Miracle-dz/UniFAR-FTUs huggingface dataset:
Robustifying Reinforcement Learning Policies with L1 Adaptive Control

A reinforcement learning (RL) policy trained in a nominal environment could fail in a new/perturbed environment due to the existence of dynamic variations. Existing robust methods try to obtain a fixed policy for all envisioned dynamic variation scenarios through robust or adversarial training. These methods could lead to conservative performance due to emphasis on the worst case, and often involve tedious modifications to the training environment. We propose an approach to robustifying a pre-trained non-robust RL policy with $\mathcal{L}_1$ adaptive control. Leveraging the capability of an $\mathcal{L}_1$ control law in the fast estimation of and active compensation for dynamic variations, our approach can significantly improve the robustness of an RL policy trained in a standard (i.e., non-robust) way, either in a simulator or in the real world. Numerical experiments are provided to validate the efficacy of the proposed approach.
Approximation Algorithms for Min-Distance Problems in DAGs

Graph parameters such as the diameter, radius, and vertex eccentricities are not deﬁned in a useful way in Directed Acyclic Graphs (DAGs) using the standard measure of distance, since for any two nodes, there is no path between them in one of the two directions. So it is natural to consider the distance between two nodes as the length of the shortest path in the direction in which this path exists, motivating the deﬁnition of the min-distance. The min-distance between two nodes u and v is the minimum of the shortest path distances from u to v and from v to u . As with the standard distance problems, the Strong Exponential Time Hypothesis [Impagliazzo-Paturi-Zane 2001, Calabro-Impagliazzo-Paturi 2009] leaves little hope for computing min-distance problems faster than computing All Pairs Shortest Paths, which can be solved in ˜ O ( mn ) time. So it is natural to resort to approximation algorithms in ˜ O ( mn 1 − (cid:15) ) time for some positive (cid:15) . Abboud, Vassilevska W. , and Wang [SODA 2016] ﬁrst studied min-distance problems achieving constant factor approximation algorithms on DAGs, and Dalirrooyfard et al [ICALP 2019] gave the ﬁrst constant factor approximation algorithms on general graphs for min-diameter, min-radius and min-eccentricities. Abboud et al obtained a 3 -approximation algorithm for min-radius on DAGs which works in ˜ O ( m √ n ) time, and showed that any (2 − δ ) -approximation requires n 2 − o (1) time for any δ > 0 , under the Hitting Set Conjecture. We close the gap, obtaining a 2 -approximation algorithm which runs in ˜ O ( m √ n ) time. As the lower bound of Abboud et al only works for sparse DAGs, we further show that our algorithm is conditionally tight for dense DAGs using a reduction from Boolean matrix multiplication. Moreover, Abboud et al obtained a linear time 2 -approximation algorithm for min-diameter along with a lower bound stating that any (3 / 2 − δ ) -approximation algorithm for sparse DAGs requires n 2 − o (1) time under SETH. We close this gap for dense DAGs up to an additive factor, by obtaining an O ( n 2 . 350 ) -time near- 3 / 2 -approximation algorithm, i.e. an algorithm which achieves a multiplicative approximation factor of 3 / 2 plus an additive error, and showing that the approximation factor is unlikely to be improved within O ( n ω − o (1) ) time under the high dimensional Orthogonal Vectors Conjecture, where ω is the matrix multiplication exponent.
A diachronic evaluation of gender asymmetry in euphemism

The use of euphemisms is a known driver of language change. It has been proposed that women use euphemisms more than men. Although there have been several studies investigating gender differences in language, the claim about euphemism usage has not been tested comprehensively through time. If women do use euphemisms more, this could mean that women also lead the formation of new euphemisms and language change over time. Using four large diachronic text corpora of English, we evaluate the claim that women use euphemisms more than men through a quantitative analysis. We assembled a list of 106 euphemism-taboo pairs to analyze their relative use through time by each gender in the corpora. Contrary to the existing belief, our results show that women do not use euphemisms with a higher proportion than men. We repeated the analysis using different subsets of the euphemism-taboo pairs list and found that our result was robust. Our study indicates that in a broad range of settings involving both speech and writing, and with varying degrees of formality, women do not use or form euphemisms more than men.
Revealing superradiant emission in the single-to-bulk transition of quantum emitters in nanodiamond agglomerates

Individual quantum emitters form a fundamental building block for emerging quantum technologies. Collective effects of emitters, such as superradiance, might improve the performance of applications even further. When scaling materials to larger sizes, however, the optical density of states is modified by the surrounding material, and the collective coupling in small domains might be covered by transitions to bulk properties due to the presence of multiple collectively emitting domains, which inhomogeneously add. Here, we probe the optical properties of nitrogen vacancy centers in agglomerates of nanodiamonds. We quantify the transition from individual emitters to bulk emission by fluorescence lifetime measurements, and find a transition to occur on a length scale of ∼3 wavelengths around the emitter. While our lifetime measurements are consistent with superradiant decay, the second-order correlation function, which is a standard measure to reveal collective properties, fails to probe collective effects for our case of an ensemble of collectively contributing domains to the emission. Therefore, we propose and apply a new measure to trace collective effects based on the intensity fluctuations of the emitted light. Our work points toward systematically studying collective effects in a scalable solid-state quantum system, and using them for quantum optical applications in agglomerates of highly-doped nanodiamonds.
The Orbit and Density of the Jupiter Trojan Satellite System Eurybates–Queta

We report observations of the Jupiter Trojan asteroid (3548) Eurybates and its satellite Queta with the Hubble Space Telescope and use these observations to perform an orbital fit to the system. Queta orbits Eurybates with a semimajor axis of 2350 ± 11 km at a period of 82.46 ± 0.06 days and an eccentricity of 0.125 ± 0.009. From this orbit we derive a mass of Eurybates of 1.51 ± 0.03 × 1017 kg, corresponding to an estimated density of 1.1 ± 0.3 g cm−3, broadly consistent with densities measured for other Trojans, C-type asteroids in the outer main asteroid belt, and small icy objects from the Kuiper Belt. Eurybates is the parent body of the only major collisional family among the Jupiter Trojans; its low density suggests that it is a typical member of the Trojan population. Detailed study of this system in 2027 with the Lucy spacecraft flyby should allow significant insight into collisional processes among what appear to be the icy bodies of the Trojan belt.
The Closer You Look, The More You Learn: A Grey-box Approach to Protocol State Machine Learning

We propose a new approach to infer state machine models from protocol implementations. Our new tool, StateInspector, learns protocol states by using novel program analyses to combine observations of run-time memory and I/O. It requires no access to source code and only lightweight execution monitoring of the implementation under test. We demonstrate and evaluate StateInspector's effectiveness on numerous TLS and WPA/2 implementations. In the process, we show StateInspector enables deeper state discovery, increased learning efficiency, and more insight compared to existing approaches. Our method led us to discover several concerning deviations from the standards and vulnerabilities in IWD and WolfSSL, both of which were assigned CVEs.
Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective

ResNets constrained to be bi-Lipschitz, that is, approximately distance preserving, have been a crucial component of recently proposed techniques for deterministic uncertainty quantification in neural models. We show that theoretical justifications for recent regularisation schemes trying to enforce such a constraint suffer from a crucial flaw -- the theoretical link between the regularisation scheme used and bi-Lipschitzness is only valid under conditions which do not hold in practice, rendering existing theory of limited use, despite the strong empirical performance of these models. We provide a theoretical explanation for the effectiveness of these regularisation schemes using a frequency analysis perspective, showing that under mild conditions these schemes will enforce a lower Lipschitz bound on the low-frequency projection of images. We then provide empirical evidence supporting our theoretical claims, and perform further experiments which demonstrate that our broader conclusions appear to hold when some of the mathematical assumptions of our proof are relaxed, corresponding to the setup used in prior work. In addition, we present a simple constructive algorithm to search for counter examples to the distance preservation condition, and discuss possible implications of our theory for future model design.
The Weyl problem for unbounded convex domains in $\HH^3$

Let K ⊂ H be a convex subset in H with smooth, strictly convex boundary. The induced metric on ∂K then has curvature K > −1. It was proved by Alexandrov that if K is bounded, then it is uniquely determined by the induced metric on the boundary, and any smooth metric with curvature K > −1 can be obtained. We propose here an extension of the existence part of this result to unbounded convex domains in H. The induced metric on ∂K is then clearly not sufficient to determine K. However one can consider a richer data on the boundary including the ideal boundary of K. Specifically, we consider the data composed of the conformal structure on the boundary of K in the Poincaré model of H, together with the induced metric on ∂K. We show that a wide range of “reasonable” data of this type, satisfying mild curvature conditions, can be realized on the boundary of a convex subset in H. We do not consider here the uniqueness of a convex subset with given boundary data.
Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.
Tidal deformation of dynamical horizons in binary black hole mergers

An important physical phenomenon that manifests itself during the inspiral of two orbiting compact objects is the tidal deformation of each under the gravitational influence of its companion. In the case of binary neutron star mergers, this tidal deformation and the associated Love numbers have been used to probe properties of dense matter and the nuclear equation of state. Non-spinning black holes on the other hand have a vanishing (field) tidal Love number in General Relativity. This pertains to the deformation of the asymptotic gravitational field. In certain cases, especially in the late stages of the inspiral phase when the black holes get close to each other, the source multipole moments might be more relevant in probing their properties and the No-Hair conjecture; contrastingly, these Love numbers do not vanish. In this paper, we track the source multipole moments in simulations of several binary black hole mergers and calculate these Love numbers. We present evidence that, at least for modest mass ratios, the behavior of the source multipole moments is universal.
Influence Estimation and Maximization via Neural Mean-Field Dynamics

We propose a novel learning framework using neural mean-field (NMF) dynamics for inference and estimation problems on heterogeneous diffusion networks. Our new framework leverages the Mori-Zwanzig formalism to obtain an exact evolution equation of the individual node infection probabilities, which renders a delay differential equation with memory integral approximated by learnable time convolution operators. Directly using information diffusion cascade data, our framework can simultaneously learn the structure of the diffusion network and the evolution of node infection probabilities. Connections between parameter learning and optimal control are also established, leading to a rigorous and implementable algorithm for training NMF. Moreover, we show that the projected gradient descent method can be employed to solve the challenging influence maximization problem, where the gradient is computed extremely fast by integrating NMF forward in time just once in each iteration. Extensive empirical studies show that our approach is versatile and robust to variations of the underlying diffusion network models, and significantly outperform existing approaches in accuracy and efficiency on both synthetic and real-world data.
Antioxidant and Cytotoxic Activities of Gmelina arborea ROXB. Leaves

Aims: The leaves of Gmelina arborea (ROXB.) (Family Verbenaceae) are widely used in the folklore to treat various types of diseases. In this study, the antioxidant and cytotoxic activities of different methanolic extracts and the derived subfractions of 90% methanolic extract of this plant were evaluated. Methodology: The antioxidant activity was carried out via three different quantitative assays as well as qualitative one. Total phenolic was determined via Folin-Ciocalteu and total flavonoid via AlCl3 assays. The cytotoxic activity was carried out via brine shrimp test and toward human cancer cell line; HepG2 using Sulphorhodamine-B assay. The 90% methanolic extract was fractionated using pet. ether then the 90% defatted methanol undergoes fractionation using (CHCl3, EtOAc and n-BuOH). Results: The antioxidant results showed that the; DPPH antioxidant activity was (19.20, 14.10 and 28.94 μg/ml); total antioxidant capacity was (412.69, 518.45 and 390.41; mg AAE /g extract); reducing power was (0.649, 0.715 and 0.396; 200 mg/ml) and total phenolic was (330.22, 400.66 and 244.76; mg GAE/g extract), respectively for 90% methanol, n-BuOH and EtOAc. The cytotoxic results showed that the; mortality of brine shrimp larvae (LC50) against different dosages of defatted 90% methanol, n-BuOH and Original Research Article British Journal of Pharmaceutical Research, 4(1): 125-144, 2014 126 EtOAc respectively was (158.48, 39.81 and 199.52; μg/ml) and the results of HepG2 assay showed that n-BuOH fractions have cytotoxic activity with IC50 ≤ 20 μg/ml (IC50 = 17.3 μg/ml) which falls within the American Cancer Institute criteria followed by 90% methanol and EtOAc (IC50 = 22.1 μg/ml). Conclusion: It was concluded that Gmelina arborea extracts possess a powerful antioxidant and cytotoxic activities.
Spectral Hypergraph Sparsifiers of Nearly Linear Size

Graph sparsification has been studied extensively over the past two decades, culminating in spectral sparsifiers of optimal size (up to constant factors). Spectral hypergraph sparsification is a natural analogue of this problem, for which optimal bounds on the sparsifier size are not known, mainly because the hypergraph Laplacian is non-linear, and thus lacks the linear-algebraic structure and tools that have been so effective for graphs. Our main contribution is the first algorithm for constructing $\epsilon$-spectral sparsifiers for hypergraphs with $O^{\ast}(n)$ hyperedges, where $O^{\ast}$ suppresses $(\epsilon^{-1}\log n)^{O(1)}$ factors. This bound is independent of the rank $r$ (maximum cardinality of a hyperedge), and is essentially best possible due to a recent bit complexity lower bound of $\Omega(nr)$ for hypergraph sparsification. This result is obtained by introducing two new tools. First, we give a new proof of spectral concentration bounds for sparsifiers of graphs; it avoids linear-algebraic methods, replacing e.g. the usual application of the matrix Bernstein inequality and therefore applies to the (non-linear) hypergraph setting. To achieve the result, we design a new sequence of hypergraph-dependent $\epsilon$-nets on the unit sphere in $\mathbb{R}^{n}$. Second, we extend the weight-assignment technique of Chen, Khanna and Nagda [FOCS'20] to the spectral sparsification setting. Surprisingly, the number of spanning trees after the weight assignment can serve as a potential function guiding the reweighting process in the spectral setting.
Prediction or Comparison: Toward Interpretable Qualitative Reasoning

Qualitative relationships illustrate how changing one property (e.g., moving velocity) affects another (e.g., kinetic energy) and constitutes a considerable portion of textual knowledge. Current approaches use either semantic parsers to transform natural language inputs into logical expressions or a"black-box"model to solve them in one step. The former has a limited application range, while the latter lacks interpretability. In this work, we categorize qualitative reasoning tasks into two types: prediction and comparison. In particular, we adopt neural network modules trained in an end-to-end manner to simulate the two reasoning processes. Experiments on two qualitative reasoning question answering datasets, QuaRTz and QuaRel, show our methods' effectiveness and generalization capability, and the intermediate outputs provided by the modules make the reasoning process interpretable.
Human-Adversarial Visual Question Answering

Performance on the most commonly used Visual Question Answering dataset (VQA v2) is starting to approach human accuracy. However, in interacting with state-of-the-art VQA models, it is clear that the problem is far from being solved. In order to stress test VQA models, we benchmark them against human-adversarial examples. Human subjects interact with a state-of-the-art VQA model, and for each image in the dataset, attempt to find a question where the model's predicted answer is incorrect. We find that a wide range of state-of-the-art models perform poorly when evaluated on these examples. We conduct an extensive analysis of the collected adversarial examples and provide guidance on future research directions. We hope that this Adversarial VQA (AdVQA) benchmark can help drive progress in the field and advance the state of the art.
Kinetic Promotion and Inhibition of Methane Hydrate Formation by Morpholinium Ionic Liquids with Chloride and Tetrafluoroborate Anions

Ionic liquids of 1-hydroxyethyl-1-methylmorpholinium chloride (HEMM-Cl) and 1-hydroxyethyl-1-methylmorpholinium tetrafluoroborate (HEMM-BF4) were synthesized to investigate the different effects of anions in the ionic liquids on the methane hydrate formation kinetics. HEMM-Cl and HEMM-BF4 acted as the kinetic hydrate promoter and inhibitor, respectively. The induction time of HEMM-BF4 solutions increased in proportion to the HEMM-BF4 concentration, and both ionic liquids showed thermodynamic hydrate inhibition effect on methane hydrate formation. The X-ray diffraction pattern of hydrates formed in the presence of both ionic liquids showed that there was no influence on, and no incorporation of ionic liquids into, the crystal structure. Using the dynamic light scattering (DLS) technique, HEMM-Cl was revealed not to form micelles, which implies that HEMM-Cl is nonamphiphilic and its hydrate promotion mechanism is different from that of amphiphilic surfactant promoters. HEMM-Cl might distort the rigid hydrat.
Approximate Fixed-Points in Recurrent Neural Networks

Recurrent neural networks are widely used in speech and language processing. Due to dependency on the past, standard algorithms for training these models, such as back-propagation through time (BPTT), cannot be efficiently parallelised. Furthermore, applying these models to more complex structures than sequences requires inference time approximations, which introduce inconsistency between inference and training. This paper shows that recurrent neural networks can be reformulated as fixed-points of non-linear equation systems. These fixed-points can be computed using an iterative algorithm exactly and in as many iterations as the length of any given sequence. Each iteration of this algorithm adds one additional Markovian-like order of dependencies such that upon termination all dependencies modelled by the recurrent neural networks have been incorporated. Although exact fixed-points inherit the same parallelization and inconsistency issues, this paper shows that approximate fixed-points can be computed in parallel and used consistently in training and inference including tasks such as lattice rescoring. Experimental validation is performed in two tasks, Penn Tree Bank and WikiText-2, and shows that approximate fixed-points yield competitive prediction performance to recurrent neural networks trained using the BPTT algorithm.
A C-V2X Platform Using Transportation Data and Spectrum-Aware Sidelink Access

Intelligent transportation systems and autonomous vehicles are expected to bring new experiences with enhanced efficiency and safety to road users in the near future. However, an efficient and robust vehicular communication system should act as a strong backbone to offer the needed infrastructure connectivity. Deep learning (DL)-based algorithms are widely adopted recently in various vehicular communication applications due to their achieved low latency and fast reconfiguration properties. Yet, collecting actual and sufficient transportation data to train DL-based vehicular communication models is costly and complex. This paper introduces a cellular vehicle-to-everything (C-V2X) verification platform based on an actual traffic simulator and spectrum-aware access. This integrated platform can generate realistic transportation and communication data, benefiting the development and adaptivity of DL-based solutions. Accordingly, vehicular spectrum recognition and management are further investigated to demonstrate the potentials of dynamic slidelink access. Numerical results show that our platform can effectively train and realize DL-based C-V2X algorithms. The developed slidelink communication scheme can adopt different operating bands with remarkable spectrum detection performance, validating its practicality in real-world vehicular environments.