Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Eectiveness of Fine Tuning versus Meta-reinforcement Learning

Intelligent agents should have the ability to leverage knowledge from previously learned tasks in order to learn new ones quickly and efﬁciently. Meta-learning approaches have emerged as a popular solution to achieve this. However, meta-reinforcement learning (meta-RL) algorithms have thus far been restricted to simple environments with narrow task distributions. Moreover, the paradigm of pretraining followed by ﬁne-tuning to adapt to new tasks has emerged as a simple yet effective solution in supervised and self-supervised learning. This calls into question the beneﬁts of meta-learning approaches in reinforcement learning, which typically come at the cost of high complexity. We therefore investigate meta-RL approaches in a variety of vision-based benchmarks, including Procgen, RLBench, and Atari, where evaluations are made on completely novel tasks. Our ﬁndings show that when meta-learning approaches are evaluated on different tasks (rather than different variations of the same task), multi-task pretraining with ﬁne-tuning on new tasks performs equally as well, or better, than meta-pretraining with meta test-time adaptation. This is encouraging for future research, as multi-task pretraining tends to be simpler and computationally cheaper than meta-RL. From these ﬁndings, we advocate for evaluating future meta-RL methods on more challenging tasks and including multi-task pretraining with ﬁne-tuning as a simple, yet strong baseline. 1
Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds

We consider a linear stochastic bandit problem involving $M$ agents that can collaborate via a central server to minimize regret. A fraction $\alpha$ of these agents are adversarial and can act arbitrarily, leading to the following tension: while collaboration can potentially reduce regret, it can also disrupt the process of learning due to adversaries. In this work, we provide a fundamental understanding of this tension by designing new algorithms that balance the exploration-exploitation trade-off via carefully constructed robust confidence intervals. We also complement our algorithms with tight analyses. First, we develop a robust collaborative phased elimination algorithm that achieves $\tilde{O}\left(\alpha+ 1/\sqrt{M}\right) \sqrt{dT}$ regret for each good agent; here, $d$ is the model-dimension and $T$ is the horizon. For small $\alpha$, our result thus reveals a clear benefit of collaboration despite adversaries. Using an information-theoretic argument, we then prove a matching lower bound, thereby providing the first set of tight, near-optimal regret bounds for collaborative linear bandits with adversaries. Furthermore, by leveraging recent advances in high-dimensional robust statistics, we significantly extend our algorithmic ideas and results to (i) the generalized linear bandit model that allows for non-linear observation maps; and (ii) the contextual bandit setting that allows for time-varying feature vectors.
Goal-Space Planning with Subgoal Models

This paper investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Background planning with learned models is often worse than model-free alternatives, such as Double DQN, even though the former uses significantly more memory and computation. The fundamental problem is that learned models can be inaccurate and often generate invalid states, especially when iterated many steps. In this paper, we avoid this limitation by constraining background planning to a set of (abstract) subgoals and learning only local, subgoal-conditioned models. This goal-space planning (GSP) approach is more computationally efficient, naturally incorporates temporal abstraction for faster long-horizon planning and avoids learning the transition dynamics entirely. We show that our GSP algorithm can propagate value from an abstract space in a manner that helps a variety of base learners learn significantly faster in different domains.
Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission

A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data, but modeling this emission relies on observations of rare gas tracers only available in limited regions of the sky. Identifying this contribution is important to discriminate gamma-ray point sources from interstellar gas, and to better characterize extended gamma-ray sources. We design and train convolutional neural networks to predict this emission where observations of these rare tracers do not exist and discuss the impact of this component on the analysis of the Fermi-LAT data. In particular, we evaluate prospects to exploit this methodology in the characterization of the Fermi-LAT Galactic center excess through accurate modeling of point-like structures in the data to help distinguish between a point-like or smooth nature for the excess. We show that deep learning may be effectively employed to model the gamma-ray emission traced by these rare H2 proxies within statistical significance in data-rich regions, supporting prospects to employ these methods in yet unobserved regions.
A Bird's-Eye Tutorial of Graph Attention Architectures

Graph Neural Networks (GNNs) have shown tremendous strides in performance for graph-structured problems especially in the domains of natural language processing, computer vision and recommender systems. Inspired by the success of the transformer architecture, there has been an ever-growing body of work on attention variants of GNNs attempting to advance the state of the art in many of these problems. Incorporating"attention"into graph mining has been viewed as a way to overcome the noisiness, heterogenity and complexity associated with graph-structured data as well as to encode soft-inductive bias. It is hence crucial and advantageous to study these variants from a bird's-eye view to assess their strengths and weaknesses. We provide a systematic and focused tutorial centered around attention based GNNs in a hope to benefit researchers dealing with graph-structured problems. Our tutorial looks at GNN variants from the point of view of the attention function and iteratively builds the reader's understanding of different graph attention variants.
Correlations of network trajectories

Temporal networks model how the interaction between elements in a complex system evolve over time. Just like complex systems display collective dynamics, here we interpret temporal networks as trajectories performing a collective motion in graph space, following a latent graph dynamical system. Under this paradigm, we propose a way to measure how the network pulsates and collectively fluctuates over time and space. To this aim, we extend the notion of linear correlations function to the case of sequences of network snapshots, i.e. a network trajectory. We construct stochastic and deterministic graph dynamical systems and show that the emergent collective correlations are well captured by simple measures, and illustrate how these patterns are revealed in empirical networks arising in different domains.
CompartOS: CHERI Compartmentalization for Embedded Systems

Existing high-end embedded systems face frequent security attacks. Software compartmentalization is one technique to limit the attacks’ effects to the compromised compartment and not the entire system. Unfortunately, the existing state-of-the-art embedded hardware-software solutions do not work well to enforce software compartmentalization for high-end embedded systems. MPUs are not ﬁne-grained and suffer from signiﬁcant scalability limitations as they can only protect a small and ﬁxed number of memory regions. On the other hand, MMUs suffer from non-determinism and coarse-grained protection. This paper introduces CompartOS as a lightweight linkage-based compartmentalization model for high-end, complex, mainstream embedded systems. CompartOS builds on CHERI, a capability-based hardware architecture, to meet scalability, availability, compatibility, and ﬁne-grained security goals. Microbenchmarks show that CompartOS’ protection-domain crossing is 95% faster than MPU-based IPC. We applied the CompartOS model, with low effort, to complex existing systems, including TCP servers and a safety-critical automotive demo. CompartOS not only catches 10 out of 13 FreeRTOS-TCP published vulnerabilities that MPU-based protection (e.g., uVisor) cannot catch but can also recover from them. Further, our TCP throughput evaluations show that our CompartOS prototype is 52% faster than relevant MPU-based compartmentalization models (e.g., ACES), with a 15% overhead compared to an unprotected system. This comes at an FPGA’s LUTs overhead of 10.4% to support CHERI for an unprotected baseline RISC-V processor, compared to 7.6%
Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map

The integration of a SLAM algorithm with place recognition technology empowers it with the ability to mitigate accumulated errors and to relocalize itself. However, existing methods for point cloud-based place recognition predominantly rely on the matching of descriptors, which are mostly lidar-centric. These methods suffer from two major drawbacks: first, they cannot perform place recognition when the distance between two point clouds is significant, and second, they can only calculate the rotation angle without considering the offset in the X and Y directions. To overcome these limitations, we propose a novel local descriptor that is constructed around the Main Object. By using a geometric method, we can accurately calculate the relative pose. We have provided a theoretical analysis to demonstrate that this method can overcome the aforementioned limitations. Furthermore, we conducted extensive experiments on KITTI Odometry and KITTI360, which indicate that our proposed method has significant advantages over state-of-the-art methods.
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances

The Sliced-Wasserstein distance (SW) is a computationally efficient and theoretically grounded alternative to the Wasserstein distance. Yet, the literature on its statistical properties -- or, more accurately, its generalization properties -- with respect to the distribution of slices, beyond the uniform measure, is scarce. To bring new contributions to this line of research, we leverage the PAC-Bayesian theory and a central observation that SW may be interpreted as an average risk, the quantity PAC-Bayesian bounds have been designed to characterize. We provide three types of results: i) PAC-Bayesian generalization bounds that hold on what we refer as adaptive Sliced-Wasserstein distances, i.e. SW defined with respect to arbitrary distributions of slices (among which data-dependent distributions), ii) a principled procedure to learn the distribution of slices that yields maximally discriminative SW, by optimizing our theoretical bounds, and iii) empirical illustrations of our theoretical findings.
A Differentially Private Linear-Time fPTAS for the Minimum Enclosing Ball Problem

The Minimum Enclosing Ball (MEB) problem is one of the most fundamental problems in clustering, with applications in operations research, statistics and computational geometry. In this works, we give the first differentially private (DP) fPTAS for the Minimum Enclosing Ball problem, improving both on the runtime and the utility bound of the best known DP-PTAS for the problem, of Ghazi et al. (2020). Given $n$ points in $\R^d$ that are covered by the ball $B(\theta_{opt},r_{opt})$, our simple iterative DP-algorithm returns a ball $B(\theta,r)$ where $r\leq (1+\gamma)r_{opt}$ and which leaves at most $\tilde O(\frac{\sqrt d}{\gamma\epsilon})$ points uncovered in $\tilde O(\nicefrac n {\gamma^2})$-time. We also give a local-model version of our algorithm, that leaves at most $\tilde O(\frac{\sqrt {nd}}{\gamma\epsilon})$ points uncovered, improving on the $n^{0.67}$-bound of Nissim and Stemmer (2018) (at the expense of other parameters). In addition, we test our algorithm empirically and discuss future open problems.
Solving Non-local Fokker-Planck Equations by Deep Learning

Physics-informed neural networks (PiNNs) recently emerged as a powerful solver for a large class of partial differential equations (PDEs) under various initial and boundary conditions. In this paper, we propose trapz-PiNNs, physics-informed neural networks incorporated with a modified trapezoidal rule recently developed for accurately evaluating fractional Laplacian and solve the space-fractional Fokker-Planck equations in 2D and 3D. We describe the modified trapezoidal rule in detail and verify the second-order accuracy. We demonstrate that trapz-PiNNs have high expressive power through predicting the solution with low L 2 relative error by a variety of numerical examples. We also use local metrics, such as point-wise absolute and relative errors, to analyze where it could be further improved. We present an effective method for improving the performance of trapz-PiNN on local metrics, provided that physical observations or high-fidelity simulation of the true solution are available. The trapz-PiNN is able to solve PDEs with fractional Laplacian with arbitrary α ∈ ( 0 , 2 ) and on rectangular domains. It also has the potential to be generalized into higher dimensions or other bounded domains.
Anomaly-induced vacuum effective action with torsion: Covariant solution and ambiguities

The conformal anomaly in curved spacetime with antisymmetric torsion is reconsidered, taking into account new important details. We formulate, for the ﬁrst time, the covariant solution of the anomaly-induced eﬀective action. The covariant eﬀective action includes local terms corresponding to total derivatives in the conformal anomaly. The contribution of massless fermions to these terms is characterized by multiplicative anomaly, coming from two diﬀerent choices of doubling for the spinor operator. On the other hand, the nonlocal part of anomaly-induced action is free of ambiguities and admits a low-energy limit, similar to the eﬀective potential in the metric-scalar theory.
Improved single-shot decoding of higher dimensional hypergraph product codes

In this work we study the single-shot performance of higher dimensional hypergraph product codes decoded using belief-propagation and ordered-statistics decoding [Panteleev and Kalachev, 2021]. We find that decoding data qubit and syndrome measurement errors together in a single stage leads to single-shot thresholds that greatly exceed all previously observed single-shot thresholds for these codes. For the 3D toric code and a phenomenological noise model, our results are consistent with a sustainable threshold of 7.1% for $Z$ errors, compared to the threshold of 2.90% previously found using a two-stage decoder~[Quintavalle et al., 2021]. For the 4D toric code, for which both $X$ and $Z$ error correction is single-shot, our results are consistent with a sustainable single-shot threshold of 4.3% which is even higher than the threshold of 2.93% for the 2D toric code for the same noise model but using $L$ rounds of stabiliser measurement. We also explore the performance of balanced product and 4D hypergraph product codes which we show lead to a reduction in qubit overhead compared the surface code for phenomenological error rates as high as 1%.
Engineering Floquet topological phases using elliptically polarized light

We study a two-dimensional topological system driven out of equilibrium by the application of elliptically polarized light. In particular, we analyze the Bernevig-Hughes-Zhang model when it is perturbed using an elliptically polarized light of frequency Ω described in general by a vector potential A ( t ) = ( A 0 x cos(Ω t ) , A 0 y cos(Ω t + φ 0 )). (Linear and circular polarizations can be obtained as special cases of this general form by appropriately choosing A 0 x , A 0 y , and φ 0 ). Even for a ﬁxed value of φ 0 , we can change the topological character of the system by changing the ratio of the x and y components of the drive. We therefore ﬁnd a rich topological phase diagram as a function of A 0 x , A 0 y and φ 0 . In each of these phases, the topological invariant given by the Chern number is consistent with the number of spin-polarized states present at the edges of a nanoribbon.
Towards Meta-learned Algorithm Selection using Implicit Fidelity Information

Automatically selecting the best performing algorithm for a given dataset or ranking multiple algorithms by their expected performance supports users in developing new machine learning applications. Most approaches for this problem rely on pre-computed dataset meta-features and landmarking performances to capture the salient topology of the datasets and those topologies that the algorithms attend to. Landmarking usually exploits cheap algorithms not necessarily in the pool of candidate algorithms to get inexpensive approximations of the topology. While somewhat indicative, hand-crafted dataset meta-features and landmarks are likely insufficient descriptors, strongly depending on the alignment of the topologies that the landmarks and the candidate algorithms search for. We propose IMFAS, a method to exploit multi-fidelity landmarking information directly from the candidate algorithms in the form of non-parametrically non-myopic meta-learned learning curves via LSTMs in a few-shot setting during testing. Using this mechanism, IMFAS jointly learns the topology of the datasets and the inductive biases of the candidate algorithms, without the need to expensively train them to convergence. Our approach produces informative landmarks, easily enriched by arbitrary meta-features at a low computational cost, capable of producing the desired ranking using cheaper fidelities. We additionally show that IMFAS is able to beat Successive Halving with at most 50% of the fidelity sequence during test time.
Protein-lipid domains in heterogeneous membranes beyond spontaneous curvature effects

We study a model of a lipid bilayer membrane described by two order parameters: the chemical composition described using the Gaussian model and the spatial configuration described with the elastic deformation model of a membrane with a finite thickness, or equivalently, for an adherent membrane. We assume and explain on physical grounds the linear coupling between the two order parameters. Using the exact solution, we calculate the correlation functions and order parameters profiles. We also study the domains that form around inclusions on the membrane. We propose and compare six distinct ways to quantify the size of such domains. Despite of its simplicity, the model has many interesting features like Fisher-Widom line or two distinct critical regions.
Minimising statistical errors in calibration of quantum-gate sets

Calibration of quantum gates is a necessary hurdle to overcome on the way to a reliable quantum computer. In a recent paper, a protocol called Gate Set Calibration protocol (GSC) has been introduced and used to learn coherent errors from multi-qubit quantum gates. Here, we extend this study in a number of ways: First, we perform a statistical analysis of the measurement uncertainties. Second, we find explicit measurement settings that minimize this uncertainty, while also requiring that the protocol involves only a small number of distinct gates, aiding physical realizability. We numerically demonstrate that, just by adding two more single-qubit gates to GSC, the statistical error produced in the calibration of a CNOT gate is divided by a factor of more than two.
Detecting Global Community Structure in a COVID-19 Activity Correlation Network

The global pandemic of COVID-19 over the last 2.5 years have produced an enormous amount of epidemic/public health datasets, which may also be useful for studying the underlying structure of our globally connected world. Here we used the Johns Hopkins University COVID-19 dataset to construct a correlation network of countries/regions and studied its global community structure. Specifically, we selected countries/regions that had at least 100,000 cumulative positive cases from the dataset and generated a 7-day moving average time series of new positive cases reported for each country/region. We then calculated a time series of daily change exponents by taking the day-to-day difference in log of the number of new positive cases. We constructed a correlation network by connecting countries/regions that had positive correlations in their daily change exponent time series using their Pearson correlation coefficient as the edge weight. Applying the modularity maximization method revealed that there were three major communities: (1) Mainly Europe + North America + Southeast Asia that showed similar six-peak patterns during the pandemic, (2) mainly Near/Middle East + Central/South Asia + Central/South America that loosely followed Community 1 but had a notable increase of activities because of the Delta variant and was later impacted significantly by the Omicron variant, and (3) mainly Africa + Central/East Canada + Australia that did not have much activities until a huge spike was caused by the Omicron variant. These three communities were robustly detected under varied settings. Constructing a 3D"phase space"by using the median curves in those three communities for x-y-z coordinates generated an effective summary trajectory of how the global pandemic progressed.
Quantum Network Tomography with Multi-party State Distribution

The fragile nature of quantum information makes it practically impossible to completely isolate a quantum state from noise under quantum channel transmissions. Quantum networks are complex systems formed by the interconnection of quantum processing devices through quantum channels. In this context, characterizing how channels introduce noise in transmitted quantum states is of paramount importance. Precise descriptions of the error distributions introduced by non-unitary quantum channels can inform quantum error correction protocols to tailor operations for the particular error model. In addition, characterizing such errors by monitoring the network with end-to-end measurements enables end-nodes to infer the status of network links. In this work, we address the end-to-end characterization of quantum channels in a quantum network by introducing the problem of Quantum Network Tomography. The solution for this problem is an estimator for parameters that define a Kraus decomposition for all quantum channels in the network, using measurements performed exclusively in the end-nodes. We study this problem in detail for the case of arbitrary star quantum networks with quantum channels described by a single Pauli operator, like bit-flip quantum channels. We provide solutions for such networks with polynomial sample complexity. Our solutions provide evidence that pre-shared entanglement brings advantages for estimation in terms of the identifiability of parameters.