Miracle-dz/UniFAR-FTUs huggingface dataset:
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis

Despite achieving superior performance in human-level control problems, unlike humans, deep reinforcement learning (DRL) lacks high-order intelligence (e.g., logic deduction and reuse), thus it behaves ineffectively than humans regarding learning and generalization in complex problems. Previous works attempt to directly synthesize a white-box logic program as the DRL policy, manifesting logic-driven behaviors. However, most synthesis methods are built on imperative or declarative programming, and each has a distinct limitation, respectively. The former ignores the cause-effect logic during synthesis, resulting in low generalizability across tasks. The latter is strictly proof-based, thus failing to synthesize programs with complex hierarchical logic. In this paper, we combine the above two paradigms together and propose a novel Generalizable Logic Synthesis (GALOIS) framework to synthesize hierarchical and strict cause-effect logic programs. GALOIS leverages the program sketch and defines a new sketch-based hybrid program language for guiding the synthesis. Based on that, GALOIS proposes a sketch-based program synthesis method to automatically generate white-box programs with generalizable and interpretable cause-effect logic. Extensive evaluations on various decision-making tasks with complex logic demonstrate the superiority of GALOIS over mainstream baselines regarding the asymptotic performance, generalizability, and great knowledge reusability across different environments.
Galaxy pairs in the sloan digital sky survey XV: Properties of ionised outflows

Powerful outflows are thought to play a critical role in galaxy evolution and black hole growth. We present the first large-scale systematic study of ionised outflows in paired galaxies and post-mergers compared to a robust control sample of isolated galaxies. We isolate the impact of the merger environment to determine if outflow properties depend on merger stage. Our sample contains ∼4,000 paired galaxies and ∼250 post-mergers in the local universe (0.02 ≤ z ≤ 0.2) from the SDSS DR 7 matched in stellar mass, redshift, local density of galaxies, and [OIII] λ5007 luminosity to a control sample of isolated galaxies. By fitting the [OIII] λ5007 line, we find ionised outflows in ∼15 per cent of our entire sample. Outflows are much rarer in star-forming galaxies compared to AGN, and outflow incidence and velocity increase with [OIII] λ5007 luminosity. Outflow incidence is significantly elevated in the optical+mid-infrared selected AGN compared to purely optical AGN; over 60 per cent show outflows at the highest luminosities (L[OIII] λ5007 ≳ 1042 erg s−1), suggesting mid-infrared AGN selection favours galaxies with powerful outflows, at least for higher [OIII] λ5007 luminosities. However, we find no statistically significant difference in outflow incidence, velocity, and luminosity in mergers compared to isolated galaxies, and there is no dependence on merger stage. Therefore, while interactions are predicted to drive gas inflows and subsequently trigger nuclear star formation and accretion activity, when the power source of the outflow is controlled for, the merging environment has no further impact on the large-scale ionised outflows as traced by [OIII] λ5007.
How to Peel a Million: Validating and Expanding Bitcoin Clusters

One of the defining features of Bitcoin and the thousands of cryptocurrencies that have been derived from it is a globally visible transaction ledger. While Bitcoin uses pseudonyms as a way to hide the identity of its participants, a long line of research has demonstrated that Bitcoin is not anonymous. This has been perhaps best exemplified by the development of clustering heuristics, which have in turn given rise to the ability to track the flow of bitcoins as they are sent from one entity to another. In this paper, we design a new heuristic that is designed to track a certain type of flow, called a peel chain, that represents many transactions performed by the same entity; in doing this, we implicitly cluster these transactions and their associated pseudonyms together. We then use this heuristic to both validate and expand the results of existing clustering heuristics. We also develop a machine learning-based validation method and, using a ground-truth dataset, evaluate all our approaches and compare them with the state of the art. Ultimately, our goal is to not only enable more powerful tracking techniques but also call attention to the limits of anonymity in these systems.
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias

Bayesian adaptive experimental design is a form of active learning, which chooses samples to maximize the information they give about uncertain parameters. Prior work has shown that other forms of active learning can suffer from active learning bias, where unrepresentative sampling leads to inconsistent parameter estimates. We show that active learning bias can also afflict Bayesian adaptive experimental design, depending on model misspecification. We analyze the case of estimating a linear model, and show that worse misspecification implies more severe active learning bias. At the same time, model classes incorporating more"noise"- i.e., specifying higher inherent variance in observations - suffer less from active learning bias. Finally, we demonstrate empirically that insights from the linear model can predict the presence and degree of active learning bias in nonlinear contexts, namely in a (simulated) preference learning experiment.
Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification

Abstract As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for matching problems of structured data like point clouds and graphs. However, its application in practice is limited due to the high computational complexity. To overcome this challenge, we propose a novel importance sparsification method, called Spar-GW, to approximate GW distance efficiently. In particular, instead of considering a dense coupling matrix, our method leverages a simple but effective sampling strategy to construct a sparse coupling matrix and update it with few computations. The proposed Spar-GW method is applicable to the GW distance with arbitrary ground cost, and it reduces the complexity from to for an arbitrary small . Theoretically, the convergence and consistency of the proposed estimation for GW distance are established under mild regularity conditions. In addition, this method can be extended to approximate the variants of GW distance, including the entropic GW distance, the fused GW distance, and the unbalanced GW distance. Experiments show the superiority of our Spar-GW to state-of-the-art methods in both synthetic and real-world tasks. Supplementary materials for this article are available online.
Comparing the Digital Annealer with Classical Evolutionary Algorithm

In more recent years, there has been increasing research interest in exploiting the use of application specific hardware for solving optimisation problems. Examples of solvers that use specialised hardware are IBM’s Quantum System One and D-wave’s Quantum Annealer (QA) and Fujitsu’s Digital Annealer (DA). These solvers have been developed to optimise problems faster than traditional meta-heuristics implemented on general purpose machines. Previous research has shown that these solvers (can optimise many problems much quicker than exact solvers such as GUROBI and CPLEX. Such conclusions have not been made when comparing hardware solvers with classical evolutionary algorithms. Making a fair comparison between traditional evolutionary algorithms, such as Genetic Algorithm (GA), and the DA (or other similar solvers) is challenging because the later benefits from the use of application specific hardware while evolutionary algorithms are often implemented on general-purpose machines. Moreover, quantum or quantum-inspired solvers are limited to solving problems in a specific format. A common formulation used is Quadratic Unconstrained Binary Optimisation (QUBO). Many optimisation problems are however constrained and have natural representations that are non-binary. Converting such problems to QUBO can lead to more problem difficulty and/or larger search space. The question addressed in this paper is whether quantum or quantum-inspired solvers can optimise QUBO transformations of combinatorial optimisation problems faster than classical evolutionary algorithms applied to the same problems in their natural representations. We show that the DA often present better average objective function values than GA on Travelling Salesman, Quadratic Assignment and Multi-dimensional Knapsack Problem instances.
NaviDroid: A Tool for Guiding Manual Android Testing via Hint Moves

Manual testing, as a complement to automated GUI testing, is the last line of defense for app quality especially in spotting usability and accessibility issues. However, the repeated actions and easy missing of some functionalities make manual testing time-consuming, labor-extensive and ineﬃcient. Inspired by the game candy crush with flashy candies as hint moves for players, we develop a tool named NaviDroid for navigating human testers via highlighted next operations for more effective and eﬃcient testing. Within NaviDroid, it constructs an enriched state transition graph (STG) with the trigger actions as the edges for two involved states. Based on the STG, NaviDroid utilizes the dynamic programming algorithm to plan the exploration path, and augment the run-time GUI with visualized hint moves for testers to quickly explore untested states and avoid duplication. The automated experiments demonstrate the high coverage and eﬃcient path planning of NaviDroid. A user study further confirms its usefulness in the participants covering more states and activities, detecting more bugs within less time compared with the control group.NaviDroid demo video: https://youtu.be/lShFyg_nTA0.
Isolating and Leveraging Controllable and Noncontrollable Visual Dynamics in World Models

World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios such as autonomous driving, there commonly exists noncontrollable dynamics independent of the action signals, making it difﬁcult to learn effective world models. To tackle this problem, we present a novel reinforcement learning approach named Iso-Dream, which improves the Dream-to-Control framework [22] in two aspects. First, by optimizing the inverse dynamics , we encourage the world model to learn controllable and noncontrollable sources of spatiotemporal changes on isolated state transition branches. Second, we optimize the behavior of the agent on the decoupled latent imaginations of the world model. Speciﬁcally, to estimate state values, we roll-out the noncontrollable states into the future and associate them with the current controllable state . In this way, the isolation of dynamics sources can greatly beneﬁt long-horizon decision-making of the agent, such as a self-driving car that can avoid potential risks by anticipating the movement of other vehicles. Experiments show that Iso-Dream is effective in decoupling the mixed dynamics and remarkably outperforms existing approaches in a wide range of visual control and prediction domains.
Attention Awareness Multiple Instance Neural Network

Multiple instance learning is qualiﬁed for many pattern recognition tasks with weakly annotated data. The combination of artiﬁcial neural network and multiple instance learning oﬀers an end-to-end solution and has been widely utilized. However, challenges remain in two-folds. Firstly, current MIL pooling operators are usually pre-deﬁned and lack ﬂexibility to mine key instances. Secondly, in current solutions, the bag-level representation can be inaccurate or inaccessible. To this end, we propose an attention awareness multiple instance neural network framework in this paper. It consists of an instance-level classiﬁer, a trainable MIL pooling operator based on spatial attention and a bag-level classiﬁcation layer. Exhaustive experiments on a series of pattern recognition tasks demonstrate that our framework outperforms many state-of-the-art MIL methods and val-idates the eﬀectiveness of our proposed attention MIL pooling operators.
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power

It is well-known that modern neural networks are vulnerable to adversarial examples. To mitigate this problem, a series of robust learning algorithms have been proposed. However, although the robust training error can be near zero via some methods, all existing algorithms lead to a high robust generalization error. In this paper, we provide a theoretical understanding of this puzzling phenomenon from the perspective of expressive power for deep neural networks. Specifically, for binary classification problems with well-separated data, we show that, for ReLU networks, while mild over-parameterization is sufficient for high robust training accuracy, there exists a constant robust generalization gap unless the size of the neural network is exponential in the data dimension $d$. This result holds even if the data is linear separable (which means achieving standard generalization is easy), and more generally for any parameterized function classes as long as their VC dimension is at most polynomial in the number of parameters. Moreover, we establish an improved upper bound of $\exp({\mathcal{O}}(k))$ for the network size to achieve low robust generalization error when the data lies on a manifold with intrinsic dimension $k$ ($k \ll d$). Nonetheless, we also have a lower bound that grows exponentially with respect to $k$ -- the curse of dimensionality is inevitable. By demonstrating an exponential separation between the network size for achieving low robust training and generalization error, our results reveal that the hardness of robust generalization may stem from the expressive power of practical models.
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits

We extend the notion of regret with a welfarist perspective. Focussing on the classic multi-armed bandit (MAB) framework, the current work quantifies the performance of bandit algorithms by applying a fundamental welfare function, namely the Nash social welfare (NSW) function. This corresponds to equating algorithm's performance to the geometric mean of its expected rewards and leads us to the study of Nash regret, defined as the difference between the - a priori unknown - optimal mean (among the arms) and the algorithm's performance. Since NSW is known to satisfy fairness axioms, our approach complements the utilitarian considerations of average (cumulative) regret, wherein the algorithm is evaluated via the arithmetic mean of its expected rewards. This work develops an algorithm that, given the horizon of play T, achieves a Nash regret of O ( sqrt{(k log T)/T} ), here k denotes the number of arms in the MAB instance. Since, for any algorithm, the Nash regret is at least as much as its average regret (the AM-GM inequality), the known lower bound on average regret holds for Nash regret as well. Therefore, our Nash regret guarantee is essentially tight. In addition, we develop an anytime algorithm with a Nash regret guarantee of O( sqrt{(k log T)/T} log T ).
VectorAdam for Rotation Equivariant Geometry Optimization

The Adam optimization algorithm has proven remarkably effective for optimization problems across machine learning and even traditional tasks in geometry processing. At the same time, the development of equivariant methods, which preserve their output under the action of rotation or some other transformation, has proven to be important for geometry problems across these domains. In this work, we observe that Adam $-$ when treated as a function that maps initial conditions to optimized results $-$ is not rotation equivariant for vector-valued parameters due to per-coordinate moment updates. This leads to significant artifacts and biases in practice. We propose to resolve this deficiency with VectorAdam, a simple modification which makes Adam rotation-equivariant by accounting for the vector structure of optimization variables. We demonstrate this approach on problems in machine learning and traditional geometric optimization, showing that equivariant VectorAdam resolves the artifacts and biases of traditional Adam when applied to vector-valued data, with equivalent or even improved rates of convergence.
Physics-Guided Hierarchical Reward Mechanism for LearningBased Multi-Finger Object Grasping

 Abstract —Autonomous grasping is challenging due to the high computational cost caused by multi-fingered robotic hands and their interactions with objects. Various analytical methods have been developed yet their high computational cost limits the adoption in real-world applications. Learning-based grasping can afford real-time motion planning thanks to its high computational efficiency. However, it needs to explore large search spaces during its learning process. The search space causes low learning efficiency, which has been the main barrier to its practical adoption. In this work, we develop a novel Physics-Guided Deep Reinforcement Learning with a Hierarchical Reward Mechanism, which combines the benefits of both analytical methods and learning-based methods for autonomous grasping. Different from conventional observation-based grasp learning, physics-informed metrics are utilized to convey correlations between features associated with hand structures and objects to improve learning efficiency and learning outcomes. Further, a hierarchical reward mechanism is developed to enable the robot to learn the grasping task in a prioritized way. It is validated in a grasping task with a MICO robot arm in simulation and physical experiments. The results show that our method outperformed the baseline in task performance by 48% and learning efficiency by 40%.
Exploration. Exploitation, and Engagement in Multi-Armed Bandits with Abandonment

Recommendation algorithms have become increasingly important in many online platforms such as online education, TikTok, YouTube Shorts, advertising platforms, etc. Multiarmed bandit (MAB) [2] is a classic problem which can model these recommendation systems. Each arm in MAB corresponds to a specific type of item in the recommendation system. The recommendation of an item of the $i\text{th}$ type is regarded as a pull of arm $a_{i}$. Taking recommending short videos as an example, each arm $a_{i}$ represents a class of similar videos (e.g. videos from the same dancer). For simplicity, we assume the reward is 1 if the user likes the recommended item and is 0 otherwise. In a traditional MAB problem, the learner can continue to play the arms with the goal of maximizing the average reward, which either assumes a single user stays in the system for a long period of time or assumes the learner is recommending a single item to each user with a large number of users. While this traditional MAB formulation models recommendation systems such as online advertising well, there are new recommendation systems that are significantly different from these traditional models. In these new recommendation systems, such as TikTok or ALEKS, the learner continuously recommends videos/contents to a user, and the user, other than like or dislike the item, may abandon the system if the recommended items cannot engage the user, and come back later. For example, a user watches TikTok or YouTube Shorts for some period of time, where the duration depends on how interesting/engaging the videos are, then leaves the systems, and comes back later.
Robust distributed control within a curve virtual tube for a robotic swarm under self‐localization drift and precise relative navigation

To guide the movement of a robotic swarm in a corridor‐like environment, a curve virtual tube with no obstacle inside was designed in our previous work. This article generalizes the controller design to the condition that all robots have self‐localization drifts and precise relative navigation, where the flocking algorithm is introduced to reduce the negative impact of the self‐localization drift. Similar to the “many wrongs principle,” it is shown that the cohesion behavior and the velocity alignment behavior are able to reduce the influence of the position measurement drift and the velocity measurement error, respectively. For convenience in practical use, a modified vector field controller with five control terms is put forward. Finally, the effectiveness of the proposed method is validated by numerical simulations and real experiments.
Combining observational datasets from multiple environments to detect hidden confounding

A common assumption in causal inference from observational data is the assumption of no hidden confounding. Yet it is, in general, impossible to verify the presence of hidden confounding factors from a single dataset. However, under the assumption of independent causal mechanisms underlying the data generative process, we demonstrate a way to detect unobserved confounders when having multiple observational datasets coming from different environments. We present a theory for testable conditional independencies that are only violated during hidden confounding and examine cases where we break its assumptions: degenerate & dependent mechanisms, and faithfulness violations. Additionally, we propose a procedure to test these independencies and study its empirical finite-sample behavior using simulation studies.
Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data

Functional magnetic resonance imaging (fMRI) data contain complex spatiotemporal dynamics, thus researchers have developed approaches that reduce the dimensionality of the signal while extracting relevant and interpretable dynamics. Models of fMRI data that can perform whole-brain discovery of dynamical latent factors are understudied. The benefits of approaches such as linear independent component analysis models have been widely appreciated, however, nonlinear extensions of these models present challenges in terms of identification. Deep learning methods provide a way forward, but new methods for efficient spatial weight-sharing are critical to deal with the high dimensionality of the data and the presence of noise. Our approach generalizes weight sharing to non-Euclidean neuroimaging data by first performing spectral clustering based on the structural and functional similarity between voxels. The spectral clusters and their assignments can then be used as patches in an adapted multi-layer perceptron (MLP)-mixer model to share parameters among input points. To encourage temporally independent latent factors, we use an additional total correlation term in the loss. Our approach is evaluated on data with multiple motor sub-tasks to assess whether the model captures disentangled latent factors that correspond to each sub-task. Then, to assess the latent factors we find further, we compare the spatial location of each latent factor to the motor homunculus. Finally, we show that our approach captures task effects better than the current gold standard of source signal separation, independent component analysis (ICA).