Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-Path-Free Representation Learning on Heterogeneous Networks

Real-world networks and knowledge graphs are usually heterogeneous networks. Representation learning on heterogeneous networks is not only a popular but a pragmatic research field. The main challenge comes from the heterogeneity -- the diverse types of nodes and edges. Besides, for a given node in a HIN, the significance of a neighborhood node depends not only on the structural distance but semantics. How to effectively capture both structural and semantic relations is another challenge. The current state-of-the-art methods are based on the algorithm of meta-path and therefore have a serious disadvantage -- the performance depends on the arbitrary choosing of meta-path(s). However, the selection of meta-path(s) is experience-based and time-consuming. In this work, we propose a novel meta-path-free representation learning on heterogeneous networks, namely Heterogeneous graph Convolutional Networks (HCN). The proposed method fuses the heterogeneity and develops a $k$-strata algorithm ($k$ is an integer) to capture the $k$-hop structural and semantic information in heterogeneous networks. To the best of our knowledge, this is the first attempt to break out of the confinement of meta-paths for representation learning on heterogeneous networks. We carry out extensive experiments on three real-world heterogeneous networks. The experimental results demonstrate that the proposed method significantly outperforms the current state-of-the-art methods in a variety of analytic tasks.
Optimal Distributed Frequency and Voltage Control for Zonal Electricity Markets

Zonal pricing is a well-suited mechanism to incentivize grid-supporting behavior of profit-maximizing producers and consumers operating on a large-scale power system. In zonal electricity markets, local system operators create individual price zones, which provide appropriate price signals depending on local grid conditions such as an excess or shortage of electrical energy in certain regions. In this paper, a real-time zonal pricing controller for AC power networks is presented that ensures frequency and voltage stability as well as Pareto efficiency of the resulting closed-loop equilibria. Based on a dynamic network model that takes line losses and power exchange with adjacent price zones into account, distributed continuous-time control laws are derived which require only neighbor-to-neighbor communication. Application to a real-time congestion management strategy illustrates how spatially and temporally differentiated prices enable grid-supportive operation of the participants without interventions by a superordinate control authority. Effectiveness of different zonal pricing concepts compared to an isolated grid operation is demonstrated through simulations on the IEEE-57 bus system.
Chandra Probes the X-Ray Variability of M51 ULX-7: Evidence of Propeller Transition and X-Ray Dips on Orbital Periods

We report on the temporal properties of the ultraluminous X-ray (ULX) pulsar M51 ULX-7 inferred from the analysis of the 2018–2020 Swift/X-ray Telescope monitoring data and archival Chandra data obtained over a period of 33 days in 2012. We find an extended low flux state, which might be indicative of propeller transition, lending further support to the interpretation that the neutron star is rotating near equilibrium. Alternatively, this off-state could be related to a variable superorbital period. Moreover, we report the discovery of periodic dips in the X-ray light curve that are associated with the binary orbital period. The presence of the dips implies a configuration where the orbital plane of the binary is closer to an edge-on orientation, and thus demonstrates that favorable geometries are not necessary in order to observe ULX pulsars. These characteristics are similar to those seen in prototypical X-ray pulsars such as Her X-1 and SMC X-1 or other ULX pulsars such as NGC 5907 ULX1.
Trees-Based Models for Correlated Data

This paper presents a new approach for trees-based regression, such as simple regression tree, random forest and gradient boosting, in settings involving correlated data. We show the problems that arise when implementing standard trees-based regression models, which ignore the correlation structure. Our new approach explicitly takes the correlation structure into account in the splitting criterion, stopping rules and fitted values in the leaves, which induces some major modifications of standard methodology. The superiority of our new approach over trees-based models that do not account for the correlation is supported by simulation experiments and real data analyses.
Positivity-preserving methods for ordinary differential equations

Many important applications are modelled by differential equations with positive solutions. However, it remains an outstanding open problem to develop numerical methods that are both (i) of a high order of accuracy and (ii) capable of preserving positivity. It is known that the two main families of numerical methods, Runge-Kutta methods and multistep methods, face an order barrier. If they preserve positivity, then they are constrained to low accuracy: they cannot be better than first order. We propose novel methods that overcome this barrier: second order methods that preserve positivity unconditionally and a third order method that preserves positivity under very mild conditions. Our methods apply to a large class of differential equations that have a special graph Laplacian structure, which we elucidate. The equations need be neither linear nor autonomous and the graph Laplacian need not be symmetric. This algebraic structure arises naturally in many important applications where positivity is required. We showcase our new methods on applications where standard high order methods fail to preserve positivity, including infectious diseases, Markov processes, master equations and chemical reactions.
Magnetic phase diagram of rare-earth orthorhombic perovskite oxides

Spin reorientation and magnetisation reversal are two important features of the rare-earth orthorhombic provskites ($RM$O$_{3}$'s) that have attracted a lot of attention, though their exact microscopic origin has eluded researchers. Here, using density functional theory and classical atomistic spin dynamics we build a general Heisenberg magnetic model that allows to explore the whole phase diagram of the chromite and ferrite compounds and to scrutinize the microscopic mechanism responsible for spin reorientations and magnetisation reversals. We show that the occurrence of a magnetization reversal transition depends on the relative strength and sign of two interactions between rare-earth and transition-metal atoms: superexchange and Dzyaloshinsky-Moriya. We also conclude that the presence of a smooth spin reorientation transition between the so-called $\Gamma_4$ and the $\Gamma_2$ phases through a coexisting region, and the temperature range in which it occurs, depends on subtle balance of metal--metal (superexchange and Dzyaloshinsky-Moriya) and metal--rare-earth (Dzyaloshinsky-Moriya) couplings. In particular, we show that the intermediate coexistence region occurs because the spin sublattices rotate at different rates.
Steadily Learn to Drive with Virtual Memory

Reinforcement learning has shown great potential in developing high-level autonomous driving systems. However, for high-dimensional tasks, current RL methods suffer from low data efficiency and oscillation in the training process. This paper proposes an algorithm called Learn to drive with Virtual Memory (LVM) to overcome these problems. LVM compresses the high-dimensional information into compact latent states and learns a latent dynamic model to summarize the agent's experience. Various imagined latent trajectories are generated as virtual memory by the latent dynamic model. The policy is learned by propagating gradient through the learned latent model with the imagined latent trajectories and thus leads to high data efficiency. Furthermore, a double critic structure is designed to reduce the oscillation during the training process. The effectiveness of LVM is demonstrated by an image-input autonomous driving task, in which LVM outperforms the existing method in terms of data efficiency, learning stability, and control performance.
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary

Current models for Word Sense Disambiguation (WSD) struggle to disambiguate rare senses, despite reaching human performance on global WSD metrics. This stems from a lack of data for both modeling and evaluating rare senses in existing WSD datasets. In this paper, we introduce FEWS (Few-shot Examples of Word Senses), a new low-shot WSD dataset automatically extracted from example sentences in Wiktionary. FEWS has high sense coverage across different natural language domains and provides: (1) a large training set that covers many more senses than previous datasets and (2) a comprehensive evaluation set containing few- and zero-shot examples of a wide variety of senses. We establish baselines on FEWS with knowledge-based and neural WSD approaches and present transfer learning experiments demonstrating that models additionally trained with FEWS better capture rare senses in existing WSD datasets. Finally, we find humans outperform the best baseline models on FEWS, indicating that FEWS will support significant future work on low-shot WSD.
Mobility-based prediction of SARS-CoV-2 spreading

The rapid spreading of SARS-CoV-2 and its dramatic consequences, are forcing policymakers to take strict measures in order to keep the population safe. At the same time, societal and economical interactions are to be safeguarded. A wide spectrum of containment measures have been hence devised and implemented, in different countries and at different stages of the pandemic evolution. Mobility towards workplace or retails, public transit usage and permanence in residential areas constitute reliable tools to indirectly photograph the actual grade of the imposed containment protocols. In this paper, taking Italy as an example, we will develop and test a deep learning model which can forecast various spreading scenarios based on different mobility indices, at a regional level. We will show that containment measures contribute to "flatten the curve" and quantify the minimum time frame necessary for the imposed restrictions to result in a perceptible impact, depending on their associated grade.
Fast Validated Byzantine Broadcast

Byzantine fault-tolerant (BFT) state machine replication (SMR) has been studied for over 30 years. Recently it has received more attention due to its application in permissioned blockchain systems. A sequence of research efforts focuses on improving the commit latency of the SMR protocol in the common good case, including PBFT [6] with 3-round latency and 𝑛 ≥ 3 𝑓 + 1 and FaB [14] with 2-round latency and 𝑛 ≥ 5 𝑓 + 1. In this paper, we abstract a single-shot BFT SMR with a new broadcast formulation named partially synchronous validated Byzantine broadcast (psync-VBB), and propose a 2-round psync-VBB protocol under the optimal resilience 𝑛 ≥ 5 𝑓 − 1 with a matching lower bound. Our protocol solves 2-round BFT SMR with only 𝑛 ≥ 5 𝑓 − 1 replicas, which refutes the optimal resiliency claim made in FaB for needing 𝑛 ≥ 5 𝑓 + 1 for 2-round PBFT-style BFT protocols. For the special case when 𝑓 = 1, our protocol needs only 4 replicas, and strictly improves PBFT by reducing the latency by one round (even when one backup is faulty).
Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining

Few-shot learning aims to generalize unseen classes that appear during testing but are unavailable during training. Prototypical networks incorporate few-shot metric learning, by constructing a class prototype in the form of a mean vector of the embedded support points within a class. The performance of prototypical networks in extreme few-shot scenarios (like one-shot) degrades drastically, mainly due to the desuetude of variations within the clusters while constructing prototypes. In this paper, we propose to replace the typical prototypical loss function with an Episodic Triplet Mining (ETM) technique. The conventional triplet selection leads to overfitting, because of all possible combinations being used during training. We incorporate episodic training for mining the semi hard positive and the semi hard negative triplets to overcome the overfitting. We also propose an adaptation to make use of unlabeled training samples for better modeling. Experimenting on two different audio processing tasks, namely speaker recognition and audio event detection; show improved performances and hence the efficacy of ETM over the prototypical loss function and other meta-learning frameworks. Further, we show improved performances when unlabeled training samples are used.
Posterior-Aided Regularization for Likelihood-Free Inference

The recent development of likelihood-free inference aims training a ﬂexible density estimator for the target posterior with a set of input-output pairs from simulation . Given the diversity of simulation structures, it is difﬁcult to ﬁnd a single uniﬁed inference method for each simulation model. This paper proposes a universally applicable regularization technique, called Posterior-Aided Regular-ization (PAR), which is applicable to learning the density estimator, regardless of the model structure. Particularly, PAR solves the mode collapse problem that arises as the output dimension of the simulation increases. PAR resolves this posterior mode degeneracy through a mixture of 1) the reverse KL divergence with the mode seeking property; and 2) the mutual information for the high quality representation on likelihood. Because of the estimation intractability of PAR, we provide a uniﬁed estimation method of PAR to estimate both reverse KL term and mutual information term with a single neural network. Af-terwards, we theoretically prove the asymptotic convergence of the regularized optimal solution to the unregularized optimal solution as the regularization magnitude converges to zero. Additionally, we empirically show that past sequential neural likelihood inferences in conjunction with PAR present the statistically signiﬁcant gains on diverse simulation tasks.
Vision-Based Drone Flocking in Outdoor Environments

Decentralized deployment of drone swarms usually relies on inter-agent communication or visual markers that are mounted on the vehicles to simplify their mutual detection. This letter proposes a vision-based detection and tracking algorithm that enables groups of drones to navigate without communication or visual markers. We employ a convolutional neural network to detect and localize nearby agents onboard the quadcopters in real-time. Rather than manually labeling a dataset, we automatically annotate images to train the neural network using background subtraction by systematically flying a quadcopter in front of a static camera. We use a multi-agent state tracker to estimate the relative positions and velocities of nearby agents, which are subsequently fed to a flocking algorithm for high-level control. The drones are equipped with multiple cameras to provide omnidirectional visual inputs. The camera setup ensures the safety of the flock by avoiding blind spots regardless of the agent configuration. We evaluate the approach with a group of three real quadcopters that are controlled using the proposed vision-based flocking algorithm. The results show that the drones can safely navigate in an outdoor environment despite substantial background clutter and difficult lighting conditions. The source code, image dataset, and trained detection model are available at https://github.com/lis-epfl/vswarm.
CWY Parametrization: a Solution for Parallelized Optimization of Orthogonal and Stiefel Matrices

We introduce an eﬃcient approach for optimization over orthogonal groups on highly parallel computation units such as GPUs or TPUs. As in earlier work, we parametrize an orthogonal matrix as a product of House-holder reﬂections. However, to overcome low parallelization capabilities of computing Householder reﬂections sequentially, we propose employing an accumulation scheme called the compact WY (or CWY) transform – a compact parallelization-friendly matrix representation for the series of Householder reﬂections. We further develop a novel Trun-cated CWY (or T-CWY) approach for Stiefel manifold parametrization which has a competitive complexity and, again, yields ben-eﬁts when computed on GPUs and TPUs. We prove that our CWY and T-CWY meth-ods lead to convergence to a stationary point of the training objective when coupled with stochastic gradient descent. We apply our methods to train recurrent neural network architectures in the tasks of neural machine translation and video prediction.
In-silico strategies of some selected phytoconstituents from Melissa officinalis as SARS CoV-2 main protease and spike protein (COVID-19) inhibitors

ABSTRACT Melissa officinalis (Lamiaceae) was used to treat multiple human afflictions. Literary works demonstrated that it has many biological activities. Today’s research aims to recognise Melissa officinalis phyto-derived anti-viral compounds against main protease and spike protein of COVID-19, to gain insight into the molecular interactions. In the current study, 12 molecules taken from Melissa officinalis were analysed through docking, which is derived from the PubMed database. Docking experiments were conducted with Autodock tool. AdmetSAR and Data warrior servers were eventually used for drug-like prediction. Our research shows that three phytoconstituents from Melissa officinalis, namely, Luteolin-7-glucoside-3′-glucuronide, Melitric acid-A and Quadranoside-III have exhibited better binding affinity and stability with the targets of COVID-19 main protease and spike protein. The identified substances can be further extended for in vitro and in vivo studies to assess their effectiveness against COVID-19. GRAPHICAL ABSTRACT
Predator size affects the intensity of mutual interference in a predatory mirid

Abstract Interference competition occurs when access to an available resource is negatively affected by interactions with other individuals, where mutual interference involves individuals of the same species. The interactive phenomena among individuals may be size‐dependent, since body size is a major factor that may alter prey consumption rates and ultimately the dynamics and structure of food webs. A study was initiated in order to evaluate the effect of mutual interference in the prey‐specific attack rates and handling times of same size class predators, incorporating variation in consumer size. For this purpose, laboratory functional response experiments were conducted using same age predators, that is, newly hatched (first instar) or mature (fifth instar) nymphs of the polyphagous mirid predator Macrolophus pygmaeus preying on Ephestia kuehniella (Lepidoptera: Pyralidae) eggs. The experiments involved four predator density treatments, that is, one, two, three, or four predators of same age, that is, either first‐ or fifth‐instar nymphs, which were exposed to several prey densities. The Crowley–Martin model, which allows for interference competition between foraging predators, was used to fit the data. The results showed that mutual interference between predator's nymphs may occur that affect their foraging efficiency. The values of the attack rate coefficient were dependent on the predator density and for the first‐instar nymphs were significantly lower at the highest predator density than the lower predator densities, whereas for the fifth‐instar nymphs in all density treatments were significantly lower to that of the individual foragers' ones. These results indicate that mutual interference is more intense for larger predators and is more obvious at low prey densities where the competition level is higher. The wider use of predator‐dependent functional response models will help toward a mechanistic understanding of intraspecific interactions and its consequences on the stability and structure of food webs.
The hepatoprotective effects of fennel seeds extract and trans‐Anethole in streptozotocin‐induced liver injury in rats

Abstract Hypoglycemic, anti‐inflammatory, and antioxidant activities of fennel have been recorded in numerous investigations. The study aimed to evaluate the protective effects of fennel or its active component trans‐Anethole (TA) on streptozotocin‐induced liver injury in rats. Rats were injected with a single dose of STZ (65 mg/kg) and treated with fennel (200 and 400 mg/kg), TA (80 mg/kg), or metformin (300 mg/kg) for 35 days. Serum lipid profile and liver enzyme activity (aminotransferases), oxidative stress markers, and the degree of fibrosis in the liver tissue were assessed. Both fennel and TA decreased blood glucose levels, reduced liver enzyme activity, food, and water intake, and intensity of weight loss, reduced serum triglycerides (TG), total cholesterol (TC), low‐density lipoprotein cholesterol (LDL‐c), and increased high‐density lipoprotein cholesterol (HDL‐c). Additionally, fennel and TA significantly reduced MDA concentration while increased CAT activity and thiol content and reduced the degree of injury and fibrosis in the liver of diabetic rats. Our results suggest that fennel seed extract and its active compound TA are able to protect the liver against diabetes‐induced hepatic injury in rats, probably via hypoglycemic and antioxidant effects.
Impacts of growth form and phylogenetic relatedness on seed germination: A large‐scale analysis of a subtropical regional flora

Abstract Plant regeneration strategy plays a critical role in species survival and can be used as a proxy for the evolutionary response of species to climate change. However, information on the effects of key plant traits and phylogenetic relatedness on seed germination is limited at large regional scales that vary in climate. To test the hypotheses that phylogenetic niche conservatism plays a critical force in shaping seed ecophysiological traits across species, and also drives their response to climatic fluctuation, we conducted a controlled experiment on seed germination and determined the percentage and rate of germination for 249 species in subtropical China under two temperature regimes (i.e., daily 25°C; daily alternating 25/15°C for each 12 hr). Germination was low with a skewed distribution (mean = 38.9% at 25°C, and 43.3% at 25/15°C). One fifth of the species had low (<10%) and slow (4–30 days) germination, and only a few (8%) species had a high (>80%) and rapid (1.2–6.6 days) germination. All studied plant traits (including germination responses) showed a significant phylogenetic signal, with an exception of seed germination percentage under the alternating temperature scenario. Generalized linear models (GLMs) and phylogenetic generalized estimation equations (GEEs) demonstrated that growth form and seed dispersal mode were strong drivers of germination. Our experimental study highlights that integrating plant key traits and phylogeny is critical to predicting seed germination response to future climate change.