Miracle-dz/UniFAR-FTUs huggingface dataset:
Abstracting Fairness: Oracles, Metrics, and Interpretability

It is well understood that classification algorithms, for example, for deciding on loan applications, cannot be evaluated for fairness without taking context into account. We examine what can be learned from a fairness oracle equipped with an underlying understanding of ``true'' fairness. The oracle takes as input a (context, classifier) pair satisfying an arbitrary fairness definition, and accepts or rejects the pair according to whether the classifier satisfies the underlying fairness truth. Our principal conceptual result is an extraction procedure that learns the underlying truth; moreover, the procedure can learn an approximation to this truth given access to a weak form of the oracle. Since every ``truly fair'' classifier induces a coarse metric, in which those receiving the same decision are at distance zero from one another and those receiving different decisions are at distance one, this extraction process provides the basis for ensuring a rough form of metric fairness, also known as individual fairness. Our principal technical result is a higher fidelity extractor under a mild technical constraint on the weak oracle's conception of fairness. Our framework permits the scenario in which many classifiers, with differing outcomes, may all be considered fair. Our results have implications for interpretablity -- a highly desired but poorly defined property of classification systems that endeavors to permit a human arbiter to reject classifiers deemed to be ``unfair'' or illegitimately derived.
Group Based Deep Shared Feature Learning for Fine-grained Image Classification

Fine-grained image classification has emerged as a significant challenge because objects in such images have small inter-class visual differences but with large variations in pose, lighting, and viewpoints, etc. Most existing work focuses on highly customized feature extraction via deep network architectures which have been shown to deliver state of the art performance. Given that images from distinct classes in fine-grained classification share significant features of interest, we present a new deep network architecture that explicitly models shared features and removes their effect to achieve enhanced classification results. Our modeling of shared features is based on a new group based learning wherein existing classes are divided into groups and multiple shared feature patterns are discovered (learned). We call this framework Group based deep Shared Feature Learning (GSFL) and the resulting learned network as GSFL-Net. Specifically, the proposed GSFL-Net develops a specially designed autoencoder which is constrained by a newly proposed Feature Expression Loss to decompose a set of features into their constituent shared and discriminative components. During inference, only the discriminative feature component is used to accomplish the classification task. A key benefit of our specialized autoencoder is that it is versatile and can be combined with state-of-the-art fine-grained feature extraction models and trained together with them to improve their performance directly. Experiments on benchmark datasets show that GSFL-Net can enhance classification accuracy over the state of the art with a more interpretable architecture.
A study on hypertension and its correlates of lifestyle habits among the medical students of Indira Gandhi Institute of Medical Sciences, Patna

1 Assistant Professor, 2 Professor, 3 Additional Professor, 4 Associate Professor Department of General Medicine, Indira Gandhi Institute of Medical Sciences, Patna, Bihar Corresponding Author Bhim Ram Associate Professor, Department of General Medicine, Indira Gandhi Institute of Medical Sciences, Sheikhpura, Patna, Bihar Email: dr.bhimram@yahoo.com Abstract Hypertension is the commonest cardiovascular disorder and one of the leading causes of global burden of diseases. Medical students are significant investment to the society and increasing emergence of hypertension among them is a topic of interest today. With this background a study was conducted to assess the prevalence 0f hypertension and its correlates of lifestyle habits among the M 385 students of IGIMS, Patna. In this cross sectional study total 200 MBBS students were selected (50 students from each year) using stratified random sampling method. Data was obtained using a pre-designed semi structured questionnaire; weight and height was measured and to show association Chi square test, Pearson's correlation test, univariate logistic regression model was used. In this study we found the prevalence of hypertension among study participants was 10%, also there was statistically significant correlation found between systolic blood pressure and age, height, weight and BM of the respondents. Although in our study no significant association was found among hypertension and its correlates of lifestyle habits. Still the prevalence of pre-hypertension was 48.5% among the study participants which is a risk factor for development of hypertension in future, therefore dietary and lifestyle modification programmes directed especially to them is necessary.
Discriminating Accretion States via Rotational Symmetry in Simulated Polarimetric Images of M87

In 2017 April, the Event Horizon Telescope observed the shadow of the supermassive black hole at the core of the elliptical galaxy Messier 87. While the original image was constructed from measurements of the total intensity, full polarimetric data were also collected, and linear polarimetric images are expected in the near future. We propose a modal image decomposition of the linear polarization field into basis functions with varying azimuthal dependence of the electric vector position angle. We apply this decomposition to images of ray traced general relativistic magnetohydrodynamics simulations of the Messier 87 accretion disk. For simulated images that are physically consistent with previous observations, the magnitude of the coefficient associated with rotational symmetry, β2, is a useful discriminator between accretion states. We find that at 20 μas resolution, is greater than 0.2 only for models of disks with horizon-scale magnetic pressures large enough to disrupt steady accretion. We also find that images with a more radially directed electric vector position angle correspond to models with higher black hole spin. Our analysis demonstrates the utility of the proposed decomposition as a diagnostic framework to improve constraints on theoretical models.
Sparse Linear Ensemble Systems and Structural Controllability

The article introduces and solves a structural controllability problem for continuum ensembles of linear time-invariant systems. All the individual linear systems of an ensemble are sparse, governed by the same sparsity pattern. Controllability of an ensemble system is, by convention, the capability of using a common control input to simultaneously steer every individual systems in it. A sparsity pattern is structurally controllable if it admits a controllable linear ensemble system. A main contribution of the article is to provide a graphical condition that is necessary and sufficient for a sparsity pattern to be structurally controllable. Like other structural problems, the property of being structural controllable is monotone. We provide a complete characterization of minimal sparsity patterns as well.
KERNEL ESTIMATION OF SPOT VOLATILITY WITH MICROSTRUCTURE NOISE USING PRE-AVERAGING

We revisit the problem of estimating the spot volatility of an Itô semimartingale using a kernel estimator. A central limit theorem (CLT) with an optimal convergence rate is established for a general two-sided kernel. A new pre-averaging/kernel estimator for spot volatility is also introduced to handle the microstructure noise of ultra high-frequency observations. A CLT for the estimation error of the new estimator is obtained, and the optimal selection of the bandwidth and kernel function is subsequently studied. It is shown that the pre-averaging/kernel estimator’s asymptotic variance is minimal for two-sided exponential kernels, hence justifying the need of working with kernels of unbounded support. Feasible implementation of the proposed estimators with optimal bandwidth is developed as well. Monte Carlo experiments confirm the superior performance of the new method.
Directional Necessary Optimality Conditions for Bilevel Programs

The bilevel program is an optimization problem in which the constraint involves solutions to a parametric optimization problem. It is well known that the value function reformulation provides an equivalent single-level optimization problem, but it results in a nonsmooth optimization problem that never satisfies the usual constraint qualification, such as the Mangasarian–Fromovitz constraint qualification (MFCQ). In this paper, we show that even the first order sufficient condition for metric subregularity (which is, in general, weaker than MFCQ) fails at each feasible point of the bilevel program. We introduce the concept of a directional calmness condition and show that, under the directional calmness condition, the directional necessary optimality condition holds. Although the directional optimality condition is, in general, sharper than the nondirectional one, the directional calmness condition is, in general, weaker than the classical calmness condition and, hence, is more likely to hold. We perform the directional sensitivity analysis of the value function and propose the directional quasi-normality as a sufficient condition for the directional calmness. An example is given to show that the directional quasi-normality condition may hold for the bilevel program.
A Simple Baseline for Multi-Object Tracking

There has been remarkable progress on object detection and re-identification in recent years which are the core components for multi-object tracking. However, little attention has been focused on accomplishing the two tasks in a single network to improve the inference speed. The initial attempts along this path ended up with degraded results mainly because the re-identification branch is not appropriately learned. In this work, we study the essential reasons behind the failure, and accordingly present a simple baseline to addresses the problem. It remarkably outperforms the state-of-the-arts on the public datasets at $30$ fps. We hope this baseline could inspire and help evaluate new ideas in this field. The code and the pre-trained models are available at \url{this https URL}.
Infrared luminosity functions and dust mass functions in the EAGLE simulation

We present infrared luminosity functions and dust mass functions for the EAGLE cosmological simulation, based on synthetic multiwavelength observations generated with the SKIRT radiative transfer code. In the local Universe, we reproduce the observed infrared luminosity and dust mass functions very well. Some minor discrepancies are encountered, mainly in the high luminosity regime, where the EAGLE-SKIRT luminosity functions mildly but systematically underestimate the observed ones. The agreement between the EAGLE-SKIRT infrared luminosity functions and the observed ones gradually worsens with increasing lookback time. Fitting modified Schechter functions to the EAGLE-SKIRT luminosity and dust mass functions at different redshifts up to z = 1, we find that the evolution is compatible with pure luminosity/mass evolution. The evolution is relatively mild: within this redshift range, we find an evolution of L⋆,250 ∝ (1 + z)1.68, L⋆,TIR ∝ (1 + z)2.51 and M⋆,dust ∝ (1 + z)0.83 for the characteristic luminosity/mass. For the luminosity/mass density we find ε250 ∝ (1 + z)1.62, εTIR ∝ (1 + z)2.35, and ρdust ∝ (1 + z)0.80, respectively. The mild evolution of the dust mass density is in relatively good agreement with observations, but the slow evolution of the infrared luminosity underestimates the observed luminosity evolution significantly. We argue that these differences can be attributed to increasing limitations in the radiative transfer treatment due to increasingly poorer resolution, combined with a slower than observed evolution of the SFR density in the EAGLE simulation and the lack of AGN emission in our EAGLE-SKIRT post-processing recipe.
An Analysis of the Utility of Explicit Negative Examples to Improve the Syntactic Abilities of Neural Language Models

We explore the utilities of explicit negative examples in training neural language models. Negative examples here are incorrect words in a sentence, such as barks in *The dogs barks. Neural language models are commonly trained only on positive examples, a set of sentences in the training data, but recent studies suggest that the models trained in this way are not capable of robustly handling complex syntactic constructions, such as long-distance agreement. In this paper, we first demonstrate that appropriately using negative examples about particular constructions (e.g., subject-verb agreement) will boost the model’s robustness on them in English, with a negligible loss of perplexity. The key to our success is an additional margin loss between the log-likelihoods of a correct word and an incorrect word. We then provide a detailed analysis of the trained models. One of our findings is the difficulty of object-relative clauses for RNNs. We find that even with our direct learning signals the models still suffer from resolving agreement across an object-relative clause. Augmentation of training sentences involving the constructions somewhat helps, but the accuracy still does not reach the level of subject-relative clauses. Although not directly cognitively appealing, our method can be a tool to analyze the true architectural limitation of neural models on challenging linguistic constructions.
Analysis and approximation of mixed-dimensional PDEs on 3D-1D domains coupled with Lagrange multipliers

Coupled partial differential equations defined on domains with different dimensionality are usually called mixed dimensional PDEs. We address mixed dimensional PDEs on three-dimensional (3D) and one-dimensional domains, giving rise to a 3D-1D coupled problem. Such problem poses several challenges from the standpoint of existence of solutions and numerical approximation. For the coupling conditions across dimensions, we consider the combination of essential and natural conditions, basically the combination of Dirichlet and Neumann conditions. To ensure a meaningful formulation of such conditions, we use the Lagrange multiplier method, suitably adapted to the mixed dimensional case. The well posedness of the resulting saddle point problem is analyzed. Then, we address the numerical approximation of the problem in the framework of the finite element method. The discretization of the Lagrange multiplier space is the main challenge. Several options are proposed, analyzed and compared, with the purpose to determine a good balance between the mathematical properties of the discrete problem and flexibility of implementation of the numerical scheme. The results are supported by evidence based on numerical experiments.
Gaussian Process Boosting

We introduce a novel way to combine boosting with Gaussian process and mixed effects models. This allows for relaxing, first, the linearity assumption for the mean function in Gaussian process and mixed effects models in a flexible non-parametric way and, second, the independence assumption made in most boosting algorithms. The former is advantageous for predictive accuracy and for avoiding model misspecifications. The latter is important for more efficient learning of the mean function and for obtaining probabilistic predictions. In addition, we present an extension that scales to large data using a Vecchia approximation for the Gaussian process model relying on novel results for covariance parameter inference. We obtain increased predictive accuracy compared to existing approaches on several simulated and real-world data sets.
Retweeting for COVID-19: Consensus building, information sharing, dissent, and lockdown life

Purpose: Public attitudes towards COVID-19 and social distancing are critical in reducing its spread. It is therefore important to understand public reactions, information dissemination and consensus building in all major forms, including social media. This article investigates important issues reflected on Twitter. Design/methodology/approach: A thematic analysis of the most retweeted English-language tweets on Twitter mentioning COVID-19 during March 10-29, 2020. Findings: The main themes identified for the 87 qualifying tweets accounting for 14 million retweets were: lockdown life; attitude towards social restrictions; politics; safety messages; people with COVID-19; support for key workers; work; and COVID-19 facts/news. Research limitations/implications: Twitter played many positive roles, mainly through unofficial tweets. Users shared social distancing information, helped build support for social distancing, criticised government responses, expressed support for key workers, and helped each other to cope with social isolation. A few popular tweets not supporting social distancing show that government messages sometimes failed. Practical implications: Public health campaigns in future may consider encouraging grass roots social web activity to support campaign goals. At a methodological level, analysing retweet counts emphasised politics and ignored practical implementation issues. Originality/value: This is the first qualitative analysis of general COVID-19-related retweeting.
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal

This work considers the sample and computational complexity of obtaining an $\epsilon$-optimal policy in a discounted Markov Decision Process (MDP), given only access to a generative model. In this work, we study the effectiveness of the most natural plug-in approach to model-based planning: we build the maximum likelihood estimate of the transition model in the MDP from observations and then find an optimal policy in this empirical MDP. We ask arguably the most basic and unresolved question in model based planning: is the naive "plug-in" approach, non-asymptotically, minimax optimal in the quality of the policy it finds, given a fixed sample size? Here, the non-asymptotic regime refers to when the sample size is sublinear in the model size. With access to a generative model, we resolve this question in the strongest possible sense: our main result shows that \emph{any} high accuracy solution in the plug-in model constructed with $N$ samples, provides an $\epsilon$-optimal policy in the true underlying MDP (where $\epsilon$ is the minimax accuracy with $N$ samples at every state, action pair). In comparison, all prior (non-asymptotically) minimax optimal results use model free approaches, such as the Variance Reduced Q-value iteration algorithm (Sidford et al 2018), while the best known model-based results (e.g. Azar et al 2013) require larger sample sizes in their dependence on the planning horizon or the state space. Notably, we show that the model-based approach allows the use of \emph{any} efficient planning algorithm in the empirical MDP, which simplifies algorithm design as this approach does not tie the algorithm to the sampling procedure. The core of our analysis is avnovel "absorbing MDP" construction to address the statistical dependency issues that arise in the analysis of model-based planning approaches, a construction which may be helpful more generally.
Modeling and Analysis of Networked Discrete Event Systems with Multiple Control Channels

In this paper, we propose a novel framework for modeling and analysis of networked discrete-event systems (DES). We assume that the plant is controlled by a feedback supervisor whose control decisions are subject to communication delays and losses. Furthermore, we consider a general setting where the supervisor sends control decisions to different actuators via different communication channels whose dynamics are independent. We provide a system theoretic approach by identifying the state-space of overall networked system and investigating the dynamic of the entire state-space. Our approach precisely specifies the roles of the supervisor, the communication channels and the actuators. Also, we compare the proposed networked DES model with the existing one and show that the proposed networked model captures physical situations of networked systems more precisely.
Panchromatic properties of the extreme Fe ii emitter PHL 1092

We present near-infrared spectroscopy of the NLS1 galaxy PHL1092 (z=0.394), the strongest FeII emitter ever reported, combined with optical and UV data. We modeled the continuum and the broad emission lines using a power-law plus a black body function and Lorentzian functions, respectively. The strength of the FeII emission was estimated using the latest FeII templates in the literature. We re-estimate the ratio between the FeII complex centered at 4570Ang and the broad component of H-Beta, R_FeII, obtaining a value of 2.58, nearly half of that previously reported (R_FeII=6.2), but still placing PHL1092 among extreme FeII emitters. The FWHM found for low ionization lines are very similar (FWHM~1200km/s), but significantly narrower than those of the Hydrogen lines (FWHM(H-Beta)~1900km/s). Our results suggest that the FeII emission in PHL1092 follows the same trend as in normal FeII emitters, with FeII being formed in the outer portion of the BLR and co-spatial with CaII, and OI, while H-Beta is formed closer to the central source. The flux ratio between the UV lines suggest high densities, log(n_H)~13.0 cm^{-3}, and a low ionization parameter, log(U)~-3.5. The flux excess found in the FeII bump at 9200Ang after the subtraction of the NIR FeII template and its comparison with optical FeII emission suggests that the above physical conditions optimize the efficiency of the ly-Alpha fluorescence process, which was found to be the main excitation mechanism in the FeII production. We discuss the role of PHL1092 in the Eigenvector 1 context.
ZeroER: Entity Resolution using Zero Labeled Examples

Entity resolution (ER) refers to the problem of matching records in one or more relations that refer to the same real-world entity. While supervised machine learning (ML) approaches achieve the state-of-the-art results, they require a large amount of labeled examples that are expensive to obtain and often times infeasible. We investigate an important problem that vexes practitioners: is it possible to design an effective algorithm for ER that requires Zero labeled examples, yet can achieve performance comparable to supervised approaches? In this paper, we answer in the affirmative through our proposed approach dubbed ZeroER. Our approach is based on a simple observation --- the similarity vectors for matches should look different from that of unmatches. Operationalizing this insight requires a number of technical innovations. First, we propose a simple yet powerful generative model based on Gaussian Mixture Models for learning the match and unmatch distributions. Second, we propose an adaptive regularization technique customized for ER that ameliorates the issue of feature overfitting. Finally, we incorporate the transitivity property into the generative model in a novel way resulting in improved accuracy. On five benchmark ER datasets, we show that ZeroER greatly outperforms existing unsupervised approaches and achieves comparable performance to supervised approaches.