Miracle-dz/UniFAR-FTUs huggingface dataset:
GANgster: A Fraud Review Detector based on Regulated GAN with Data Augmentation

Financial implications of written reviews provide great incentives for businesses to pay fraudsters to write or use bots to generate fraud reviews. The promising performance of Deep Neural Networks (DNNs) in text classification, has attracted research to use them for fraud review detection. However, the lack of trusted labeled data has limited the performance of the current solutions in detecting fraud reviews. Unsupervised and semi-supervised methods are among the most applicable methods to deal with the data scarcity problem. Generative Adversarial Network (GAN) as a semi-supervised method has demonstrated to be effective for data augmentation purposes. The state-of-the-art solution utilizes GAN to overcome the data limitation problem. However, it fails to incorporate the behavioral clues in both fraud generation and detection. Besides, the state-of-the-art approach suffers from a common limitation in the training convergence of the GAN, slowing down the training procedure. In this work, we propose a regularised GAN for fraud review detection that makes use of both review text and review rating scores. Scores are incorporated through Information Gain Maximization in to the loss function for two reasons. One is to generate near-authentic and more human like score-correlated reviews. The other is to improve the stability of the GAN. Experimental results have shown better convergence of the regulated GAN. In addition, the scores are also used in combination with word embeddings of review text as input for the discriminators for better performance. Results show that the proposed framework relatively outperformed existing state-of-the-art framework; namely FakeGAN; in terms of AP by 7%, and 5% on the Yelp and TripAdvisor datasets, respectively.
External reconnection and resultant reconfiguration of overlying magnetic fields during sympathetic eruptions of two filaments

Context. Sympathetic eruptions of two solar filaments have been studied for several decades, but the detailed physical process through which one erupting filament triggers another is still under debate. Aims. Here we aim to investigate the physical nature of a sympathetic event involving successive eruptions of two filaments on 2015 November 15-16, which presented abundant sympathetic characteristics. Methods. Combining data from the Solar Dynamics Observatory and other observatories as well as results of nonlinear force-free field (NLFFF) extrapolations, we study the evolution of observational features and magnetic topology during the sympathetic event. Results. The two filaments (north F1 and south F2) were separated by a narrow region of negative polarity, and F1 first erupted, producing a two-ribbon flare. When the outward-spreading ribbon produced by F1 approached stable F2, a weak brightening was observed to the south of F2 and then spread northward, inward approaching F2. Behind this inward-spreading brightening, a dimming region characterized by a plasma density reduction of 30% was extending. NLFFF extrapolations with a time sequence reveal that fields above pre-eruption F1 and F2 constituted a quadrupolar magnetic system with a possible null point. Moreover, the null point kept moving toward F2 and descending within the following hours. We infer that the rising F1 pushed its overlying fields toward the fields above stable F2 and caused successive external reconnection between the overlying fields. From outside to inside (lower and lower in height), the fields above pre-eruption F2 were gradually involved in the reconnection, manifesting as the inward-spreading brightening and extending dimming on the south side of F2. Furthermore, the external reconnection could reconfigure the overlying fields of F2 by transporting magnetic flux from its west part to the east part, which is further verified by the subsequent partial eruption of F2. Conclusions. We propose an integrated evidence chain to demonstrate the critical roles of external magnetic reconnection and the resultant reconfiguration of overlying fields on the sympathetic eruptions of two filaments.
Improved Algorithms for Convex-Concave Minimax Optimization

This paper studies minimax optimization problems $\min_x \max_y f(x,y)$, where $f(x,y)$ is $m_x$-strongly convex with respect to $x$, $m_y$-strongly concave with respect to $y$ and $(L_x,L_{xy},L_y)$-smooth. \citet{zhang2019lower} provided the following lower bound of the gradient complexity for any first-order method: $\Omega\Bigl(\sqrt{\frac{L_x}{m_x}+\frac{L_{xy}^2}{m_x m_y}+\frac{L_y}{m_y}}\ln(1/\epsilon)\Bigr). $ This paper proposes a new algorithm with gradient complexity upper bound $\tilde{O}\Bigl(\sqrt{\frac{L_x}{m_x}+\frac{L\cdot L_{xy}}{m_x m_y}+\frac{L_y}{m_y}}\ln\left(1/\epsilon\right)\Bigr),$ where $L=\max\{L_x,L_{xy},L_y\}$. This improves over the best known upper bound $\tilde{O}\left(\sqrt{\frac{L^2}{m_x m_y}} \ln^3\left(1/\epsilon\right)\right)$ by \citet{lin2020near}. Our bound achieves linear convergence rate and tighter dependency on condition numbers, especially when $L_{xy}\ll L$ (i.e., when the interaction between $x$ and $y$ is weak). Via reduction, our new bound also implies improved bounds for strongly convex-concave and convex-concave minimax optimization problems. When $f$ is quadratic, we can further improve the upper bound, which matches the lower bound up to a small sub-polynomial factor.
Systematic status of Oribatula Berlese, 1895 (Acari: Oribatida: Oribatulidae) in the light of the ontogeny of three species

The systematic status of Oribatula Berlese, 1895 in light of the ontogeny of Oribatula interrupta (Willmann, 1939), Oribatula propinqua (Oudemans, 1900), Oribatula exarata (Berlese, 1916), and the literature was investigated. Oribatula and Zygoribatula Berlese, 1917 are distinguished as separate genera or subgenera mainly by the absence or presence of translamella in adults, respectively, but the shape of the translamella varies in O. interrupta, Oribatula colemani (Franklin, Norton & Crossley, 2008), and other species, which presents problems with their classification and puts into question the importance of translamella as a generic or subgeneric character. The morphology of juveniles of the species investigated here also does not support the importance of translamella; these juveniles have similar body shape and excentrosclerites at gastronotal setae c 2, la, lp, and h 1 in the larva and c 2, la, lp, h-series, and p 1 in the nymphs, but differ mainly by the pattern of cuticle of gastronotum, length of certain setae, and number of setae of p-series in nymphs, which are species specific. Their adults differ mainly by the shape of translamella, number of setae of p-series, and pattern of integument on the notogaster, which are also species specific. In the light of these investigations, Zygoribatula should be considered a junior synonym of Oribatula.
Directional convergence and alignment in deep learning

In this paper, we show that although the minimizers of cross-entropy and related classification losses are off at infinity, network weights learned by gradient flow converge in direction, with an immediate corollary that network predictions, training errors, and the margin distribution also converge. This proof holds for deep homogeneous networks -- a broad class of networks allowing for ReLU, max pooling, linear, and convolutional layers -- and we additionally provide empirical support not just close to the theory (e.g., the AlexNet), but also on non-homogeneous networks (e.g., the ResNet). If the network further has locally Lipschitz gradients, we show that these gradients converge in direction, and asymptotically align with the gradient flow path, with consequences on margin maximization. Our analysis complements and is distinct from the well-known neural tangent and mean-field theories, and in particular makes no requirements on network width and initialization, instead merely requiring perfect classification accuracy. The proof proceeds by developing a theory of unbounded nonsmooth Kurdyka-Lojasiewicz inequalities for functions definable in an o-minimal structure, and is also applicable outside deep learning.
Fine-tuned distribution of feather mites (Astigmata) on the wing of birds: the case of blackcaps Sylvia atricapilla

20.The distribution of feather mites (Astigmata) along the wing of passerine birds couldchange dramatically within minutes because of the rapid movement of mites betweenfeathers. However, no rigorous study has answered how fine-tuned is the pattern ofdistribution of feather mites at a given time. Here we present a multiscale study of thedistribution of feather mites on the wing of non-moulting blackcaps Sylvia atricapillain a short time period and at a single locality. We found that the number anddistribution of mites differed among birds, but it was extremely similar between thewings of each bird. Moreover, mites consistently avoided the first secondary feather,despite that it is placed at the centre of the feathers most used by them. Thus, ourresults suggest that feather mites do precise, feather-level decisions on where to live,contradicting the current view that mites perform ‘‘mass’’, or ‘‘blind’’ movementsacross wing feathers. Moreover, our findings indicate that ‘‘rare’’ distributions are notspurious data or sampling errors, but each distribution of mites on the wing of eachbird is the outcome of the particular conditions operating on each ambient-bird-feathermite system at a given time. This study indicates that we need to focus on thedistribution of feather mites at the level of the individual bird and at the feather level toimprove our understanding of the spatial ecology of mites on the wings of birds.R. Jovani (correspondence) and D. Serrano, Department of Applied Biology, Estacio´nBiolo´gica de Don˜ana, C.S. I.C. , Avda. M
Mid-infrared Studies of HD 113766 and HD 172555: Assessing Variability in the Terrestrial Zone of Young Exoplanetary Systems

We present multiepoch infrared photometry and spectroscopy obtained with warm Spitzer, Subaru, and the Stratospheric Observatory for Infrared Astronomy to assess variability for the young (∼20 Myr) and dusty debris systems around HD 172555 and HD 113766A. No variations (within 0.5%) were found for the former at either 3.6 or 4.5 μm, while significant nonperiodic variations (peak to peak of ∼10%–15% relative to the primary star) were detected for the latter. Relative to the Spitzer Infrared Spectrograph spectra taken in 2004, multiepoch mid-infrared spectra reveal no change in either the shape of the prominent 10 μm solid-state features or the overall flux levels (no more than 20%) for both systems, corroborating the fact that the population of submicron-size grains that produce the pronounced solid-state features is stable over a decadal timescale. We suggest that these submicron-size grains were initially generated in an optically thick clump of debris of millimeter-size vapor condensates resulting from a recent violent impact between large asteroidal or planetary bodies. Because of the shielding from the stellar photons provided by this clump, intense collisions led to an overproduction of fine grains that would otherwise be ejected from the system by radiation pressure. As the clump is sheared by its orbital motion and becomes optically thin, a population of very fine grains could remain in stable orbits until Poynting–Robertson drag slowly spirals them into the star. We further suggest that the 3–5 μm disk variation around HD 113766A is consistent with a clump/arc of such fine grains on a modestly eccentric orbit in its terrestrial zone.
Is Juvenile Natural Mortality in Marine Demersal Fish Variable

We test the hypothesis that the interannual variability of the density-independent component of juvenile natural mortality is a major source of variability in abundance of marine demersal fish. Our tests use data on populations for which there are research surveys consisting of two simultaneous estimates of abundance of young juveniles soon after settlement and two more simultaneous estimates of the same cohort a year later. We applied our test to 14 populations of Atlantic cod (Gadus morhua), haddock (Melanogrammus aeglefinus), whiting (Merlangius merlangus), plaice (Pleuronectes platessa), and sole (Solea vulgaris). We conclude that, in all populations examined except North Sea sole, there was very little or no interannual variability in the density-independent component of juvenile mortality.
Privacy-Cost Management in Smart Meters: An Information-Theoretic Deep Reinforcement Learning Approach

Smart meters (SMs) share fine-grained electricity consumption of households with utility providers almost in real-time. This can violate the users' privacy since sensitive information is leaked through the SMs data. In this study, a novel privacy-aware method which exploits the availability of a rechargeable battery (RB) is proposed. It is based on a Markov decision process (MDP) formulation in which the reward received by the agent is designed to control the trade-off between privacy and electricity cost. To obtain a robust and general privacy measure, we adopt the mutual information (MI) between the user's demand load and the masked load seen by the grid. Unlike previous studies, we model the whole temporal correlation in the data to estimate the MI in its general form. The training of the agent is done using a model-free deep reinforcement learning algorithm known as the deep double Q-learning (DDQL) method. In order to estimate the MI-based privacy signal, a neural network termed the H-network is included in the scheme. The performance of the DDQL-MI algorithm is assessed empirically using actual SMs data and compared with simpler privacy measures. The results show significant improvements over the state-of-the-art privacy-aware SMs methods.
Learning compositional models of robot skills for task and motion planning

The objective of this work is to augment the basic abilities of a robot by learning to use sensorimotor primitives to solve complex long-horizon manipulation problems. This requires flexible generative planning that can combine primitive abilities in novel combinations and, thus, generalize across a wide variety of problems. In order to plan with primitive actions, we must have models of the actions: under what circumstances will executing this primitive successfully achieve some particular effect in the world? We use, and develop novel improvements to, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the constraints on skill effectiveness from small numbers of expensive-to-collect training examples. In addition, we develop efficient adaptive sampling methods for generating a comprehensive and diverse sequence of continuous candidate control parameter values (such as pouring waypoints for a cup) during planning. These values become end-effector goals for traditional motion planners that then solve for a full robot motion that performs the skill. By using learning and planning methods in conjunction, we take advantage of the strengths of each and plan for a wide variety of complex dynamic manipulation tasks. We demonstrate our approach in an integrated system, combining traditional robotics primitives with our newly learned models using an efficient robot task and motion planner. We evaluate our approach both in simulation and in the real world through measuring the quality of the selected primitive actions. Finally, we apply our integrated system to a variety of long-horizon simulated and real-world manipulation problems.
Dispersion relations and self-localization of quasiparticles in coupled elongated Bose-Einstein condensates

We present a detailed study of the spectrum and dispersion of Bogoliubov quasiparticles in two coupled elongated Bose-Einstein condensates. We develop an analytically solvable model that approximates two infinite homogeneous condensates and compare its predictions to a numerical simulation of a realistic trapped system. While the comparisons show a reasonable agreement between the two models, they also manifest the existence of several anomalous Bogoliubov modes in the spectrum. These modes show degeneracy in both energy and momentum together with self-localization in the coordinate space.
Granulocyte colony‐stimulating factor attenuates LPS‐stimulated IL‐1β release via suppressed processing of proIL‐1β, whereas TNF‐α release is inhibited on the level of proTNF‐α formation

In the presence of granulocyte colony‐stimulating factor (G‐CSF), the release of IL‐1β and TNF‐α by LPS‐stimulated human whole blood was suppressed. Via measurement of cytokine mRNA, inactive precursor and mature protein, we investigated whether this inhibition occurs at the transcriptional, translational or post‐translational level of cytokine production. G‐CSF inhibited IL‐1β release, but the formation of proIL‐1β was not attenuated, indicating that G‐CSF interferes with the proteolytic processing of proIL‐1β. Since the release of IL‐1β in LPS‐stimulated whole blood was blocked by the caspase‐1 inhibitor YVAD‐cmk, processing of proIL‐1β appears to depend on caspase‐1 activity. The conclusion that G‐CSF inhibits caspase‐1 activity was supported bythe finding that the release of IL‐18 was also inhibited by G‐CSF, similar to IL‐1β release. Intracellular caspase‐1 activity in monocytes was measured by flow cytometry with the cell‐permeablecaspase substrate Asp2‐rhodamine. In the presence of G‐CSF the cleavage of this substrate was inhibited by more than 50%. G‐CSF had no effect on LPS‐induced doubling of caspase‐1 mRNA, indicating that G‐CSF affects caspase‐1 activation and not its formation. For TNF‐α another mechanism of G‐CSF action was identified: TNF‐α as well as proTNF‐α formation were inhibited by G‐CSF, butG‐CSF had no influence on LPS‐induced TNF‐α mRNA level. We therefore suggest that G‐CSF causes translational silencing of LPS‐induced TNF‐α mRNA.
Diel rhythmicity of prey‐search activity and its predominance over starvation in the lady beetle, Coccinella septempunctata bruckii

ABSTRACT. The locomotor activity of the lady beetle, Coccinella septempunctata bruckii Mulsant (Coleoptera: Coccinellidae), was recorded during food deprivation in LD 16:8 h and continuous light. Activity was relatively low immediately after the lady beetle was fed to satiation but increased from 4 to 24 h later. It thereafter gradually decreased to a low level when the food was withheld for several days. Over a period of 10 days of starvation the beetles showed a high level of activity throughout the photophase and a low level during the scotophase in LD 16:8 h. In continuous light this rhythmic pattern persisted, with a free‐running period of c. 22 h, significantly shorter than 24 h. The results suggest that the endogenous circadian timing predominates over ‘hunger’ as a determinant of search activity. The lady beetle accepted and consumed aphid prey presented at night, but the number of prey consumed was significantly lower than in the light. This suggests that, during the dark, activity related to prey‐searching is suppressed, though lady beetles are able to accept and consume prey even at night.
Intelligent Surfaces for 6G Wireless Networks: A Survey of Optimization and Performance Analysis Techniques

This paper surveys the optimization frameworks and performance analysis methods for large intelligent surfaces (LIS), which have been emerging as strong candidates to support the sixth-generation wireless physical platforms (6G). Due to their ability to adjust the behavior of interacting electromagnetic (EM) waves through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency of wireless networks. In this context, researchers have been recently exploring LIS technology in depth as a means to achieve programmable, virtualized, and distributed wireless network infrastructures. From a system level perspective, LIS have also been proven to be a low-cost, green, sustainable, and energy-efficient solution for 6G systems. This paper provides a unique blend that surveys the principles of operation of LIS, together with their optimization and performance analysis frameworks. The paper first introduces the LIS technology and its physical working principle. Then, it presents various optimization frameworks that aim to optimize specific objectives, namely, maximizing energy efficiency, sum-rate, secrecy-rate, and coverage. The paper afterwards discusses various relevant performance analysis works including capacity analysis, the impact of hardware impairments on capacity, uplink/downlink data rate analysis, and outage probability. The paper further presents the impact of adopting the LIS technology for positioning applications. Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid radio frequency/visible light communication (RF-VLC) systems, health considerations, and localization.
Multi-task Batch Reinforcement Learning with Metric Learning

We tackle the Multi-task Batch Reinforcement Learning problem. Given multiple datasets collected from different tasks, we train a multi-task policy to perform well in unseen tasks sampled from the same distribution. The task identities of the unseen tasks are not provided. To perform well, the policy must infer the task identity from collected transitions by modelling its dependency on states, actions and rewards. Because the different datasets may have state-action distributions with large divergence, the task inference module can learn to ignore the rewards and spuriously correlate \textit{only} state-action pairs to the task identity, leading to poor test time performance. To robustify task inference, we propose a novel application of the triplet loss. To mine hard negative examples, we relabel the transitions from the training tasks by approximating their reward functions. When further training is allowed on the unseen tasks, using the trained policy as an initialization leads to significantly faster convergence compared to randomly initialized policies.
Seasonal changes in the diet of the European rabbit (Oryctolagus cuniculus) from three different Mediterranean habitats in south-western Australia

Seasonal changes in the diet of rabbits from three temperate (Mediterranean) areas in south-western Australia were identified using microscopic determination of the percentage occurrence of various food groups in sampled stom- achs. The sites differed in soil type and in the availability of summer perennials, native vegetation bush remnants (size of, and number of plant species), improved pastures, and summer rainfall, and hence, enabled a comparison of the diet of rabbits from the different vegetation communities. Although the diet of these rabbits was quite flexible, with some switch- ing in food items occurring between seasons, there were marked differences in the proportion of monocotyledonous and dicotyledonous species eaten in each habitat. There was a strong reliance on seeds (1-5 species) during late spring and summer in all three habitats. Guildford grass (Romulea rosea) leaf and corms were a major component of the diet in the two habitats where this species was common. Further, as a result of the summer die-off of pasture species, there was a shift in where rabbits sourced food items during winter and summer. Pasture species were eaten during winter, but rabbits fed mainly on those dicotyledons found only in the surrounding scrub during summer. This suggests that rabbits may impact negatively upon such remnant vegetation at this time. Rabbits in all three habitats consumed several plant species with high water content (>54%) during summer, presumably to help maintain their water balance. Rabbits also consumed the seeds and foliage of several weed/nuisance species in each habitat, but any role of rabbits in weed dispersal was not determined.