Miracle-dz/UniFAR-FTUs huggingface dataset:
Falsification before Extrapolation in Causal Effect Estimation

Randomized Controlled Trials (RCTs) represent a gold standard when developing policy guidelines. However, RCTs are often narrow, and lack data on broader populations of interest. Causal effects in these populations are often estimated using observational datasets, which may suffer from unobserved confounding and selection bias. Given a set of observational estimates (e.g. from multiple studies), we propose a meta-algorithm that attempts to reject observational estimates that are biased. We do so using validation effects, causal effects that can be inferred from both RCT and observational data. After rejecting estimators that do not pass this test, we generate conservative confidence intervals on the extrapolated causal effects for subgroups not observed in the RCT. Under the assumption that at least one observational estimator is asymptotically normal and consistent for both the validation and extrapolated effects, we provide guarantees on the coverage probability of the intervals output by our algorithm. To facilitate hypothesis testing in settings where causal effect transportation across datasets is necessary, we give conditions under which a doubly-robust estimator of group average treatment effects is asymptotically normal, even when flexible machine learning methods are used for estimation of nuisance parameters. We illustrate the properties of our approach on semi-synthetic and real world datasets, and show that it compares favorably to standard meta-analysis techniques.
SoftTreeMax: Policy Gradient with Tree Search

Policy-gradient methods are widely used for learning control policies. They can be easily distributed to multiple workers and reach state-of-the-art results in many domains. Unfortunately, they exhibit large variance and subsequently suffer from high-sample complexity since they aggregate gradients over entire trajectories. At the other extreme, planning methods, like tree search, optimize the policy using single-step transitions that consider future lookahead. These approaches have been mainly considered for value-based algorithms. Planning-based algorithms require a forward model and are computationally intensive at each step, but are more sample efficient. In this work, we introduce SoftTreeMax, the first approach that integrates tree-search into policy gradient. Traditionally, gradients are computed for single state-action pairs. Instead, our tree-based policy structure leverages all gradients at the tree leaves in each environment step. This allows us to reduce the variance of gradients by three orders of magnitude and to benefit from better sample complexity compared with standard policy gradient. On Atari, SoftTreeMax demonstrates up to 5x better performance in faster run-time compared with distributed PPO.
ON THE CURVATURE OF DUST LANES IN GALACTIC BARS

We test the theoretical prediction that the straightest dust lanes in bars are found in strongly barred galaxies, or more specifically, that the degree of curvature of the dust lanes is inversely proportional to the strength of the bar. The test uses archival images of barred galaxies for which a reliable nonaxisymmetric torque parameter (Qb) and the radius at which Qb has been measured (r(Qb)) have been published in the literature. Our results confirm the theoretical prediction but show a large spread that cannot be accounted for by measurement errors. We simulate 238 galaxies with different bar and bulge parameters in order to investigate the origin of the spread in the dust lane curvature versus Qb relation. From these simulations, we conclude that the spread is greatly reduced when describing the bar strength as a linear combination of the bar parameters Qb and the quotient of the major and minor axes of the bar, a/b. Thus, we conclude that the dust lane curvature is predominantly determined by the parameters of the bar.
Set-Valued Shadow Matching Using Zonotopes for 3-D Map-Aided GNSS Localization

Unlike many urban localization methods that return point-valued estimates, a set-valued representation enables robustness by ensuring that a continuum of possible positions obeys safety constraints. One strategy with the potential for set-valued estimation is GNSS-based shadow matching~(SM), where one uses a three-dimensional (3-D) map to compute GNSS shadows (where line-of-sight is blocked). However, SM requires a point-valued grid for computational tractability, with accuracy limited by grid resolution. We propose zonotope shadow matching (ZSM) for set-valued 3-D map-aided GNSS localization. ZSM represents buildings and GNSS shadows using constrained zonotopes, a convex polytope representation that enables propagating set-valued estimates using fast vector concatenation operations. Starting from a coarse set-valued position, ZSM refines the estimate depending on the receiver being inside or outside each shadow as judged by received carrier-to-noise density. We demonstrated our algorithm's performance using simulated experiments on a simple 3-D example map and on a dense 3-D map of San Francisco.
How to solve a classification problem using a cooperative tiling Multi-Agent System?

Adaptive Multi-Agent Systems (AMAS) transform dynamic problems into problems of local cooperation between agents. We present smapy, an ensemble based AMAS implementation for mobility prediction, whose agents are provided with machine learning models in addition to their cooperation rules. With a detailed methodology, we propose a framework to transform a classification problem into a cooperative tiling of the input variable space. We show that it is possible to use linear classifiers for online non-linear classification on three benchmark toy problems chosen for their different levels of linear separability, if they are integrated in a cooperative Multi-Agent structure. The results obtained show a significant improvement of the performance of linear classifiers in non-linear contexts in terms of classification accuracy and decision boundaries, thanks to the cooperative approach.
From Understanding the Population Dynamics of the NSGA-II to the First Proven Lower Bounds

Due to the more complicated population dynamics of the NSGA-II, none of the existing runtime guarantees for this algorithm is accompanied by a non-trivial lower bound. Via a first mathematical understanding of the population dynamics of the NSGA-II, that is, by estimating the expected number of individuals having a certain objective value, we prove that the NSGA-II with suitable population size needs Omega(Nn log n) function evaluations to find the Pareto front of the OneMinMax problem and Omega(Nn^k) evaluations on the OneJumpZeroJump problem with jump size k. These bounds are asymptotically tight (that is, they match previously shown upper bounds) and show that the NSGA-II here does not even in terms of the parallel runtime (number of iterations) profit from larger population sizes. For the OneJumpZeroJump problem and when the same sorting is used for the computation of the crowding distance contributions of the two objectives, we even obtain a runtime estimate that is tight including the leading constant.
Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model

Network analysis has been a powerful tool to unveil relationships and interactions among a large number of objects. Yet its effectiveness in accurately identifying important node-node interactions is challenged by the rapidly growing network size, with data being collected at an unprecedented granularity and scale. Common wisdom to overcome such high dimensionality is collapsing nodes into smaller groups and conducting connectivity analysis on the group level. Dividing efforts into two phases inevitably opens a gap in consistency and drives down efficiency. Consensus learning emerges as a new normal for common knowledge discovery with multiple data sources available. To this end, this paper features developing a unified framework of simultaneous grouping and connectivity analysis by combining multiple data sources. The algorithm also guarantees a statistically optimal estimator.
Personalization of Web Search During the 2020 US Elections

Search engines play a central role in routing political information to citizens. The algorithmic personalization of search results by large search engines like Google implies that different users may be offered systematically different information. However, measuring the causal effect of user characteristics and behavior on search results in a politically relevant context is challenging. We set up a population of 150 synthetic internet users ("bots") who are randomly located across 25 US cities and are active for several months during the 2020 US Elections and their aftermath. These users differ in their browsing preferences and political ideology, and they build up realistic browsing and search histories. We run daily experiments in which all users enter the same election-related queries. Search results to these queries differ substantially across users. Google prioritizes previously visited websites and local news sites. Yet, it does not generally prioritize websites featuring the user's ideology.
Longitudinal variability analysis on low-dose abdominal CT with deep learning-based segmentation

Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitatively characterizing these relationships. 2D low dose single slice computed tomography (CT) provides a high resolution, quantitative tissue map, albeit with a limited field of view. Although numerous potential analyses have been proposed in quantifying image context, there has been no comprehensive study for low-dose single slice CT longitudinal variability with automated segmentation. We studied a total of 1816 slices from 1469 subjects of Baltimore Longitudinal Study on Aging (BLSA) abdominal dataset using supervised deep learning-based segmentation and unsupervised clustering method. 300 out of 1469 subjects that have two year gap in their first two scans were pick out to evaluate longitudinal variability with measurements including intraclass correlation coefficient (ICC) and coefficient of variation (CV) in terms of tissues/organs size and mean intensity. We showed that our segmentation methods are stable in longitudinal settings with Dice ranged from 0.821 to 0.962 for thirteen target abdominal tissues structures. We observed high variability in most organ with ICC<0.5, low variability in the area of muscle, abdominal wall, fat and body mask with average ICC≥0.8. We found that the variability in organ is highly related to the cross-sectional position of the 2D slice. Our efforts pave quantitative exploration and quality control to reduce uncertainties in longitudinal analysis.
Popularity Driven Data Integration

More and more, with the growing focus on large scale ana-lytics, we are confronted with the need of integrating data from multiple sources. The problem is that these data are impossible to reuse as-is . The net result is high cost, with the further drawback that the resulting integrated data will again be hardly reusable as-is . iTelos 3 is a general purpose methodology aiming at minimizing the eﬀects of this process. The intuition is that data will be treated diﬀerently based on their popularity : the more a certain set of data have been reused, the more they will be reused and the less they will be changed across reuses, thus decreasing the overall data preprocessing costs, while increasing backward compatibility and future sharing.
THE GAMMA-RAY BURST CATALOG OBTAINED WITH THE GAMMA-RAY BURST MONITOR ABOARD BeppoSAX

We report on the catalog of gamma-ray bursts (GRBs) detected with the Gamma-Ray Burst Monitor aboard the BeppoSAX satellite. It includes 1082 GRBs with 40–700 keV fluences in the range from 1.3 × 10−7 to 4.5 × 10−4 erg cm−2, and 40–700 keV peak fluxes from 3.7 × 10−8 to 7.0 × 10−5 erg cm−2 s−1. We report in the catalog some relevant parameters of each GRB and discuss the derived statistical properties.
Safe Balancing Control of a Soft Legged Robot

Legged robots constructed from soft materials are commonly claimed to demonstrate safer, more robust environmental interactions than their rigid counterparts. However, this motivating feature of soft robots requires more rigorous development for comparison to rigid locomotion. This article presents a soft legged robot platform, Horton, and a feedback control system with safety guarantees on some aspects of its operation. The robot is constructed using a series of soft limbs, actuated by thermal shape memory alloy (SMA) wire muscles, with sensors for its position and its actuator temperatures. A supervisory control scheme maintains safe actuator states during the operation of a separate controller for the robot's pose. Experiments demonstrate that Horton can lift its leg and maintain a balancing stance, a precursor to locomotion. The supervisor is verified in hardware via a human interaction test during balancing, keeping all SMA muscles below a temperature threshold. This work represents the first demonstration of a safety-verified feedback system on any soft legged robot.
X-RAY STUDIES OF THE BLACK WIDOW PULSAR PSR B1957+20

We report on Chandra observations of the black widow pulsar, PSR B1957+20. Evidence for a binary-phase dependence of the X-ray emission from the pulsar is found with a deep observation. The binary-phase-resolved spectral analysis reveals non-thermal X-ray emission of PSR B1957+20, confirming the results of previous studies. This suggests that the X-rays are mostly due to intra-binary shock emission, which is strongest when the pulsar wind interacts with the ablated material from the companion star. The geometry of the peak emission is determined in our study. The marginal softening of the spectrum of the non-thermal X-ray tail may indicate that particles injected at the termination shock are dominated by synchrotron cooling.
Mining approximate frequent patterns from graph databases

Graph analytics is the process of discovering patterns and insights from data that can be modeled as graphs. Algorithms for graph analytics fall into two broad categories : Mining and Management. Graph mining algorithms are often used in graph management and vice versa. In recent times, these algorithms have become an indispensable tool for analyzing networks in domains such as i) Computational biology, ii) Infrastructure and mobile sectors, iii) Cybersecurity. Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to compute some cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns. In this thesis, we present methods for mining approximate frequent patterns from a single large graph. The approximate pattern mining algorithms that we consider in this thesis cover two main categories: Firstly, instead of computing the exact support of a pattern we approximate the support by an upper-bound. Secondly, we tolerate bounded label and structural mismatches when finding matches of a pattern in the database. A common theme across both these paradigms is that the problems are NP-Hard to even approximate upto a constant factor of the optimal value. We formally prove the exponential complexity of all these problems and also present approximation algorithms for solving the problems at scale. Approximate Support: The problem of mining frequent subgraph patterns from a database of graphs is a well studied problem in the literature. However, the methods proposed for the multiple input graphs scenario cannot be directly extended for mining patterns from a single large graph. This is due to the fact that the support function, is defined in terms of all the embeddings of a pattern, which can be exponential. We propose a network-flow based approach that gives a polynomial time approximation for the support function. We also propose a three step procedure to summarize the output of graph mining algorithms.
ViPER: Video-based Perceiver for Emotion Recognition

Recognizing human emotions from videos requires a deep understanding of the underlying multimodal sources, including images, audio, and text. Since the input data sources are highly variable across different modality combinations, leveraging multiple modalities often requires ad hoc fusion networks. To predict the emotional arousal of a person reacting to a given video clip we present ViPER, a multimodal architecture leveraging a modality-agnostic transformer based model to combine video frames, audio recordings, and textual annotations. Specifically, it relies on a modality-agnostic late fusion network which makes ViPER easily adaptable to different modalities. The experiments carried out on the Hume-Reaction datasets of the MuSe-Reaction challenge confirm the effectiveness of the proposed approach.
FLUID-GRAVITY CORRESPONDENCE UNDER THE PRESENCE OF VISCOSITY

This work addresses the analogy between the speed of sound of a viscous, barotropic, and irrotational fluid and the equation of motion for a non-massive field in a curved manifold. It will be shown that the presence of viscosity implies the introduction, into the equation of motion of the gravitational analogue, of a source term which entails the flow of energy from the non-massive field to the curvature of the spacetime manifold. The stress–energy tensor is also computed and it is found not to be constant, which is consistent with such energy interchange.
Quantum Go

We introduce a new board game based on the ancient Chinese game of Go (Weiqi, Igo, Baduk). The key difference from the original game is that players no longer alternatively play single stones on the board but instead they take turns placing pairs of entangled go stones. A phenomenon of quantum-like collapse occurs when one of the stones in an entangled pair is directly in contact with at least one other stone. The player to whom the entangled pair belongs must then remove one of the stones in the pair from the board. The aim of the game is still to surround more territory than the opponent and as the number of stones increases, all the entangled pairs of stones eventually reduce to single stones. Quantum Go provides an interesting and tangible illustration of quantum concepts such as superposition, entanglement and collapse.
Towards a Calibrated 360° Stereoscopic HDR Image Dataset for Architectural Lighting Studies

High fidelity 360 degree images enhance user experience and offer realistic representations for architectural design studies. Specifically, VR and hyper-realistic imaging technologies can be helpful tools to study daylight in architectural places thanks to the high level of immersion and its ability to create perceptually accurate and faithful scene representations. In this paper, we present a novel method for collecting and processing physically calibrated 360 degree stereoscopic high-dynamic range images of daylit indoor places. The future dataset aims to provide a higher degree of realism, a wide range of various luminous interior spaces supplied with information on the physical characterization of the space. This paper presents the first applications of this method on different places and discusses challenges for assessing visual perception in these images. In the near future, this dataset will be publicly available for architectural as well as multimedia studies.
Intellectual Capital Management in Industry: Focusing on Superprofit

Over the past decades, the intellectual resource has become a key element in the strategic development of enterprises and sectors of the national economy. Nevertheless, there is still no universal apparatus for managing the processes connected with intellectual capital, which determines the relevance of the search for new methods and approaches for managing intellectual capital (IC). In an industrial environment IC makes the main contribution to the intensive development of production that can bring super-profits. The purpose of this paper is to develop an algorithm for assessing the effectiveness of IC management in industrial enterprises, taking into account the need to get a super profit. To achieve this goal, the following tasks were fulfilled: an approach was proposed to compare the indicators of value added intellectual capital (VAIC) and economic value added (EVA) with the average industry values; the rating of enterprises from the standpoint of IC management efficiency (highlighting outsiders and leaders) was substantiated; the toolkit to determine the relationship between super profits in the form of rental income and controlled parameters of intellectualization was developed. Thus, at the enterprise level, it becomes possible to change individual parameters to obtain additional economic effects. The authors note that since the resulting indicator is super-profit, the development of practical recommendations for the rationalization of IC management will determine the potential for increasing its intellectual components (intellectual rent). The materials of the research can be used in the development of economic and mathematical models for increasing the efficiency of IC, taking into account the specifics of industrial production. The proposed approach will make it possible to develop measures to intensify intellectual processes in production, which is a determinant of innovative economic growth.
Digitalization and Knowledge-Based Economy of Smart Cities: Development Scenarios

This study aims to assess potential changes in the existing approaches to urban development within the national scale. The paper focuses on the development of such an approach that would encourage digital transformation in cities with the maximum number of benefits for their residents. Cluster analysis of the Russian regions allowed articulating various development scenarios for each cluster in terms of digitalization and improvements in the knowledge-based economy. Apart from changing the urban environment as it is, information technologies alter the very notion of how the regions can be integrated into the social and economic space. This fact makes even more sense regarding the emergence of digital inequality and disproportions in the development and distribution of new knowledge. Thereby, decent living standards in the regions end up undermined. Global changes of early 2020 will certainly lead to transformations in consumer behavior and thinking behind business processes, which will change the needs for the urban environment to meet. The core requirements include infrastructure security, state regulation, social security, localization of production and consumption, as well as changes in the consumption patterns. It should be noted that improvements in the public policy of the Russian Federation can be implemented only when guided by the identified scenarios and factors influencing urban transformation. In this regard, all the incorporated digital solutions are expected to enhance comfort, which does not necessarily mean an overwhelming set of tools, but high quality and an idea behind their use. The introduction of digital technologies is no longer a matter that can be neglected or prioritized by the authorities. It has been immersed in the system of public administration to such an extent that there is now no other way but to use its effectiveness as a competitive advantage of the region. At the regional level, the competition for investors is so harsh at the moment that the ability to embrace the full potential of digitalization turns out to be the key to success, determining the speed for an investor to access the required infrastructure and start their commercial activities