Miracle-dz/UniFAR-FTUs huggingface dataset:
SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction

In the field of 3D scene understanding, 3D scene graphs have emerged as a new scene representation that combines geometric and semantic information about objects and their relationships. However, learning semantic 3D scene graphs in a fully supervised manner is inherently difficult as it requires not only object-level annotations but also relationship labels. While pre-training approaches have helped to boost the performance of many methods in various fields, pre-training for 3D scene graph prediction has received little attention. Furthermore, we find in this paper that classical contrastive point cloud-based pre-training approaches are ineffective for 3D scene graph learning. To this end, we present SGRec3D, a novel self-supervised pre-training method for 3D scene graph prediction. We propose to reconstruct the 3D input scene from a graph bottleneck as a pretext task. Pre-training SGRec3D does not require object relationship labels, making it possible to exploit large-scale 3D scene understanding datasets, which were off-limits for 3D scene graph learning before. Our experiments demonstrate that in contrast to recent point cloud-based pre-training approaches, our proposed pre-training improves the 3D scene graph prediction considerably, which results in SOTA performance, outperforming other 3D scene graph models by +10% on object prediction and +4% on relationship prediction. Additionally, we show that only using a small subset of 10% labeled data during fine-tuning is sufficient to outperform the same model without pre-training.
Temporal graph models fail to capture global temporal dynamics

A recently released Temporal Graph Benchmark is analyzed in the context of Dynamic Link Property Prediction. We outline our observations and propose a trivial optimization-free baseline of"recently popular nodes"outperforming other methods on medium and large-size datasets in the Temporal Graph Benchmark. We propose two measures based on Wasserstein distance which can quantify the strength of short-term and long-term global dynamics of datasets. By analyzing our unexpectedly strong baseline, we show how standard negative sampling evaluation can be unsuitable for datasets with strong temporal dynamics. We also show how simple negative-sampling can lead to model degeneration during training, resulting in impossible to rank, fully saturated predictions of temporal graph networks. We propose improved negative sampling schemes for both training and evaluation and prove their usefulness. We conduct a comparison with a model trained non-contrastively without negative sampling. Our results provide a challenging baseline and indicate that temporal graph network architectures need deep rethinking for usage in problems with significant global dynamics, such as social media, cryptocurrency markets or e-commerce. We open-source the code for baselines, measures and proposed negative sampling schemes.
Teach and Repeat Navigation: A Robust Control Approach

Robot navigation requires an autonomy pipeline that is robust to environmental changes and effective in varying conditions. Teach and Repeat (T&R) navigation has shown high performance in autonomous repeated tasks under challenging circumstances, but research within T&R has predominantly focused on motion planning as opposed to motion control. In this paper, we propose a novel T&R system based on a robust motion control technique for a skid-steering mobile robot using sliding-mode control that effectively handles uncertainties that are particularly pronounced in the T&R task, where sensor noises, parametric uncertainties, and wheel-terrain interaction are common challenges. We first theoretically demonstrate that the proposed T&R system is globally stable and robust while considering the uncertainties of the closed-loop system. When deployed on a Clearpath Jackal robot, we then show the global stability of the proposed system in both indoor and outdoor environments covering different terrains, outperforming previous state-of-the-art methods in terms of mean average trajectory error and stability in these challenging environments. This paper makes an important step towards long-term autonomous T&R navigation with ensured safety guarantees.
Microscopic mechanism of structural and volume relaxation below glass transition temperature in a soda-lime silicate glass revealed by Raman spectroscopy and its first principle calculations

To elucidate the atomistic origin of volume relaxation in soda-lime silicate glass annealed below the glass transition temperature (Tg), the experimental and calculated Raman spectra were compared. By decomposing the calculated Raman spectra into a specific group of atoms, we found that the Raman peak at 1050 cm-1 corresponds to bridging oxygen with a small Si-O-Si bond angle. The experimental Raman spectra indicated that, during annealing below Tg, a homogenization reaction Q2+Q4->2Q3 proceeds in the early stage of structural relaxation. Then, the Si-O-Si units with relatively small angles decrease even in the later stages, which is first evidence of ring deformation causing volume relaxation of soda-lime silicate glass because decreasing small Si-O-Si angles corresponds to the reduce of acute O-O-O angle in a ring and can expand the space inside the rings, and Na can be inserted into the ring center. In conclusion the ring deformation and Na displacement is the origin of the volume relaxation of soda-lime silicate glass below Tg.
Scaling solutions as Early Dark Energy resolutions to the Hubble tension

A wide class of scalar field models including Quintessence and K-essence have the attractive property of tracker regimes, where the energy density stored in the field evolves so as to mimic that of the dominant background component for a period of time. During this evolution, for a brief period of time there is an increase in the energy density of the field as it spirals in towards it's attractor solution. We show that when the peak of this energy density occurs around the epoch of equality, we can address a key requirement of early dark energy (EDE), postulated as a solution to the Hubble tension. In particular we demonstrate how this can occur in a wide class of Quintessence, axion and K-essence models, before showing that the Quintessence models suffer in that they generally lead to sound speeds incompatible with the requirements of EDE, whereas the K-essence and axion models can do a better job of fitting the data.
Characters of $GL_n(\mathbb F_q)$ and vertex operators

In this paper, we present a vertex operator approach to construct and compute all complex irreducible characters of the general linear group $\mathrm{GL}_n(\mathbb F_q)$. Green's theory of $\mathrm{GL}_n(\mathbb F_q)$ is recovered and enhanced under the realization of the Grothendieck ring of representations $R_G=\bigoplus_{n\geq 0}R(\mathrm{GL}_n(\mathbb F_q))$ as two isomorphic Fock spaces associated to two infinite-dimensional $F$-equivariant Heisenberg Lie algebras $\widehat{\mathfrak{h}}_{\hat{\overline{\mathbb F}}_q}$ and $\widehat{\mathfrak{h}}_{\overline{\mathbb F}_q}$, where $F$ is the Frobenius automorphism of the algebraically closed field $\overline{\mathbb F}_q$. Under this picture, the irreducible characters are realized by the Bernstein vertex operators for Schur functions, the characteristic functions of the conjugacy classes are realized by the vertex operators for the Hall-Littlewood functions, and the character table is completely given by matrix coefficients of vertex operators of these two types. One of the features of the current approach is a simpler identification of the Fock space $R_G$ as the Hall algebra of symmetric functions via vertex operator calculus, and another is that we are able to compute in general the character table, where Green's degree formula is demonstrated as an example.
Prospective cohort study of parathyroid function and quality of life after total thyroidectomy for thyroid cancer: robotic surgery vs. open surgery

Objective: To compare robot-assisted thyroidectomy (RT) and open thyroidectomy (OT) through a prospective cohort study focusing on the rate of postoperative hypoparathyroidism, efficacy, and quality of life (QoL). Summary background data: Hypoparathyroidism is a frequent complication after thyroidectomy. Reducing the risk of hypoparathyroidism after total thyroidectomy is a crucial and difficult task for thyroid surgeons. Methods: We prospectively enroled 306 patients with papillary thyroid carcinoma into an RT group and OT group. The former used “super-meticulous” capsular dissection) and the latter used traditional meticulous capsular dissection. Patients were evaluated by scales [Short Form (SF)-36, Visual Impairment Scale (VIS), Swallowing Impairment Scale (SIS), Neck Impairment Scale (NIS), Scar questionnaire (SCAR-Q)]. Results: The rates of transient hypoparathyroidism, permanent hypoparathyroidism, and transient hypocalcemia after surgery in the OT group and RT group were significantly different (P<0.001). SIS and VIS scores in the two groups were significantly different (P<0.001). SF-36 showed significant differences (P<0.001) in the subsections of “physiological function”, “body pain”, “general health”, “vitality”, “social function”, “role emotional”, and “mental health” between the two groups. SCAR-Q showed that the length and appearance of scars showed significant differences between the two groups. Conclusions: RT with Super-meticulous capsular dissection can protect parathyroid function and improve postoperative QoL, and could be a new option for robot-assisted surgery against thyroid cancer.
Large Language Model Routing with Benchmark Datasets

There is a rapidly growing number of open-source Large Language Models (LLMs) and benchmark datasets to compare them. While some models dominate these benchmarks, no single model typically achieves the best accuracy in all tasks and use cases. In this work, we address the challenge of selecting the best LLM out of a collection of models for new tasks. We propose a new formulation for the problem, in which benchmark datasets are repurposed to learn a"router"model for this LLM selection, and we show that this problem can be reduced to a collection of binary classification tasks. We demonstrate the utility and limitations of learning model routers from various benchmark datasets, where we consistently improve performance upon using any single model for all tasks.
Guided Frequency Loss for Image Restoration

Image Restoration has seen remarkable progress in recent years. Many generative models have been adapted to tackle the known restoration cases of images. However, the interest in benefiting from the frequency domain is not well explored despite its major factor in these particular cases of image synthesis. In this study, we propose the Guided Frequency Loss (GFL), which helps the model to learn in a balanced way the image's frequency content alongside the spatial content. It aggregates three major components that work in parallel to enhance learning efficiency; a Charbonnier component, a Laplacian Pyramid component, and a Gradual Frequency component. We tested GFL on the Super Resolution and the Denoising tasks. We used three different datasets and three different architectures for each of them. We found that the GFL loss improved the PSNR metric in most implemented experiments. Also, it improved the training of the Super Resolution models in both SwinIR and SRGAN. In addition, the utility of the GFL loss increased better on constrained data due to the less stochasticity in the high frequencies' components among samples.
Multiple Case Physics-Informed Neural Network for Biomedical Tube Flows

Fluid dynamics computations for tube-like geometries are important for biomedical evaluation of vascular and airway fluid dynamics. Physics-Informed Neural Networks (PINNs) have recently emerged as a good alternative to traditional computational fluid dynamics (CFD) methods. The vanilla PINN, however, requires much longer training time than the traditional CFD methods for each specific flow scenario and thus does not justify its mainstream use. Here, we explore the use of the multi-case PINN approach for calculating biomedical tube flows, where varied geometry cases are parameterized and pre-trained on the PINN, such that results for unseen geometries can be obtained in real time. Our objective is to identify network architecture, tube-specific, and regularization strategies that can optimize this, via experiments on a series of idealized 2D stenotic tube flows.
Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions

Accurate trajectory prediction is crucial for safe and efficient autonomous driving, but handling partial observations presents significant challenges. To address this, we propose a novel trajectory prediction framework called Partial Observations Prediction (POP) for congested urban road scenarios. The framework consists of two stages: self-supervised learning (SSL) and feature distillation. In SSL, a reconstruction branch reconstructs the hidden history of partial observations using a mask procedure and reconstruction head. The feature distillation stage transfers knowledge from a fully observed teacher model to a partially observed student model, improving prediction accuracy. POP achieves comparable results to top-performing methods in open-loop experiments and outperforms the baseline method in closed-loop simulations, including safety metrics. Qualitative results illustrate the superiority of POP in providing reasonable and safe trajectory predictions.
DECO: Dense Estimation of 3D Human-Scene Contact In The Wild

Understanding how humans use physical contact to interact with the world is key to enabling human-centric artificial intelligence. While inferring 3D contact is crucial for modeling realistic and physically-plausible human-object interactions, existing methods either focus on 2D, consider body joints rather than the surface, use coarse 3D body regions, or do not generalize to in-the-wild images. In contrast, we focus on inferring dense, 3D contact between the full body surface and objects in arbitrary images. To achieve this, we first collect DAMON, a new dataset containing dense vertex-level contact annotations paired with RGB images containing complex human-object and human-scene contact. Second, we train DECO, a novel 3D contact detector that uses both body-part-driven and scene-context-driven attention to estimate vertex-level contact on the SMPL body. DECO builds on the insight that human observers recognize contact by reasoning about the contacting body parts, their proximity to scene objects, and the surrounding scene context. We perform extensive evaluations of our detector on DAMON as well as on the RICH and BEHAVE datasets. We significantly outperform existing SOTA methods across all benchmarks. We also show qualitatively that DECO generalizes well to diverse and challenging real-world human interactions in natural images. The code, data, and models are available at https://deco.is. tue.mpg. de.
Memory-Efficient Continual Learning Object Segmentation for Long Video

Recent state-of-the-art semi-supervised Video Object Segmentation (VOS) methods have shown significant improvements in target object segmentation accuracy when information from preceding frames is used in segmenting the current frame. In particular, such memory-based approaches can help a model to more effectively handle appearance changes (representation drift) or occlusions. Ideally, for maximum performance, Online VOS methods would need all or most of the preceding frames (or their extracted information) to be stored in memory and be used for online learning in later frames. Such a solution is not feasible for long videos, as the required memory size grows without bound, and such methods can fail when memory is limited and a target object experiences repeated representation drifts throughout a video. We propose two novel techniques to reduce the memory requirement of Online VOS methods while improving modeling accuracy and generalization on long videos. Motivated by the success of continual learning techniques in preserving previously-learned knowledge, here we propose Gated-Regularizer Continual Learning (GRCL), which improves the performance of any Online VOS subject to limited memory, and a Reconstruction-based Memory Selection Continual Learning (RMSCL), which empowers Online VOS methods to efficiently benefit from stored information in memory. We also analyze the performance of a hybrid combination of the two proposed methods. Experimental results show that the proposed methods are able to improve the performance of Online VOS models by more than 8%, with improved robustness on long-video datasets while maintaining comparable performance on short-video datasets such as DAVIS16, DAVIS17, and YouTube-VOS18.
Detlef D\"urr, arrival-time distributions, and spin in Bohmian mechanics: Personal recollections and state-of-the-art

I recount here my association with Prof. Detlef D\"urr leading to our memorable research collaboration on arrival-time distributions in quantum mechanics. He influenced my life, both personally and professionally, as few others have or ever will. Detlef is my role model for what a brilliant, discerning scientist, academic, and mentor can and should be. The"arrival-time problem"in quantum mechanics is examined selectively, with an emphasis on the arrival-time distributions of Bohmian particles. In what follows, the"exotic"Bohmian arrival-time distributions of spin-polarized electrons accelerating down a cylindrical waveguide [S. Das and D. D\"urr, Sci. Rep. 9: 2242 (2019)], and some variations thereof are discussed. I shall not go into the mathematical treatment more than is necessary to spell out the key results. The intention is to document the circumstances and motivations underlying the ideas.
Long-Term Temporal Trends in Agri-Environment and Agricultural Land Use in Ontario, Canada: Transformation, Transition and Significance

To help ground agri-environmental policy in evidence, long-term trends in agri-environmental and agricultural land use data in Ontario, Canada (1826-2011, 1921-2011) were identified using correlation and trend detection (Mann-Kendal test) and compared to other parts of Canada and other developed countries. After historic expansion 1826-1931, farm area decreased 1931-2011 by 4 million ha or 44.5%, similar to decreases in eastern Canada, eastern US and other developed countries, but contrasting continued but slowing expansion in western Canada. Ontario crop area varied little 1921-2011, but with large shifts in crop types and production increases. Crop area in eastern Canada was declining or stable, while in western Canada continued increasing. Major changes 1921-2011 included significant declining pasture area (-78.7%), hay area (-44.9%) and cattle numbers (-33.9%), along with increases in soybean and corn area and in chicken and hog numbers-transforming landscapes, similar to trends in eastern Canada but contrasting western Canada. Two periods of change are identified using principal components analysis, 1921-1970s, a long modernization period (69.1% of variance) and the 1970s-2011 period (20.1% of variance), with decreases in cattle numbers, hay and pasture and increases in hogs, chickens, soybeans, corn and wheat. After the 1970s, agri-environmental stewardship increased, and some environmental risks decreased with reduced pesticide use (-45%, 1983-2008), phosphorus fertilizer use (-30.1%, 1981-2011) and estimated manure volumes (-42.6%, 1976-2011). Illustrating the regional diversity of agricultural change, Ontario trends are unique, differing considerably from western Canada, but having some similarities with other eastern provinces, the mid-west and eastern US and many other developed countries. The research emphasizes the need to base agri-environmental policy in sound empirical trend analysis.
Exploiting the Signal-Leak Bias in Diffusion Models

There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signal-leak bias is particularly significant when models are tuned to a specific style, causing sub-optimal style matching. Recent research tries to avoid the signal leakage during training. We instead show how we can exploit this signal-leak bias in existing diffusion models to allow more control over the generated images. This enables us to generate images with more varied brightness, and images that better match a desired style or color. By modeling the distribution of the signal leak in the spatial frequency and pixel domains, and including a signal leak in the initial latent, we generate images that better match expected results without any additional training.
High-Resolution Full-field Structural Microscopy of the Voltage Induced Filament Formation in Neuromorphic Devices

Neuromorphic functionalities in memristive devices are commonly associated with the ability to electrically create local conductive pathways by resistive switching. The archetypal correlated material, VO2, has been intensively studied for its complex electronic and structural phase transition as well as its filament formation under applied voltages. Local structural studies of the filament behavior are often limited due to time-consuming rastering which makes impractical many experiments aimed at investigating large spatial areas or temporal dynamics associated with the electrical triggering of the phase transition. Utilizing Dark Field X-ray Microscopy (DFXM), a novel full-field x-ray imaging technique, we study this complex filament formation process in-operando in VO2 devices from a structural perspective. We show that prior to filament formation, there is a significant gain of the metallic rutile phase beneath the metal electrodes that define the device. We observed that the filament formation follows a preferential path determined by the nucleation sites within the device. These nucleation sites are predisposed to the phase transition and can persistently maintain the high-temperature rutile phase even after returning to room temperature, which can enable a novel training/learning mechanism. Filament formation also appears to follow a preferential path determined by a nucleation site within the device which is predisposed to the rutile transition even after returning to room temperature. Finally, we found that small isolated low-temperature phase clusters can be present inside the high-temperature filaments indicating that the filament structure is not uniform. Our results provide a unique perspective on the electrically induced filament formation in metal-insulator transition materials, which further the basic understanding of this resistive switching.