Miracle-dz/UniFAR-FTUs huggingface dataset:
OmniControl: Control Any Joint at Any Time for Human Motion Generation

We present a novel approach named OmniControl for incorporating flexible spatial control signals into a text-conditioned human motion generation model based on the diffusion process. Unlike previous methods that can only control the pelvis trajectory, OmniControl can incorporate flexible spatial control signals over different joints at different times with only one model. Specifically, we propose analytic spatial guidance that ensures the generated motion can tightly conform to the input control signals. At the same time, realism guidance is introduced to refine all the joints to generate more coherent motion. Both the spatial and realism guidance are essential and they are highly complementary for balancing control accuracy and motion realism. By combining them, OmniControl generates motions that are realistic, coherent, and consistent with the spatial constraints. Experiments on HumanML3D and KIT-ML datasets show that OmniControl not only achieves significant improvement over state-of-the-art methods on pelvis control but also shows promising results when incorporating the constraints over other joints.
From Podes to Antipodes: Positionalities and Global Airline Geographies

Abstract Questions of cost and time distance have long been of interest to geographers and have become a more central concern as globalization advances. We analyze the global air travel system by examining the differences in the costs, distances, and times of one aspect of globalization. We review the extant literature on airline transportation by geographers and others, noting especially the near-century-long interest in unraveling cost, time, and distance issues and designing innovative ways to map these interrelated variables. We expand on this base to bring recent scholarship on power and positionality of cities to our understanding of air travel. Our analysis expands on previous work on airline transport geographies in four distinct ways. First, we developed an international database for a large number of cities worldwide that includes measures of distance, cost, frequency and flight duration of airline connections. Second, this database is examined statistically through ordinary least squares (OLS) regressions to measure variations in airline volume with selected socioeconomic variables. Third, these global airline data are mapped using conventional mapping techniques, and finally, we prepare a set of “position-grams” or intersecting spheres of regional variation that measure and map regional patterns and variations in airline connectedness.
Quasi‐biennial oscillation in ozone in a coupled chemistry‐climate model

[1] Tropical ozone variability is analysed in a version of the Met Office Unified Model with stratospheric chemistry and transport. The model reproduced the stratospheric quasi-biennial oscillation (QBO) in the equatorial winds, forced by a broad spectrum of atmospheric waves. This resulted in a corresponding ozone QBO with vertical phase reversal near 10 hPa and amplitude consistent with observations. It was driven mainly by the QBO-induced vertical velocity anomaly, with the 10 hPa phase reversal a consequence of the sign change in the mean ozone vertical gradient. Chemistry was important for determining phase differences between the ozone and dynamical QBOs. Simulated column ozone anomalies were in reasonable agreement with observations in the tropics, but too weak in the sub-tropics. Nonetheless, the observed sub-tropical to tropical phase reversal and seasonal synchronization of the sub-tropical anomalies were reproduced. In contrast to some other studies our ozone QBO was obtained without QBO-induced variations in NOy transport, implying that ozone transport is an important forcing term in the upper, as well as the lower, stratosphere. In the lower stratosphere (21.5 hPa), diabatic effects of the ozone QBO increased the amplitude of the temperature oscillation by 35%. The mean period of the wind oscillation was also extended by about 10%, due to a longer westerly phase. These results imply that a better representation of chemical, as well as dynamical processes, will be required for improving the inter-annual variability in the tropical stratosphere in climate models.
LangNav: Language as a Perceptual Representation for Navigation

We explore the use of language as a perceptual representation for vision-and-language navigation. Our approach uses off-the-shelf vision systems (for image captioning and object detection) to convert an agent's egocentric panoramic view at each time step into natural language descriptions. We then finetune a pretrained language model to select an action, based on the current view and the trajectory history, that would best fulfill the navigation instructions. In contrast to the standard setup which adapts a pretrained language model to work directly with continuous visual features from pretrained vision models, our approach instead uses (discrete) language as the perceptual representation. We explore two use cases of our language-based navigation (LangNav) approach on the R2R vision-and-language navigation benchmark: generating synthetic trajectories from a prompted large language model (GPT-4) with which to finetune a smaller language model; and sim-to-real transfer where we transfer a policy learned on a simulated environment (ALFRED) to a real-world environment (R2R). Our approach is found to improve upon strong baselines that rely on visual features in settings where only a few gold trajectories (10-100) are available, demonstrating the potential of using language as a perceptual representation for navigation tasks.
Why Train More? Effective and Efficient Membership Inference via Memorization

Membership Inference Attacks (MIAs) aim to identify specific data samples within the private training dataset of machine learning models, leading to serious privacy violations and other sophisticated threats. Many practical black-box MIAs require query access to the data distribution (the same distribution where the private data is drawn) to train shadow models. By doing so, the adversary obtains models trained"with"or"without"samples drawn from the distribution, and analyzes the characteristics of the samples under consideration. The adversary is often required to train more than hundreds of shadow models to extract the signals needed for MIAs; this becomes the computational overhead of MIAs. In this paper, we propose that by strategically choosing the samples, MI adversaries can maximize their attack success while minimizing the number of shadow models. First, our motivational experiments suggest memorization as the key property explaining disparate sample vulnerability to MIAs. We formalize this through a theoretical bound that connects MI advantage with memorization. Second, we show sample complexity bounds that connect the number of shadow models needed for MIAs with memorization. Lastly, we confirm our theoretical arguments with comprehensive experiments; by utilizing samples with high memorization scores, the adversary can (a) significantly improve its efficacy regardless of the MIA used, and (b) reduce the number of shadow models by nearly two orders of magnitude compared to state-of-the-art approaches.
The statistics and sensitivity of axion wind detection with the homogeneous precession domain of superfluid helium-3

The homogeneous precession domain (HPD) of superfluid $^{3}$He has recently been identified as a detection medium which might provide sensitivity to the axion-nucleon coupling $g_{aNN}$ competitive with, or surpassing, existing experimental proposals. In this work, we make a detailed study of the statistical and dynamical properties of the HPD system in order to make realistic projections for a full-fledged experimental program. We include the effects of clock error and measurement error in a concrete readout scheme using superconducting qubits and quantum metrology. This work also provides a more general framework to describe the statistics associated with the axion gradient coupling through the treatment of a transient resonance with a non-stationary background in a time-series analysis. Incorporating an optimal data-taking and analysis strategy, we project a sensitivity approaching $g_{aNN} \sim 10^{-12}$ GeV$^{-1}$ across a decade in axion mass.
Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation

Despite the great progress of unsupervised domain adaptation (UDA) with the deep neural networks, current UDA models are opaque and cannot provide promising explanations, limiting their applications in the scenarios that require safe and controllable model decisions. At present, a surge of work focuses on designing deep interpretable methods with adequate data annotations and only a few methods consider the distributional shift problem. Most existing interpretable UDA methods are post-hoc ones, which cannot facilitate the model learning process for performance enhancement. In this paper, we propose an inherently interpretable method, named Transferable Conceptual Prototype Learning (TCPL), which could simultaneously interpret and improve the processes of knowledge transfer and decision-making in UDA. To achieve this goal, we design a hierarchically prototypical module that transfers categorical basic concepts from the source domain to the target domain and learns domain-shared prototypes for explaining the underlying reasoning process. With the learned transferable prototypes, a self-predictive consistent pseudo-label strategy that fuses confidence, predictions, and prototype information, is designed for selecting suitable target samples for pseudo annotations and gradually narrowing down the domain gap. Comprehensive experiments show that the proposed method can not only provide effective and intuitive explanations but also outperform previous state-of-the-arts.
A Rotating Accretion Disk around MWC 297, a Young B1.5Ve Star

High-resolution spectra with iSHELL on IRTF in the K and M bands of the young, heavily accreting B1.5Ve star MWC 297 show numerous double-peaked CO lines. These CO lines originate in an inclined gaseous disk in Keplerian rotation. MWC 297 is the only early B star known to show a Keplerian disk in CO. Analysis of the spectra shows that 12CO 1–0 is optically thick for the low excitation lines. Even the 13CO 1–0 and 12CO 2–1 have somewhat optically thick lines at low J levels. We find that the CO emission in the disk can be fitted with CO being in a narrow ring at a radius of 12 au, with a temperature of 1500 K and a CO column density of 1.6 × 1018 cm−2. This model underestimates the line strength of high-J lines, indicating that they are excited by fluorescence. The CO overtone lines have a similar temperature. The 13CO lines are much brighter than expected from interstellar isotope ratios. The 13CO lines are wider than the 12CO ones, suggesting different excitation conditions. The same is true for 12CO 2–1. We see strong absorption in 12CO and 13CO 1–0 at low J levels, which is due to two cold foreground clouds. These clouds, one with a temperature of 8.3 K and a column density of 6.7 1017 cm−2 and the other one colder and with lower column density, can fully account for the observed extinction toward MWC 297.
Faithfulness Measurable Masked Language Models

A common approach to explain NLP models, is to use importance measures that express which tokens are important for a prediction. Unfortunately, such explanations are often wrong despite being persuasive. Therefore, it is essential to measure their faithfulness. One such metric is if tokens are truly important, then masking them should result in worse model performance. However, token masking introduces out-of-distribution issues and existing solutions are computationally expensive and employ proxy-models. Furthermore, other metrics are very limited in scope. In this work, we propose an inherently faithfulness measurable model that addresses these challenges. This is achieved by using a novel fine-tuning method that incorporates masking, such that masking tokens become in-distribution by design. This differs from existing approaches, which are completely model-agnostic but are inapplicable in practice. We demonstrate the generality of our approach by applying it to various tasks and validate it using statistical in-distribution tests. Additionally, because masking is in-distribution, importance measures which themselves use masking become more faithful, thus our model becomes more explainable.
RT-SRTS: Angle-Agnostic Real-Time Simultaneous 3D Reconstruction and Tumor Segmentation from Single X-Ray Projection

Radiotherapy is one of the primary treatment methods for tumors, but the organ movement caused by respiration limits its accuracy. Recently, 3D imaging from a single X-ray projection has received extensive attention as a promising approach to address this issue. However, current methods can only reconstruct 3D images without directly locating the tumor and are only validated for fixed-angle imaging, which fails to fully meet the requirements of motion control in radiotherapy. In this study, a novel imaging method RT-SRTS is proposed which integrates 3D imaging and tumor segmentation into one network based on multi-task learning (MTL) and achieves real-time simultaneous 3D reconstruction and tumor segmentation from a single X-ray projection at any angle. Furthermore, the attention enhanced calibrator (AEC) and uncertain-region elaboration (URE) modules have been proposed to aid feature extraction and improve segmentation accuracy. The proposed method was evaluated on fifteen patient cases and compared with three state-of-the-art methods. It not only delivers superior 3D reconstruction but also demonstrates commendable tumor segmentation results. Simultaneous reconstruction and segmentation can be completed in approximately 70 ms, significantly faster than the required time threshold for real-time tumor tracking. The efficacies of both AEC and URE have also been validated in ablation studies. The code of work is available at https://github.com/ZywooSimple/RT-SRTS.
Regional trends in the platinum-group mineralogy of the critical zone of the Bushveld Complex, South Africa

A wealth of quantitative data on the types, volume distribution, and mode of occurrence of the platinum-group minerals residing in the Merensky Reef, the UG-2 chromitite layer, and Platreef has been obtained over a twelve-year period with the result that general platinum-group mineral types for unexplored areas can perhaps now be predicted. The platinum-group minerals of the UG-2 and overlying Merensky layers, although locally different in composition, mode of occurrence, and size, show close ties when viewed on a regional scale, especially with regard to their Pt-Pd sulfide and Pt-Fe alloy contents. Thus, if in one particular area the Merensky Reef contains Pt-Pd sulfides as the predominant platinum-group minerals, then it can be safely inferred that Pt-Pd sulfides will be a significant component of the UG-2 layer in that area. The occurrence of Pt-Fe alloy also follows this principle. The distribution of Pt-Fe alloy is in fact a major factor in differentiating the ores in the vicinity (north and south) of the Pilansberg Alkaline Complex from those of the same layers farther along the arcuate strike (northeastern and southeastern horns of the western Bushveld) and has led to an overall concept relating platinum-group mineral types to postulated centers of increased volatile activity or perhaps feeder sites of the complex. The mineralogy of reef disturbances such as potholes is discussed. The mode of occurrence of the platinum-group minerals varies between base metal sulfide, silicate, and chromite associations and the size of platinum-group minerals in the various reefs is given. Solid solution or lattice substitutions of platinum-group elements in the base metal sulfides account for the very important latent fraction of the total platinum-group elements (as distinct from platinum-group minerals) of the Bushveld Complex. Rhodium in the Merensky Reef occurs almost wholly in solid solution within pentlandite, whereas Rh minerals occur in the UG-2 layer. Chromite tends to host microscopic to submicroscopic inclusions of laurite and does not appear to take Ru into the crystal lattice. The probability of solid solution of platinum-group elements in silicate and other oxide phases is discussed.
Neural Sampling in Hierarchical Exponential-family Energy-based Models

Bayesian brain theory suggests that the brain employs generative models to understand the external world. The sampling-based perspective posits that the brain infers the posterior distribution through samples of stochastic neuronal responses. Additionally, the brain continually updates its generative model to approach the true distribution of the external world. In this study, we introduce the Hierarchical Exponential-family Energy-based (HEE) model, which captures the dynamics of inference and learning. In the HEE model, we decompose the partition function into individual layers and leverage a group of neurons with shorter time constants to sample the gradient of the decomposed normalization term. This allows our model to estimate the partition function and perform inference simultaneously, circumventing the negative phase encountered in conventional energy-based models (EBMs). As a result, the learning process is localized both in time and space, and the model is easy to converge. To match the brain's rapid computation, we demonstrate that neural adaptation can serve as a momentum term, significantly accelerating the inference process. On natural image datasets, our model exhibits representations akin to those observed in the biological visual system. Furthermore, for the machine learning community, our model can generate observations through joint or marginal generation. We show that marginal generation outperforms joint generation and achieves performance on par with other EBMs.
UniPose: Detecting Any Keypoints

This work proposes a unified framework called UniPose to detect keypoints of any articulated (e.g., human and animal), rigid, and soft objects via visual or textual prompts for fine-grained vision understanding and manipulation. Keypoint is a structure-aware, pixel-level, and compact representation of any object, especially articulated objects. Existing fine-grained promptable tasks mainly focus on object instance detection and segmentation but often fail to identify fine-grained granularity and structured information of image and instance, such as eyes, leg, paw, etc. Meanwhile, prompt-based keypoint detection is still under-explored. To bridge the gap, we make the first attempt to develop an end-to-end prompt-based keypoint detection framework called UniPose to detect keypoints of any objects. As keypoint detection tasks are unified in this framework, we can leverage 13 keypoint detection datasets with 338 keypoints across 1,237 categories over 400K instances to train a generic keypoint detection model. UniPose can effectively align text-to-keypoint and image-to-keypoint due to the mutual enhancement of textual and visual prompts based on the cross-modality contrastive learning optimization objectives. Our experimental results show that UniPose has strong fine-grained localization and generalization abilities across image styles, categories, and poses. Based on UniPose as a generalist keypoint detector, we hope it could serve fine-grained visual perception, understanding, and generation.
How Secure is the Time-Modulated Array-Enabled OFDM Directional Modulation?

Time-modulated arrays (TMA) transmitting orthogonal frequency division multiplexing (OFDM) waveforms achieve physical layer security by allowing the signal to reach the legitimate destination undistorted, while making the signal appear scrambled in all other directions. In this paper, we examine how secure the TMA OFDM system is, and show that it is possible for the eavesdropper to defy the scrambling. In particular, we show that, based on the scrambled signal, the eavesdropper can formulate a blind source separation problem and recover data symbols and TMA parameters via independent component analysis (ICA) techniques. We show how the scaling and permutation ambiguities arising in ICA can be resolved by exploiting the Toeplitz structure of the corresponding mixing matrix, and knowledge of data constellation, OFDM specifics, and the rules for choosing TMA parameters. We also introduce a novel TMA implementation to defend the scrambling against the eavesdropper.
Climate feedbacks under a very broad range of forcing

An atmospheric general circulation model, coupled to a mixed layer ocean, is subjected to a broad range of forcing away from the current climate between 1/16 to 32 times current CO2 in halving/doubling steps. As climate warms climate sensitivity weakens (although not monotonically), albedo feedback weakens (driving much of the sensitivity weakening), water vapour feedback strengthens (at a rate slightly larger than it would if relative humidity remained unchanged), and lapse rate feedback increases (negatively); this latter change essentially offsetting the water vapour increases. Longwave cloud feedbacks are relatively stable (moderate and positive) across the full range; shortwave cloud feedback remains relatively weak, apart from under the coldest climates. Cloud optical property related components (from total water content, water/ice fraction and cloud thickness) remain remarkably stable. Cloud ‘amount’ feedbacks show the greatest trends: weakening as temperatures increase. Although cloud feedbacks show an overall consistency of features in different latitudes, precise patterns of changes differ substantially for different baseline climates.
Changes in Soil Organic Matter Fractions under Subtropical No‐Till Cropping Systems

Conservation management systems increase soil C and N pools. However, their effects on particulate (>53 μm) and mineral-associated (<53 μm) soil organic matter (SOM) fractions are less understood under subtropical climatic conditions. This study evaluated the long-term (12-yr) effects of three no-till cropping systems [bare soil, BS; oat (Avena strigosa Schreb.) + vetch (Vicia sativa L.)/maize (Zea mays L.) - cowpea (Vigna unguiculata L. Walp.), O + V/M + C; and maize - Cajanus {Cajanus cajan (L.) Millsp. ], M + C} on C and N pools in particulate and mineral-associated SOM. The study was performed in southern Brazil, on a sandy clay loam Acrisol. Cropping systems that included cover crops increased C and N pools in both particulate and mineral-associated SOM when compared with BS. Mineral-associated SOM had five to nine times more C and 13 to 26 times more N than particulate organic matter and was responsible for 69 to 80% of total atmospheric CO 2 sequestred by soil in O + V/M + C (38 Mg ha -1 ) and M + C (51 Mg ha -1 ). The higher C and N pools were associated with greater recalcitrance of mineral-associated SOM to biological decomposition, resulting from its interaction with variable charge minerals. The negative relationship between decay rates of SOM and the concentrations of Fe oxides and kaolinite demonstrated the physical stability of SOM caused by interaction with variable charge minerals. Power saturation curves of electron spin resonance (ESR) spectroscopy in the 20- to 53-, 2- to 20-, and <2-μm granulometric fractions also reinforced this hypothesis. The SOM interaction with variable charge minerals plays an important role in preserving SOM storage, enhancing the potential of tropical and subtropical soils to act as an atmospheric CO 2 sink.