Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Byzantine Fault-Tolerance in Distributed Optimization

This paper considers the problem of Byzantine fault-tolerance in distributed multi-agent optimization. In this problem, each agent has a local cost function, and in the fault-free case, the goal is to design a distributed algorithm that allows all the agents to find a minimum point of all the agents' aggregate cost function. We consider a scenario where some agents might be Byzantine faulty that renders the original goal of computing a minimum point of all the agents' aggregate cost vacuous. A more reasonable objective for an algorithm in this scenario is to allow all the non-faulty agents to compute the minimum point of only the non-faulty agents' aggregate cost. Prior work shows that if there are up to f (out of n) Byzantine agents then a minimum point of the non-faulty agents' aggregate cost can be computed exactly if and only if the non-faulty agents' costs satisfy a certain redundancy property called 2f-redundancy. However, 2f-redundancy is an ideal property that can be satisfied only in systems free from noise or uncertainties, which can make the goal of exact fault-tolerance unachievable in some applications. Thus, we introduce the notion of (f,ε)-resilience, a generalization of exact fault-tolerance wherein the objective is to find an approximate minimum point of the non-faulty aggregate cost, with ε accuracy. This approximate fault-tolerance can be achieved under a weaker condition that is easier to satisfy in practice, compared to 2f-redundancy. We obtain necessary and sufficient conditions for achieving (f, ε)-resilience characterizing the correlation between relaxation in redundancy and approximation in resilience. In case when the agents' cost functions are differentiable, we obtain conditions for (f, ε)-resilience of the distributed gradient-descent method when equipped with robust gradient aggregation; such as comparative gradient elimination or coordinate-wise trimmed mean.
4D Atlas: Statistical Analysis of the Spatiotemporal Variability in Longitudinal 3D Shape Data

We propose a novel framework to learn the spatiotemporal variability in longitudinal 3D shape data sets, which contain observations of objects that evolve and deform over time. This problem is challenging since surfaces come with arbitrary parameterizations and thus, they need to be spatially registered. Also, different deforming objects, hereinafter referred to as <italic>4D surfaces</italic>, evolve at different speeds and thus they need to be temporally aligned. We solve this spatiotemporal registration problem using a Riemannian approach. We treat a 3D surface as a point in a shape space equipped with an elastic Riemannian metric that measures the amount of bending and stretching that the surfaces undergo. A 4D surface can then be seen as a trajectory in this space. With this formulation, the statistical analysis of 4D surfaces can be cast as the problem of analyzing trajectories embedded in a nonlinear Riemannian manifold. However, performing the spatiotemporal registration, and subsequently computing statistics, on such nonlinear spaces is not straightforward as they rely on complex nonlinear optimizations. Our core contribution is the mapping of the surfaces to the space of Square-Root Normal Fields (SRNF) where the <inline-formula><tex-math notation="LaTeX">$\mathbb {L}^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mi mathvariant="double-struck">L</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="laga-ieq1-3163720.gif"/></alternatives></inline-formula> metric is equivalent to the partial elastic metric in the space of surfaces. Thus, by solving the spatial registration in the SRNF space, the problem of analyzing 4D surfaces becomes the problem of analyzing trajectories embedded in the SRNF space, which has a euclidean structure. In this paper, we develop the building blocks that enable such analysis. These include: <bold>(1)</bold> the spatiotemporal registration of arbitrarily parameterized 4D surfaces even in the presence of large elastic deformations and large variations in their execution rates; <bold>(2)</bold> the computation of geodesics between 4D surfaces; <bold>(3)</bold> the computation of statistical summaries, such as means and modes of variation, of collections of 4D surfaces; and <bold>(4)</bold> the synthesis of random 4D surfaces. We demonstrate the performance of the proposed framework using 4D facial surfaces and 4D human body shapes.
Obsolete Personal Information Update System for the Prevention of Falls among Elderly Patients

Falls are a common problem affecting the older adults and a major public health issue. Centers for Disease Control and Prevention, and World Health Organization report that one in three adults over the age of 65 and half of the adults over 80 fall each year. In recent years, an ever-increasing range of applications have been developed to help deliver more effective falls prevention interventions. All these applications rely on a huge elderly personal database collected from hospitals, mutual health, and other organizations in caring for elderly. The information describing an elderly is continually evolving and may become obsolete at a given moment and contradict what we already know on the same person. So, it needs to be continuously checked and updated in order to restore the database consistency and then provide better service. This paper provides an outline of an Obsolete personal Information Update System (OIUS) designed in the context of the elderly-fall prevention project. Our OIUS aims to control and update in real-time the information acquired about each older adult, provide on-demand consistent information and supply tailored interventions to caregivers and fall-risk patients. The approach outlined for this purpose is based on a polynomial-time algorithm build on top of a causal Bayesian network representing the elderly data. The result is given as a recommendation tree with some accuracy level. We conduct a thorough empirical study for such a model on an elderly personal information base. Experiments confirm the viability and effectiveness of our OIUS.
With Measured Words: Simple Sentence Selection for Black-Box Optimization of Sentence Compression Algorithms

Sentence Compression is the task of generating a shorter, yet grammatical, version of a given sentence, preserving the essence of the original sentence. This paper proposes a Black-Box Optimizer for Compression (B-BOC): given a black-box compression algorithm and assuming not all sentences need be compressed – find the best candidates for compression in order to maximize both compression rate and quality. Given a required compression ratio, we consider two scenarios: (i) single-sentence compression, and (ii) sentences-sequence compression. In the first scenario our optimizer is trained to predict how well each sentence could be compressed while meeting the specified ratio requirement. In the latter, the desired compression ratio is applied to a sequence of sentences (e.g., a paragraph) as a whole, rather than on each individual sentence. To achieve that we use B-BOC to assign an optimal compression ratio to each sentence, then cast it as a Knapsack problem which we solve using bounded dynamic programming. We evaluate B-BOC on both scenarios on three datasets, demonstrating that our optimizer improves both accuracy and Rouge-F1-score compared to direct application of other compression algorithms.
Reinforcement Learning Based Temporal Logic Control with Soft Constraints Using Limit-deterministic Büchi Automata

—This paper studies the control synthesis of motion planning subject to uncertainties. The uncertainties are considered in robot motion and environment properties, giving rise to the probabilistic labeled Markov decision process (MDP). A model-free reinforcement learning (RL) is developed to generate a ﬁnite-memory control policy to satisfy high-level tasks expressed in linear temporal logic (LTL) formulas. One of the novelties is to translate LTL into a limit deterministic generalized Büchi automaton (LDGBA) and develop a corresponding embedded LDGBA (E-LDGBA) by incorporating a tracking-frontier function to overcome the issue of sparse accepting rewards, resulting in improved learning performance without increasing computational complexity. Due to potentially conﬂicting tasks, a relaxed product MDP is developed to allow the agent to revise its motion plan without strictly following the desired LTL constraints if the desired tasks can only be partially fulﬁlled. An expected return composed of violation rewards and accepting rewards is developed. The designed violation function quantiﬁes the differences between the revised and the desired motion planning, while the accepting rewards are designed to enforce the satisfaction of the acceptance condition of the relaxed product MDP. Rigorous analysis shows that any RL algorithm that optimizes the expected return is guaranteed to ﬁnd policies that, in decreasing order, can 1) satisfy acceptance condition of relaxed product MDP and 2) reduce the violation cost over long-term behaviors. Also, we validate the control synthesis approach via simulation and experimental results.
Minconvnets: a New Class of Multiplication-Less Neural Networks

In this article, MinConvNets where the multiplications in the forward propagation path of CNNs are approximated by minimum comparator operations are introduced. Hardware complexity of minimum operator is of the order of O(N), whereas for multiplication it is O(N2). Firstly, a methodology to find approximate operations based on statistical correlation is presented. We show that it is possible to replace multipliers by minimum operations in the forward propagation under certain constraints, i.e. given similar mean and variances of the feature and the weight vectors. A modified training method which guarantees the above constraints is proposed. And it is shown that equivalent precision can be achieved during inference with MinConvNets by using transfer learning from well trained exact CNNs.
Debiasing Pre-trained Contextualised Embeddings

In comparison to the numerous debiasing methods proposed for the static non-contextualised word embeddings, the discriminative biases in contextualised embeddings have received relatively little attention. We propose a fine-tuning method that can be applied at token- or sentence-levels to debias pre-trained contextualised embeddings. Our proposed method can be applied to any pre-trained contextualised embedding model, without requiring to retrain those models. Using gender bias as an illustrative example, we then conduct a systematic study using several state-of-the-art (SoTA) contextualised representations on multiple benchmark datasets to evaluate the level of biases encoded in different contextualised embeddings before and after debiasing using the proposed method. We find that applying token-level debiasing for all tokens and across all layers of a contextualised embedding model produces the best performance. Interestingly, we observe that there is a trade-off between creating an accurate vs. unbiased contextualised embedding model, and different contextualised embedding models respond differently to this trade-off.
The Fast-evolving Type Ib Supernova SN 2015dj in NGC 7371

We present the detailed optical evolution of a Type Ib SN 2015dj in NGC 7371, using data spanning up to ∼170 days after discovery. SN 2015dj shares similarity in light-curve shape with SN 2007gr and peaks at M V = −17.37 ± 0.02 mag. Analytical modeling of the quasi bolometric light curve yields 0.06 ± 0.01 M ⊙ of 56Ni, ejecta mass M ⊙, and kinetic energy erg. The spectral features show a fast evolution and resemble those of spherically symmetric ejecta. The analysis of nebular phase spectral lines indicates a progenitor mass between 13–20 M ⊙, suggesting a binary scenario.
Near-Optimal Detection for Both Data and Sneak-Path Interference in Resistive Memories With Random Cell Selector Failures

Resistive random-access memory is one of the most promising candidates for the next generation of non-volatile memory technology. However, its crossbar array structure causes severe “sneak-path” interference, which also leads to strong inter-cell correlation. Recent works have mainly focused on sub-optimal data detection schemes by ignoring inter-cell correlation and assuming sneak-path interference is independent between different array cells. In this paper, we propose a near-optimal data detection scheme that can approach the performance bound of the optimal detection scheme. Our detection scheme leverages a joint data and sneak-path interference recovery and can use all inter-cell correlations. The proposed scheme is suitable for data detection of large memory arrays with only linear operation complexity.
Weave-realizability for D–type

We study exact Lagrangian fillings of Legendrian links of $D_n$-type in the standard contact 3-sphere. The main result is the existence of a Lagrangian filling, represented by a weave, such that any algebraic quiver mutation of the associated intersection quiver can be realized as a geometric weave mutation. The method of proof is via Legendrian weave calculus and a construction of appropriate 1-cycles whose geometric intersections realize the required algebraic intersection numbers. In particular, we show that in $D$-type, each cluster chart of the moduli of microlocal rank-1 sheaves is induced by at least one embedded exact Lagrangian filling. Hence, the Legendrian links of $D_n$-type have at least as many Hamiltonian isotopy classes of Lagrangian fillings as cluster seeds in the $D_n$-type cluster algebra, and their geometric exchange graph for Lagrangian disk surgeries contains the cluster exchange graph of $D_n$-type.
Chromium solubility in perovskite at high pressure: The structure of (Mg1–xCrx)(Si1–xCrx)O3 (with x = 0.07) synthesized at 23 GPa and 1600 °C

Abstract The crystal structure and chemical composition of a crystal of (Mg1-xCrx)(Si1-xCrx)O3 perovskite (with x = 0.07) synthesized in the model system Mg3Cr2Si3O12-Mg4Si4O12 at 23 GPa and 1600 °C have been investigated. The compound was found to be orthorhombic, space group Pbnm, with lattice parameters a = 4.8213(5), b = 4.9368(6), c = 6.9132(8) Å, V = 164.55(3) Å3. The structure was refined to R = 0.046 using 473 independent reflections. Chromium was found to substitute for both Mg at the dodecahedral X site (with a mean bond distance of 2.187 Å) and Si at the octahedral Y site (mean: 1.814 Å), according to the reaction Mg2+ + Si4+ = 2Cr3+. Such substitutions cause a shortening of the and a lengthening of the distances with respect to the values typically observed for pure MgSiO3 perovskite. Although high Cr-contents are not considered in the pyrolite model, Cr-bearing perovskite may be an important host for chromium in the lower mantle. The successful synthesis of perovskite with high-Cr content and its structural characterization are of key importance because the study of its thermodynamic constants combined with the data on phase relations in the lower-mantle systems can provide new constraints on thermobarometry of perovskite-bearing assemblages.
Point-hyperplane Incidence Geometry and the Log-rank Conjecture

We study the log-rank conjecture from the perspective of point-hyperplane incidence geometry. We formulate the following conjecture: Given a point set in ℝd that is covered by constant-sized sets of parallel hyperplanes, there exists an affine subspace that accounts for a large (i.e., 2–polylog(d)) fraction of the incidences, in the sense of containing a large fraction of the points and being contained in a large fraction of the hyperplanes. In other words, the point-hyperplane incidence graph for such configurations has a large complete bipartite subgraph. Alternatively, our conjecture may be interpreted linear-algebraically as follows: Any rank-d matrix containing at most O(1) distinct entries in each column contains a submatrix of fractional size 2–polylog(d), in which each column is constant. We prove that our conjecture is equivalent to the log-rank conjecture; the crucial ingredient of this proof is a reduction from bounds for parallel k-partitions to bounds for parallel (k-1)-partitions. We also introduce an (apparent) strengthening of the conjecture, which relaxes the requirements that the sets of hyperplanes be parallel. Motivated by the connections above, we revisit well-studied questions in point-hyperplane incidence geometry without structural assumptions (i.e., the existence of partitions). We give an elementary argument for the existence of complete bipartite subgraphs of density Ω (ε 2d/d) in any d-dimensional configuration with incidence density ε, qualitatively matching previous results proved using sophisticated geometric techniques. We also improve an upper-bound construction of Apfelbaum and Sharir [2], yielding a configuration whose complete bipartite subgraphs are exponentially small and whose incidence density is Ω (1/√ d). Finally, we discuss various constructions (due to others) of products of Boolean matrices which yield configurations with incidence density Ω (1) and complete bipartite subgraph density 2-Ω (√ d), and pose several questions for this special case in the alternative language of extremal set combinatorics. Our framework and results may help shed light on the difficulty of improving Lovett’s Õ(√ rank(f)) bound [20] for the log-rank conjecture. In particular, any improvement on this bound would imply the first complete bipartite subgraph size bounds for parallel 3-partitioned configurations which beat our generic bounds for unstructured configurations.
Rethinking Exploration for Sample-Efficient Policy Learning

Oﬀ-policy reinforcement learning for control has made great strides in terms of performance and sample eﬃciency. We suggest that for many tasks the sample eﬃciency of modern methods is now limited by the richness of the data collected rather than the diﬃculty of policy ﬁtting. We examine the reasons that directed exploration methods in the bonus-based exploration (BBE) family have not been more inﬂuential in the sample eﬃcient control problem. Three issues have limited the applicability of BBE: bias with ﬁnite samples, slow adaptation to decaying bonuses, and lack of optimism on unseen transitions. We propose modiﬁcations to the bonus-based exploration recipe to address each of these limitations. The resulting algorithm, which we call UFO, produces policies that are Unbiased with ﬁnite samples, Fast-adapting as the exploration bonus changes, and Optimistic with respect to new transitions. We include experiments showing that rapid directed exploration is a promising direction to improve sample eﬃciency for control.
The intracluster magnetic field in the double relic galaxy cluster Abell 2345

Magnetic fields are ubiquitous in galaxy clusters, yet their radial profile, power spectrum, and connection to host cluster properties are poorly known. Merging galaxy clusters hosting diffuse polarized emission in the form of radio relics offer a unique possibility to study the magnetic fields in these complex systems. In this paper, we investigate the intracluster magnetic field in Abell 2345. This cluster hosts two radio relics that we detected in polarization with 1–2 GHz Jansky Very Large Array observations. X-ray XMM–Newton images show a very disturbed morphology. We derived the rotation measure (RM) of five polarized sources within ∼1 Mpc from the cluster centre applying the RM synthesis. Both, the average RM and the RM dispersion radial profiles probe the presence of intracluster magnetic fields. Using the thermal electron density profile derived from X-ray analysis and simulating a 3D magnetic field with fluctuations following a power spectrum derived from magneto-hydrodynamical cosmological simulations, we build mock RM images of the cluster. We constrained the magnetic field profile in the eastern radio relic sector by comparing simulated and observed RM images. We find that, within the framework of our model, the data require a magnetic field scaling with thermal electron density as B(r) ∝ ne(r). The best model has a central magnetic field (within a 200 kpc radius) of 2.8$\pm 0.1 \ \mu$G. The average magnetic field at the position of the eastern relic is $\sim 0.3 \ \mu$G, a factor 2.7 lower than the equipartition estimate.
Recycling of polyethene and polypropene in a novel bench-scale rotating cone reactor by high temperature pyrolysis.

The high-temperature pyrolysis of polyethene (PE), polypropene (PP), and mixtures of these polymers was studied in a novel bench-scale rotating cone reactor (RCR). Experiments showed that the effect of the sand or reactor temperature on the product spectrum obtained is large compared to the effect of other parameters (for instance, residence time). In general, it can be concluded that the amount of polymer converted into propene and butene decreases with higher cracking severity (higher temperatures or longer residence times), while the fraction methane increases. About 80 wt % of the polymer is converted into gas at a reactor temperature of 898 K, while 20 wt % is converted into intermediate waxlike compounds or aromatics in the case of PE. The gas yield increases slightly with the reactor and/or sand temperature to 88 wt % at higher temperatures. The total amount of alkenes decreases with increasing cracking intensity, which suggests that the reactor should be operated at the lowest possible temperature. Our results indicate that the reactor offers a few significant advantages compared to other reactors (no fluidization gas necessary, good solid−polymer mixing, no cyclones necessary) and a competitive product spectrum. However, significant improvements are still possible to make the reactor concept technically and economically more attractive.
ATRM: Attention-based Task-level Relation Module for GNN-based Few-shot Learning

Recently, graph neural networks (GNNs) have shown powerful ability to handle few-shot classiﬁcation problem, which aims at classifying unseen samples when trained with limited labeled samples per class. GNN-based few-shot learning architectures mostly replace traditional metric with a learnable GNN. In the GNN, the nodes are set as the samples’ embedding, and the relationship between two connected nodes can be obtained by a network, the input of which is the difference of their embedding features. We consider this method of measuring relation of samples only models the sample-to-sample relation, while neglects the speciﬁcity of different tasks. That is, this method of measuring relation does not take the task-level information into account. To this end, we propose a new relation measure method, namely the attention-based task-level relation module (ATRM) , to explicitly model the task-level relation of one sample to all the others. The proposed module captures the relation representations between nodes by considering the sample-to-task instead of sample-to-sample embedding features. We conducted extensive experiments on four benchmark datasets: mini-ImageNet, tiered-ImageNet, CUB-200 - 2011 , and CIFAR-FS. Experimental re-sults demonstrate that the proposed module is effective for GNN-based few-shot learning.