Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning

Deep reinforcement learning (RL) agents are becoming increasingly proficient in a range of complex control tasks. However, the agent’s behavior is usually difficult to interpret due to the introduction of black-box function, making it difficult to acquire the trust of users. Although there have been some interesting interpretation methods for vision-based RL, most of them cannot uncover temporal causal information, raising questions about their reliability. To address this problem, we present a temporal-spatial causal interpretation (TSCI) model to understand the agent’s long-term behavior, which is essential for sequential decision-making. TSCI model builds on the formulation of temporal causality, which reflects the temporal causal relations between sequential observations and decisions of RL agent. Then a separate causal discovery network is employed to identify temporal-spatial causal features, which are constrained to satisfy the temporal causality. TSCI model is applicable to recurrent agents and can be used to discover causal features with high efficiency once trained. The empirical results show that TSCI model can produce high-resolution and sharp attention masks to highlight task-relevant temporal-spatial information that constitutes most evidence about how vision-based RL agents make sequential decisions. In addition, we further demonstrate that our method is able to provide valuable causal interpretations for vision-based RL agents from the temporal perspective.
JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering

Existing KG-augmented models for commonsense question answering primarily focus on designing elaborate Graph Neural Networks (GNNs) to model knowledge graphs (KGs). However, they ignore (i) the effectively fusing and reasoning over question context representations and the KG representations, and (ii) automatically selecting relevant nodes from the noisy KGs during reasoning. In this paper, we propose a novel model, JointLK, which solves the above limitations through the joint reasoning of LM and GNN and the dynamic KGs pruning mechanism. Specifically, JointLK performs joint reasoning between LM and GNN through a novel dense bidirectional attention module, in which each question token attends on KG nodes and each KG node attends on question tokens, and the two modal representations fuse and update mutually by multi-step interactions. Then, the dynamic pruning module uses the attention weights generated by joint reasoning to prune irrelevant KG nodes recursively. We evaluate JointLK on the CommonsenseQA and OpenBookQA datasets, and demonstrate its improvements to the existing LM and LM+KG models, as well as its capability to perform interpretable reasoning.
Under the Sand: Navigation and Localization of a Micro Aerial Vehicle for Landmine Detection with Ground-Penetrating Synthetic Aperture Radar

Ground-penetrating radar mounted on a micro aerial vehicle (MAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar images depends on accurate and precise motion estimation of the radar antennas as well as generating informative viewpoints with the MAV. This paper presents a complete and automatic airborne ground-penetrating synthetic aperture radar (GPSAR) system. The system consists of a spatially calibrated and temporally synchronized industrial grade sensor suite that enables navigation above ground level, radar imaging, and optical imaging. A custom mission planning framework allows generation and automatic execution of stripmap and circular GPSAR trajectories controlled above ground level as well as aerial imaging survey flights. A factor graph based state estimator fuses measurements from dual receiver real-time kinematic (RTK) global navigation satellite system (GNSS) and an inertial measurement unit (IMU) to obtain precise, high-rate platform positions and orientations. Ground truth experiments showed sensor timing as accurate as 0.8 µs and as precise as 0.1 µs with localization rates of 1 kHz. The dual position factor formulation improves online localization accuracy up to 40 % and batch localization accuracy up to 59 % compared to a single position factor with uncertain heading initialization. Our field trials validated a localization accuracy and precision that enables coherent radar measurement addition and detection of radar targets buried in sand. This validates the potential as an aerial landmine detection system.
Restitution Modeling for Crush Analysis: Theory and Validation

This paper describes, demonstrates and validates a method for incorporating the effects of restitution into crush analysis. The paper first defines the impact coefficient of restitution in a manner consistent with the assumptions of crush analysis. Second, modified equations of crush analysis are presented that incorporate this coefficient of restitution. Next, the paper develops equations that model restitution response on a vehicle-specific basis. These equations utilize physically meaningful empirical constants and thus improve on restitution modeling equations already in the literature of accident reconstruction. Finally, the paper presents analysis of four vehicle-to-vehicle crash tests, demonstrating that the application of the restitution model derived in this paper results in crush analysis yielding more accurate ΔV calculations.
Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations

Learning visual concepts from raw images without strong supervision is a challenging task. In this work, we show the advantages of prototype representations for understanding and revising the latent space of neural concept learners. For this purpose, we introduce interactive Concept Swapping Networks (iCSNs), a novel framework for learning concept-grounded representations via weak supervision and implicit prototype representations. iCSNs learn to bind conceptual information to specific prototype slots by swapping the latent representations of paired images. This semantically grounded and discrete latent space facilitates human understanding and human-machine interaction. We support this claim by conducting experiments on our novel data set “Elementary Concept Reasoning” (ECR), focusing on visual concepts shared by geometric objects.11Code available at: https://github.com/ml-research/ XIConceptLearning
Crystal Growth, Exfoliation and Magnetic Phase Diagram of Quaternary Quasi-Two-Dimensional CuCrP2S6

S. Selter, Y. Shemerliuk, K. K. Bestha, L. T. Corredor, B. Özer, L. Veyrat, 2 A. U. B. Wolter, B. Büchner, 2 and S. Aswartham Institute for Solid State Research, Leibniz IFW Dresden, Helmholtzstr. 20, 01069 Dresden, Germany Institute of Solid State and Materials Physics and Würzburg-Dresden Cluster of Excellence ct.qmat, Technische Universität Dresden, 01062 Dresden, Germany (Dated: December 7, 2021)
Contextual Multi-View Query Learning for Short Text Classification in User-Generated Data

Mining user-generated content–e.g., for the early detection of outbreaks or for extracting personal observations–often suffers from the lack of enough training data, short document length, and informal language model. We propose a novel multi-view active learning model, called Context-aware Co-testing with Bagging (COCOBA), to address these issues in the classiﬁcation tasks tailored for a query word–e.g., detecting illness reports given the disease name. COCOBA employs the context of user postings to construct two views. Then it uses the distribution of the representations in each view to detect the regions that are assigned to the opposite classes. This effectively leads to detecting the contexts that the two base learners disagree on. Our model also employs a query-by-committee model to address the usually noisy language of user postings. The experiments testify that our model is applicable to multiple important representative Twitter tasks and also signiﬁcantly outperforms the existing baselines.
A variational approach to first order kinetic mean field games with local couplings

Abstract First order kinetic mean field games formally describe the Nash equilibria of deterministic differential games where agents control their acceleration, asymptotically in the limit as the number of agents tends to infinity. The known results for the well-posedness theory of mean field games with control on the acceleration assume either that the running and final costs are regularizing functionals of the density variable, or the presence of noise, i.e. a second-order system. In this article we construct global in time weak solutions to a first order mean field games system involving kinetic transport operators, where the costs are local (hence non-regularizing) functions of the density variable with polynomial growth. We show the uniqueness of these solutions on the support of the agent density. This is achieved by characterizing solutions through two convex optimization problems in duality. As part of our approach, we develop tools for the analysis of mean field games on a non-compact domain by variational methods. We introduce a notion of ‘reachable set’, built from the initial measure, that allows us to work with initial measures with or without compact support. In this way we are able to obtain crucial estimates on minimizing sequences for merely bounded and continuous initial measures. These are then carefully combined with L 1-type averaging lemmas from kinetic theory to obtain pre-compactness for the minimizing sequence. Finally, under stronger convexity and monotonicity assumptions on the data, we prove higher order Sobolev estimates of the solutions.
A biophysical threshold for biofilm formation

Bacteria are ubiquitous in our daily lives, either as motile planktonic cells or as immobilized surface-attached biofilms. These different phenotypic states play key roles in agriculture, environment, industry, and medicine; hence, it is critically important to be able to predict the conditions under which bacteria transition from one state to the other. Unfortunately, these transitions depend on a dizzyingly complex array of factors that are determined by the intrinsic properties of the individual cells as well as those of their surrounding environments, and are thus challenging to describe. To address this issue, here, we develop a generally-applicable biophysical model of the interplay between motility-mediated dispersal and biofilm formation under positive quorum sensing control. Using this model, we establish a universal rule predicting how the onset and extent of biofilm formation depend collectively on cell concentration and motility, nutrient diffusion and consumption, chemotactic sensing, and autoinducer production. Our work thus provides a key step toward quantitatively predicting and controlling biofilm formation in diverse and complex settings.
Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey

Semantic segmentation plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image. Yet, the state-of-the-art models rely on large amount of annotated samples, which are more expensive to obtain than in tasks such as image classification. Since unlabelled data is instead significantly cheaper to obtain, it is not surprising that Unsupervised Domain Adaptation reached a broad success within the semantic segmentation community. This survey is an effort to summarize five years of this incredibly rapidly growing field, which embraces the importance of semantic segmentation itself and a critical need of adapting segmentation models to new environments. We present the most important semantic segmentation methods; we provide a comprehensive survey on domain adaptation techniques for semantic segmentation; we unveil newer trends such as multi-domain learning, domain generalization, test-time adaptation or source-free domain adaptation; we conclude this survey by describing datasets and benchmarks most widely used in semantic segmentation research. We hope that this survey will provide researchers across academia and industry with a comprehensive reference guide and will help them in fostering new research directions in the field.
An investigation of harmonics attenuation and diversity among distributed single-phase power electronic loads

Widely distributed single-phase power electronic loads are an increasingly important source of harmonics in power distribution systems. The objective of this paper is to investigate the cumulative harmonic current characteristics of a large number of such loads. A complete analytical model for the most common load type is derived. This model is then used to investigate the impact of (1) interaction due to a shared source impedance, (2) variation in power level, and (3) variations in circuit parameters, on individual and cumulative current harmonics. The key findings of the paper are that diversity and attenuation are very important factors in predicting the behavior of distributed single-phase power electronic loads, especially for the higher-order harmonics, and that due to these two factors, the commonly-used fixed current injection method, using arithmetic sums of harmonic current magnitudes, can significantly overestimate the cumulative harmonic currents produced by these loads. <<ETX>>
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning

Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agent systems, coordinated exploration and behaviour is critical for agents to jointly achieve optimal outcomes. In this paper, we introduce a new general framework for improving coordination and performance of multi-agent reinforcement learners (MARL). Our framework, named Learnable Intrinsic-Reward Generation Selection algorithm (LIGS) introduces an adaptive learner, Generator that observes the agents and learns to construct intrinsic rewards online that coordinate the agents’ joint exploration and joint behaviour. Using a novel combination of MARL and switching controls, LIGS determines the best states to learn to add intrinsic rewards which leads to a highly efficient learning process. LIGS can subdivide complex tasks making them easier to solve and enables systems of MARL agents to quickly solve environments with sparse rewards. LIGS can seamlessly adopt existing MARL algorithms and, our theory shows that it ensures convergence to policies that deliver higher system performance. We demonstrate its superior performance in challenging tasks in Foraging and StarCraft II.
Closed‐loop Nash competition for liquidity

We study a multiplayer stochastic differential game, where agents interact through their joint price impact on an asset that they trade to exploit a common trading signal. In this context, we prove that a closed‐loop Nash equilibrium exists if the price impact parameter is small enough. Compared to the corresponding open‐loop Nash equilibrium, both the agents' optimal trading rates and their performance move towards the central‐planner solution, in that excessive trading due to lack of coordination is reduced. However, the size of this effect is modest for plausible parameter values.
Mather classes of Schubert varieties via small resolutions

We express a Schubert expansion of the Chern-Mather class for Schubert varieties in the even orthogonal Grassmannian via integrals involving Pfaffians and pushforward of the small resolutions in the sense of Intersection Cohomology (IH) constructed by Sankaran and Vanchinathan, instead of the Nash blowup. The equivariant localization is employed to show the way of computing the integral. As a byproduct, we present the computations. For analogy and the completion of the method in ordinary Grassmannians, we also suggest Kazhdan-Lusztig classes associated to Schubert varieties in the Lagrangian and odd orthogonal Grassmannian.
Magnetic Energy Conversion in Magnetohydrodynamics: Curvature Relaxation and Perpendicular Expansion of Magnetic Fields

The mechanisms and pathways of magnetic energy conversion are an important subject for many laboratory, space, and astrophysical systems. Here, we present a perspective on magnetic energy conversion in magnetohydrodynamics through magnetic field curvature relaxation (CR) and perpendicular expansion (PE) due to magnetic pressure gradients, and quantify their relative importance in two representative cases, namely 3D magnetic reconnection and 3D kink-driven instability in an astrophysical jet. We find that the CR and PE processes have different temporal and spatial evolutions in these systems. The relative importance of the two processes tends to reverse as the system enters the nonlinear stage from the instability growth stage. Overall, the two processes make comparable contributions to magnetic energy conversion, with the PE process somewhat stronger than the CR process. We further explore how these energy conversion terms can be related to particle energization in these systems.
Advanced encapsulant systems for flip chip

Flip chip on board (FCOB) technology is a novel electronic assembly structure whereby a bare integrated circuit (IC) die is interconnected directly to an organic printed circuit board (PCB). Mechanical coupling of the die and PCB by an epoxy encapsulant enhances the performance of the FCOB assembly during reliability testing. Advanced encapsulant materials for FCOB must address the issues observed during assembly of consumer electronic products on a high volume manufacturing line. The development of these materials is critical to continue the integration of FCOB assemblies as an alternative packaging system in electronic products. Materials characterization studies were performed to determine the glass transition temperatures (Tg), tensile elastic and loss moduli (E' and E"), flow profiles, coefficients of thermal expansion (CTE), and apparent strengths of adhesion for several advanced encapsulants. In addition, reliability tests were conducted to observe the relationship between encapsulant materials properties and reliability responses.
Hα Dots: Direct-method Metal Abundances of Low-luminosity Star-forming Systems

Utilizing low-luminosity star-forming systems discovered in the Hα Dots survey, we present spectroscopic observations undertaken using the Kitt Peak National Observatory 4 m telescope for 26 sources. With determinations of robust, “direct”-method metal abundances, we examine the properties of these dwarf systems, exploring their utility in characterizing starburst galaxies at low luminosities and stellar masses. We find that the Hα Dots survey provides an effective new avenue for identifying star-forming galaxies in these regimes. In addition, we examine abundance characteristics and metallicity scaling relations with these sources, highlighting a flattening of both the luminosity–metallicity (L–Z) and stellar mass–metallicity (M *–Z) relation slopes in these regimes as compared with those utilizing samples covering wider respective dynamic ranges. These local, accessible analogs to the kinds of star-forming dwarfs common at high redshift will help shed light on the building blocks that assembled into the massive galaxies common today.
A Tale of Color Variants: Representation and Self-Supervised Learning in Fashion E-Commerce

In this paper, we address a crucial problem in fashion e-commerce (with respect to customer experience, as well as revenue): color variants identification, i.e., identifying fashion products that match exactly in their design (or style), but only to differ in their color. We propose a generic framework, that leverages deep visual Representation Learning at its heart, to address this problem for our fashion e-commerce platform. Our framework could be trained with supervisory signals in the form of triplets, that are obtained manually. However, it is infeasible to obtain manual annotations for the entire huge collection of data usually present in fashion e-commerce platforms, such as ours, while capturing all the difficult corner cases. But, to our rescue, interestingly we observed that this crucial problem in fashion e-commerce could also be solved by simple color jitter based image augmentation, that recently became widely popular in the contrastive Self-Supervised Learning (SSL) literature, that seeks to learn visual representations without using manual labels. This naturally led to a question in our mind: Could we leverage SSL in our use-case, and still obtain comparable performance to our supervised framework? The answer is, Yes! because, color variant fashion objects are nothing but manifestations of a style, in different colors, and a model trained to be invariant to the color (with, or without supervision), should be able to recognize this! This is what the paper further demonstrates, both qualitatively, and quantitatively, while evaluating a couple of state-of-the-art SSL techniques, and also proposing a novel method.
Analysis of Pointing Loss Effects in Deep Space Optical Links

Owing to the extremely narrow beams, a main issue in optical deep space communications is represented by miss-pointing errors, which may severely degrade the system performance and availability. In this paper, we address pointing losses in the case in which both the receiver and the transmitter are affected by angular errors. Pointing losses are evaluated through two approaches. The first approach is deterministic and only requires knowledge of a maximum angular error. The second approach requires knowledge of the angular error statistical distribution and tackles the problem from an outage probability viewpoint. These tools are then applied to analyze the impact of pointing losses in deep space optical links in which both terminals suffer from miss-pointing effects. The antenna gains are first optimized to maximize the effective system gain. The optimum antenna gains are then applied to evaluate maximum achievable ranges and to perform link design by means of optical link budgets.
Mean and median bias reduction: A concise review and application to adjacent-categories logit models

The estimation of categorical response models using bias-reducing adjusted score equations has seen extensive theoretical research and applied use. The resulting estimates have been found to have superior frequentist properties to what maximum likelihood generally delivers and to be finite, even in cases where the maximum likelihood estimates are infinite. We briefly review mean and median bias reduction of maximum likelihood estimates via adjusted score equations in an illustration-driven way, and discuss their particular equivariance properties under parameter transformations. We then apply mean and median bias reduction to adjacent-categories logit models for ordinal responses. We show how ready bias reduction procedures for Poisson log-linear models can be used for mean and median bias reduction in adjacent-categories logit models with proportional odds and mean bias-reduced estimation in models with non-proportional odds. As in binomial logistic regression, the reduced-bias estimates are found to be finite even in cases where the maximum likelihood estimates are infinite. We also use the approximation of the bias of transformations of mean bias-reduced estimators to correct for the mean bias of model-based ordinal superiority measures. All developments are motivated and illustrated using real-data case studies and simulations.