Miracle-dz/UniFAR-FTUs huggingface dataset:
SeqGenSQL - A Robust Sequence Generation Model for Structured Query Language

We explore using T5 (Raffel et al. (2019)) to directly translate natural language questions into SQL statements. General purpose natural language that interfaces to information stored within databases requires flexibly translating natural language questions into database queries. The best performing text-to-SQL systems approach this task by first converting questions into an intermediate logical form (LF) (Lyu et al. (2020)). While LFs provide a convenient intermediate representation and simplify query generation, they introduce an additional layer of complexity and annotation requirements. However, weakly supervised modeling that directly converts questions to SQL statements has proven more difficult without the scaffolding provided by LFs (Min et al. (2019)). We approach direct conversion of questions to SQL statements using T5 (Raffel et al. (2019)), a pre-trained textto-text generation model, modified to support pointer-generator style decoding (See et al. (2017)). We explore using question augmentation with table schema information and the use of automatically generated silver training data. The resulting model achieves 90.5% execution accuracy on the WikiSQL (Zhong et al. (2017)) test data set, a new state-of-the-art on weakly supervised SQL generation. The performance improvement is 6.6% absolute over the prior state-of-the-art (Min et al. (2019)) and approaches the performance of state-ofthe-art systems making use of LFs.
Knowledge-driven Self-supervision for Zero-shot Commonsense Question Answering

Recent developments in pre-trained neural language modeling have led to leaps in accuracy on commonsense question-answering benchmarks. However, there is increasing concern that models overfit to specific tasks, without learning to utilize external knowledge or perform general semantic reasoning. In contrast, zero-shot evaluations have shown promise as a more robust measure of a model's general reasoning abilities. In this paper, we propose a novel neuro-symbolic framework for zero-shot question answering across commonsense tasks. Guided by a set of hypotheses, the framework studies how to transform various pre-existing knowledge resources into a form that is most effective for pre-training models. We vary the set of language models, training regimes, knowledge sources, and data generation strategies, and measure their impact across tasks. Extending on prior work, we devise and compare four constrained distractor-sampling strategies. We provide empirical results across five commonsense question-answering tasks with data generated from five external knowledge resources. We show that, while an individual knowledge graph is better suited for specific tasks, a global knowledge graph brings consistent gains across different tasks. In addition, both preserving the structure of the task as well as generating fair and informative questions help language models learn more effectively.
An HVS-Oriented Saliency Map Prediction Modeling

Visual attention is one of the most significant characteristics for selecting and understanding the outside world. The nature complex scenes, including larger redundancy and human vision, can't be processing all information simultaneously because of the information bottleneck. The visual system mainly focuses on dominant parts of the scenes to reduce the input visual redundancy information. It's commonly known as visual attention prediction or visual saliency map. This paper proposes a new saliency prediction architecture inspired by human low-level visual cortex function. The model considered the opponent color channel, wavelet energy map, and contrast sensitivity function for extract image features and maximum approach to real visual neural network function in the brain. The proposed model is evaluated several datasets, including MIT1003, MIT300, TORONTO, and SID4VAM to explain its efficiency. The proposed model results are quantitatively and qualitatively compared to other state-of-the-art salience prediction models and their achieved out-performing of visual saliency prediction.
New examples of Property (T) factors with trivial fundamental group and unique prime factorization

In this paper we provide new examples of property (T) group factors with trivial fundamental group thereby providing more evidence towards Popa's conjecture on triviality of fundamental groups for property (T) group factors (page 9 \cite{Po13}; see also Problem 2, page 551 in Connes' book \cite{Co94}). Our groups arise as direct product of groups either in Class $\mathscr S$ or Class $\mathscr V$, as introduced in \cite{CDHK20}. We establish a unique prime factorization result for these product groups, thereby providing more evidence towards Popa's conjecture on prime decomposition of property (T) factors (page 9 \cite{Po13}).
Minimal Controllability Problems on Linear Structural Descriptor Systems

We consider minimal controllability problems (MCPs) on linear structural descriptor systems. We address two problems of determining the minimum number of input nodes such that a descriptor system is structurally controllable. We show that MCP0 for structural descriptor systems can be solved in polynomial time. This is the same as the existing results on typical structural linear time-invariant (LTI) systems. However, the derivation of the result is considerably different because the derivation technique of the existing result cannot be used for descriptor systems. Instead, we use the Dulmage–Mendelsohn decomposition. Moreover, we prove that the results for MCP1 are different from those for usual LTI systems. In fact, MCP1 for descriptor systems is an NP-hard problem, while MCP1 for LTI systems can be solved in polynomial time.
Generating Concurrent Programs From Sequential Data Structure Knowledge

In this paper we tackle the problem of automatically designing concurrent data structure operations given a sequential data structure specification and knowledge about concurrent behavior. Designing concurrent code is a non-trivial task even in simplest of cases. Humans often design concurrent data structure operations by transforming sequential versions into their respective concurrent versions. This requires an understanding of the data structure, its sequential behavior, thread interactions during concurrent execution and shared memory synchronization primitives. We mechanize this design process using automated commonsense reasoning. We assume that the data structure description is provided as axioms alongside the sequential code of its algebraic operations. This information is used to automatically derive concurrent code for that data structure, such as dictionary operations for linked lists and binary search trees. Knowledge in our case is expressed using Answer Set Programming (ASP), and we employ deduction, induction and abduction---just as humans do---in the reasoning involved. ASP allows for succinct modeling of first order theories of pointer data structures, run-time thread interactions and shared memory synchronization. Our reasoner can systematically make the same judgments as a human reasoner while constructing provably safe concurrent code. We present several reasoning challenges involved in transforming the sequential data structure into its equivalent concurrent version. All the reasoning tasks are encoded in ASP and our reasoner can make sound judgments to transform sequential code into concurrent code. To the best of our knowledge, our work is the first one to use commonsense reasoning to automatically transform sequential programs into concurrent code.
Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks

Surface anomaly detection plays an important quality control role in many manufacturing industries to reduce scrap production. Machine-based visual inspections have been utilized in recent years to conduct this task instead of human experts. In particular, deep learning Convolutional Neural Networks (CNNs) have been at the forefront of these image processing-based solutions due to their predictive accuracy and efficiency. Training a CNN on a classification objective requires a sufficiently large amount of defective data, which is often not available. In this paper, we address that challenge by training the CNN on surface texture patches with a distance-based anomaly detection objective instead. A deep residual-based triplet network model is utilized, and defective training samples are synthesized exclusively from non-defective samples via random erasing techniques to directly learn a similarity metric between the same-class samples and out-of-class samples. Evaluation results demonstrate the approach's strength in detecting different types of anomalies, such as bent, broken, or cracked surfaces, for known surfaces that are part of the training data and unseen novel surfaces.
Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments

Reinforcement learning was carried out in a simulated environment to learn continuous velocity control over multiple motor axes. This was then applied to a real-world optical tweezers experiment with the objective of moving a laser-trapped microsphere to a target location whilst avoiding collisions with other free-moving microspheres. The concept of training a neural network in a virtual environment has significant potential in the application of machine learning for experimental optimization and control, as the neural network can discover optimal methods for problem solving without the risk of damage to equipment, and at a speed not limited by movement in the physical environment. As the neural network treats both virtual and physical environments equivalently, we show that the network can also be applied to an augmented environment, where a virtual environment is combined with the physical environment. This technique may have the potential to unlock capabilities associated with mixed and augmented reality, such as enforcing safety limits for machine motion or as a method of inputting observations from additional sensors.
Complete classification of photon escape in the Kerr black hole spacetime

We consider necessary and sufficient conditions for photons emitted from the vicinity of a Kerr black hole horizon to escape to infinity. The radial equation of motion determines necessary conditions for photons to reach infinity, and the polar angle equation of motion further restricts the allowed region of photon motion. Unlike emission from the equatorial plane, the latter restrictions are crucial for photon escape when the initial polar angle of the emission point is arbitrary. We provide a visualization tool to analyze these two conditions and demonstrate a procedure for revealing photon escape. Finally, we completely identify the two-dimensional impact parameter space in which photons can escape.
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial

Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have led to multiple successes in solving sequential decision-making problems in various domains, particularly in wireless communications. The next generation of wireless networks is expected to provide scalable, low-latency, ultra-reliable services empowered by the application of data-driven Artificial Intelligence (AI). The key enabling technologies of future wireless networks, such as intelligent meta-surfaces, aerial networks, and AI at the edge, involve more than one agent which motivates the importance of multi-agent learning techniques. Furthermore, cooperation is central to establishing self-organizing, self-sustaining, and decentralized networks. In this context, this tutorial focuses on the role of DRL with an emphasis on deep Multi-Agent Reinforcement Learning (MARL) for AI-enabled wireless networks. The first part of this paper will present a clear overview of the mathematical frameworks for single-agent RL and MARL. The main idea of this work is to motivate the application of RL beyond the model-free perspective which was extensively adopted in recent years. Thus, we provide a selective description of RL algorithms such as Model-Based RL (MBRL) and cooperative MARL and we highlight their potential applications in future wireless networks. Finally, we overview the state-of-the-art of MARL in fields such as Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAV) networks, and cell-free massive MIMO, and identify promising future research directions. We expect this tutorial to stimulate more research endeavors to build scalable and decentralized systems based on MARL.
Multi-Objective Resource Allocation for D2D and Enabled MC-NOMA Networks by Tchebycheff Method

This paper considers a resource allocation problem in device-to-device (D2D) communications sharing the same frequency spectrum. In particular, the cellular users (CUs) utilize non-orthogonal multiple access (NOMA) while D2D users (DUs) adopt the orthogonal frequency division multiple access (OFDMA). A multi-objective optimization problem (MOOP) is formulated, which jointly maximizes the sum rate of DUs and CUs in uplink communications while taking into account the maximum transmit power budget and minimum data rate requirement for DUs and CUs. This MOOP is handled by the weighted Tchebycheff method, which converts it into a single-objective optimization (SOOP). Then, the monotonic optimization approach is employed to solve this SOOP optimally. Numerical results unveil an interesting tradeoff between D2D and CUs.
Centrally concentrated molecular gas driving galactic-scale ionized gas outflows in star-forming galaxies

We perform a joint analysis of high spatial resolution molecular gas and star-formation rate (SFR) maps in main-sequence star-forming galaxies experiencing galactic-scale outflows of ionized gas. Our aim is to understand the mechanism that determines which galaxies are able to launch these intense winds. We observed CO(1→0) at 1-arcsec resolution with ALMA in 16 edge-on galaxies, which also have 2-arcsec spatial-resolution optical integral field observations from the SAMI Galaxy Survey. Half the galaxies in the sample were previously identified as harbouring intense and large-scale outflows of ionized gas (‘outflow types’) and the rest serve as control galaxies. The data set is complemented by integrated CO(1→0) observations from the IRAM 30-m telescope to probe the total molecular gas reservoirs. We find that the galaxies powering outflows do not possess significantly different global gas fractions or star-formation efficiencies when compared with a control sample. However, the ALMA maps reveal that the molecular gas in the outflow-type galaxies is distributed more centrally than in the control galaxies. For our outflow-type objects, molecular gas and star-formation are largely confined within their inner effective radius (reff), whereas in the control sample, the distribution is more diffuse, extending far beyond reff. We infer that outflows in normal star-forming galaxies may be caused by dynamical mechanisms that drive molecular gas into their central regions, which can result in locally enhanced gas surface density and star-formation.
Differentiable Predictive Control: An MPC Alternative for Unknown Nonlinear Systems using Constrained Deep Learning

We present an alternative to model predictive control (MPC) for unknown nonlinear systems in low-resource embedded device settings. The structure of the presented data-driven control policy learning method, Differentiable Predictive Control (DPC), echos the structure of classical MPC, by i) using a prediction model capturing controlled system dynamics, ii) receding horizon optimal control action predictions, and iii) enforcing inequality constraints via penalty methods. However, contrary to MPC, the presented control architecture does not require the system dynamics model to synthesize the control policy. Instead, a dynamics model is learned end-to-end from time-series measurements of the system dynamics in the off-policy setup. The control policy is then optimized via gradient descent by differentiating the closed-loop system dynamics model. The proposed architecture allows to train the control policy to track the distribution of reference signals and handle time-varying inequality constraints. We experimentally demonstrate that it is possible to train generalizing constrained optimal control policies purely based on the observations of the dynamics of the unknown nonlinear system. The proposed control method is applied to a laboratory device in embedded implementation using a Raspberry Pi micro-controller. We demonstrate superior reference tracking control performance compared to classical explicit MPC and a baseline PI controller, and pivotal efficiency gains in online computational demands, memory requirements, policy complexity, and construction. Beyond improved control performance, the DPC method scales linearly compared to exponential scalability of the explicit MPC solved via multiparametric programming, hence, opening doors for applications in nonlinear systems with a large number of variables and fast sampling rates which are beyond the reach of classical explicit MPC.
On the accept-reject mechanism for Metropolis-Hastings algorithms

This work develops a powerful and versatile framework for determining acceptance ratios in Metropolis-Hastings type Markov kernels widely used in statistical sampling problems. Our approach allows us to derive new classes of kernels which unify popular random walk or diffusion-type sampling methods with more complicated "extended phase space" algorithms based on Hamiltonian dynamics. The starting point for our approach is an abstract result developed in the generality of measurable state spaces that addresses proposal kernels that possess a certain involution structure. While this underlying involution proposal structure suggests a scope which includes Hamiltonian-type kernel structures, we demonstrate that our result also recovers a broad range of kernels encompassed by a previous general state space approach in [Tierney, Annals of Applied Probability, 1998]. Altogether, the theoretical unity and reach of our main result provides a basis for deriving novel sampling algorithms while laying bare important relationships between existing methods.
High-Dimensional Sparse Linear Bandits

Stochastic linear bandits with high-dimensional sparse features are a practical model for a variety of domains, including personalized medicine and online advertising. We derive a novel $\Omega(n^{2/3})$ dimension-free minimax regret lower bound for sparse linear bandits in the data-poor regime where the horizon is smaller than the ambient dimension and where the feature vectors admit a well-conditioned exploration distribution. This is complemented by a nearly matching upper bound for an explore-then-commit algorithm showing that that $\Theta(n^{2/3})$ is the optimal rate in the data-poor regime. The results complement existing bounds for the data-rich regime and provide another example where carefully balancing the trade-off between information and regret is necessary. Finally, we prove a dimension-free $O(\sqrt{n})$ regret upper bound under an additional assumption on the magnitude of the signal for relevant features.
Noise sensitivity of percolation via differential inequalities

Consider critical Bernoulli percolation in the plane. We give a new proof of the sharp noise sensitivity theorem shown by Garban, Pete, and Schramm (Acta Math. 205 (2010), 19–104). Contrary to the previous approaches, we do not use any spectral tool. We rather study differential inequalities satisfied by a dynamical four‐arm event, in the spirit of Kesten's proof of scaling relations in Kesten (Comm. Math. Phys. 109 (1987), 109–156). We also obtain new results in dynamical percolation. In particular, we prove that the Hausdorff dimension of the set of times with both primal and dual percolation equals 2/3$2/3$ almost surely.
Reinforced Deep Markov Models With Applications in Automatic Trading

Inspired by the developments in deep generative models, we propose a model-based RL approach, coined Reinforced Deep Markov Model (RDMM), designed to integrate desirable properties of a reinforcement learning algorithm acting as an automatic trading system. The network architecture allows for the possibility that market dynamics are partially visible and are potentially modified by the agent's actions. The RDMM filters incomplete and noisy data, to create better-behaved input data for RL planning. The policy search optimisation also properly accounts for state uncertainty. Due to the complexity of the RKDF model architecture, we performed ablation studies to understand the contributions of individual components of the approach better. To test the financial performance of the RDMM we implement policies using variants of Q-Learning, DynaQ-ARIMA and DynaQ-LSTM algorithms. The experiments show that the RDMM is data-efficient and provides financial gains compared to the benchmarks in the optimal execution problem. The performance improvement becomes more pronounced when price dynamics are more complex, and this has been demonstrated using real data sets from the limit order book of Facebook, Intel, Vodafone and Microsoft.
Polarization simulations of stellar wind bow shock nebulae – II. The case of dust scattering

We study the polarization produced by scattering from dust in a bow shock-shaped region of enhanced density surrounding a stellar source, using the Monte Carlo radiative transfer code SLIP. Bow shocks are structures formed by the interaction of the winds of fast-moving stars with the interstellar medium. Our previous study focused on the polarization produced in these structures by electron scattering; we showed that polarization is highly dependent on inclination angle and that multiple scattering changes the shape and degree of polarization. In contrast to electron scattering, dust scattering is wavelength-dependent, which changes the polarization behaviour. Here we explore different dust particle sizes and compositions and generate polarized spectral energy distributions for each case. We find that the polarization SED behaviour depends on the dust composition and grain size. Including dust emission leads to polarization changes with temperature at higher optical depth in ways that are sensitive to the orientation of the bow shock. In various scenarios and under certain assumptions, our simulations can constrain the optical depth and dust properties of resolved and unresolved bow shock-shaped scattering regions.Constraints on optical depth can provide estimates of local ISM density for observed bow shocks. We also study the impact of dust grains filling the region between the star and bow shock. We see that as the density of dust between the star and bow shock increases, the resulting polarization is suppressed for all the optical depth regimes.