Miracle-dz/UniFAR-FTUs huggingface dataset:
Demand-Side Scheduling Based on Deep Actor-Critic Learning for Smart Grids

We consider the problem of demand-side energy management, where each household is equipped with a smart meter that is able to schedule home appliances online. The goal is to minimise the overall cost under a real-time pricing scheme. While previous works have introduced centralised approaches, we formulate the smart grid environment as a Markov game, where each household is a decentralised agent, and the grid operator produces a price signal that adapts to the energy demand. The main challenge addressed in our approach is partial observability and perceived non-stationarity of the environment from the viewpoint of each agent. We propose a multi-agent extension of a deep actor-critic algorithm that shows success in learning in this environment. This algorithm learns a centralised critic that coordinates training of all agents. Our approach thus uses centralised learning but decentralised execution. Simulation results show that our online deep reinforcement learning method can reduce both the peak-to-average ratio of total energy consumed and the cost of electricity for all households based purely on instantaneous observations and a price signal.
Foraging in a Dynamic Mimicry Complex

Chipmunks encountering a simulated Batesian mimicry complex with the models and mimics randomly distributed only foraged extensively when mimics were 90% of the population. If the models and mimics were nonrandomly distributed, however, extensive foraging occurred when mimics were 70% of the complex. The groups that did not forage extensively were tested to see how rapidly they could detect a favorable change in the frequency of mimics. I found that success in escaping detection for the mimics was closely linked to how well sampling behavior was eliminated in the prior treatment. Animals which experienced nonrandom distributions seemed particularly adept at detecting a favorable change. The groups that did forage extensively were presented with a new, readily available alternative food source. The results demonstrate that the effectiveness of Batesian mimicry can be increased in environments when alternative prey are available.
Suspicious Behavior Detection on Shoplifting Cases for Crime Prevention by Using 3D Convolutional Neural Networks

Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks are generating vast amounts of data, and the surveillance staff can not process all the information in real-time. The human sight has its limitations, where the visual focus is among the most critical ones when dealing with surveillance. A crime can occur in a different screen segment or on a distinct monitor, and the staff may not notice it. Our proposal focuses on shoplifting crimes by analyzing special situations that an average person will consider as typical conditions, but may lead to a crime. While other approaches identify the crime itself, we instead model suspicious behavior -- the one that may occur before a person commits a crime -- by detecting precise segments of a video with a high probability to contain a shoplifting crime. By doing so, we provide the staff with more opportunities to act and prevent crime. We implemented a 3DCNN model as a video feature extractor and tested its performance on a dataset composed of daily-action and shoplifting samples. The results are encouraging since it correctly identifies 75% of the cases where a crime is about to happen.
Chasing Lions: Co-Designing Human-Drone Interaction in Sub-Saharan Africa

Drones are an exciting technology that is quickly being adopted in the global consumer market. Africa has become a center of deployment with the first drone airport established in Rwanda and drones currently being used for applications such as medical deliveries, agriculture, and wildlife monitoring. Despite this increasing presence of drones, there is a lack of research on stakeholders' perspectives from this region. We ran a human-drone interaction user study (N=15) with experts from several sub-Saharan countries using a co-design methodology. Participants described novel applications and identified important design aspects for the integration of drones in this context. Our results highlight the potential of drones to address real world problems, the need for them to be culturally situated, and the importance of considering the social aspects of their interaction with humans. This research highlights the need for diverse perspectives in the human-drone interaction design process.
Clustering for Graph Datasets via Gumbel Softmax

Recently, in many systems such as speech recognition and visual processing, deep learning has been widely implemented. In this research, we are exploring the possibility of using deep learning in graph clustering. Graphs have gained growing traction in different fields, including social networks, information graphs, the recommender system, and also life sciences. In this paper, we propose a method of clustering the nodes of various graph datasets. We cluster different category datasets that belong to Affiliation networks, Animal networks, Human contact networks, Human social networks, Miscellaneous networks. The deep learning role in modeling the interaction between nodes in a network allows a revolution in the field of science relevant to graph network analysis. In this paper, we extend the gumbel softmax approach to graph network clustering. The experimental findings on specific graph datasets reveal that the new approach outperforms traditional clustering significantly, which strongly shows the efficacy of deep learning in graph clustering. We do a series of experiments on our graph clustering algorithm, using various datasets: Zachary karate club, Highland Tribe, Train bombing Italian Gangs, Jazz musicians, American Revolution, Dolphins, Zebra, Windsurfers, Les Miserables, Political books.
Localized active learning of Gaussian process state space models

The performance of learning-based control techniques crucially depends on how effectively the system is explored. While most exploration techniques aim to achieve a globally accurate model, such approaches are generally unsuited for systems with unbounded state spaces. Furthermore, a globally accurate model is not required to achieve good performance in many common control applications, e.g., local stabilization tasks. In this paper, we propose an active learning strategy for Gaussian process state space models that aims to obtain an accurate model on a bounded subset of the state-action space. Our approach aims to maximize the mutual information of the exploration trajectories with respect to a discretization of the region of interest. By employing model predictive control, the proposed technique integrates information collected during exploration and adaptively improves its exploration strategy. To enable computational tractability, we decouple the choice of most informative data points from the model predictive control optimization step. This yields two optimization problems that can be solved in parallel. We apply the proposed method to explore the state space of various dynamical systems and compare our approach to a commonly used entropy-based exploration strategy. In all experiments, our method yields a better model within the region of interest than the entropy-based method.
A Comparative Study of Host Galaxy Properties between Fast Radio Bursts and Stellar Transients

Recent arcsecond localizations of fast radio bursts and identifications of their host galaxies confirmed their extragalactic origin. While FRB 121102 resides in the bright region of a dwarf star-forming galaxy, other FRBs reside in more massive galaxies and are related to older stellar populations. We compare the host galaxy properties of nine FRBs with those of several types of stellar transients: from young to old populations, long-duration gamma-ray bursts (LGRBs), superluminous supernovae (SLSNe), SNe Ibc, SNe II, SNe Ia, and short-duration gamma-ray bursts (SGRBs). We find that the stellar mass and star formation rate of the FRB host galaxies, taken as a whole sample, prefer a medium to old population, and are against a young population, similar to LGRBs and SLSNe by a null probability of 0.02. Individually, the host of FRB 121102 is consistent with that of young population objects; the environment of FRB 180924 is similar to that of SGRBs; and the environment of FRB 190523 is similar to those of SNe Ia. These results are consistent with the magnetar engine model for FRBs, if magnetars produced from extreme explosions (GRBs/SLSNe) and those from regular channels (e.g., those producing Galactic magnetars) can both produce FRBs.
Soundness of dark energy properties

Type Ia Supernovae (SNeIa) used as standardizable candles have been instrumental in the discovery of cosmic acceleration, usually attributed to some form of dark energy (DE). Recent studies have raised the issue of whether intrinsic SNeIa luminosities might evolve with redshift. While the evidence for cosmic acceleration is robust to this possible systematic, the question remains of how much the latter can affect the inferred properties of the DE component responsible for cosmic acceleration. This is the question we address in this work. We use SNeIa distance moduli measurements from the Pantheon and JLA samples. We consider models where the DE equation of state is a free parameter, either constant or time-varying, as well as models where DE and dark matter interact, and finally a model-agnostic parametrization of effects due to modified gravity (MG). When SNeIa data are combined with Cosmic Microwave Background (CMB) temperature and polarization anisotropy measurements, we find strong degeneracies between parameters governing the SNeIa systematics, the DE parameters, and the Hubble constant H0. These degeneracies significantly broaden the DE parameter uncertainties, in some cases leading to 𝒪(σ) shifts in the central values. However, including low-redshift Baryon Acoustic Oscillation and Cosmic Chronometer measurements, as well as CMB lensing measurements, considerably improves the previous constraints, and the only remaining effect of the examined systematic is a ≲ 40% broadening of the uncertainties on the DE parameters. The constraints we derive on the MG parameters are instead basically unaffected by the systematic in question. We therefore confirm the overall soundness of dark energy properties.
Bayesian longitudinal models for exploring European sardine fishing in the Mediterranean Sea

In the Mediterranean Sea, catches are dominated by small pelagic fish, representing nearly the 49\% of the total harvest. Among them, the European sardine (Sardina pilchardus) is one of the most commercially important species showing high over-exploitation rates in recent last years. In this study we analysed the European sardine landings in the Mediterranean Sea from 1970 to 2014. We made use of Bayesian longitudinal linear mixed models in order to assess differences in the temporal evolution of fishing between and within countries. Furthermore, we modelled the subsequent joint evolution of artisanal and industrial fisheries. Overall results confirmed that Mediterranean fishery time series were highly diverse along their dynamics and this heterogeneity was persistent throughout the time. In addition, results highlighted a positive relationship between the two types of fishing.
A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017

Framing is an indispensable narrative device for news media because even the same facts may lead to conflicting understandings if deliberate framing is employed. Therefore, identifying media framing is a crucial step to understanding how news media influence the public. Framing is, however, difficult to operationalize and detect, and thus traditional media framing studies had to rely on manual annotation, which is challenging to scale up to massive news datasets. Here, by developing a media frame classifier that achieves state-of-the-art performance, we systematically analyze the media frames of 1.5 million New York Times articles published from 2000 to 2017. By examining the ebb and flow of media frames over almost two decades, we show that short-term frame abundance fluctuation closely corresponds to major events, while there also exist several long-term trends, such as the gradually increasing prevalence of the “Cultural identity” frame. By examining specific topics and sentiments, we identify characteristics and dynamics of each frame. Finally, as a case study, we delve into the framing of mass shootings, revealing three major framing patterns. Our scalable, computational approach to massive news datasets opens up new pathways for systematic media framing studies.
Neural subdivision

This paper introduces Neural Subdivision, a novel framework for data-driven coarse-to-fine geometry modeling. During inference, our method takes a coarse triangle mesh as input and recursively subdivides it to a finer geometry by applying the fixed topological updates of Loop Subdivision, but predicting vertex positions using a neural network conditioned on the local geometry of a patch. This approach enables us to learn complex non-linear subdivision schemes, beyond simple linear averaging used in classical techniques. One of our key contributions is a novel self-supervised training setup that only requires a set of high-resolution meshes for learning network weights. For any training shape, we stochastically generate diverse low-resolution discretizations of coarse counterparts, while maintaining a bijective mapping that prescribes the exact target position of every new vertex during the subdivision process. This leads to a very efficient and accurate loss function for conditional mesh generation, and enables us to train a method that generalizes across discretizations and favors preserving the manifold structure of the output. During training we optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category. Our network encodes patch geometry in a local frame in a rotation- and translation-invariant manner. Jointly, these design choices enable our method to generalize well, and we demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
OpinionDigest: A Simple Framework for Opinion Summarization

We present OpinionDigest, an abstractive opinion summarization framework, which does not rely on gold-standard summaries for training. The framework uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extractions. At summarization time, we merge extractions from multiple reviews and select the most popular ones. The selected opinions are used as input to the trained Transformer model, which verbalizes them into an opinion summary. OpinionDigest can also generate customized summaries, tailored to specific user needs, by filtering the selected opinions according to their aspect and/or sentiment. Automatic evaluation on Yelp data shows that our framework outperforms competitive baselines. Human studies on two corpora verify that OpinionDigest produces informative summaries and shows promising customization capabilities.
GPU-accelerated massive black hole binary parameter estimation with LISA

The Laser Interferometer Space Antenna (LISA) is slated for launch in the early 2030s. A main target of the mission is massive black hole binaries that have an expected detection rate of $\sim20$ yr$^{-1}$. We present a parameter estimation analysis for a variety of massive black hole binaries. This analysis is performed with a graphics processing unit (GPU) implementation comprising the phenomhm waveform with higher-order harmonic modes and aligned spins; a fast frequency-domain LISA detector response function; and a GPU-native likelihood computation. The computational performance achieved with the GPU is shown to be 500 times greater than with a similar CPU implementation, which allows us to analyze full noise-infused injections at a realistic Fourier bin width for the LISA mission in a tractable and efficient amount of time. With these fast likelihood computations, we study the effect of adding aligned spins to an analysis with higher-order modes by testing different configurations of spins in the injection, as well as the effect of varied and fixed spins during sampling. Within these tests, we examine three different binaries with varying mass ratios, redshifts, sky locations, and detector-frame total masses ranging over three orders of magnitude. We discuss varied correlations between the total masses and mass ratios; unique spin posteriors for the larger mass binaries; and the constraints on parameters when fixing spins during sampling, allowing us to compare to previous analyses that did not include aligned spins.
Comment on the Comment on the paper "Can oscillating neutrino states be formulated universally?''

Recently, our work regarding the definition of oscillating neutrino states in Eur. Phys. J. C 80: 68 (2020), arXiv:1902.01232 [hep-ph] has been commented upon in arXiv:2004.04739 [hep-ph]. In this note we show that, contrary to the claim in the comment, our above-mentioned work cannot be reproduced in the scheme of the so-called flavour Fock space approach described in the comment. Moreover, we prove explicitly that a flavour Fock space cannot exist in a model with massive mixed neutrinos. If the flavour Fock space scheme were viable, it would necessarily lead, as a consequence of Coleman's theorem, to the flavour number invariance of the Hamiltonian of mixed neutrino fields, while the Hamiltonian is by construction flavour number violating. Quod est absurdum.
Frequency Detection and Change Point Estimation for Time Series of Complex Oscillation

We consider detecting the evolutionary oscillatory pattern of a signal when it is contaminated by non-stationary noises with complexly time-varying data generating mechanism. A high-dimensional dense progressive periodogram test is proposed to accurately detect all oscillatory frequencies. A further phase-adjusted local change point detection algorithm is applied in the frequency domain to detect the locations at which the oscillatory pattern changes. Our method is shown to be able to detect all oscillatory frequencies and the corresponding change points within an accurate range with a prescribed probability asymptotically. This study is motivated by oscillatory frequency estimation and change point detection problems encountered in physiological time series analysis. An application to spindle detection and estimation in sleep EEG data is used to illustrate the usefulness of the proposed methodology. A Gaussian approximation scheme and an overlapping-block multiplier bootstrap methodology for sums of complex-valued high dimensional non-stationary time series without variance lower bounds are established, which could be of independent interest.
Spray retention, foliar uptake and translocation of glufosinate and glyphosate in Ambrosia artemisiifolia

Summary Ambrosia artemisiifolia plants exhibit stomata on both leaf surfaces and three types of trichomes: (i) small ( 80% of the applied label, and half maximum uptake being reached within 6 h. The foliar uptake of glyphosate was nearly complete and half of it was attained after 3 h. Glufosinate and glyphosate were ambimobile and their translocation out of the treated leaves amounted to 13–16% and 11–15% of the absorbed radioactive label respectively. Glufosinate was mainly directed to the apical developing tissues, with less amounts reaching the tissues below the treated leaves. Glyphosate was directed towards the sink tissues (apical developing tissues and roots). The sensitivity of A. artemisiifolia to glufosinate and glyphosate can be explained by high spray retention, rapid and important foliar uptake, and appreciable migration out of the parts of the plant hit by the spray.
Effect of fomesafen on glutathione S-transferase and cellulase activity and DNA damage in the earthworm (Eisenia fetida)

Biochemical responses and DNA damage of earthworm (Eisenia fetida) exposed to various concentrations of fomesafen (0, 10, 100, and 500 μg kg−1) for 3, 7, 14, 21, and 28 days were investigated in vivo. Glutathione S-transferase (GST) activity was significantly stimulated upon treatment with 500 μg kg−1 fomesafen for 21 days, while the cellulase activity was markedly inhibited after 14 days of treatment. On day 28, the activities of GST and cellulase had recovered to the level similar to that of controls. Low or mild DNA damage in earthworm was induced within 14 days of exposure, and the damage was reduced or disappeared with the extension of exposure.
Localization of type II collagen, long form α1(IX) collagen, and short form α1(IX) collagen transcripts in the developing chick notochord and axial skeleton

In this study we compare, by in situ hybridization, the spatial and temporal expression patterns of transcripts of avian type II collagen and the long and short forms of the (α1) chain of type IX collagen during the development of the notochord and axial skeleton. We observed type II collagen and short form type IX collagen transcripts in the developing (stage 25–28) nonchondrogenic notochord. Conversely, long form type IX transcripts were not detectable in the notochord or perinotochordal sheath. Interestingly, all three transcripts colocalized in the developing chondrogenic vertebrae of the axial skeleton as well as in the chondrocranium and Meckel's cartilage. The expression of the short form of type IX collagen in these regions was more restricted than that of the long form. This report provides additional support for a complex regulatory pathway of cartilage marker gene expression in chondrogenic vs. nonchondrogenic tissues during avian embryogenesis. © 1992 Wiley‐Liss, Inc.
Tree species distributions in an upper Amazonian forest

Not a single tree species distribution in the Amazon basin has been reliably mapped, though speculation regarding such distributions has been extensive. We present data from a network of 21 forest plots in Manu National Park, Peru, totaling >36 ha and sited over an area of -400 km2, to explore how tree species are distributed across upper Amazonia at a variety of spatial scales. For each of 825 tree species occurring in the plots we asked three questions: (1) Does the species have a large or small geographic range? (2) Is the species restricted to a single forest type, or is it found in several? (3) Is the species locally abundant anywhere or is it scarce everywhere? The answers served to classify a subset of species under Rabinowitz's classification scheme for rare species. Three main conclusions emerged. First, the great majority of tree species at Manu are geographically widespread. Every species identified to date occurs elsewhere in South America, outside the department of Madre de Dios; more than two-thirds of them have been collected 1500 km away in Amazonian Ecuador. Second, 15-26% of species appear to be restricted to a single forest type, when forest types are defined by historical river dynamics (i.e., terra firme forest, mature floodplain forest, swamp forest, and primary successional floodplain forest). The proportion of restricted species declined with increasing sampling effort, mak- ing 15% a more reliable figure. Third, while 88% of species occurred at densities of 1.5 individuals/ha. Extrapolating these results provides a first guess at how tree species are distributed across the western portion of the Amazon basin. We conclude with the suggestion that most tree species in the region are habitat generalists occurring over large areas of the Amazonian lowlands at low densities but large absolute population sizes.