Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of vortex patterns of oscillating foils in side-by-side configurations

Abstract The unsteady hydrodynamics of two pitching foils arranged in a side-by-side (parallel) configuration is examined for a range of Strouhal numbers, phase differences, oscillation amplitudes and separation distances. Three distinct vortex patterns are identified in the wake maps, which include separated wake, merged wake and transitional-merged wake. Furthermore, a novel model is introduced based on fundamental flow variables including velocity, location and circulation of dipole structures to quantitatively distinguish vortex patterns in the wake. The physical mechanism of the wake merging process is also elucidated. When an oscillating foil experiences the jet deflection phenomenon, secondary structures separated from the primary street traverse in the other direction by making an angle with its parent vortex street. For in-phase pitching parallel foils, secondary structures from the vortex street of the lower foil interact with the primary vortex street of the upper foil under certain kinematic conditions. This interaction triggers the wake merging process by influencing circulation of coherent structures in the upper part of the wake. It is unveiled that merging of the wakes leads to enhancements in propulsive efficiency by increasing thrust generation without a significant alteration in power requirements. These are attributed to the formation of a high-momentum jet by the merged vortex street, which possesses significantly larger circulation due to the amalgamation of the vortices, and major alterations in the evolution of leading edge vortices. Thus, flow physics, which is thoroughly explored here, is crucial in providing novel insights for the future development of flow control techniques for efficient designs of bio-inspired underwater propulsors.
The Extremality of 2-Partite Turán Graphs with Respect to the Number of Colorings

We consider a problem proposed by Linial and Wilf to determine the structure of graphs that allows the maximum number of $q$-colorings among graphs with $n$ vertices and $m$ edges. Let $T_r(n)$ denote the Tur\'{a}n graph - the complete $r$-partite graph on $n$ vertices with partition sizes as equal as possible. We prove that for all odd integers $q\geq 5$ and sufficiently large $n$, the Tur\'{a}n graph $T_2(n)$ has at least as many $q$-colorings as any other graph $G$ with the same number of vertices and edges as $T_2(n)$, with equality holding if and only if $G=T_2(n)$. Our proof builds on methods by Norine and by Loh, Pikhurko, and Sudakov, which reduces the problem to a quadratic program.
MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning

Inferring reward functions from demonstrations and pairwise preferences are auspicious approaches for aligning Reinforcement Learning (RL) agents with human intentions. However, state-of-the art methods typically focus on learning a single reward model, thus rendering it difficult to trade off different reward functions from multiple experts. We propose Multi-Objective Reinforced Active Learning (MORAL), a novel method for combining diverse demonstrations of social norms into a Pareto-optimal policy. Through maintaining a distribution over scalarization weights, our approach is able to interactively tune a deep RL agent towards a variety of preferences, while eliminating the need for computing multiple policies. We empirically demonstrate the effectiveness of MORAL in two scenarios, which model a delivery and an emergency task that require an agent to act in the presence of normative conflicts. Overall, we consider our research a step towards multi-objective RL with learned rewards, bridging the gap between current reward learning and machine ethics literature.
Lock-free locks revisited

This paper presents a new and practical approach to lock-free locks based on helping, which allows the user to write code using fine-grained locks, but run it in a lock-free manner. Although lock-free locks have been suggested in the past, they are widely viewed as impractical, have some key limitations, and, as far as we know, have never been implemented. The paper presents some key techniques that make lock-free locks practical and more general. The most important technique is an approach to idempotence---i.e. making code that runs multiple times appear as if it ran once. The idea is based on using a shared log among processes running the same protected code. Importantly, the approach can be library based, requiring very little if any change to standard code---code just needs to use the idempotent versions of memory operations (load, store, LL/SC, allocation, free). We have implemented a C++ library called Flock based on the ideas. Flock allows lock-based data structures to run in either lock-free or blocking (traditional locks) mode. We implemented a variety of tree and list-based data structures with Flock and compare the performance of the lock-free and blocking modes under a variety of workloads. The lock-free mode is almost as fast as blocking mode under almost all workloads, and significantly faster when threads are over-subscribed (more threads than processors). We also compare with several existing lock-based and lock-free alternatives.
The Accretion Flow in M87 is Really MAD

The supermassive black holes in most galaxies in the universe are powered by hot accretion flows. Both theoretical analysis and numerical simulations have indicated that, depending on the degree of magnetization, black hole hot accretion flow is divided into two modes, namely SANE (standard and normal evolution) and MAD (magnetically arrested disk). It has been an important question which mode the hot accretion flows in individual sources should belong to in reality, SANE or MAD. This issue has been investigated in some previous works but they all suffer from various uncertainties. By using the measured rotation measure (RM) values in the prototype low-luminosity active galactic nuclei in M87 at 2, 5, and 8 GHz along the jet at various distances from the black hole, combined with three-dimensional general relativity magnetohydrodynamical numerical simulations of SANE and MAD, we show in this paper that the RM values predicted by MAD are well consistent with observations, while the SANE model overestimates the RM by over two orders of magnitude and thus is ruled out.
Class-Incremental Continual Learning Into the eXtended DER-Verse

The staple of human intelligence is the capability of acquiring knowledge in a continuous fashion. In stark contrast, Deep Networks forget catastrophically and, for this reason, the sub-field of Class-Incremental Continual Learning fosters methods that learn a sequence of tasks incrementally, blending sequentially-gained knowledge into a comprehensive prediction. This work aims at assessing and overcoming the pitfalls of our previous proposal Dark Experience Replay (DER), a simple and effective approach that combines rehearsal and Knowledge Distillation. Inspired by the way our minds constantly rewrite past recollections and set expectations for the future, we endow our model with the abilities to i) revise its replay memory to welcome novel information regarding past data ii) pave the way for learning yet unseen classes. We show that the application of these strategies leads to remarkable improvements; indeed, the resulting method – termed eXtended-DER (X-DER) – outperforms the state of the art on both standard benchmarks (such as CIFAR-100 and miniImageNet) and a novel one here introduced. To gain a better understanding, we further provide extensive ablation studies that corroborate and extend the findings of our previous research (e.g., the value of Knowledge Distillation and flatter minima in continual learning setups). We make our results fully reproducible; the codebase is available at https://github.com/aimagelab/mammoth.
Transformer Embeddings of Irregularly Spaced Events and Their Participants

The neural Hawkes process (Mei&Eisner, 2017) is a generative model of irregularly spaced sequences of discrete events. To handle complex domains with many event types, Mei et al. (2020a) further consider a setting in which each event in the sequence updates a deductive database of facts (via domain-specific pattern-matching rules); future events are then conditioned on the database contents. They show how to convert such a symbolic system into a neuro-symbolic continuous-time generative model, in which each database fact and the possible event has a time-varying embedding that is derived from its symbolic provenance. In this paper, we modify both models, replacing their recurrent LSTM-based architectures with flatter attention-based architectures (Vaswani et al., 2017), which are simpler and more parallelizable. This does not appear to hurt our accuracy, which is comparable to or better than that of the original models as well as (where applicable) previous attention-based methods (Zuo et al., 2020; Zhang et al., 2020a).
The fixed point property of quasi-point-separable topological vector spaces

In this paper, we introduce the concept of quasi-point-separable topological vector spaces, which has the following important properties: 1. In general, the conditions for a topological vector space to be quasi-pointseparable is not very difficult to check; 2. The class of quasi-point-separable topological vector spaces is very large that includes locally convex topological vector spaces and pseudonorm adjoint topological vector spaces as special cases; 3. Every quasi-point-separable Housdorrf topological vector space has the fixed point property (that is, every continuous self-mapping on any given nonempty closed and convex subset has a fixed point), which is the result of the main theorem of this paper (Theorem 4.1); Furthermore, we provide some concrete examples of quasi-point-separable topological vector spaces, which are not locally convex. It follows that the main theorem of this paper is a proper extension of Tychonoff’s fixed point theorem on locally convex topological vector spaces.
Effects of concurrency on epidemic spreading in Markovian temporal networks

The concurrency of edges, quantified by the number of edges that share a common node at a given time point, may be an important determinant of epidemic processes in temporal networks. We propose theoretically tractable Markovian temporal network models in which each edge flips between the active and inactive states in continuous time. The different models have different amounts of concurrency while we can tune the models to share the same statistics of edge activation and deactivation (and hence the fraction of time for which each edge is active) and the structure of the aggregate (i.e., static) network. We analytically calculate the amount of concurrency of edges sharing a node for each model. We then numerically study effects of concurrency on epidemic spreading in the stochastic susceptible-infectious-susceptible and susceptible-infectious-recovered dynamics on the proposed temporal network models. We find that the concurrency enhances epidemic spreading near the epidemic threshold, while this effect is small in many cases. Furthermore, when the infection rate is substantially larger than the epidemic threshold, the concurrency suppresses epidemic spreading in a majority of cases. In sum, our numerical simulations suggest that the impact of concurrency on enhancing epidemic spreading within our model is consistently present near the epidemic threshold but modest. The proposed temporal network models are expected to be useful for investigating effects of concurrency on various collective dynamics on networks including both infectious and other dynamics.
Wikipedia Reader Navigation: When Synthetic Data Is Enough

Every day millions of people read Wikipedia. When navigating the vast space of available topics using hyperlinks, readers describe trajectories on the article network. Understanding these navigation patterns is crucial to better serve readers' needs and address structural biases and knowledge gaps. However, systematic studies of navigation on Wikipedia are hindered by a lack of publicly available data due to the commitment to protect readers' privacy by not storing or sharing potentially sensitive data. In this paper, we ask: How well can Wikipedia readers' navigation be approximated by using publicly available resources, most notably the Wikipedia clickstream data? We systematically quantify the differences between real navigation sequences and synthetic sequences generated from the clickstream data, in 6 analyses across 8 Wikipedia language versions. Overall, we find that the differences between real and synthetic sequences are statistically significant, but with small effect sizes, often well below 10%. This constitutes quantitative evidence for the utility of the Wikipedia clickstream data as a public resource: clickstream data can closely capture reader navigation on Wikipedia and provides a sufficient approximation for most practical downstream applications relying on reader data. More broadly, this study provides an example for how clickstream-like data can generally enable research on user navigation on online platforms while protecting users' privacy.
Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfe

Unbalanced optimal transport (UOT) extends optimal transport (OT) to take into account mass variations to compare distributions. This is crucial to make OT successful in ML applications, making it robust to data normalization and outliers. The baseline algorithm is Sinkhorn, but its convergence speed might be significantly slower for UOT than for OT. In this work, we identify the cause for this deficiency, namely the lack of a global normalization of the iterates, which equivalently corresponds to a translation of the dual OT potentials. Our first contribution leverages this idea to develop a provably accelerated Sinkhorn algorithm (coined 'translation invariant Sinkhorn') for UOT, bridging the computational gap with OT. Our second contribution focusses on 1-D UOT and proposes a Frank-Wolfe solver applied to this translation invariant formulation. The linear oracle of each steps amounts to solving a 1-D OT problems, resulting in a linear time complexity per iteration. Our last contribution extends this method to the computation of UOT barycenter of 1-D measures. Numerical simulations showcase the convergence speed improvement brought by these three approaches.
WHOCARES: data-driven WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions

Cardiac pulsation is a physiological confound of functional magnetic resonance imaging (fMRI) time-series that introduces spurious signal fluctuations in proximity to blood vessels. fMRI alone is not sufficiently fast to resolve cardiac pulsation. Depending on the ratio between the instantaneous heartrate and the acquisition sampling frequency ( 1 TR , with TR being the repetition time), the cardiac signal may alias into the frequency band of neural activation. The introduction of simultaneous multi-slice (SMS) imaging has significantly reduced the chances of cardiac aliasing. However, the necessity of covering the entire brain at high spatial resolution restrain the shortest TR to just over 0.5 seconds, which is in turn not sufficiently short to resolve cardiac pulsation beyond 60 beats per minute. Recently, hyper-sampling of the fMRI time-series has been introduced to overcome this issue. While each anatomical location is sampled every TR seconds, the time between consecutive excitations is shorter ∗Corresponding author Email address: giulio.ferrazzi@hsancamillo.it (Giulio Ferrazzi) 1IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy 2Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, 3001, Belgium 3Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium 4Brain Imaging Center, McLean Hospital, 115 Mill St., Belmont, MA, 02478, USA 5Department of Psychiatry, Harvard University Medical School, 25 Shattuck St., Boston, MA, 02115, USA Submitted preprint ar X iv :2 20 1. 00 74 4v 1 [ ph ys ic s. m ed -p h] 3 J an 2 02 2 and thus adequate to resolve cardiac pulsation. In this study, we show that it is feasible to temporally and spatially resolve cardiac waveforms at each voxel location by combining a dedicated hyper-sampling decomposition scheme with SMS. We developed the technique on 774 healthy subjects selected from the Human Connectome Project (HCP) and validated the technology against the RETROICOR method. The proposed approach, which we name Data-driven WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), is fully data-driven, does not make specific assumptions on cardiac pulsatility, and is independent from external physiological recordings so that the retrospective correction of fMRI data becomes possible when such measurements are not available. WHOCARES is freely available at https://github.com/gferrazzi/WHOCARES.
On robustness and local differential privacy

It is of soaring demand to develop statistical analysis tools that are robust against contamination as well as preserving individual data owners' privacy. In spite of the fact that both topics host a rich body of literature, to the best of our knowledge, we are the first to systematically study the connections between the optimality under Huber's contamination model and the local differential privacy (LDP) constraints. In this paper, we start with a general minimax lower bound result, which disentangles the costs of being robust against Huber's contamination and preserving LDP. We further study four concrete examples: a two-point testing problem, a potentially-diverging mean estimation problem, a nonparametric density estimation problem and a univariate median estimation problem. For each problem, we demonstrate procedures that are optimal in the presence of both contamination and LDP constraints, comment on the connections with the state-of-the-art methods that are only studied under either contamination or privacy constraints, and unveil the connections between robustness and LDP via partially answering whether LDP procedures are robust and whether robust procedures can be efficiently privatised. Overall, our work showcases a promising prospect of joint study for robustness and local differential privacy.
Decoding Nonbinary LDPC Codes via Proximal-ADMM Approach

In this paper, we focus on decoding nonbinary low-density parity-check (LDPC) codes in Galois fields of characteristic two via the proximal alternating direction method of multipliers (proximal-ADMM). By exploiting Flanagan/Constant-Weighting embedding techniques and the decomposition technique based on three-variables parity-check equations, two efficient proximal-ADMM decoders for nonbinary LDPC codes are proposed. We show that both of them are theoretically guaranteed convergent to some stationary point of the decoding model and either of their computational complexities in each proximal-ADMM iteration scales linearly with LDPC code’s length and the size of the considered Galois field. Moreover, the decoder based on the Constant-Weight embedding technique satisfies the favorable property of codeword symmetry. Simulation results demonstrate their effectiveness in comparison with state-of-the-art LDPC decoders.
SporeAgent: Reinforced Scene-level Plausibility for Object Pose Refinement

Observational noise, inaccurate segmentation and ambiguity due to symmetry and occlusion lead to inaccurate object pose estimates. While depth- and RGB-based pose refinement approaches increase the accuracy of the resulting pose estimates, they are susceptible to ambiguity in the observation as they consider visual alignment. We propose to leverage the fact that we often observe static, rigid scenes. Thus, the objects therein need to be under physically plausible poses. We show that considering plausibility reduces ambiguity and, in consequence, allows poses to be more accurately predicted in cluttered environments. To this end, we extend a recent RL-based registration approach towards iterative refinement of object poses. Experiments on the LINEMOD and YCB-VIDEO datasets demonstrate the state-of-the-art performance of our depth-based refinement approach. Code is available at github.com/dornik/sporeagent.
Learning cortical representations through perturbed and adversarial dreaming

Humans and other animals learn to extract general concepts from sensory experience without extensive teaching. This ability is thought to be facilitated by offline states like sleep where previous experiences are systemically replayed. However, the characteristic creative nature of dreams suggests that learning semantic representations may go beyond merely replaying previous experiences. We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs). Learning in our model is organized across three different global brain states mimicking wakefulness, non-rapid eye movement (NREM), and REM sleep, optimizing different, but complementary, objective functions. We train the model on standard datasets of natural images and evaluate the quality of the learned representations. Our results suggest that generating new, virtual sensory inputs via adversarial dreaming during REM sleep is essential for extracting semantic concepts, while replaying episodic memories via perturbed dreaming during NREM sleep improves the robustness of latent representations. The model provides a new computational perspective on sleep states, memory replay, and dreams, and suggests a cortical implementation of GANs.
Approximate quantum encryption with even shorter keys

Perfect encryption of a qubit state using the Quantum One-Time Pad (QOTP) requires 2 classical key bits. More generally, perfect QOTP encryption of a 2 n -dimensional state requires 2 n classical bits. However, almost-perfect encryption, with information-theoretic security, can be achieved with only little more than 1 key bit per qubit. In this paper we slightly improve the key length. We show that key length n + 23 log 1 ε suﬃces to encrypt n qubits in such a way that the cipherstate has trace distance ≤ ε from the fully mixed state. The previous best result was n + 2 log 1 ε . Similar to the previous literature, we expand the key to a 2 n -bit pseudorandom string which is then used as a QOTP key. In this expansion we make use of 2 n bits of public randomness which are included as a classical part of the cipherstate. Our key expansion is slightly faster than in previous works.
SalyPath360: Saliency and Scanpath Prediction Framework for Omnidirectional Images

This paper introduces a new framework to predict visual attention of omnidirectional images. The key setup of our architecture is the simultaneous prediction of the saliency map and a corresponding scanpath for a given stimulus. The framework implements a fully encoder-decoder convolutional neural network augmented by an attention module to generate representative saliency maps. In addition, an auxiliary network is employed to generate probable viewport center fixation points through the SoftArgMax function. The latter allows to derive fixation points from feature maps. To take advantage of the scanpath prediction, an adaptive joint probability distribution model is then applied to construct the final unbiased saliency map by leveraging the encoder decoder-based saliency map and the scanpath-based saliency heatmap. The proposed framework was evaluated in terms of saliency and scanpath prediction, and the results were compared to state-of-the-art methods on Salient360! dataset. The results showed the relevance of our framework and the benefits of such architecture for further omnidirectional visual attention prediction tasks.