Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning by Watching: A Review of Video-based Learning Approaches for Robot Manipulation

Robot learning of manipulation skills is hindered by the scarcity of diverse, unbiased datasets. While curated datasets can help, challenges remain in generalizability and real-world transfer. Meanwhile, large-scale"in-the-wild"video datasets have driven progress in computer vision through self-supervised techniques. Translating this to robotics, recent works have explored learning manipulation skills by passively watching abundant videos sourced online. Showing promising results, such video-based learning paradigms provide scalable supervision while reducing dataset bias. This survey reviews foundations such as video feature representation learning techniques, object affordance understanding, 3D hand/body modeling, and large-scale robot resources, as well as emerging techniques for acquiring robot manipulation skills from uncontrolled video demonstrations. We discuss how learning only from observing large-scale human videos can enhance generalization and sample efficiency for robotic manipulation. The survey summarizes video-based learning approaches, analyses their benefits over standard datasets, survey metrics, and benchmarks, and discusses open challenges and future directions in this nascent domain at the intersection of computer vision, natural language processing, and robot learning.
Unsupervised Optimisation of GNNs for Node Clustering

Graph Neural Networks (GNNs) can be trained to detect communities within a graph by learning from the duality of feature and connectivity information. Currently, the common approach for optimisation of GNNs is to use comparisons to ground-truth for hyperparameter tuning and model selection. In this work, we show that nodes can be clustered into communities with GNNs by solely optimising for modularity, without any comparison to ground-truth. Although modularity is a graph partitioning quality metric, we show that this can be used to optimise GNNs that also encode features without a drop in performance. We take it a step further and also study whether the unsupervised metric performance can predict ground-truth performance. To investigate why modularity can be used to optimise GNNs, we design synthetic experiments that show the limitations of this approach. The synthetic graphs are created to highlight current capabilities in distinct, random and zero information space partitions in attributed graphs. We conclude that modularity can be used for hyperparameter optimisation and model selection on real-world datasets as well as being a suitable proxy for predicting ground-truth performance, however, GNNs fail to balance the information duality when the spaces contain conflicting signals.
Can machine learning predict citizen-reported angler behavior?

Prediction of angler behaviors, such as catch rates and angler pressure, is essential to maintaining fish populations and ensuring angler satisfaction. Angler behavior can partly be tracked by online platforms and mobile phone applications that provide fishing activities reported by recreational anglers. Moreover, angler behavior is known to be driven by local site attributes. Here, the prediction of citizen-reported angler behavior was investigated by machine-learning methods using auxiliary data on the environment, socioeconomics, fisheries management objectives, and events at a freshwater body. The goal was to determine whether auxiliary data alone could predict the reported behavior. Different spatial and temporal extents and temporal resolutions were considered. Accuracy scores averaged 88% for monthly predictions at single water bodies and 86% for spatial predictions on a day in a specific region across Canada. At other resolutions and scales, the models only achieved low prediction accuracy of around 60%. The study represents a first attempt at predicting angler behavior in time and space at a large scale and establishes a foundation for potential future expansions in various directions.
Feed-Forward Neural Networks as a Mixed-Integer Program

Deep neural networks (DNNs) are widely studied in various applications. A DNN consists of layers of neurons that compute affine combinations, apply nonlinear operations, and produce corresponding activations. The rectified linear unit (ReLU) is a typical nonlinear operator, outputting the max of its input and zero. In scenarios like max pooling, where multiple input values are involved, a fixed-parameter DNN can be modeled as a mixed-integer program (MIP). This formulation, with continuous variables representing unit outputs and binary variables for ReLU activation, finds applications across diverse domains. This study explores the formulation of trained ReLU neurons as MIP and applies MIP models for training neural networks (NNs). Specifically, it investigates interactions between MIP techniques and various NN architectures, including binary DNNs (employing step activation functions) and binarized DNNs (with weights and activations limited to $-1,0,+1$). The research focuses on training and evaluating proposed approaches through experiments on handwritten digit classification models. The comparative study assesses the performance of trained ReLU NNs, shedding light on the effectiveness of MIP formulations in enhancing training processes for NNs.
Food Recommendation as Language Processing (F-RLP): A Personalized and Contextual Paradigm

State-of-the-art rule-based and classification-based food recommendation systems face significant challenges in becoming practical and useful. This difficulty arises primarily because most machine learning models struggle with problems characterized by an almost infinite number of classes and a limited number of samples within an unbalanced dataset. Conversely, the emergence of Large Language Models (LLMs) as recommendation engines offers a promising avenue. However, a general-purpose Recommendation as Language Processing (RLP) approach lacks the critical components necessary for effective food recommendations. To address this gap, we introduce Food Recommendation as Language Processing (F-RLP), a novel framework that offers a food-specific, tailored infrastructure. F-RLP leverages the capabilities of LLMs to maximize their potential, thereby paving the way for more accurate, personalized food recommendations.
Graph Structure Inference with BAM: Introducing the Bilinear Attention Mechanism

In statistics and machine learning, detecting dependencies in datasets is a central challenge. We propose a novel neural network model for supervised graph structure learning, i.e., the process of learning a mapping between observational data and their underlying dependence structure. The model is trained with variably shaped and coupled simulated input data and requires only a single forward pass through the trained network for inference. By leveraging structural equation models and employing randomly generated multivariate Chebyshev polynomials for the simulation of training data, our method demonstrates robust generalizability across both linear and various types of non-linear dependencies. We introduce a novel bilinear attention mechanism (BAM) for explicit processing of dependency information, which operates on the level of covariance matrices of transformed data and respects the geometry of the manifold of symmetric positive definite matrices. Empirical evaluation demonstrates the robustness of our method in detecting a wide range of dependencies, excelling in undirected graph estimation and proving competitive in completed partially directed acyclic graph estimation through a novel two-step approach.
Representations of the $su(1,1)$ current algebra and probabilistic perspectives

We construct three representations of the $su(1,1)$ current algebra: in extended Fock space, with Gamma random measures, and with negative binomial (Pascal) point processes. For the second and third representations, the lowering and neutral operators are generators of measure-valued branching processes (Dawson-Watanabe superprocesses) and spatial birth-death processes. The vacuum is the constant function $1$ and iterated application of raising operators yields Laguerre and Meixner polynomials. In addition, we prove a Baker-Campbell-Hausdorff formula and give an explicit formula for the action of unitaries $\exp( k^+(\xi) - k^-(\xi))\exp(2 \mathrm i k^0(\theta))$ on exponential vectors. We explain how the representations fit in with a general scheme proposed by Araki and with representations of the $SL(2,\mathbb{R})$ current group with Vershik, Gelfand and Graev's multiplicative measure.
Carleman estimates for higher step Grushin operators

The higher step Grushin operators $\Delta_{\alpha}$ are a family of sub-elliptic operators which degenerate on a sub-manifold of $\mathbb{R}^{n+m}$. This paper establishes Carleman-type inequalities for these operators. It is achieved by deriving a weighted $L^{p}-L^{q}$ estimate for the Grushin-harmonic projector. The crucial ingredient in the proof is the addition formula for Gegenbauer polynomials due to T. Koornwinder and Y. Xu. As a consequence, we obtain the strong unique continuation property for the Schr\"odinger operators $-\Delta_{\alpha}+V$ at points of the degeneracy manifold, where $V$ belongs to certain $ L^{r}_{{\rm loc}}(\mathbb{R}^{n+m})$.
Impurities in graphene and their influence on the Casimir interaction

We study the influence of impurities in graphene described by a scattering rate $\Gamma$ on the Casimir interaction between graphene and an ideal conductor or between two identical sheets of graphene at zero temperature and chemical potential. To this end, we compute the polarization tensor of quasiparticles in graphene and corresponding conductivities for TE and TM channels. The Casimir energy density is evaluated with the help of the Lifshitz formula. We find that depending on the value of mass gap parameter the presence of $\Gamma$ may lead to a slight damping or to a considerable enhancement of the Casimir interaction.
$\Lambda_{\rm s}$CDM cosmology from a type-II minimally modified gravity

We have successfully integrated $\Lambda_{\rm s}$CDM, a promising model for alleviating cosmological tensions, into a theoretical framework by endowing it with a specific Lagrangian from the VCDM model, a type-II minimally modified gravity. In this theory, we demonstrate that an auxiliary scalar field with a linear potential induces an effective cosmological constant, enabling the realization of an abrupt mirror AdS-dS transition in the late universe through a piecewise linear potential. To eliminate the sudden singularity in this setup and ensure stable transitions, we smooth out this potential. Realized within the VCDM theory, the $\Lambda_{\rm s}$CDM model facilitates two types of rapid smooth mirror AdS-dS transitions: (i) the agitated transition, associated with a smooth jump in the potential, where $\Lambda_{\rm s}$, and consequently $H$, exhibits a bump around the transition's midpoint; and (ii) the quiescent transition, associated with a smooth change in the slope of the potential, where $\Lambda_{\rm s}$ transitions gracefully. These transitions are likely to leave distinct imprints on the background and perturbation dynamics, potentially allowing the observational data to distinguish between them. This novel theoretical framework propels $\Lambda_{\rm s}$CDM into a fully predictive model capable of exploring the evolution of the Universe including the late-time AdS-dS transition epoch, and extends the applicability of the model. We believe further research is crucial in establishing $\Lambda_{\rm s}$CDM as a leading candidate or guide for a new concordance cosmological model.
Cost optimisation of individual-based institutional reward incentives for promoting cooperation in finite populations

In this paper, we study the problem of cost optimisation of individual-based institutional incentives (reward, punishment, and hybrid) for guaranteeing a certain minimal level of cooperative behaviour in a well-mixed, finite population. In this scheme, the individuals in the population interact via cooperation dilemmas (Donation Game or Public Goods Game) in which institutional reward is carried out only if cooperation is not abundant enough (i.e., their number is below a threshold $1\leq t\leq N-1$, where $N$ is the population size); and similarly, institutional punishment is carried out only when defection is too abundant. We study analytically the cases $t=1$ for the reward incentive, showing that the cost function is always non-decreasing. We also derive the neutral drift and strong selection limits when the intensity of selection tends to zero and infinity, respectively. We also numerically investigate the problem for other values of $t$.
A note on the capacities of Lagrangian $p$-sum

In this short note, we construct an explicit embedding of the rescaling of the $p$-sum $K\oplus_p K^{\circ}$ of the centrally symmetric convex domain $K$ and its polar $K^{\circ}$ to the product $K \times K^{\circ}$. The rescaling constant is sharp in some cases. Additionally, we comment on the strong Viterbo conjecture for $K\oplus_p K^{\circ}$.
Optimal score estimation via empirical Bayes smoothing

We study the problem of estimating the score function of an unknown probability distribution $\rho^*$ from $n$ independent and identically distributed observations in $d$ dimensions. Assuming that $\rho^*$ is subgaussian and has a Lipschitz-continuous score function $s^*$, we establish the optimal rate of $\tilde \Theta(n^{-\frac{2}{d+4}})$ for this estimation problem under the loss function $\|\hat s - s^*\|^2_{L^2(\rho^*)}$ that is commonly used in the score matching literature, highlighting the curse of dimensionality where sample complexity for accurate score estimation grows exponentially with the dimension $d$. Leveraging key insights in empirical Bayes theory as well as a new convergence rate of smoothed empirical distribution in Hellinger distance, we show that a regularized score estimator based on a Gaussian kernel attains this rate, shown optimal by a matching minimax lower bound. We also discuss the implication of our theory on the sample complexity of score-based generative models.
Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse

Our understanding of learning dynamics of deep neural networks (DNNs) remains incomplete. Recent research has begun to uncover the mathematical principles underlying these networks, including the phenomenon of"Neural Collapse", where linear classifiers within DNNs converge to specific geometrical structures during late-stage training. However, the role of geometric constraints in learning extends beyond this terminal phase. For instance, gradients in fully-connected layers naturally develop a low-rank structure due to the accumulation of rank-one outer products over a training batch. Despite the attention given to methods that exploit this structure for memory saving or regularization, the emergence of low-rank learning as an inherent aspect of certain DNN architectures has been under-explored. In this paper, we conduct a comprehensive study of gradient rank in DNNs, examining how architectural choices and structure of the data effect gradient rank bounds. Our theoretical analysis provides these bounds for training fully-connected, recurrent, and convolutional neural networks. We also demonstrate, both theoretically and empirically, how design choices like activation function linearity, bottleneck layer introduction, convolutional stride, and sequence truncation influence these bounds. Our findings not only contribute to the understanding of learning dynamics in DNNs, but also provide practical guidance for deep learning engineers to make informed design decisions.
Benchmarking Frameworks and Comparative Studies of Controller Area Network (CAN) Intrusion Detection Systems: A Review

The development of intrusion detection systems (IDS) for the in-vehicle Controller Area Network (CAN) bus is one of the main efforts being taken to secure the in-vehicle network against various cyberattacks, which have the potential to cause vehicles to malfunction and result in dangerous accidents. These CAN IDS are evaluated in disparate experimental conditions that vary in terms of the workload used, the features used, the metrics reported, etc., which makes direct comparison difficult. Therefore, there have been several benchmarking frameworks and comparative studies designed to evaluate CAN IDS in similar experimental conditions to understand their relative performance and facilitate the selection of the best CAN IDS for implementation in automotive networks. This work provides a comprehensive survey of CAN IDS benchmarking frameworks and comparative studies in the current literature. A CAN IDS evaluation design space is also proposed in this work, which draws from the wider CAN IDS literature. This is not only expected to serve as a guide for designing CAN IDS evaluation experiments but is also used for categorizing current benchmarking efforts. The surveyed works have been discussed on the basis of the five aspects in the design space-namely IDS type, attack model, evaluation type, workload generation, and evaluation metrics-and recommendations for future work have been identified.
Early Paleozoic adakitic rocks in Heishishan, Gansu and their significance for tectonodynamics.

The adakitic rocks recently revealed in Heishishan Region, Gansu Province, intruding into the volcanic rocks of Baiyin continental island arc, eastern section of northern Qilian orogenic belt, consist mainly of trondjemite, granodiorite and graniteporphyry, with high contents of Sr and low contents of Y, Sr/Y > 40; enrichment in LREE, Na Na-2 O/K-2 O > 2) and SiO2, but depletion in HREE, La/Yb > 20, and no negative Eu anomalies (delta Eu = 1. 0 similar to 1. 19). Compared with the typical subducted slab-melting generated adakites, the adakitic rocks in Heishishan have higher SiO2 and lower MgO, Mg-# (similar to 46), Cr ( similar to 11. 6 mu g/g) and Ni ( similar to 6. 52 mu g/g), indicating no obvious interaction of the primary adakitic melt with mantle peridotite, so they can not directly origin from the subducted slab-melting; compared with the high-K calc-alkaline adakitic rocks of Yanshanian period in eastern China, they have higher Na(2)0 ( Na2O > 4.0%), similar to Cordilera Blanca granitoid (non-slab-melting generated adakites) I may origin from the partial melting of the thickened lower crust of intermediate-basic rocks. Existence of the adakitie rocks in Heishishan Region shows that the eastern section of north Qilian may predominately experience the tectonic evolution of close of north Qilian ocean to collision of the plates, and then thickness of the continental marginal crust, underplating of basaltic magma, and to the reworking of the lower crust during late early-paleozoic. Meanwhile, it should be pointed out that porphyry copper and gold deposits related to the adakitic rocks in the region can be new ore-prospecting strategy.
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs

Vision language models (VLMs) have shown impressive capabilities across a variety of tasks, from logical reasoning to visual understanding. This opens the door to richer interaction with the world, for example robotic control. However, VLMs produce only textual outputs, while robotic control and other spatial tasks require outputting continuous coordinates, actions, or trajectories. How can we enable VLMs to handle such settings without fine-tuning on task-specific data? In this paper, we propose a novel visual prompting approach for VLMs that we call Prompting with Iterative Visual Optimization (PIVOT), which casts tasks as iterative visual question answering. In each iteration, the image is annotated with a visual representation of proposals that the VLM can refer to (e.g., candidate robot actions, localizations, or trajectories). The VLM then selects the best ones for the task. These proposals are iteratively refined, allowing the VLM to eventually zero in on the best available answer. We investigate PIVOT on real-world robotic navigation, real-world manipulation from images, instruction following in simulation, and additional spatial inference tasks such as localization. We find, perhaps surprisingly, that our approach enables zero-shot control of robotic systems without any robot training data, navigation in a variety of environments, and other capabilities. Although current performance is far from perfect, our work highlights potentials and limitations of this new regime and shows a promising approach for Internet-Scale VLMs in robotic and spatial reasoning domains. Website: pivot-prompt.github. io and HuggingFace: https://huggingface.co/spaces/pivot-prompt/pivot-prompt-demo.
Groups Acting Generically Multiply Transitively on Solvable Groups

In this work, we complete the classification of generically multiply transitive actions of groups on solvable groups in the finite Morley rank setting. We prove that if $G$ is a group of finite Morley rank acting definably, faithfully and generically $m$-transitively on a connected solvable group $V$ of finite Morley rank where $\operatorname{rk}(V)\leqslant m$, then $\operatorname{rk}(V)=m$, $V$ is a vector space of dimension $m$ over an algebraically closed field $F$, $G\cong \operatorname{GL}_m(F)$, and the action is equivalent to the natural action of $\operatorname{GL}_m(F)$ on $F^m$. This generalises our previous work arXiv:2107.09997.
A Big Ring on the Sky

We present the discovery of `A Big Ring on the Sky' (BR), the second ultra-large-scale structure (uLSS) found in MgII-absorber catalogues, following the previously reported Giant Arc (GA). In cosmological terms the BR is close to the GA - at the same redshift $z \sim 0.8$ and with a separation on the sky of only $\sim 12^\circ$. Two extraordinary uLSSs in such close configuration raises the possibility that together they form an even more extraordinary cosmological system. The BR is a striking circular, annulus-like, structure of diameter $\sim 400$ Mpc (proper size, present epoch). The method of discovery is as described in the GA paper, but here using the new MgII-absorber catalogues restricted to DR16Q quasars. Using the Convex Hull of Member Spheres (CHMS) algorithm, we estimate that the annulus and inner absorbers of the BR have departures from random expectations, at the density of the control field, of up to $5.2\sigma$. We present the discovery of the BR, assess its significance using the CHMS, Minimal Spanning Tree (MST), FilFinder and Cuzick&Edwards (CE) methods, show it in the context of the GA+BR system, and suggest some implications for the origins of uLSS and for our understanding of cosmology. For example, it may be that unusual geometric patterns, such as these uLSSs, have an origin in cosmic strings.
Reproducibility, Replicability, and Repeatability: A survey of reproducible research with a focus on high performance computing

Reproducibility is widely acknowledged as a fundamental principle in scientific research. Currently, the scientific community grapples with numerous challenges associated with reproducibility, often referred to as the ''reproducibility crisis. '' This crisis permeated numerous scientific disciplines. In this study, we examined the factors in scientific practices that might contribute to this lack of reproducibility. Significant focus is placed on the prevalent integration of computation in research, which can sometimes function as a black box in published papers. Our study primarily focuses on highperformance computing (HPC), which presents unique reproducibility challenges. This paper provides a comprehensive review of these concerns and potential solutions. Furthermore, we discuss the critical role of reproducible research in advancing science and identifying persisting issues within the field of HPC.