Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards A Unified Neural Architecture for Visual Recognition and Reasoning

Recognition and reasoning are two pillars of visual understanding. However, these tasks have an imbalance in focus; whereas recent advances in neural networks have shown strong empirical performance in visual recognition, there has been comparably much less success in solving visual reasoning. Intuitively, unifying these two tasks under a singular framework is desirable, as they are mutually dependent and beneficial. Motivated by the recent success of multi-task transformers for visual recognition and language understanding, we propose a unified neural architecture for visual recognition and reasoning with a generic interface (e.g., tokens) for both. Our framework enables the principled investigation of how different visual recognition tasks, datasets, and inductive biases can help enable spatiotemporal reasoning capabilities. Noticeably, we find that object detection, which requires spatial localization of individual objects, is the most beneficial recognition task for reasoning. We further demonstrate via probing that implicit object-centric representations emerge automatically inside our framework. Intriguingly, we discover that certain architectural choices such as the backbone model of the visual encoder have a significant impact on visual reasoning, but little on object detection. Given the results of our experiments, we believe that visual reasoning should be considered as a first-class citizen alongside visual recognition, as they are strongly correlated but benefit from potentially different design choices.
Invariance principle and local limit theorem for a class of random conductance models with long-range jumps

We study continuous time random walks on $\mathbb{Z}^d$ (with $d \geq 2$) among random conductances $\{ \omega(\{x,y\}) : x,y \in \mathbb{Z}^d\}$ that permit jumps of arbitrary length. The law of the random variables $\omega(\{x,y\})$, taking values in $[0, \infty)$, is assumed to be stationary and ergodic with respect to space shifts. Assuming that the first moment of $\sum_{x \in \mathbb{Z}^d} \omega(\{0,x\}) |x|^2$ and the $q$-th moment of $1/\omega(0,x)$ for $x$ neighbouring the origin are finite for some $ q>d/2$, we show a quenched invariance principle and a quenched local limit theorem, where the moment condition is optimal for the latter. We also obtain H\"older regularity estimates for solutions of the heat equation for the associated non-local discrete operator, and deduce that the pointwise spectral dimension equals $d$ almost surely. Our results apply to random walks on long-range percolation graphs with connectivity exponents larger than $d+2$ when all nearest-neighbour edges are present.
Data-Efficient Task Generalization via Probabilistic Model-Based Meta Reinforcement Learning

We introduce PACOH-RL, a novel model-based Meta-Reinforcement Learning (Meta-RL) algorithm designed to efficiently adapt control policies to changing dynamics. PACOH-RL meta-learns priors for the dynamics model, allowing swift adaptation to new dynamics with minimal interaction data. Existing Meta-RL methods require abundant meta-learning data, limiting their applicability in settings such as robotics, where data is costly to obtain. To address this, PACOH-RL incorporates regularization and epistemic uncertainty quantification in both the meta-learning and task adaptation stages. When facing new dynamics, we use these uncertainty estimates to effectively guide exploration and data collection. Overall, this enables positive transfer, even when access to data from prior tasks or dynamic settings is severely limited. Our experiment results demonstrate that PACOH-RL outperforms model-based RL and model-based Meta-RL baselines in adapting to new dynamic conditions. Finally, on a real robotic car, we showcase the potential for efficient RL policy adaptation in diverse, data-scarce conditions.
On Measuring Faithfulness or Self-consistency of Natural Language Explanations

Large language models (LLMs) can explain their predictions through post-hoc or Chain-of-Thought (CoT) explanations. But an LLM could make up reasonably sounding explanations that are unfaithful to its underlying reasoning. Recent work has designed tests that aim to judge the faithfulness of post-hoc or CoT explanations. In this work we argue that these faithfulness tests do not measure faithfulness to the models' inner workings -- but rather their self-consistency at output level. Our contributions are three-fold: i) We clarify the status of faithfulness tests in view of model explainability, characterising them as self-consistency tests instead. This assessment we underline by ii) constructing a Comparative Consistency Bank for self-consistency tests that for the first time compares existing tests on a common suite of 11 open LLMs and 5 tasks -- including iii) our new self-consistency measure CC-SHAP. CC-SHAP is a fine-grained measure (not a test) of LLM self-consistency. It compares how a model's input contributes to the predicted answer and to generating the explanation. Our fine-grained CC-SHAP metric allows us iii) to compare LLM behaviour when making predictions and to analyse the effect of other consistency tests at a deeper level, which takes us one step further towards measuring faithfulness by bringing us closer to the internals of the model than strictly surface output-oriented tests. Our code is available at \url{https://github.com/Heidelberg-NLP/CC-SHAP}
A Closer Look at Small-scale Magnetic Flux Ropes in the Solar Wind at 1 au: Results from Improved Automated Detection

Small-scale interplanetary magnetic flux ropes (SMFRs) are similar to ICMEs in magnetic structure, but are smaller and do not exhibit coronal mass ejection plasma signatures. We present a computationally efficient and GPU-powered version of the single-spacecraft automated SMFR detection algorithm based on the Grad–Shafranov (GS) technique. Our algorithm can process higher resolution data, eliminates selection bias caused by a fixed 〈B〉 threshold, has improved detection criteria demonstrated to have better results on an MHD simulation, and recovers full 2.5D cross sections using GS reconstruction. We used it to detect 512,152 SMFRs from 27 yr (1996–2022) of 3 s cadence Wind measurements. Our novel findings are the following: (1) the SMFR filling factor (∼ 35%) is independent of solar activity, distance to the heliospheric current sheet, and solar wind plasma type, although the minority of SMFRs with diameters greater than ∼0.01 au have a strong solar activity dependence; (2) SMFR diameters follow a log-normal distribution that peaks below the resolved range (≳104 km), although the filling factor is dominated by SMFRs between 105 and 106 km; (3) most SMFRs at 1 au have strong field-aligned flows like those from Parker Solar Probe measurements; (4) the radial density (generally ∼1 detected per 106 km) and axial magnetic flux density of SMFRs are higher in faster solar wind types, suggesting that they are more compressed. Implications for the origin of SMFRs and switchbacks are briefly discussed. The new algorithm and SMFR dataset are made freely available.
Finding planted cliques using Markov chain Monte Carlo

The planted clique problem is a paradigmatic model of statistical-to-computational gaps: the planted clique is information-theoretically detectable if its size $k\ge 2\log_2 n$ but polynomial-time algorithms only exist for the recovery task when $k= \Omega(\sqrt{n})$. By now, there are many simple and fast algorithms that succeed as soon as $k = \Omega(\sqrt{n})$. Glaringly, however, no MCMC approach to the problem had been shown to work, including the Metropolis process on cliques studied by Jerrum since 1992. In fact, Chen, Mossel, and Zadik recently showed that any Metropolis process whose state space is the set of cliques fails to find any sub-linear sized planted clique in polynomial time if initialized naturally from the empty set. Here, we redeem MCMC performance for the planted clique problem by relaxing the state space to all vertex subsets and adding a corresponding energy penalty for missing edges. With that, we prove that energy-minimizing Markov chains (gradient descent and a low-temperature relaxation of it) succeed at recovering planted cliques of size $k = \Omega(\sqrt{n})$ if initialized from the full graph. Importantly, initialized from the empty set, the relaxation still does not help the gradient descent find sub-linear planted cliques. We also demonstrate robustness of these Markov chain approaches under a natural contamination model.
Tunable Soft Prompts are Messengers in Federated Learning

Federated learning (FL) enables multiple participants to collaboratively train machine learning models using decentralized data sources, alleviating privacy concerns that arise from directly sharing local data. However, the lack of model privacy protection in FL becomes an unneglectable challenge, especially when people want to federally finetune models based on a proprietary large language model. In this study, we propose a novel FL training approach that accomplishes information exchange among participants via tunable soft prompts. These soft prompts, updated and transmitted between the server and clients, assume the role of the global model parameters and serve as messengers to deliver useful knowledge from the local data and global model. As the global model itself is not required to be shared and the local training is conducted based on an auxiliary model with fewer parameters than the global model, the proposed approach provides protection for the global model while reducing communication and computation costs in FL. Extensive experiments show the effectiveness of the proposed approach compared to several baselines. We have released the source code at \url{https://github.com/alibaba/FederatedScope/tree/fedsp/federatedscope/nlp/fedsp}.
Regimes of stratified turbulence at low Prandtl number

Quantifying transport by strongly stratified turbulence in low Prandtl number ($Pr$) fluids is critically important for the development of better models for the structure and evolution of stellar interiors. Motivated by recent numerical simulations showing strongly anisotropic flows suggestive of scale-separated dynamics, we perform a multiscale asymptotic analysis of the governing equations. We find that, in all cases, the resulting slow-fast system naturally takes a quasilinear form. Our analysis also reveals the existence of several distinct dynamical regimes depending on the emergent buoyancy Reynolds and P\'eclet numbers, $Re_b = \alpha^2 Re$ and $Pe_b = Pr Re_b$, respectively, where $\alpha$ is the aspect ratio of the large-scale turbulent flow structures, and $Re$ is the outer scale Reynolds number. Scaling relationships relating the aspect ratio, the characteristic vertical velocity, and the strength of the stratification (measured by the Froude number $Fr$) naturally emerge from the analysis. When $Pe_b \ll \alpha$, the dynamics at all scales is dominated by buoyancy diffusion, and our results recover the scaling laws empirically obtained from direct numerical simulations by Cope et al. (2020). For $Pe_b \ge O(1)$, diffusion is negligible (or at least subdominant) at all scales and our results are consistent with those of Chini et al. (2022) for strongly stratified geophysical turbulence at $Pr = O(1)$. Finally, we have identified a new regime for $\alpha \ll Pe_b \ll 1$, in which slow, large scales are diffusive while fast, small scales are not. We conclude by presenting a map of parameter space that clearly indicates the transitions between isotropic turbulence, non-diffusive stratified turbulence, diffusive stratified turbulence and viscously-dominated flows.
Anchor Data Augmentation

We propose a novel algorithm for data augmentation in nonlinear over-parametrized regression. Our data augmentation algorithm borrows from the literature on causality and extends the recently proposed Anchor regression (AR) method for data augmentation, which is in contrast to the current state-of-the-art domain-agnostic solutions that rely on the Mixup literature. Our Anchor Data Augmentation (ADA) uses several replicas of the modified samples in AR to provide more training examples, leading to more robust regression predictions. We apply ADA to linear and nonlinear regression problems using neural networks. ADA is competitive with state-of-the-art C-Mixup solutions.
A brief review of hybrid skin-topological effect.

The finding of non-Hermitian skin effect has revolutionized our understanding of non-Hermitian topological phases, where the usual bulk-boundary correspondence is broken and new topological phases specific to non-Hermitian system are uncovered. Hybrid skin-topological effect (HSTE) is a class of newly discovered non-Hermitian topological states that simultaneously supports skin-localized topological edge states and extended bulk states. Here we provide a brief review of HSTE, starting from different mechanics that have been used to realize HSTE, including non-reciprocal couplings, onsite gain/loss, and non-Euclidean lattice geometries. We also review some theoretical developments closely related to the HSTE, including the concept of higher-order non-Hermitian skin effect, parity-time symmetry engineering, and non-Hermitian chiral skin effect. Finally, we summarize recent experimental exploration of HSTE, including its realization in electric circuits systems, non-Hermitian photonic crystals, and active matter systems. We hope this review can make the concept of hybrid-skin effect clearer and inspire new finding of non-Hermitian topological states in higher dimensional systems.
GraNNDis: Efficient Unified Distributed Training Framework for Deep GNNs on Large Clusters

Graph neural networks (GNNs) are one of the most rapidly growing fields within deep learning. According to the growth in the dataset and the model size used for GNNs, an important problem is that it becomes nearly impossible to keep the whole network on GPU memory. Among numerous attempts, distributed training is one popular approach to address the problem. However, due to the nature of GNNs, existing distributed approaches suffer from poor scalability, mainly due to the slow external server communications. In this paper, we propose GraNNDis, an efficient distributed GNN training framework for training GNNs on large graphs and deep layers. GraNNDis introduces three new techniques. First, shared preloading provides a training structure for a cluster of multi-GPU servers. We suggest server-wise preloading of essential vertex dependencies to reduce the low-bandwidth external server communications. Second, we present expansion-aware sampling. Because shared preloading alone has limitations because of the neighbor explosion, expansion-aware sampling reduces vertex dependencies that span across server boundaries. Third, we propose cooperative batching to create a unified framework for full-graph and minibatch training. It significantly reduces redundant memory usage in mini-batch training. From this, GraNNDis enables a reasonable trade-off between full-graph and mini-batch training through unification especially when the entire graph does not fit into the GPU memory. With experiments conducted on a multi-server/multi-GPU cluster, we show that GraNNDis provides superior speedup over the state-of-the-art distributed GNN training frameworks.
Crop yield, soil erosion, and net returns from five tillage systems in the Mississippi blackland prairie

ABSTRACT: Five tillage systems were used on two Blackland Prairie soils in Mississippi. Monocrop soybeans were grown with four systems ranging from zero tillage to conventional tillage plus fall chisel plowing. A double-crop of soft red winter wheat and soybeans was the fifth system. One soil was a Leeper silty clay loam with less than 1% slope, the other an upland soil, Brooksville silty clay, with a 3% slope. Relative crop yields as a function of tillage were similar on both soils. There was no soybean yield response to fall chisel plowing. Minimum tillage and zero tillage reduced soybean yields significantly. The double-crop treatment produced the highest net return and was the most soil-conserving practice on the upland soil. Soil loss with the double-crop treatment averaged 2.48 tons/ha/year, compared to 6.53 and 40.13 tons/ha/year for the zero tillage and bare fallow reference plot, respectively. The double-crop treatment also produced the least runoff (11.48 cm/year), which was 10% of the average annual rainfall. Of the five tillage systems studied, only the fall chisel treatment resulted in erosion rates greater than the soil loss tolerance of 9.1 tons/ha/year for the Brooksville soil.
Mitigating Pooling Bias in E-commerce Search via False Negative Estimation

Efficient and accurate product relevance assessment is critical for user experiences and business success. Training a proficient relevance assessment model requires high-quality query-product pairs, often obtained through negative sampling strategies. Unfortunately, current methods introduce pooling bias by mistakenly sampling false negatives, diminishing performance and business impact. To address this, we present Bias-mitigating Hard Negative Sampling (BHNS), a novel negative sampling strategy tailored to identify and adjust for false negatives, building upon our original False Negative Estimation algorithm. Our experiments in the Instacart search setting confirm BHNS as effective for practical e-commerce use. Furthermore, comparative analyses on public dataset showcase its domain-agnostic potential for diverse applications.
DocGen: Generating Detailed Parameter Docstrings in Python

Documentation debt hinders the effective utilization of open-source software. Although code summarization tools have been helpful for developers, most would prefer a detailed account of each parameter in a function rather than a high-level summary. However, generating such a summary is too intricate for a single generative model to produce reliably due to the lack of high-quality training data. Thus, we propose a multi-step approach that combines multiple task-specific models, each adept at producing a specific section of a docstring. The combination of these models ensures the inclusion of each section in the final docstring. We compared the results from our approach with existing generative models using both automatic metrics and a human-centred evaluation with 17 participating developers, which proves the superiority of our approach over existing methods.
High-resolution magnetostratigraphy of four sediment cores from the Greenland Sea—I. Identification of the Mono Lake excursion, Laschamp and Biwa I/Jamaica geomagnetic polarity events

SUMMARY High-resolution magnetostratigraphic analysis of three sediment cores from the base of the volcanic seamount Vesteris Banken in the Greenland Basin and one core from the Jan Mayen Fracture Zone revealed records of three pronounced geomagnetic events within the last 200 ka. Dating by stable carbon and oxygen isotope analysis, AMS14C measurements and biostratigraphic data (foraminifera abundances) yielded ages of 28-27 ka for the Mono Lake excursion, 37-33 ka for the Laschamp event, and 189179 ka for the Biwa I event. In at least one of the cores the Laschamp event exhibits a full reversal of the local geomagnetic field vector. The same is true of the Biwa I event, documented in one of the cores.
Cut finite element method for divergence free approximation of incompressible flow: a Lagrange multiplier approach

In this note we design a cut finite element method for a low order divergence free element applied to a boundary value problem subject to Stokes' equations. For the imposition of Dirichlet boundary conditions we consider either Nitsche's method or a stabilized Lagrange multiplier method. In both cases the normal component of the velocity is constrained using a multiplier, different from the standard pressure approximation. The divergence of the approximate velocities is pointwise zero over the whole mesh domain, and we derive optimal error estimates for the velocity and pressures, where the error constant is independent of how the physical domain intersects the computational mesh, and of the regularity of the pressure multiplier imposing the divergence free condition.
VGSG: Vision-Guided Semantic-Group Network for Text-Based Person Search

Text-based Person Search (TBPS) aims to retrieve images of target pedestrian indicated by textual descriptions. It is essential for TBPS to extract fine-grained local features and align them crossing modality. Existing methods utilize external tools or heavy cross-modal interaction to achieve explicit alignment of cross-modal fine-grained features, which is inefficient and time-consuming. In this work, we propose a Vision-Guided Semantic-Group Network (VGSG) for text-based person search to extract well-aligned fine-grained visual and textual features. In the proposed VGSG, we develop a Semantic-Group Textual Learning (SGTL) module and a Vision-guided Knowledge Transfer (VGKT) module to extract textual local features under the guidance of visual local clues. In SGTL, in order to obtain the local textual representation, we group textual features from the channel dimension based on the semantic cues of language expression, which encourages similar semantic patterns to be grouped implicitly without external tools. In VGKT, a vision-guided attention is employed to extract visual-related textual features, which are inherently aligned with visual cues and termed vision-guided textual features. Furthermore, we design a relational knowledge transfer, including a vision-language similarity transfer and a class probability transfer, to adaptively propagate information of the vision-guided textual features to semantic-group textual features. With the help of relational knowledge transfer, VGKT is capable of aligning semantic-group textual features with corresponding visual features without external tools and complex pairwise interaction. Experimental results on two challenging benchmarks demonstrate its superiority over state-of-the-art methods.
Orbital Hall Conductivity in Bilayer Graphene

We investigate the orbital Hall conductivity in bilayer graphene (G/G), by modifying one of the layer as Haldane type with the next nearest neighbour (NNN) hopping strength and flux. The Haldane flux in one of the layer breaks the time reversal symmetry in both the layers and induces the gap opening at the Dirac points $K$ and $K'$ points. It is observed that the low energy isolated bands show large orbital magnetization and induce the Hall potential for opposite magnetic polarization under the external fields, thereby contribute to the orbital Hall conductivity (OHC). The self-rotation of the isolated electrons in their respective orbits leads to strong orbital angular momentum, which is more fundamental in non-magnetic materials. The observed OHC is similar to the anomalous Hall conductivity (AHC). Moreover, the orbital magnetization with opposite sign among the occupied states adds up to the higher OHC in the gap, whereas the AHC get vanishes. We further show the results of bilayer graphene with both the layers as Haldane type ($\rm \tilde G/ \tilde G$), and found that the OHC behaves similar to AHC which indicate that, the OHC is strongly depends on the band dispersion. Similarly, we show that in the heterobilayers with one of the layer is Haldane type generates the orbital magnetization and induces the OHC. It is concluded that, the isolated bands in Graphene bilayers with external stimuli are of orbital nature and show orbital ferromagnetism in the valleys in BZ.