Miracle-dz/UniFAR-FTUs huggingface dataset:
One to Rule Them All: Towards Joint Indic Language Hate Speech Detection

This paper is a contribution to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) 2021 shared task. Social media today is a hotbed of toxic and hateful conversations, in various languages. Recent news reports have shown that current models struggle to automatically identify hate posted in minority languages. Therefore, efficiently curbing hate speech is a critical challenge and problem of interest. We present a multilingual architecture using state-of-the-art transformer language models to jointly learn hate and offensive speech detection across three languages namely, English, Hindi, and Marathi. On the provided testing corpora, we achieve Macro F1 scores of 0.7996, 0.7748, 0.8651 for sub-task 1A and 0.6268, 0.5603 during the fine-grained classification of sub-task 1B. These results show the efficacy of exploiting a multilingual training scheme.
Reinforcement Learning for Quantitative Trading

Quantitative trading (QT), which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement learning (RL) has garnered significant interest in many domains such as robotics and video games, owing to its outstanding ability on solving complex sequential decision making problems. RL’s impact is pervasive, recently demonstrating its ability to conquer many challenging QT tasks. It is a flourishing research direction to explore RL techniques’ potential on QT tasks. This paper aims at providing a comprehensive survey of research efforts on RL-based methods for QT tasks. More concretely, we devise a taxonomy of RL-based QT models, along with a comprehensive summary of the state of the art. Finally, we discuss current challenges and propose future research directions in this exciting field.
Fake News Detection using Semi-Supervised Graph Convolutional Network

Social media becomes the central way for people to obtain and utilise news, due to its rapidness and inexpensive value of data distribution. Though, such features of social media platforms also present it a root cause of fake news distribution, causing adverse consequences on both people and culture. Hence, detecting fake news has become a significant research interest for bringing feasible real time solutions to the problem. Most current techniques of fake news disclosure are supervised, that need large cost in terms of time and effort to make a certainly interpreted dataset. The proposed framework concentrates on the text-based detection of fake news items while considering that only limited number of labels are available. Graphs are functioned extensively under several purposes of real-world problems on the strength of their property to structure things easily. Deep neural networks are used to generate great results within tasks that utilizes graph classification. The Graph Convolution Network works as a deep learning paradigm which works on graphs. Our proposed framework deals with limited amount of labelled data; we go for a semi-supervised learning method. We come up with a semi-supervised fake news detection technique based on GCN (Graph Convolutional Networks). The recommended architecture comprises of three basic components: collecting word embeddings from the news articles in datasets utilising GloVe, building similarity graph using Word Movers Distance (WMD) and finally applying Graph Convolution Network (GCN) for binary classification of news articles in semi-supervised paradigm. The implemented technique is validated on three different datasets by varying the volume of labelled data achieving 95.27 % highest accuracy on Real or Fake dataset. Comparison with other contemporary techniques also reinforced the supremacy of the proposed framework.
Extinction in the Large Magellanic Cloud Bar around NGC 1854, NGC 1856, and NGC 1858

We report on the extinction properties in the fields around the clusters NGC 1854, NGC 1856, and NGC 1858 in the bar of the Large Magellanic Cloud. The color–magnitude diagrams of the stars in all these regions show an elongated red giant clump that reveals a variable amount of extinction across these fields, ranging from A V ≃ 0.2 to A V ≃ 1.9, including Galactic foreground extinction. The extinction properties nonetheless are remarkably uniform. The slope of the reddening vectors measured in the (V − I, V) and (B − I, B) color–magnitude planes is fully in line with the A V /E(B − V) ≃ 5.5 value found in the outskirts of 30 Dor. This indicates the presence of an additional gray extinction component in the optical requiring big grains to be about twice as abundant as in the diffuse Galactic interstellar medium (ISM). Areas of higher extinction appear to be systematically associated with regions of more intense star formation, as measured by the larger number of stars more massive than 8 M ⊙, thus making injection of big grains into the ISM by a SNII explosion the likely mechanism at the origin of the observed gray extinction component.
On the Empirical Evaluation of Information Gain Criteria for Active Action Selection

—Accurate object pose estimation using multi-modal perception such as visual and tactile sensing have been used for autonomous robotic manipulators in literature. Due to variation in density of visual and tactile data, a novel probabilistic Bayesian ﬁlter-based approach termed translation-invariant Quaternion ﬁlter (TIQF) is proposed for pose estimation using point cloud registration. Active tactile data collection is preferred by reasoning over multiple potential actions for maximal expected information gain as tactile data collection is time consuming. In this paper, we empirically evaluate various information gain criteria for action selection in the context of object pose estimation. We demonstrate the adaptability and effectiveness of our proposed TIQF pose estimation approach with various information gain criteria. We ﬁnd similar performance in terms of pose accuracy with sparse measurements ( < 15 points) across all the selected criteria. Furthermore, we explore the use of uncommon information theoretic criteria in the robotics domain for action selection.
Model-based Motion Imitation for Agile, Diverse and Generalizable Quadupedal Locomotion

—Robots operating in human environments need a variety of skills, like slow and fast walking, turning, and side-stepping. However, building robot controllers that can exhibit such a large range of behaviors is challenging, and unsolved. We present an approach that uses a model-based controller for imitating different animal gaits without requiring any real-world ﬁne-tuning. Unlike previous works that learn one policy per motion, we present a uniﬁed controller which is capable of generating four different animal gaits on the A1 robot. Our framework includes a trajectory optimization procedure that improves the quality of real-world imitation. We demonstrate our results in simulation and on a real 12-DoF A1 quadruped robot. Our result shows that our approach can mimic four animal motions, and outperform baselines learned per motion.
Conditional cross‐design synthesis estimators for generalizability in Medicaid

While much of the causal inference literature has focused on addressing internal validity biases, both internal and external validity are necessary for unbiased estimates in a target population of interest. However, few generalizability approaches exist for estimating causal quantities in a target population that is not well‐represented by a randomized study but is reflected when additionally incorporating observational data. To generalize to a target population represented by a union of these data, we propose a novel class of conditional cross‐design synthesis estimators that combine randomized and observational data, while addressing their estimates' respective biases—lack of overlap and unmeasured confounding. These methods enable estimating the causal effect of managed care plans on health care spending among Medicaid beneficiaries in New York City, which requires obtaining estimates for the 7% of beneficiaries randomized to a plan and 93% who choose a plan, who do not resemble randomized beneficiaries. Our new estimators include outcome regression, propensity weighting, and double robust approaches. All use the covariate overlap between the randomized and observational data to remove potential unmeasured confounding bias. Applying these methods, we find substantial heterogeneity in spending effects across managed care plans. This has major implications for our understanding of Medicaid, where this heterogeneity has previously been hidden. Additionally, we demonstrate that unmeasured confounding rather than lack of overlap poses a larger concern in this setting.
Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments

Place recognition is a key module in robotic navigation. The existing line of studies mostly focuses on visual place recognition to recognize previously visited places solely based on their appearance. In this article, we address structural place recognition by recognizing a place based on structural appearance, namely from range sensors. Extending our previous work on a rotation invariant spatial descriptor, the proposed descriptor completes a generic descriptor robust to both rotation (heading) and translation when roll–pitch motions are not severe. We introduce two subdescriptors and enable topological place retrieval followed by the 1-degree of freedom semimetric localization, thereby bridging the gap between topological place retrieval and metric localization. The proposed method has been evaluated thoroughly in terms of environmental complexity and scale. The source code is available and can easily be integrated into existing light detection and ranging simultaneous localization and mapping.
Classical string solutions in non-relativistic AdS5 × S5: closed and twisted sectors

We find classical closed string solutions to the non-relativistic AdS5 × S5 string theory which are the analogue of the BMN and GKP solutions for the relativistic theory. We show that non-relativistic AdS5 × S5 string theory admits a Z2 orbifold symmetry which allows us to impose twisted boundary conditions. Among the solutions in the twisted sector, we find the one around which the semiclassical expansion in arXiv:2102.00008 takes place.
Identifying and Mitigating Gender Bias in Hyperbolic Word Embeddings

Euclidean word embedding models such as GloVe and Word2Vec have been shown to reflect human-like gender biases. In this paper, we extend the study of gender bias to the recently popularized hyperbolic word embeddings. We propose gyrocosine bias, a novel measure for quantifying gender bias in hyperbolic word representations and observe a significant presence of gender bias. To address this problem, we propose Poincar\'e Gender Debias (PGD), a novel debiasing procedure for hyperbolic word representations. Experiments on a suit of evaluation tests show that PGD effectively reduces bias while adding a minimal semantic offset.
The Tensor Brain: A Unified Theory of Perception, Memory, and Semantic Decoding

Abstract We present a unified computational theory of an agent's perception and memory. In our model, both perception and memory are realized by different operational modes of the oscillating interactions between a symbolic index layer and a subsymbolic representation layer. The two layers form a bilayer tensor network (BTN). The index layer encodes indices for concepts, predicates, and episodic instances. The representation layer broadcasts information and reflects the cognitive brain state; it is our model of what authors have called the “mental canvas” or the “global workspace. ” As a bridge between perceptual input and the index layer, the representation layer enables the grounding of indices by their subsymbolic embeddings, which are implemented as connection weights linking both layers. The propagation of activation to earlier perceptual processing layers in the brain can lead to embodiments of indices. Perception and memories first create subsymbolic representations, which are subsequently decoded semantically to produce sequences of activated indices that form symbolic triple statements. The brain is a sampling engine: only activated indices are communicated to the remaining parts of the brain. Triple statements are dynamically embedded in the representation layer and embodied in earlier processing layers: the brain speaks to itself. Although memory appears to be about the past, its main purpose is to support the agent in the present and the future. Recent episodic memory provides the agent with a sense of the here and now. Remote episodic memory retrieves relevant past experiences to provide information about possible future scenarios. This aids the agent in decision making. “Future” episodic memory, based on expected future events, guides planning and action. Semantic memory retrieves specific information, which is not delivered by current perception, and defines priors for future observations. We argue that it is important for the agent to encode individual entities, not just classes and attributes. Perception is learning: episodic memories are constantly being formed, and we demonstrate that a form of self-supervised learning can acquire new concepts and refine existing ones. We test our model on a standard benchmark data set, which we expanded to contain richer representations for attributes, classes, and individuals. Our key hypothesis is that obtaining a better understanding of perception and memory is a crucial prerequisite to comprehending human-level intelligence.
Dust traffic jams in inclined circumbinary protoplanetary discs – I. Morphology and formation theory

Gas and dust in inclined orbits around binaries experience precession induced by the binary gravitational torque. The difference in precession between gas and dust alters the radial drift of weakly coupled dust and leads to density enhancements where the radial drift is minimized. We explore this phenomenon using 3D hydrodynamical simulations to investigate the prominence of these ‘dust traffic jams’ and the evolution of the resulting dust sub-structures at different disc inclinations and binary eccentricities. We then derive evolution equations for the angular momentum of warped dust discs and implement them in a 1D code and present calculations to further explain these traffic jams. We find that dust traffic jams in inclined circumbinary discs provide significant dust density enhancements that are long lived and can have important consequences for planetesimal formation.
IGAN: Inferent and Generative Adversarial Networks

I present IGAN (Inferent Generative Adversarial Networks), a neural architecture that learns both a generative and an inference model on a complex high dimensional data distribution, i.e. a bidirectional mapping between data samples and a simpler low-dimensional latent space. It extends the traditional GAN framework with inference by rewriting the adversarial strategy in both the image and the latent space with an entangled game between data-latent encoded posteriors and priors. It brings a measurable stability and convergence to the classical GAN scheme, while keeping its generative quality and remaining simple and frugal in order to run on a lab PC. IGAN fosters the encoded latents to span the full prior space: this enables the exploitation of an enlarged and self-organised latent space in an unsupervised manner. An analysis of previously published articles sets the theoretical ground for the proposed algorithm. A qualitative demonstration of potential applications like self-supervision or multi-modal data translation is given on common image datasets including SAR and optical imagery.
Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning

Transfer learning where the behavior of extracting transferable knowledge from the source domain(s) and reusing this knowledge to target domain has become a research area of great interest in the field of artificial intelligence. Probabilistic graphical models (PGMs) have been recognized as a powerful tool for modeling complex systems with many advantages, e.g., the ability to handle uncertainty and possessing good interpretability. Considering the success of these two aforementioned research areas, it seems natural to apply PGMs to transfer learning. However, although there are already some excellent PGMs specific to transfer learning in the literature, the potential of PGMs for this problem is still grossly underestimated. This paper aims to boost the development of PGMs for transfer learning by 1) examining the pilot studies on PGMs specific to transfer learning, i.e., analyzing and summarizing the existing mechanisms particularly designed for knowledge transfer; 2) discussing examples of real-world transfer problems where existing PGMs have been successfully applied; and 3) exploring several potential research directions on transfer learning using PGM.
Multiplier ideals of plane curve singularities via Newton polygons

We give an effective method to determine the multiplier ideals and jumping numbers associated with a curve singularity $C$ in a smooth surface. We characterize the multiplier ideals in terms of certain Newton polygons, generalizing a theorem of Howald, which holds when $C$ is Newton non-degenerate with respect to some local coordinate system. The method uses toroidal embedded resolutions and generating sequences of families of valuations, and can be extended to some classes of higher dimensional hypersurface singularities.
Shape Resonances in H_{2} as Photolysis Reaction Intermediates.

Shape resonances in H_{2}, produced as reaction intermediates in the photolysis of H_{2}S precursor molecules, are measured in a half-collision approach. Before disintegrating into two ground state H atoms, the reaction is quenched by two-photon Doppler-free excitation to the F electronically excited state of H_{2}. For J=13, 15, 17, 19, and 21, resonances with lifetimes in the range of nano- to milliseconds were observed with an accuracy of 30 MHz (1.4 mK). The experimental resonance positions are found to be in excellent agreement with theoretical predictions when nonadiabatic and quantum electrodynamical corrections are included. This is the first time such effects are observed in collisions between neutral atoms. From the potential energy curve of the H_{2} molecule, now tested at high accuracy over a wide range of internuclear separations, the s-wave scattering length for singlet H(1s)+H(1s) scattering is determined at a=0. 2735_{31}^{39} a_{0}.
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence

While recent advances in AI-based automated decision-making have shown many benefits for businesses and society, they also come at a cost. It has for long been known that a high level of automation of decisions can lead to various drawbacks, such as automation bias and deskilling. In particular, the deskilling of knowledge workers is a major issue, as they are the same people who should also train, challenge and evolve AI. To address this issue, we conceptualize a new class of DSS, namely Intelligent Decision Assistance (IDA) based on a literature review of two different research streams -- DSS and automation. IDA supports knowledge workers without influencing them through automated decision-making. Specifically, we propose to use techniques of Explainable AI (XAI) while withholding concrete AI recommendations. To test this conceptualization, we develop hypotheses on the impacts of IDA and provide first evidence for their validity based on empirical studies in the literature.
Faster Improvement Rate Population Based Training

The successful training of neural networks typically involves careful and time consuming hyperparameter tuning. Population Based Training (PBT) has recently been proposed to automate this process. PBT trains a population of neural networks concurrently, frequently mutating their hyperparameters throughout their training. However, the decision mechanisms of PBT are greedy and favour short-term improvements which can, in some cases, lead to poor long-term performance. This paper presents Faster Improvement Rate PBT (FIRE PBT) which addresses this problem. Our method is guided by an assumption: given two neural networks with similar performance and training with similar hyperparameters, the network showing the faster rate of improvement will lead to a better final performance. Using this, we derive a novel fitness metric and use it to make some of the population members focus on long-term performance. Our experiments show that FIRE PBT is able to outperform PBT on the ImageNet benchmark and match the performance of networks that were trained with a hand-tuned learning rate schedule. We apply FIRE PBT to reinforcement learning tasks and show that it leads to faster learning and higher final performance than both PBT and random hyperparameter search.
The edge of chaos: quantum field theory and deep neural networks

We explicitly construct the quantum field theory corresponding to a general class of deep neural networks encompassing both recurrent and feedforward architectures. We first consider the mean-field theory (MFT) obtained as the leading saddlepoint in the action, and derive the condition for criticality via the largest Lyapunov exponent. We then compute the loop corrections to the correlation function in a perturbative expansion in the ratio of depth T to width N, and find a precise analogy with the well-studied O(N) vector model, in which the variance of the weight initializations plays the role of the 't Hooft coupling. In particular, we compute both the O(1) corrections quantifying fluctuations from typicality in the ensemble of networks, and the subleading O(T/N) corrections due to finite-width effects. These provide corrections to the correlation length that controls the depth to which information can propagate through the network, and thereby sets the scale at which such networks are trainable by gradient descent. Our analysis provides a first-principles approach to the rapidly emerging NN-QFT correspondence, and opens several interesting avenues to the study of criticality in deep neural networks.
Coordinated motion of active filaments on spherical surfaces

Coordinated cilia are used throughout the natural world for micronscale fluid transport. They are often modelled with regular filament arrays on fixed, planar surfaces. Here, we simulate hundreds of interacting active filaments on spherical surfaces, where defects in the cilia displacement field must be present. We see synchronised beating towards or about two defects for spheres held fixed. Defects alter filament beating which causes the sphere to move once released. This motion feeds back to the filaments resulting in a whirling state with metachronal behaviour along the equator.