset of vertices and iteratively expands it until further growth is neither possible nor beneficial. Traditionally, obtaining solutions with bounds for an optimization problem involves solving a relaxation, modifying the relaxed solution to a feasible one, and then comparing the two solutions to establish bounds. However, for SPP-GCS, we demonstrate that reversing this process can be more advantageous, especially with Euclidean travel costs. In other words, we initially employ $A^*$ to find a feasible solution for SPP-GCS, then solve a convex relaxation restricted to the vertices explored by $A^*$ to obtain a relaxed solution, and finally, compare the solutions to derive bounds. We present numerical results to highlight the advantages of our algorithm over the existing approach in terms of the sizes of the convex programs solved and computation time.

Scanning tunneling microscopy is the method of choice for characterizing charge density waves by imaging the variation in atomic-scale contrast of the surface. Due to the measurement principle of scanning tunneling microscopy, the electronic and lattice degrees of freedom are convoluted, making it difficult to disentangle a structural displacement from spatial variations in the electronic structure. In this work, we quantify the influence of the displacement of the surface-terminating Se atoms on the 3 x 3 charge density wave contrast in scanning probe microscopy images of 2H-NbSe2. In scanning tunneling microscopy images, we observe the 3 x 3 charge density wave superstructure and atomic lattice at all probed tip-sample distances. In contrast, non-contact atomic force microscopy images show both periodicities only at small tip-sample distances while, unexpectedly, a 3 x 3 superstructure is present at larger tip-sample distances. Using density functional theory calculations, we qualitatively reproduce the experimental findings and reveal that the 3 x 3 superstructure at different tip-sample distances in non-contact atomic force microscopy images is a result from different underlying interactions. In addition, we show that the displacement of the surface-terminating Se atoms has a negligible influence to the contrast in scanning tunneling microscopy images. Our work presents a method on how to discriminate the influence of the surface corrugation from the variation of the charge density to the charge density wave contrast in scanning probe microscopy images, which can foster the understanding of the charge density wave mechanism in low-dimensional materials.

Tracking plant features is crucial for various agricultural tasks like phenotyping, pruning, or harvesting, but the unstructured, cluttered, and deformable nature of plant environments makes it a challenging task. In this context, the recent advancements in foundational models show promise in addressing this challenge. In our work, we propose PlantTrack where we utilize DINOv2 which provides high-dimensional features, and train a keypoint heatmap predictor network to identify the locations of semantic features such as fruits and leaves which are then used as prompts for point tracking across video frames using TAPIR. We show that with as few as 20 synthetic images for training the keypoint predictor, we achieve zero-shot Sim2Real transfer, enabling effective tracking of plant features in real environments.

Apart from performing arithmetic operations, understanding numbers themselves is still a challenge for existing language models. Simple generalisations, such as solving 100+200 instead of 1+2, can substantially affect model performance (Sivakumar and Moosavi, 2023). Among various techniques, character-level embeddings of numbers have emerged as a promising approach to improve number representation. However, this method has limitations as it leaves the task of aggregating digit representations to the model, which lacks direct supervision for this process. In this paper, we explore the use of mathematical priors to compute aggregated digit embeddings and explicitly incorporate these aggregates into transformer models. This can be achieved either by adding a special token to the input embeddings or by introducing an additional loss function to enhance correct predictions. We evaluate the effectiveness of incorporating this explicit aggregation, analysing its strengths and shortcomings, and discuss future directions to better benefit from this approach. Our methods, while simple, are compatible with any pretrained model and require only a few lines of code, which we have made publicly available.

Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric for density estimation. For example, when estimating population densities in a geographic region, a small Wasserstein distance means that the estimate is able to capture roughly where the population mass is. In this work we study differentially private density estimation in the Wasserstein distance. We design and analyze instance-optimal algorithms for this problem that can adapt to easy instances. For distributions $P$ over $\mathbb{R}$, we consider a strong notion of instance-optimality: an algorithm that uniformly achieves the instance-optimal estimation rate is competitive with an algorithm that is told that the distribution is either $P$ or $Q_P$ for some distribution $Q_P$ whose probability density function (pdf) is within a factor of 2 of the pdf of $P$. For distributions over $\mathbb{R}^2$, we use a different notion of instance optimality. We say that an algorithm is instance-optimal if it is competitive with an algorithm that is given a constant-factor multiplicative approximation of the density of the distribution. We characterize the instance-optimal estimation rates in both these settings and show that they are uniformly achievable (up to polylogarithmic factors). Our approach for $\mathbb{R}^2$ extends to arbitrary metric spaces as it goes via hierarchically separated trees. As a special case our results lead to instance-optimal private learning in TV distance for discrete distributions.

Urban land use inference is a critically important task that aids in city planning and policy-making. Recently, the increased use of sensor and location technologies has facilitated the collection of multi-modal mobility data, offering valuable insights into daily activity patterns. Many studies have adopted advanced data-driven techniques to explore the potential of these multi-modal mobility data in land use inference. However, existing studies often process samples independently, ignoring the spatial correlations among neighbouring objects and heterogeneity among different services. Furthermore, the inherently low interpretability of complex deep learning methods poses a significant barrier in urban planning, where transparency and extrapolability are crucial for making long-term policy decisions. To overcome these challenges, we introduce an explainable framework for inferring land use that synergises heterogeneous graph neural networks (HGNs) with Explainable AI techniques, enhancing both accuracy and explainability. The empirical experiments demonstrate that the proposed HGNs significantly outperform baseline graph neural networks for all six land-use indicators, especially in terms of 'office' and 'sustenance'. As explanations, we consider feature attribution and counterfactual explanations. The analysis of feature attribution explanations shows that the symmetrical nature of the `residence' and 'work' categories predicted by the framework aligns well with the commuter's 'work' and 'recreation' activities in London. The analysis of the counterfactual explanations reveals that variations in node features and types are primarily responsible for the differences observed between the predicted land use distribution and the ideal mixed state. These analyses demonstrate that the proposed HGNs can suitably support urban stakeholders in their urban planning and policy-making.

A sudden transition to a state of high amplitude limit cycle oscillations is catastrophic in a thermo-fluid system. Conventionally, upon varying the control parameter, a sudden transition is observed as an abrupt jump in the amplitude of the fluctuations in these systems. In contrast, we present an experimental discovery of a canard explosion in a turbulent reactive flow system where we observe a continuous bifurcation with a rapid rise in the amplitude of the fluctuations within a narrow range of control parameters. The observed transition is facilitated via a state of bursting, consisting of the epochs of large amplitude periodic oscillations amidst the epochs of low amplitude periodic oscillations. The amplitude of the bursts is higher than the amplitude of the bursts of intermittency state in a conventional gradual transition, as reported in turbulent reactive flow systems. During the bursting state, we observe that temperature fluctuations of exhaust gas vary at a slower time scale in correlation with the amplitude envelope of the bursts. We also present a phenomenological model for thermoacoustic systems to describe the observed canard explosion. Using the model, we explain that the large amplitude bursts occur due to the slow-fast dynamics at the bifurcation regime of the canard explosion.

Successful deployment of mobile robots in unstructured domains requires an understanding of the environment and terrain to avoid hazardous areas, getting stuck, and colliding with obstacles. Traversability estimation--which predicts where in the environment a robot can travel--is one prominent approach that tackles this problem. Existing geometric methods may ignore important semantic considerations, while semantic segmentation approaches involve a tedious labeling process. Recent self-supervised methods reduce labeling tedium, but require additional data or models and tend to struggle to explicitly label untraversable areas. To address these limitations, we introduce a weakly-supervised method for relative traversability estimation. Our method involves manually annotating the relative traversability of a small number of point pairs, which significantly reduces labeling effort compared to traditional segmentation-based methods and avoids the limitations of self-supervised methods. We further improve the performance of our method through a novel cross-image labeling strategy and loss function. We demonstrate the viability and performance of our method through deployment on a mobile robot in outdoor environments., Comment: Accepted by RA-L. Code is available at https://github.com/andreschreiber/W-RIZZ

The study deals with the breakup behavior of swirling liquid sheets discharging from gas-centered swirl coaxial atomizers with attention focused toward the understanding of the role of central gas jet on the liquid sheet breakup. Cold flow experiments on the liquid sheet breakup were carried out by employing custom fabricated gas-centered swirl coaxial atomizers using water and air as experimental fluids. Photographic techniques were employed to capture the flow behavior of liquid sheets at different flow conditions. Quantitative variation on the breakup length of the liquid sheet and spray width were obtained from the measurements deduced from the images of liquid sheets. The sheet breakup process is significantly influenced by the central air jet. It is observed that low inertia liquid sheets are more vulnerable to the presence of the central air jet and develop shorter breakup lengths at smaller values of the air jet Reynolds number $Re_g$. High inertia liquid sheets ignore the presence of the central air jet at smaller values of $Re_g$ and eventually develop shorter breakup lengths at higher values of $Re_g$. The experimental evidences suggest that the central air jet causes corrugations on the liquid sheet surface, which may be promoting the production of thick liquid ligaments from the sheet surface. The level of surface corrugations on the liquid sheet increases with increasing $Re_g$. Qualitative analysis of experimental observations reveals that the entrainment process of air established between the inner surface of the liquid sheet and the central air jet is the primary trigger for the sheet breakup.

Certain quantum sensing protocols rely on qubits that are initialized, coherently driven in the presence of a stimulus to be measured, then read out. Most widely employed pulse sequences used to drive sensing qubits act locally in either the time or frequency domain. We introduce a generalized set of sequences that effect a measurement in any fractional Fourier domain, i.e. along a linear trajectory of arbitrary angle through the time-frequency plane. Using an ensemble of nitrogen-vacancy centers we experimentally demonstrate advantages in sensing signals with time-varying spectra., Comment: 7 pages, 4 figures

We present a vision-based navigation system for under-canopy agricultural robots using semantic keypoints. Autonomous under-canopy navigation is challenging due to the tight spacing between the crop rows ($\sim 0.75$ m), degradation in RTK-GPS accuracy due to multipath error, and noise in LiDAR measurements from the excessive clutter. Our system, CropFollow++, introduces modular and interpretable perception architecture with a learned semantic keypoint representation. We deployed CropFollow++ in multiple under-canopy cover crop planting robots on a large scale (25 km in total) in various field conditions and we discuss the key lessons learned from this., Comment: Accepted to the IEEE ICRA Workshop on Field Robotics 2024

Researchers must stay current in their fields by regularly reviewing academic literature, a task complicated by the daily publication of thousands of papers. Traditional multi-label text classification methods often ignore semantic relationships and fail to address the inherent class imbalances. This paper introduces a novel approach using the SciBERT model and CNNs to systematically categorize academic abstracts from the Elsevier OA CC-BY corpus. We use a multi-segment input strategy that processes abstracts, body text, titles, and keywords obtained via BERT topic modeling through SciBERT. Here, the [CLS] token embeddings capture the contextual representation of each segment, concatenated and processed through a CNN. The CNN uses convolution and pooling to enhance feature extraction and reduce dimensionality, optimizing the data for classification. Additionally, we incorporate class weights based on label frequency to address the class imbalance, significantly improving the classification F1 score and enhancing text classification systems and literature review efficiency.

We consider the polynomial ring in finitely many variables over an algebraically closed field of positive characteristic, and initiate the systematic study of ideals preserved by the action of the general linear group by changes of coordinates. We show that these ideals are classified by sets of carry patterns, which are finite sequences of integers introduced by Doty in the study of representation theory of the polynomial ring. We provide an algorithm to decompose an invariant ideal as a sum of carry ideals with no redundancies. Next, we study the conditions under which one carry ideal is contained in another, and completely characterize the image of the multiplication map between the space of linear forms and a subrepresentation of forms of degree d. Finally, we begin an investigation into free resolutions of these ideals. Our results are most explicit in the case of carry ideals in two variables, where we completely describe the monomial generators and syzygies using base-p expansions of the parameters involved, and we provide a formula for the structure of the Tor modules in the Grothendieck group of representations., Comment: 25 pages, comments welcome

Superconducting diode effects have recently attracted much attention for their potential applications in superconducting logic circuits. Several mechanisms such as magneto-chiral effects, finite momentum Cooper pairing, asymmetric edge currents have been proposed to give rise to a supercurrent diode effect in different materials. In this work, we establish the presence of a large intrinsic Josephson diode effect in a type-II Dirac semimetal $1T-PtTe_2$ facilitated by its helical spin-momentum locking and distinguish it from other extrinsic effects. The magnitude of the Josephson diode effect is shown to be directly correlated to the large second-harmonic component of the supercurrent that is induced by the significant contribution of the topological spin-momentum locked states that promote coherent Andreev processes in the junction. We denote such junctions, where the relative phase between the two harmonics corresponding to charge transfers of $2e$ and $4e$ can be tuned by a magnetic field, as second order ${\phi}_0$-junctions. The direct correspondence between the second harmonic supercurrent component and the diode effect in $1T-PtTe_2$ junctions makes topological semimetals with high transparency an ideal platform to study and implement the Josephson diode effect, while also enabling further research on higher order supercurrent transport in Josephson junctions.

We study the gravitational edge mode in the $\mathcal{N}=1$ Jackiw-Teitelboim~(JT) supergravity on the disk and it $osp(2|1)$ BF formulation. We revisit the derivation of the finite-temperature Schwarzian action in the conformal gauge of the bosonic JT gravity through wiggling boundary and the frame fluctuation descriptions. Extending our method to $\mathcal{N}=1$ JT supergravity, we derive the finite-temperature super-Schwarzian action for the edge mode from both the wiggling boundary and the superframe field fluctuation. We emphasize the crucial role of the supersymmetric version of the inversion formula in elucidating the relation between the isometry and the $OSp(2|1)$ gauging of the super-Schwarzian action. In $osp(2|1)$ BF formulation, we discuss the asymptotic AdS condition. We employ the Iwasawa-like decomposition of $OSp(2|1)$ group element to derive the super-Schwarzian action at finite temperature. We demonstrate that the $OSp(2|1)$ gauging arises from inherent redundancy in the Iwasawa-like decomposition. We also discuss the path integral measure obtained from the Haar measure of $OSp(2|1)$., Comment: 32 pages, 1 figure

This paper introduces a new formulation that finds the optimum for the Moving-Target Traveling Salesman Problem (MT-TSP), which seeks to find a shortest path for an agent, that starts at a depot, visits a set of moving targets exactly once within their assigned time-windows, and returns to the depot. The formulation relies on the key idea that when the targets move along lines, their trajectories become convex sets within the space-time coordinate system. The problem then reduces to finding the shortest path within a graph of convex sets, subject to some speed constraints. We compare our formulation with the current state-of-the-art Mixed Integer Conic Program (MICP) solver for the MT-TSP. The experimental results show that our formulation outperforms the MICP for instances with up to 20 targets, with up to two orders of magnitude reduction in runtime, and up to a 60\% tighter optimality gap. We also show that the solution cost from the convex relaxation of our formulation provides significantly tighter lower bounds for the MT-TSP than the ones from the MICP., Comment: 7 pages, 4 figures