Miracle-dz/UniFAR-FTUs huggingface dataset:
Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations

This paper systematically derives design dimensions for the structured evaluation of explainable artificial intelligence (XAI) approaches. These dimensions enable a descriptive characterization, facilitating comparisons between different study designs. They further structure the design space of XAI, converging towards a precise terminology required for a rigorous study of XAI. Our literature review differentiates between comparative studies and application papers, revealing methodological differences between the fields of machine learning, human-computer interaction, and visual analytics. Generally, each of these disciplines targets specific parts of the XAI process. Bridging the resulting gaps enables a holistic evaluation of XAI in real-world scenarios, as proposed by our conceptual model characterizing bias sources and trust-building. Furthermore, we identify and discuss the potential for future work based on observed research gaps that should lead to better coverage of the proposed model.
Projections for COVID-19 spread in India and its worst affected five states using the Modified SEIRD and LSTM models

The last leg of the year 2019 gave rise to a virus named COVID-19 (Corona Virus Disease 2019). Since the beginning of this infection in India, the government implemented several policies and restrictions to curtail its spread among the population. As the time passed, these restrictions were relaxed and people were advised to follow precautionary measures by themselves. These timely decisions taken by the Indian government helped in decelerating the spread of COVID-19 to a large extent. Despite these decisions, the pandemic continues to spread and hence, there is an urgent need to plan and control the spread of this disease. This is possible by finding the future predictions about the spread. Scientists across the globe are working towards estimating the future growth of COVID-19. This paper proposes a Modified SEIRD (Susceptible-Exposed-Infected-Recovered-Deceased) model for projecting COVID-19 infections in India and its five states having the highest number of total cases. In this model, exposed compartment contains individuals which may be asymptomatic but infectious. Deep Learning based Long Short-Term Memory (LSTM) model has also been used in this paper to perform short-term projections. The projections obtained from the proposed Modified SEIRD model have also been compared with the projections made by LSTM for next 30 days. The epidemiological data up to 15th August 2020 has been used for carrying out predictions in this paper. These predictions will help in arranging adequate medical infrastructure and providing proper preventive measures to handle the current pandemic. The effect of different lockdowns imposed by the Indian government has also been used in modelling and analysis in the proposed Modified SEIRD model. The results presented in this paper will act as a beacon for future policy-making to control the COVID-19 spread in India.
Shear-driven Transition to Isotropically Turbulent Solar Wind Outside the Alfvén Critical Zone

Motivated by prior remote observations of a transition from striated solar coronal structures to more isotropic “flocculated” fluctuations, we propose that the dynamics of the inner solar wind just outside the Alfvén critical zone, and in the vicinity of the first surface, is powered by the relative velocities of adjacent coronal magnetic flux tubes. We suggest that large-amplitude flow contrasts are magnetically constrained at lower altitude but shear-driven dynamics are triggered as such constraints are released above the Alfvén critical zone, as suggested by global magnetohydrodynamic (MHD) simulations that include self-consistent turbulence transport. We argue that this dynamical evolution accounts for features observed by Parker Solar Probe (PSP) near initial perihelia, including magnetic “switchbacks,” and large transverse velocities that are partially corotational and saturate near the local Alfvén speed. Large-scale magnetic increments are more longitudinal than latitudinal, a state unlikely to originate in or below the lower corona. We attribute this to preferentially longitudinal velocity shear from varying degrees of corotation. Supporting evidence includes comparison with a high Mach number three-dimensional compressible MHD simulation of nonlinear shear-driven turbulence, reproducing several observed diagnostics, including characteristic distributions of fluctuations that are qualitatively similar to PSP observations near the first perihelion. The concurrence of evidence from remote sensing observations, in situ measurements, and both global and local simulations supports the idea that the dynamics just above the Alfvén critical zone boost low-frequency plasma turbulence to the level routinely observed throughout the explored solar system.
Enhanced absorption per unit mass for infrared arrays using subwavelength metal–dielectric structures

The absorption to mass ratio of the infrared arrays is enhanced to approximately 1.33 to 7.33 times larger than the previously reported structures by incorporating two design characteristics: first, the coupling of evanescent fields in the air gaps around pixels to create effectively larger pixel sizes, and, second, the use of guided mode resonance (GMR) within the subwavelength metal-dielectric gratings. The bilayer Ti-Si3N4 gratings achieve broadband long-wave infrared (LWIR, 8 to 12 um) absorption by the combined effects of free carrier absorption by the thin Ti films and vibrational phonon absorption by the thick Si3N4 films. In the presence of GMR, this broadband absorption can be enormously enhanced even with low fill factor subwavelength grating cells. Further, the spacing and design of the cells can be modified to form a pixel array structure that couples the light falling in the air gaps via evanescent field coupling. Calculations are performed using the finite difference time domain (FDTD) technique. Excellent broadband absorption is observed for the optimized arrays, yielding maximum absorption of 90 percent across the LWIR and an average absorption-per-unit-mass (absorption/mass) per pixel of 3.45$\times$10^{13} kg^{-1}.
Star formation in CALIFA survey perturbed galaxies – I. Effects of tidal interactions

We explore the effects of tidal interactions on star formation (SF) by analysing a sample of CALIFA survey galaxies. The sample consists of tidally and non-tidally perturbed galaxies whose star-forming regions are paired at the closest stellar mass surface densities, even between the same galaxy types. The regions are then compared, both on the resolved star-formation main sequence (SFMS) plane and in annular property profiles. Star-forming regions in tidally perturbed galaxies exhibit flatter SFMS slopes compared to star-forming regions in non-tidally perturbed galaxies. Despite the fact that the annular profiles show star-forming regions in tidally perturbed galaxies as being mostly older, their SF properties are never reduced against those of non-tidally perturbed galaxies. Star-forming regions in non-tidally perturbed galaxies are better candidates for SF suppression (quenching). The lowered SF with increasing stellar mass density in tidally perturbed galaxies may suggest a lower dependence of SF on stellar mass. Though the SFMS slopes, either flatter or steeper, are found independent of stellar mass density, the effect of global stellar mass cannot be ignored when distinguishing between galaxy types. Since a phenomenon or property other than local/global stellar mass may be taking part in the modulation of SF, the integrated SF properties are related to the tidal perturbation parameter. We find weak, but detectable, positive correlations for perturbed galaxies suggesting that tidal perturbations induced by close companions increase the gas accretion rates of these objects.
Making Simulated Annealing Sample Efficient for Discrete Stochastic Optimization

We study the regret of simulated annealing (SA) based approaches to solving discrete stochastic optimization problems. The main theoretical conclusion is that the regret of the simulated annealing algorithm, with either noisy or noiseless observations, depends primarily upon the rate of the convergence of the associated Gibbs measure to the optimal states. In contrast to previous works, we show that SA does not need an increased estimation effort (number of \textit{pulls/samples} of the selected \textit{arm/solution} per round for a finite horizon $n$) with noisy observations to converge in probability. By simple modifications, we can make the total number of samples per iteration required for convergence (in probability) to scale as $\mathcal{O}\big(n)$. Additionally, we show that a simulated annealing inspired heuristic can solve the problem of stochastic multi-armed bandits (MAB), by which we mean that it suffers a $\mathcal{O}(\log \,n)$ regret. Thus, our contention is that SA should be considered as a viable candidate for inclusion into the family of efficient exploration heuristics for bandit and discrete stochastic optimization problems.
Strong duality and boundedness in conic optimization

Unlike linear programming, it is well-known that some conditions are required to achieve strong duality between a primal-dual pair of conic programs. The most common and well-known of these conditions is full-dimensionality of the cones and strict feasibility of one of the problems, also referred to as the Slater constraint qualification (CQ). Other sufficient conditions in literature for strong duality include a closedness condition for the full-dimensional case and a minimal facial property for general cones. We show that the closedness condition is also sufficient for strong duality when the cones are low-dimensional. The key step is to establish upper bounds on the duality gap through the separation of certain proximal points from the closure of an adjoint image of the cones. A consequence is a collection of specific sufficient conditions for strong duality, one of them being the generalized Slater CQ for low-dimensional cones, a few others being in terms of strict feasibility of the recession cones, and another being boundedness of the feasible region as a universal CQ. We also give various algebraic characterizations of the recession cone and its polar, thereby leading to many necessary and sufficient conditions for a bounded feasible region and also a theorem of the alternative in terms of approximate feasibility of the problem. Finally, we establish that under the generalized Slater CQ, finiteness of one problem and feasibility of the other problem are equivalent. This not only implies sufficiency of the Slater CQ for strong duality but it also allows us to characterize the projection of a conic set onto a linear subspace using extreme rays of a closed convex cone that generalizes the projection cone for polyhedral sets.
Can Fine-tuning Pre-trained Models Lead to Perfect NLP? A Study of the Generalizability of Relation Extraction

Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks. However, the resultant model generalizability remains poorly understood. We do not know, for example, how excellent performance can lead to the perfection of generalization models. In this study, we analyze a fine-tuned BERT model from different perspectives using relation extraction. We also characterize the differences in generalization techniques according to our proposed improvements. From empirical experimentation, we find that BERT suffers a bottleneck in terms of robustness by way of randomizations, adversarial and counterfactual tests, and biases (i.e., selection and semantic). These findings highlight opportunities for future improvements. Our open-sourced testbed DiagnoseRE is available in this https URL.
ALMA High-frequency Long-baseline Campaign in 2017: A Comparison of the Band-to-band and In-band Phase Calibration Techniques and Phase-calibrator Separation Angles

The Atacama Large Millimeter/submillimeter Array (ALMA) obtains spatial resolutions of 15 to 5 mas at 275–950 GHz (0.87–0.32 mm) with 16 km baselines. Calibration at higher frequencies is challenging as ALMA sensitivity and quasar density decrease. The band-to-band (B2B) technique observes a detectable quasar at a lower frequency that is closer to the target, compared to one at the target high frequency. Calibration involves a nearly constant instrumental phase offset between the frequencies and the conversion of the temporal phases to the target frequency. The instrumental offsets are solved with a differential gain calibration (DGC) sequence, consisting of alternating low- and high-frequency scans of strong quasars. Here we compare B2B and in-band phase referencing for high frequencies (>289 GHz) using 2–15 km baselines and calibrator separation angles between ∼0. °68 and ∼11. °65. The analysis shows the following: (1) DGC for B2B produces a coherence loss <7% for DGC phase rms residuals <30°. (2) B2B images using close calibrators (<1.°67) are superior to in-band images using distant ones (>2.°42). (3) For more distant calibrators, B2B is preferred if it provides a calibrator ∼2° closer than the best in-band calibrator. (4) Decreasing image coherence and poorer image quality occur with increasing phase-calibrator separation angle because of uncertainties in the antenna positions and suboptimal phase referencing. (5) To achieve >70% coherence for long-baseline (16 km) band 7 (289 GHz) observations, calibrators should be within ∼4° of the target.
Implicit Graph Neural Networks

Graph Neural Networks (GNNs) are widely used deep learning models that learn meaningful representations from graph-structured data. Due to the finite nature of the underlying recurrent structure, current GNN methods may struggle to capture long-range dependencies in underlying graphs. To overcome this difficulty, we propose a graph learning framework, called Implicit Graph Neural Networks (IGNN), where predictions are based on the solution of a fixed-point equilibrium equation involving implicitly defined "state" vectors. We use the Perron-Frobenius theory to derive sufficient conditions that ensure well-posedness of the framework. Leveraging implicit differentiation, we derive a tractable projected gradient descent method to train the framework. Experiments on a comprehensive range of tasks show that IGNNs consistently capture long-range dependencies and outperform the state-of-the-art GNN models.
Optimal testing of discrete distributions with high probability

We study the problem of testing discrete distributions with a focus on the high probability regime. Specifically, given samples from one or more discrete distributions, a property P, and parameters 0< є, δ <1, we want to distinguish with probability at least 1−δ whether these distributions satisfy P or are є-far from P in total variation distance. Most prior work in distribution testing studied the constant confidence case (corresponding to δ = Ω(1)), and provided sample-optimal testers for a range of properties. While one can always boost the confidence probability of any such tester by black-box amplification, this generic boosting method typically leads to sub-optimal sample bounds. Here we study the following broad question: For a given property P, can we characterize the sample complexity of testing P as a function of all relevant problem parameters, including the error probability δ? Prior to this work, uniformity testing was the only statistical task whose sample complexity had been characterized in this setting. As our main results, we provide the first algorithms for closeness and independence testing that are sample-optimal, within constant factors, as a function of all relevant parameters. We also show matching information-theoretic lower bounds on the sample complexity of these problems. Our techniques naturally extend to give optimal testers for related problems. To illustrate the generality of our methods, we give optimal algorithms for testing collections of distributions and testing closeness with unequal sized samples.
A Tutorial of Ultra-Reliable and Low-Latency Communications in 6G: Integrating Theoretical Knowledge into Deep Learning

As one of the key communication scenarios in the 5th and also the 6th generation (6G) cellular networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications. The state-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLC. A holistic framework that takes into account latency, reliability, availability, scalability, and decision-making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how to integrate theoretical knowledge (models, analysis tools, and optimization frameworks) of wireless communications into different kinds of deep learning algorithms for URLLC. We first introduce the background of URLLC and review promising network architectures and deep learning frameworks in 6G. To better illustrate how to improve learning algorithms with theoretical knowledge, we revisit model-based analysis tools and cross-layer optimization frameworks for URLLC. Following that, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLC and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.
Nonparametric causal mediation analysis for stochastic interventional (in)direct effects.

Causal mediation analysis has historically been limited in two important ways: (i) a focus has traditionally been placed on binary exposures and static interventions and (ii) direct and indirect effect decompositions have been pursued that are only identifiable in the absence of intermediate confounders affected by exposure. We present a theoretical study of an (in)direct effect decomposition of the population intervention effect, defined by stochastic interventions jointly applied to the exposure and mediators. In contrast to existing proposals, our causal effects can be evaluated regardless of whether an exposure is categorical or continuous and remain well-defined even in the presence of intermediate confounders affected by exposure. Our (in)direct effects are identifiable without a restrictive assumption on cross-world counterfactual independencies, allowing for substantive conclusions drawn from them to be validated in randomized controlled trials. Beyond the novel effects introduced, we provide a careful study of nonparametric efficiency theory relevant for the construction of flexible, multiply robust estimators of our (in)direct effects, while avoiding undue restrictions induced by assuming parametric models of nuisance parameter functionals. To complement our nonparametric estimation strategy, we introduce inferential techniques for constructing confidence intervals and hypothesis tests, and discuss open-source software, the $\texttt{medshift}$ $\texttt{R}$ package, implementing the proposed methodology. Application of our (in)direct effects and their nonparametric estimators is illustrated using data from a comparative effectiveness trial examining the direct and indirect effects of pharmacological therapeutics on relapse to opioid use disorder.
On Numerical Approximations of the Koopman Operator

We study numerical approaches to computation of spectral properties of composition operators. We provide a characterization of Koopman Modes in Banach spaces using Generalized Laplace Analysis. We cast the Dynamic Mode Decomposition-type methods in the context of Finite Section theory of infinite dimensional operators, and provide an example of a mixing map for which the finite section method fails. Under assumptions on the underlying dynamics, we provide the first result on the convergence rate under sample size increase in the finite-section approximation. We study the error in the Krylov subspace version of the finite section method and prove convergence in pseudospectral sense for operators with pure point spectrum. Since Krylov sequence-based approximations can mitigate the curse of dimensionality, this result indicates that they may also have low spectral error without an exponential-in-dimension increase in the number of functions needed.
Zero-shot Synthesis with Group-Supervised Learning

Visual cognition of primates is superior to that of artificial neural networks in its ability to 'envision' a visual object, even a newly-introduced one, in different attributes including pose, position, color, texture, etc. To aid neural networks to envision objects with different attributes, we propose a family of objective functions, expressed on groups of examples, as a novel learning framework that we term Group-Supervised Learning (GSL). GSL decomposes inputs into a disentangled representation with swappable components that can be recombined to synthesize new samples, trained through similarity mining within groups of exemplars. For instance, images of red boats & blue cars can be decomposed and recombined to synthesize novel images of red cars. We describe a general class of datasets admissible by GSL. We propose an implementation based on auto-encoder, termed group-supervised zero-shot synthesis network (GZS-Net) trained with our learning framework, that can produce a high-quality red car even if no such example is witnessed during training. We test our model and learning framework on existing benchmarks, in addition to new dataset that we open-source. We qualitatively and quantitatively demonstrate that GZS-Net trained with GSL outperforms state-of-the-art methods
Foodbot: A Goal-Oriented Just-in-Time Healthy Eating Interventions Chatbot

Recent research has identified a few design flaws in popular mobile health (mHealth) applications for promoting healthy eating lifestyle, such as mobile food journals. These include tediousness of manual food logging, inadequate food database coverage, and a lack of healthy dietary goal setting. To address these issues, we present Foodbot, a chatbot-based mHealth application for goal-oriented just-in-time (JIT) healthy eating interventions. Powered by a large-scale food knowledge graph, Foodbot utilizes automatic speech recognition and mobile messaging interface to record food intake. Moreover, Foodbot allows users to set goals and guides their behavior toward the goals via JIT notification prompts, interactive dialogues, and personalized recommendation. Altogether, the Foodbot framework demonstrates the use of open-source data, tools, and platforms to build a practical mHealth solution for supporting healthy eating lifestyle in the general population.