Miracle-dz/UniFAR-FTUs huggingface dataset:
Stock prices and Macroeconomic indicators: Investigating a correlation in Indian context

The objective of this paper is to find the existence of a relationship between stock market prices and the fundamental macroeconomic indicators. We build a Vector Auto Regression (VAR) model comprising of nine major macroeconomic indicators (interest rate, inflation, exchange rate, money supply, gdp, fdi, trade-gdp ratio, oil prices, gold prices) and then try to forecast them for next 5 years. Finally we calculate cross-correlation of these forecasted values with the BSE Sensex closing price for each of those years. We find very high correlation of the closing price with exchange rate and money supply of Indian economy.
Towards Controllable Agent in MOBA Games with Generative Modeling

We propose novel methods to develop action controllable agent that behaves like a human and has the ability to align with human players in Multiplayer Online Battle Arena (MOBA) games. By modeling the control problem as an action generation process, we devise a deep latent alignment neural network model for training agent, and a corresponding sampling algorithm for controlling an agent's action. Particularly, we propose deterministic and stochastic attention implementations of the core latent alignment model. Both simulated and online experiments in the game Honor of Kings demonstrate the efficacy of the proposed methods.
Nonadiabaticity of electron spin dynamics and spin torque exerted on wide magnetic domain walls: Exact results from time-dependent quantum transport

The key concept in spintronics of current-driven noncollinear magnetic textures, such as magnetic domain walls (DWs), is adiabaticity, i.e., how closely electronic spins can track spatially classical localized magnetic moments (LMMs) comprising the texture. When mistracking occurs, nonadiabatic effects arise, the salient of which is the so-called nonadiabatic spin transfer torque (STT) where spin angular momentum is exchanged between conduction electrons and LMMs to cause their dynamics and enable DW motion for arbitrary small injected current. The microscopic mechanisms behind nonadiabatic STT have been debated theoretically for nearly two decades, but with unanimous conclusion that they should be significant only in narrow DWs. This, however, sharply contradicts experiments observing nonadiabatic STT in DWs much wider than putatively relevant ∼ nm scale, and largely insensitive to further increasing of DW width w. Here we employ time-dependent quantum transport for electrons, combined self-consistently with the Landau-Lifshitz-Gilbert (LLG) equation for the dynamics of LMMs, to extract nonadiabatic and adiabatic STT from numerically exact nonequilibrium density matrix and its lowest order term (i.e., adiabatic density matrix) defined by assuming that LMMs are infinitely slow. This allows us to demonstrate that thus defined nonadiabatic STT: (i) does not decay, but instead saturates, with increasing DW width ensuring entry into the adiabatic limit which we characterize by showing that electronic spins do not reflect from the DW in this limit; and (ii) it has both out-of-DW-plane (as is the case of phenomenological nonadiabatic STT) and in-plane components, where the former remains finite with increasing w.
Crowdsourcing County-Level Data on Early COVID-19 Policy Interventions in the United States: Technical Report

Beginning in April 2020, we gathered partial county-level data on non-pharmaceutical interventions (NPIs) implemented in response to the COVID-19 pandemic in the United States, using both volunteer and paid crowdsourcing. In this report, we document the data collection process and summarize our results, to increase the utility of our open data and inform the design of future rapid crowdsourcing data collection efforts.
Interpretable Feature Learning Framework for Smoking Behavior Detection

Smoking in public has been proven to be more harmful to nonsmokers, making it a huge public health concern with urgent need for proactive measures and attention by authorities. With the world moving towards the 4th Industrial Revolution, there is a need for reliable eco-friendly detective measures towards this harmful intoxicating behavior to public health in and out of smart cities. We developed an Interpretable feature learning framework for smoking behavior detection which utilizes a Deep Learning VGG-16 pretrained network to predict and classify the input Image class and a Layer-wise Relevance Propagation (LRP) to explain the network detection or prediction of smoking behavior based on the most relevant learned features or pixels or neurons. The network's classification decision is based mainly on features located at the mouth especially the smoke seems to be of high importance to the network's decision. The outline of the smoke is highlighted as evidence for the corresponding class. Some elements are seen as having a negative effect on the smoke neuron and are consequently highlighted differently. It is interesting to see that the network distinguishes important from unimportant features based on the image regions. The technology can also detect other smokeable drugs like weed, shisha, marijuana etc. The framework allows for reliable identification of action-based smokers in unsafe zones like schools, shopping malls, bus stops, railway compartments or other violated places for smoking as per the government's regulatory health policies. With installation clearly defined in smoking zones, this technology can detect smokers out of range.
CheckDST: Measuring Real-World Generalization of Dialogue State Tracking Performance

Neural models that extend the pretrain-then- 001 finetune paradigm continue to achieve new 002 state-of-the-art results in dialogue state track- 003 ing (DST) benchmarks on joint goal accu- 004 racy ( JGA ). However, motivated by CheckList 005 (Ribeiro et al., 2020), we argue for a holistic 006 assessment of DST models since JGA is un- 007 able to capture robustness to the inevitable test- 008 time distribution shifts. To this end, we build 009 on recent work on robustness testing in task- 010 oriented dialogue and introduce CheckDST, 011 an instantiation of CheckList for DST that 012 quantifies robustness with test set augmenta- 013 tions and new metrics that measure consistency. 014 Using CheckDST, we are able to extensively 015 compare state-of-the-art DST models, finding 016 that, although span-based classification mod- 017 els achieve slightly better JGA on the original 018 test set than generation models, they are sig- 019 nificantly less robust to distribution shift. Sec- 020 ondly, we observe that while stopping training 021 early, e.g., at the first epoch, hurts JGA , the re- 022 sulting models are significantly more robust to 023 distribution shift. Lastly, guided by the weak- 024 nesses exposed by CheckDST, we explore train- 025 ing DST models that simultaneously boost JGA 026 and CheckDST metrics and report preliminary 027 success with PrefineDST, a simple generation 028 model pretrained with non-target datasets to 029 internalize reasoning skills relevant to dialogue 030 state tracking. 031
A Comparative Analysis of Machine Learning Approaches for Automated Face Mask Detection During COVID-19

The World Health Organization (WHO) has recommended wearing face masks as one of the most effective measures to prevent COVID-19 transmission. In many countries, it is now mandatory to wear face masks, specially in public places. Since manual monitoring of face masks is often infeasible in the middle of the crowd, automatic detection can be beneficial. To facilitate that, we explored a number of deep learning models (i.e., VGG1, VGG19, ResNet50) for face-mask detection and evaluated them on two benchmark datasets. We also evaluated transfer learning (i.e., VGG19, ResNet50 pre-trained on ImageNet) in this context. We find that while the performances of all the models are quite good, transfer learning models achieve the best performance. Transfer learning improves the performance by 0.10\%--0.40\% with 30\% less training time. Our experiment also shows these high-performing models are not quite robust for real-world cases where the test dataset comes from a different distribution. Without any fine-tuning, the performance of these models drops by 47\% in cross-domain settings.
Meshless Monte Carlo radiation transfer method for curved geometries using signed distance functions

Abstract. Significance: Monte Carlo radiation transfer (MCRT) is the gold standard for modeling light transport in turbid media. Typical MCRT models use voxels or meshes to approximate experimental geometry. A voxel-based geometry does not allow for the precise modeling of smooth curved surfaces, such as may be found in biological systems or food and drink packaging. Mesh-based geometry allows arbitrary complex shapes with smooth curved surfaces to be modeled. However, mesh-based models also suffer from issues such as the computational cost of generating meshes and inaccuracies in how meshes handle reflections and refractions. Aim: We present our algorithm, which we term signedMCRT (sMCRT), a geometry-based method that uses signed distance functions (SDF) to represent the geometry of the model. SDFs are capable of modeling smooth curved surfaces precisely while also modeling complex geometries. Approach: We show that using SDFs to represent the problem’s geometry is more precise than voxel and mesh-based methods. Results: sMCRT is validated against theoretical expressions, and voxel and mesh-based MCRT codes. We show that sMCRT can precisely model arbitrary complex geometries such as microvascular vessel network using SDFs. In comparison with the current state-of-the-art in MCRT methods specifically for curved surfaces, sMCRT is more precise for cases where the geometry can be defined using combinations of shapes. Conclusions: We believe that SDF-based MCRT models are a complementary method to voxel and mesh models in terms of being able to model complex geometries and accurately treat curved surfaces, with a focus on precise simulation of reflections and refractions. sMCRT is publicly available at https://github.com/lewisfish/signedMCRT.
Extensions of the Mandelstam–Tamm quantum speed limit to systems in mixed states

The Mandelstam–Tamm quantum speed limit (QSL) puts a bound on how fast a closed system in a pure state can evolve. In this paper, we derive several extensions of this QSL to closed systems in mixed states. We also compare the strengths of these extensions and examine their tightness. The most widely used extension of the Mandelstam–Tamm QSL originates in Uhlmann’s energy dispersion estimate. We carefully analyze the underlying geometry of this estimate, an analysis that makes apparent that the Bures metric, or equivalently the quantum Fisher information, will rarely give rise to tight extensions. This observation leads us to address whether there is a tightest general extension of the Mandelstam–Tamm QSL. Using a geometric construction similar to that developed by Uhlmann, we prove that this is indeed the case. In addition, we show that tight evolutions of mixed states are typically generated by time-varying Hamiltonians, which contrasts with the case for systems in pure states.
Stochastic Model Predictive Control as an Iterated Function System

We present the observation that the process of stochastic model predictive control can be formulated in the framework of an iterated function system. The latter has a rich ergodicity theory that can hence be applied to study the system’s long run behavior. We present how such a framework can be realized for speciﬁc problems and illustrate the required conditions for the application of relevant theoretical guarantees.
Knowledge-Rich Self-Supervised Entity Linking

Entity linking faces signiﬁcant challenges, such as proliﬁc variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classiﬁcation approaches suffer from the annotation bottle-neck and cannot effectively handle unseen entities. Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia. In this paper, we explore Knowledge-RIch Self-Supervision ( KRISS ) for entity linking, by leveraging readily available domain knowledge. In training, it generates self-supervised mention examples on unlabeled text using a domain ontology and trains a contextual encoder using contrastive learning. For inference, it samples self-supervised mentions as prototypes for each entity and conducts linking by mapping the test mention to the most similar prototype. Our approach subsumes zero-shot and few-shot methods, and can easily incorporate entity descriptions and gold mention labels if available. Using biomedicine as a case study, we conducted extensive experiments on seven standard datasets spanning biomedical literature and clinical notes. With-out using any labeled information, our method produces KRISSBERT , a universal entity linker for four million UMLS entities, which attains new state of the art across the board, outper-forming prior best self-supervised methods by as much as over 20 absolute points in accuracy.
Analog/Mixed-Signal Circuit Synthesis Enabled by the Advancements of Circuit Architectures and Machine Learning Algorithms

Analog mixed-signal (AMS) circuit architecture has evolved towards more digital friendly due to technology scaling and demand for higher flexibility/reconfigurability. Mean-while, the design complexity and cost of AMS circuits has substantially increased due to the necessity of optimizing the circuit sizing, layout, and verification of a complex AMS circuit. On the other hand, machine learning (ML) algorithms have been under exponential growth over the past decade and actively exploited by the electronic design automation (EDA) community. This paper will identify the opportunities and challenges brought about by this trend and overview several emerging AMS design methodologies that are enabled by the recent evolution of AMS circuit architectures and machine learning algorithms. Specifically, we will focus on using neural-network-based surrogate models to expedite the circuit design parameter search and layout iterations. Lastly, we will demonstrate the rapid synthesis of several AMS circuit examples from specification to silicon prototype, with significantly reduced human intervention.
Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering

In control design most control strategies are model-based and require accurate models to be applied successfully. Due to simplifications and the model-reality-gap physics-derived models frequently exhibit deviations from real-world-systems. Likewise, purely data-driven methods often do not generalise well enough and may violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired loss functions separately have shown promising results to conquer these drawbacks. In this contribution we extend existing methods towards the identification of non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN and a physics-inspired loss term (-L) to successfully identify the system's dynamics, while maintaining the consistency with physical laws. The proposed method is demonstrated on two real-world nonlinear systems and outperforms existing techniques regarding complexity and reliability.
One System to Rule them All: a Universal Intent Recognition System for Customer Service Chatbots

Customer service chatbots are conversational systems designed to provide information to customers about products/services offered by different companies. Particularly, intent recognition is one of the core components in the natural language understating capabilities of a chatbot system. Among the different intents that a chatbot is trained to recognize, there is a set of them that is universal to any customer service chatbot. Universal intents may include salutation, switch the conversation to a human agent, farewells, among others. A system to recognize those universal intents will be very helpful to optimize the training process of specific customer service chatbots. We propose the development of a universal intent recognition system, which is trained to recognize a selected group of 11 intents that are common in 28 different chatbots. The proposed system is trained considering state-of-the-art word-embedding models such as word2vec and BERT, and deep classifiers based on convolutional and recurrent neural networks. The proposed model is able to discriminate between those universal intents with a balanced accuracy up to 80.4\%. In addition, the proposed system is equally accurate to recognize intents expressed both in short and long text requests. At the same time, misclassification errors often occurs between intents with very similar semantic fields such as farewells and positive comments. The proposed system will be very helpful to optimize the training process of a customer service chatbot because some of the intents will be already available and detected by our system. At the same time, the proposed approach will be a suitable base model to train more specific chatbots by applying transfer learning strategies.
HyObscure: Hybrid Obscuring for Privacy-Preserving Data Publishing

Minimizing privacy leakage while ensuring data utility is a critical problem to data holders in a privacy-preserving data publishing task. Most prior research concerns only with one type of data and resorts to a single obscuring method, \eg, obfuscation or generalization, to achieve a privacy-utility tradeoff, which is inadequate for protecting real-life heterogeneous data and is hard to defend ever-growing machine learning based inference attacks. This work takes a pilot study on privacy-preserving data publishing when both generalization and obfuscation operations are employed for heterogeneous data protection. To this end, we first propose novel measures for privacy and utility quantification and formulate the hybrid privacy-preserving data obscuring problem to account for the joint effect of generalization and obfuscation. We then design a novel hybrid protection mechanism called HyObscure, to cross-iteratively optimize the generalization and obfuscation operations for maximum privacy protection under a certain utility guarantee. The convergence of the iterative process and the privacy leakage bound of HyObscure are also provided in theory. Extensive experiments demonstrate that HyObscure significantly outperforms a variety of state-of-the-art baseline methods when facing various inference attacks under different scenarios. HyObscure also scales linearly to the data size and behaves robustly with varying key parameters.
Self-Ensembling GAN for Cross-Domain Semantic Segmentation

Deep neural networks (DNNs) have greatly contributed to the performance gains in semantic segmentation. Nevertheless, training DNNs generally requires large amounts of pixel-level labeled data, which is expensive and time-consuming to collect in practice. To mitigate the annotation burden, this paper proposes a self-ensembling generative adversarial network (SE-GAN) exploiting cross-domain data for semantic segmentation. In SE-GAN, a teacher network and a student network constitute a self-ensembling model for generating semantic segmentation maps, which together with a discriminator, forms a GAN. Despite its simplicity, we find SE-GAN can significantly boost the performance of adversarial training and enhance the stability of the model, the latter of which is a common barrier shared by most adversarial training-based methods. We theoretically analyze SE-GAN and provide an $\mathcal {O}(1/\sqrt{N})$ generalization bound ($N$ is the training sample size), which suggests controlling the discriminator's hypothesis complexity to enhance the generalizability. Accordingly, we choose a simple network as the discriminator. Extensive and systematic experiments in two standard settings demonstrate that the proposed method significantly outperforms current state-of-the-art approaches.
Designing & Rapid Prototyping a Gesture-Enabled Steering Wheel

As natural user interfaces become increasingly prevalent in everyday interactions, the Automotive Research team at Intel’s Interaction and Experience Research Lab (IXR) sought to explore the potential of touch gestures in the incar experience. Leveraging common gestures now standard with touch interfaces, we looked at how these standards might extend and adapt to provide a simpler in-car control interface. Through an iterative design process, we conceived, designed, developed, and prototyped an interactive driving demo controlled by a gesture-enabled steering wheel in 8 weeks.
Public key cryptography based on twisted dihedral group algebras

<p style='text-indent:20px;'>In this paper, we propose to use a twisted dihedral group algebra for public-key cryptography. For this, we introduce a new <inline-formula><tex-math id="M1">\begin{document}$ 2 $\end{document}</tex-math></inline-formula>-cocycle <inline-formula><tex-math id="M2">\begin{document}$ \alpha_{\lambda} $\end{document}</tex-math></inline-formula> to twist the dihedral group algebra. Using the ambient space <inline-formula><tex-math id="M3">\begin{document}$ \mathbb{F}^{\alpha_{\lambda}} D_{2n} $\end{document}</tex-math></inline-formula>, we then introduce a key exchange protocol and present an analysis of its security. Moreover, we explore the properties of the resulting twisted algebra <inline-formula><tex-math id="M4">\begin{document}$ \mathbb{F}^{\alpha_{\lambda}}D_{2n} $\end{document}</tex-math></inline-formula>, exploiting them to enhance our key exchange protocol. We also introduce a probabilistic public-key scheme derived from our key-exchange protocol and obtain a key encapsulation mechanism (KEM) by applying a well-known generic transformation to our public-key scheme. Finally, we present a proof-of-concept implementation of the resulting key encapsulation mechanism. </p>
AIEADA 1.0: Efficient high-dimensional variational data assimilation with machine-learned reduced-order models

Abstract. Data assimilation (DA) in the geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical weather prediction, and is a crucial building block that has allowed dramatic improvements in weather forecasting over the past few decades. DA is commonly framed in a variational setting, where one solves an optimization problem within a Bayesian formulation using raw model forecasts as a prior, and observations as likelihood. This leads to a DA objective function that needs to be minimized, where the decision variables are the initial conditions specified to the model. In traditional DA, the forward model is numerically and computationally expensive. Here we replace the forward model with a low-dimensional, data-driven, and differentiable emulator. Consequently, gradients of our DA objective function with respect to the decision variables are obtained rapidly via automatic differentiation. We demonstrate our approach by performing an emulator-assisted DA forecast of geopotential height. Our results indicate that emulator-assisted DA is faster than traditional equation-based DA forecasts by four orders of magnitude, allowing computations to be performed on a workstation rather than a dedicated high-performance computer. In addition, we describe accuracy benefits of emulator-assisted DA when compared to simply using the emulator for forecasting (i.e., without DA). Our overall formulation is denoted AIAEDA (Artificial Intelligence Emulator Assisted Data Assimilation).