Miracle-dz/UniFAR-FTUs huggingface dataset:
D IABLO : A Distributed Analytical Blockchain Benchmark Framework Focusing on Real-World Workloads

The plethora of blockchain proposals raises the question of selecting the ideal blockchain for a given decentralised application (DApp). Most blockchain performance evaluations are obtained through tailored environments under unknown experimental settings documented through whitepapers or blog posts. Unfortunately, the few blockchain benchmark efforts typically generate workloads either from a central location, or, purely based on synthetic request patterns. We propose a benchmark framework, D IABLO , to reason about the performance of blockchains under realistic workloads. D IABLO is extensible, allowing DApp programmers to deﬁne new work-loads, and blockchain designers to integrate and test their own blockchain. D IABLO currently supports 6 blockchain implementations and 4 built-in work-load DApps. Our results indicate that blockchains with lower fault tolerance can achieve higher performance, conﬁrming that speculative execution of transactions might drastically impact performance. These results also show that one implementation can offer signiﬁcantly better performance than another implementation of the same blockchain protocol.
Ordering of molecules with π-conjugated triangular core by switching hydrogen bonding and van der Waals interactions

Using alkoxylated derivatives of triangular dehydrobenzo[12]annulene (DBA) as building blocks, we demonstrate control of a formation of 2D molecular networks on Au(111). The tunability of intermolecular interactions by substituting alkoxy groups can improve the homogeneity of the 2D molecular network by restricting the number of polymorphs, and it can induce domain-specific chirality. The π-conjugated triangular core of each alkoxylated DBA derivative is locked on a specific site on the Au(111) surface by the interaction between the oxygen atoms of the molecule and the gold (Au) surface atoms, and the relative importance of intermolecular hydrogen bonding versus van der Waals interactions depends on the length of the alkoxy groups. Such tunable intermolecular interactions balanced with surface–molecule interaction may eventually enable control of the formation of 2D molecular networks. These results could lead to potential applications in tailoring 2D molecular networks or allow the use of these networks .
A Hybrid mmWave and Camera System for Long-Range Depth Imaging

mmWave radars offer excellent depth resolution even at very long ranges owing to their high bandwidth. But their angular resolution is at least an order-of-magnitude worse than camera and lidar systems. Hence, mmWave radar is not a capable 3-D imaging solution in isolation. We propose Metamoran, a system that combines the complimentary strengths of radar and camera to obtain accurate, high resolution depth images over long ranges even in high clutter environments, all from a single fixed vantage point. Metamoran enables rich long-range depth imaging with applications in security and surveillance, roadside safety infrastructure and wide-area mapping. Our approach leverages the high angular resolution from cameras using computer vision techniques, including image segmentation and monocular depth estimation, to obtain object shape. Our core contribution is a method to convert this object shape into an RF I/Q equivalent, which we use in a novel radar processing pipeline to help declutter the scene and capture extremely weak reflections from objects at long distances. We perform a detailed evaluation of Metamoran's depth imaging capabilities in 400 diverse scenes. Our evaluation shows that Metamoran estimates the depth of static objects up to 90 m and moving objects up to 305 m and with a median error of 28 cm, an improvement of 13$\times$ compared to a naive radar+camera baseline and 23$\times$ compared to monocular depth estimation.
Human movement augmentation and how to make it a reality

Augmenting the body with artificial limbs controlled concurrently to the natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this vision possible. By allowing individuals to achieve otherwise impossible actions, this movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with their environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning, and propose a vision for the field.
Enforcing Statistical Orthogonality in Massive MIMO Systems via Covariance Shaping

This paper tackles the problem of downlink data transmission in massive multiple-input multiple-output (MIMO) systems where user equipments (UEs) exhibit high spatial correlation and channel estimation is limited by strong pilot contamination. Signal subspace separation among UEs is, in fact, rarely realized in practice and is generally beyond the control of the network designer (as it is dictated by the physical scattering environment). In this context, we propose a novel statistical beamforming technique, referred to as MIMO covariance shaping, that exploits multiple antennas at the UEs and leverages the realistic non-Kronecker structure of massive MIMO channels to target a suitable shaping of the channel statistics performed at the UE-side. To optimize the covariance shaping strategies, we propose a low-complexity block coordinate descent algorithm that is proved to converge to a limit point of the original nonconvex problem. For the two-UE case, this is shown to converge to a stationary point of the original problem. Numerical results illustrate the sum-rate performance gains of the proposed method with respect to spatial multiplexing in scenarios where the spatial selectivity of the base station is not sufficient to separate closely spaced UEs.
Extracting Global Dynamics of Loss Landscape in Deep Learning Models

Deep learning models evolve through training to learn the manifold in which the data exists to satisfy an objective. It is well known that evolution leads to different final states which produce inconsistent predictions of the same test data points. This calls for techniques to be able to empirically quantify the difference in the trajectories and highlight problematic regions. While much focus is placed on discovering what models learn, the question of how a model learns is less studied beyond theoretical landscape characterizations and local geometric approximations near optimal conditions. Here, we present a toolkit for the Dynamical Organization Of Deep Learning Loss Landscapes, or DOODL3. DOODL3 formulates the training of neural networks as a dynamical system, analyzes the learning process, and presents an interpretable global view of trajectories in the loss landscape. Our approach uses the coarseness of topology to capture the granularity of geometry to mitigate against states of instability or elongated training. Overall, our analysis presents an empirical framework to extract the global dynamics of a model and to use that information to guide the training of neural networks.
Markov equivalence of max-linear Bayesian networks

Max-linear Bayesian networks have emerged as highly applicable models for causal inference via extreme value data. However, conditional independence (CI) for max-linear Bayesian networks behaves differently than for classical Gaussian Bayesian networks. We establish the parallel between the two theories via tropicalization, and establish the surprising result that the Markov equivalence classes for max-linear Bayesian networks coincide with the ones obtained by regular CI. Our paper opens up many problems at the intersection of extreme value statistics, causal inference and tropical geometry.
Accurate dose measurements using cherenkov emission polarization imaging.

PURPOSE Cherenkov radiation carries the potential of direct in-water dose measurements, but its precision is currently limited by a strong anisotropy. Taking advantage of polarization imaging, this work proposes a new approach for high accuracy Cherenkov emission dose measurements. METHODS Cherenkov radiation produced in a 15 × 15 × 20 cm3 water tank is imaged with a cooled CCD camera from four polarizer transmission axes [0°, 45°, 90°, 135°]. The water tank is positioned at the isocenter of a 5 × 5 cm2 , 6 MV and 18 MV photon beam. Using Malus' law, the polarized portion of the signal is extracted. Corrections are applied to the polarized signal following azimuthal and polar Cherenkov emission angular distributions extracted from Monte Carlo simulations. Projected percent depth dose and beam profiles are measured and compared with the prediction from a treatment planning system (TPS). RESULTS Corrected polarized signals on the central axis reduced deviations at depth (mean ± std) from 8±5% to 0.8 ±1% at 6 MV and 8±7% to 1±3% at 18 MV. For the profile measurement, differences between the corrected polarized signal and the TPS calculations are 1±3% and 2±3% on the central axis at 6 MV and 18 MV respectively. In these conditions, Cherenkov emission is shown to be partly polarized. CONCLUSIONS This work proposes a novel polarization imaging approach enabling high precision water-based dose measurements using Cherenkov radiation. The method allows correction of the Cherenkov emission anisotropy within 4% on the beam central axis and in depth. This article is protected by copyright. All rights reserved.
Scaling Neural Tangent Kernels via Sketching and Random Features

The Neural Tangent Kernel (NTK) characterizes the behavior of infinitely-wide neural networks trained under least squares loss by gradient descent. Recent works also report that NTK regression can outperform finitely-wide neural networks trained on small-scale datasets. However, the computational complexity of kernel methods has limited its use in large-scale learning tasks. To accelerate learning with NTK, we design a near input-sparsity time approximation algorithm for NTK, by sketching the polynomial expansions of arc-cosine kernels: our sketch for the convolutional counterpart of NTK (CNTK) can transform any image using a linear runtime in the number of pixels. Furthermore, we prove a spectral approximation guarantee for the NTK matrix, by combining random features (based on leverage score sampling) of the arc-cosine kernels with a sketching algorithm. We benchmark our methods on various large-scale regression and classification tasks and show that a linear regressor trained on our CNTK features matches the accuracy of exact CNTK on CIFAR-10 dataset while achieving 150× speedup.
Unique Sparse Decomposition of Low Rank Matrices

The problem of finding a unique low dimensional decomposition of a given matrix has been a fundamental and recurrent problem in many areas. In this paper, we study the problem of seeking a unique decomposition of a low rank matrix <inline-formula> <tex-math notation="LaTeX">$\boldsymbol Y\in \mathbb R ^{p\times n}$ </tex-math></inline-formula> that admits a sparse representation. Specifically, we consider <inline-formula> <tex-math notation="LaTeX">$\boldsymbol Y= \boldsymbol A \boldsymbol X $ </tex-math></inline-formula> where the matrix <inline-formula> <tex-math notation="LaTeX">$\boldsymbol A\in \mathbb R^{p\times r}$ </tex-math></inline-formula> has full column rank, with <inline-formula> <tex-math notation="LaTeX">$r < \min \{n,p\}$ </tex-math></inline-formula>, and the matrix <inline-formula> <tex-math notation="LaTeX">$\boldsymbol X\in \mathbb R^{r\times n}$ </tex-math></inline-formula> is element-wise sparse. We prove that this low rank, sparse decomposition of <inline-formula> <tex-math notation="LaTeX">$\boldsymbol Y$ </tex-math></inline-formula> can be uniquely identified, up to some intrinsic signed permutation. Our approach relies on solving a nonconvex optimization problem constrained over the unit sphere. Our geometric analysis for its nonconvex optimization landscape shows that any <italic>strict</italic> local solution is close to the ground truth, and can be recovered by a simple data-driven initialization followed with any second order descent algorithm. Our theoretical findings are corroborated by numerical experiments. <xref ref-type="fn" rid="fn1">1</xref>
Maximum Spanning Trees Are Invariant to Temperature Scaling in Graph-based Dependency Parsing

Modern graph-based syntactic dependency parsers operate by predicting, for each token within a sentence, a probability distribution over its possible syntactic heads (i.e., all other tokens) and then extracting a maximum spanning tree from the resulting log-probabilities. Nowadays, virtually all such parsers utilize deep neural networks and may thus be susceptible to miscalibration (in particular, overconfident predictions). In this paper, we prove that temperature scaling, a popular technique for post-hoc calibration of neural networks, cannot change the output of the aforementioned procedure. We conclude that other techniques are needed to tackle miscalibration in graph-based dependency parsers in a way that improves parsing accuracy.
Hypergraph Dissimilarity Measures

In this paper, we propose two novel approaches for hypergraph comparison. The first approach transforms the hypergraph into a graph representation for use of standard graph dissimilarity measures. The second approach exploits the mathematics of tensors to intrinsically capture multi-way relations. For each approach, we present measures that assess hypergraph dissimilarity at a specific scale or provide a more holistic multi-scale comparison. We test these measures on synthetic hypergraphs and apply them to biological datasets.
RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests

Like many predictive models, random forests provide point predictions for new observations. Besides the point prediction, it is important to quantify the uncertainty in the prediction. Prediction intervals provide information about the reliability of the point predictions. We have developed a comprehensive R package, RFpredInterval, that integrates 16 methods to build prediction intervals with random forests and boosted forests. The set of methods implemented in the package includes a new method to build prediction intervals with boosted forests (PIBF) and 15 method variations to produce prediction intervals with random forests, as proposed by Roy and Larocque (2020). We perform an extensive simulation study and apply real data analyses to compare the performance of the proposed method to ten existing methods for building prediction intervals with random forests. The results show that the proposed method is very competitive and, globally, outperforms competing methods.
Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs

Recent studies have exposed that many graph neural networks (GNNs) are sensitive to adversarial attacks, and can suffer from performance loss if the graph structure is intentionally perturbed. A different line of research has shown that many GNN architectures implicitly assume that the underlying graph displays homophily, i.e., connected nodes are more likely to have similar features and class labels, and perform poorly if this assumption is not fulﬁlled. In this work, we formalize the relation between these two seemingly different issues. We theoretically show that in the standard scenario in which node features exhibit homophily, impactful structural attacks always lead to increased levels of heterophily. Then, inspired by GNN architectures that target heterophily, we present two designs—(i) separate aggregators for ego-and neighbor-embeddings, and (ii) a reduced scope of aggregation—that can signiﬁcantly improve the robustness of GNNs. Our extensive empirical evaluations show that GNNs featuring merely these two designs can achieve signiﬁcantly improved robustness compared to the best-performing unvaccinated model with 24.99% gain in average performance under targeted attacks, while having smaller computational overhead than existing defense mechanisms. Furthermore, these designs can be readily combined with explicit defense mechanisms to yield state-of-the-art robustness with up to 18.33% increase in performance under attacks compared to the best-performing vaccinated model.
Exact periodic stripes for a local/nonlocal minimization problem with volume constraint

We consider a class of generalized antiferromagnetic local/nonlocal interaction functionals in general dimension, where a short range attractive term of perimeter type competes with a long range repulsive term characterized by a reflection positive power law kernel. Breaking of symmetry with respect to coordinate permutations and pattern formation for functionals in this class have been shown in~\cite{gr,dr_arma} and previously by~\cite{gs_cmp} in the discrete setting, for a smaller range of exponents. Global minimizers of such functionals have been proved in~\cite{dr_arma} to be given by periodic stripes of volume density $1/2$ in any cube having optimal period size, also in the large volume limit. In this paper we study the minimization problem with arbitrarily prescribed volume constraint $\alpha\in(0,1)$. We show that, in the large volume limit, minimizers are periodic stripes of volume density $\alpha$, namely stripes whose one-dimensional slices in the direction orthogonal to their boundary are simple periodic with volume density $\alpha$ in each period. Results of this type in the one-dimensional setting, where no symmetry breaking occurs, have been previously obtained in \cite{muller1993singular, alberti2001new,ren2003energy,chen2005periodicity,giuliani2009modulated}.
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning

Many advances that have improved the robustness and efﬁciency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional objectives, or constraints, in the policy optimization step. This includes ideas as far ranging as exploration bonuses, entropy regularization, and regularization toward teachers or data priors when learning from experts or in ofﬂine RL. Often, task reward and auxiliary objectives are in conﬂict with each other and it is therefore natural to treat these examples as instances of multi-objective (MO) optimization problems. We study the principles underlying MORL and introduce a new algorithm, Distillation of a Mixture of Experts (DiME), that is intuitive and scale-invariant under some conditions. We highlight its strengths on standard MO benchmark problems and consider case studies in which we recast ofﬂine RL and learning from experts as MO problems. This leads to a natural algorithmic formulation that sheds light on the connection between existing approaches. For ofﬂine RL, we use the MO perspective to derive a simple algorithm, that optimizes for the standard RL objective plus a behavioral cloning term. This outperforms state-of-the-art on two established ofﬂine RL benchmarks.
Gauge Gravity Vacuum in Constraintless Clairaut-Type Formalism

The gauged Lorentz theory with torsion has been argued to have an effective theory whose non-trivial background is responsible for background gravitational curvature if torsion is treated as a quantum-mechanical variable against a background of constant curvature. We use the CDG decomposition to argue that such a background can be found without including torsion. Adapting our previously published Clairaut-based treatment of QCD, we go on to study the implications for second quantisation.
Control Variates for Slate Off-Policy Evaluation

We study the problem of off-policy evaluation from batched contextual bandit data with multidimensional actions, often termed slates. The problem is common to recommender systems and user-interface optimization, and it is particularly challenging because of the combinatorially-sized action space. Swaminathan et al. (2017) have proposed the pseudoinverse (PI) estimator under the assumption that the conditional mean rewards are additive in actions. Using control variates, we consider a large class of unbiased estimators that includes as specific cases the PI estimator and (asymptotically) its self-normalized variant. By optimizing over this class, we obtain new estimators with risk improvement guarantees over both the PI and the self-normalized PI estimators. Experiments with real-world recommender data as well as synthetic data validate these improvements in practice.
Learning Stable Classifiers by Transferring Unstable Features

While unbiased machine learning models are essential for many applications, bias is a human-defined concept that can vary across tasks. Given only input-label pairs, algorithms may lack sufficient information to distinguish stable (causal) features from unstable (spurious) features. However, related tasks often share similar biases -- an observation we may leverage to develop stable classifiers in the transfer setting. In this work, we explicitly inform the target classifier about unstable features in the source tasks. Specifically, we derive a representation that encodes the unstable features by contrasting different data environments in the source task. We achieve robustness by clustering data of the target task according to this representation and minimizing the worst-case risk across these clusters. We evaluate our method on both text and image classifications. Empirical results demonstrate that our algorithm is able to maintain robustness on the target task for both synthetically generated environments and real-world environments. Our code is available at https://github.com/YujiaBao/Tofu.
Residual Reinforcement Learning from Demonstrations

Residual reinforcement learning (RL) has been proposed as a way to solve challenging robotic tasks by adapting control actions from a conventional feedback controller to maximize a reward signal. We extend the residual formulation to learn from visual inputs and sparse rewards using demonstrations. Learning from images, proprioceptive inputs and a sparse task-completion reward relaxes the requirement of accessing full state features, such as object and target positions. In addition, replacing the base controller with a policy learned from demonstrations removes the dependency on a hand-engineered controller in favour of a dataset of demonstrations, which can be provided by non-experts. Our experimental evaluation on simulated manipulation tasks on a 6-DoF UR5 arm and a 28-DoF dexterous hand demonstrates that residual RL from demonstrations is able to generalize to unseen environment conditions more flexibly than either behavioral cloning or RL fine-tuning, and is capable of solving high-dimensional, sparse-reward tasks out of reach for RL from scratch.