Miracle-dz/UniFAR-FTUs huggingface dataset:
3-D Printed Metallic Dual-Polarized Vivaldi Arrays on Square and Triangular Lattices

We report the first Vivaldi arrays monolithically fabricated exclusively using commercial, low-cost, 3-D metal printing (direct metal laser sintering [DMLS]). Furthermore, we developed one of the first dual-polarized Vivaldi arrays on a triangular lattice, and compare it to a square lattice array. The triangular lattice is attractive because it has a 15.5% larger cell size compared to the square lattice and can be more naturally truncated into a wide range of aperture shapes such as a rectangle, hexagon, or triangle. Both arrays operate at 3–20 GHz and scan angles out to 60° from normal. The fabrication process is significantly simplified compared to previously published Vivaldi arrays since the antenna is ready for use directly after the standard printing process is complete. This rapid manufacturing is further expedited by printing the “Sub-Miniature Push-on, Micro” (SMPM) connectors directly onto the radiating elements, which simplifies assembly and reduces cost compared to utilizing discrete RF connectors. The arrays have a modular design that allows for combining multiple subarrays together for arbitrarily increasing the aperture size. Simulations and measurements show that our arrays have similar performance as previously published Vivaldi arrays, but with simpler fabrication.
Combining Coupled Skorokhod SDEs and Lattice Gas Frameworks for Multi-fidelity Modelling of Complex Behavioral Systems

To model reliably behavioral systems with complex bio-social interactions, accounting for uncertainty quantification, is critical for many application areas. However, in terms of the mathematical formulation of the corresponding problems, one of the major challenges is coming from the fact that corresponding stochastic processes should, in most cases, be considered in bounded domains, possibly with obstacles. This has been known for a long time and yet, very little has been done for the quantification of uncertainties in modelling complex behavioral systems described by such stochastic processes. In this paper, we address this challenge by considering a coupled system of Skorokhod-type stochastic differential equations (SDEs) describing interactions between active and passive participants of a mixed-population group. In developing a multi-fidelity modelling methodology for such behavioral systems, we combine low- and high-fidelity results obtained from (a) the solution of the underlying coupled system of SDEs and (b) simulations with a statistical-mechanics-based lattice gas model, where we employ a kinetic Monte Carlo procedure. Furthermore, we provide representative numerical examples of healthcare systems, subject to an epidemic, where the main focus in our considerations is given to an interacting particle system of asymptomatic and susceptible populations.
Smooth Exploration for Robotic Reinforcement Learning

Reinforcement learning (RL) enables robots to learn skills from interactions with the real world. In practice, the unstructured step-based exploration used in Deep RL -- often very successful in simulation -- leads to jerky motion patterns on real robots. Consequences of the resulting shaky behavior are poor exploration, or even damage to the robot. We address these issues by adapting state-dependent exploration (SDE) to current Deep RL algorithms. To enable this adaptation, we propose two extensions to the original SDE, using more general features and re-sampling the noise periodically, which leads to a new exploration method generalized state-dependent exploration (gSDE). We evaluate gSDE both in simulation, on PyBullet continuous control tasks, and directly on three different real robots: a tendon-driven elastic robot, a quadruped and an RC car. The noise sampling interval of gSDE permits to have a compromise between performance and smoothness, which allows training directly on the real robots without loss of performance. The code is available at https://github.com/DLR-RM/stable-baselines3.
BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System

Multi-armed bandits (MAB) provide a principled online learning approach to attain the balance between exploration and exploitation. Generally speaking, in a multi-armed bandit problem, to obtain a higher reward, the agent must choose the optimal action in various states based on previous experience (exploit) known actions to obtain a higher score; to discover these actions, the necessary discovery is required (exploration). Due to the superior performance and low feedback learning without the learning to act in multiple situations, multi-armed bandits are drawing widespread attention in applications ranging from recommender systems. Likewise, within the recommender system, collaborative filtering (CF) is arguably the earliest and most influential method in the recommender system. The meaning of collaboration is to filter the information through the relationship between the users and the feedback of the user’s rating of the items together to find the target users’ preferences. Crucially, new users and an ever-changing pool of recommended items are the challenges that recommender systems need to address. For collaborative filtering, the classical method is to train the model offline, then perform the online testing, but this approach can no longer handle the dynamic changes in user preferences, which is the so-called cold start. So, how to effectively recommend items to users in the absence of effective information? To address the aforementioned problems, a multi-armed bandit based collaborative filtering recommender system has been proposed, named BanditMF. BanditMF is designed to address two challenges in the multi-armed bandits algorithm and collaborative filtering: (1) how to solve the cold start problem for collaborative filtering under the condition of scarcity of valid information, (2) how to solve the sub-optimal problem of bandit algorithms in strong social relations domains caused by independently estimating unknown parameters associated with each user and ignoring correlations between users.
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs

As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings). However, the existing one-size-fits-all GNN implementations are insufficient to catch up with the evolving GNN architectures, the ever-increasing graph sizes, and the diverse node embedding dimensionalities. To this end, we propose \textbf{GNNAdvisor}, an adaptive and efficient runtime system to accelerate various GNN workloads on GPU platforms. First, GNNAdvisor explores and identifies several performance-relevant features from both the GNN model and the input graph, and uses them as a new driving force for GNN acceleration. Second, GNNAdvisor implements a novel and highly-efficient 2D workload management, tailored for GNN computation to improve GPU utilization and performance under different application settings. Third, GNNAdvisor capitalizes on the GPU memory hierarchy for acceleration by gracefully coordinating the execution of GNNs according to the characteristics of the GPU memory structure and GNN workloads. Furthermore, to enable automatic runtime optimization, GNNAdvisor incorporates a lightweight analytical model for an effective design parameter search. Extensive experiments show that GNNAdvisor outperforms the state-of-the-art GNN computing frameworks, such as Deep Graph Library ($3.02\times$ faster on average) and NeuGraph (up to $4.10\times$ faster), on mainstream GNN architectures across various datasets.
Dynamic Approximate Maximum Independent Set on Massive Graphs

Computing a maximum independent set (MaxIS) is a fundamental NP-hard problem in graph theory, which has important applications in a wide spectrum of fields. Since graphs in many applications are changing frequently over time, the problem of maintaining a MaxIS over dynamic graphs has attracted increasing attention over the past few years. Due to the intractability of maintaining an exact MaxIS, this paper aims to develop efficient algorithms that can maintain an approximate MaxIS with an accuracy guarantee theoretically. In particular, we propose a framework that maintains a $(\displaystyle \frac{\triangle}{2}+1)$ -approximate MaxIS over dynamic graphs and prove that it achieves a constant approximation ratio in many real-world networks. To the best of our knowledge, this is the first non-trivial approximability result for the dynamic MaxIS problem. Following the framework, we implement an efficient linear-time dynamic algorithm and a more effective dynamic algorithm with near-linear expected time complexity. Our thorough experiments over real and synthetic graphs demonstrate the effectiveness and efficiency of the pro-posed algorithms, especially when the graph is highly dynamic.
Code Generation Based on Deep Learning: a Brief Review

Automatic software development has been a research hot spot in the ﬁeld of software engineering (SE) in the past decade. In particular, deep learning (DL) has been applied and achieved a lot of progress in various SE tasks. Among all applications, automatic code generation by machines as a general concept, including code completion and code synthesis, is a common expectation in the ﬁeld of SE, which may greatly reduces the development burden of the software developers and improves the eﬃciency and quality of the software development process to a certain extent. Code completion is an important part of modern integrated development environments (IDEs). Code completion technology eﬀectively helps programmers complete code class names, method names and key-words, etc. , which improves the eﬃciency of program development and reduces spelling errors in the coding process. Such tools use static analysis on the code and provide candidates for completion arranged in alphabetical order.Codesynthesis is implemented from two aspects, ones based on input-output samples and the others based on functionality description. In this study, we introduce existing techniques of these two aspects and the corresponding DL techniques, and present some possible future research directions.
Reassessing Measures for Press Freedom

There has been an increasing interest in press freedom in the face of various global scandals, transformation of media, technological change, obstacles to deliberative democracy, and other factors. Press freedom is frequently used also as an explanatory factor in comparative empirical research. However, validations of existing measurement instruments on press freedom have been far and few between. Given these points, this paper evaluates eight cross-country instruments on press freedom in 146 countries between 2001 and 2020, replicating an earlier study with a comparable research setup. The methodology is based on principal component analysis and multi-level regression modeling. According to the results, the construct (convergence) validity of the instruments is good; they all measure the same underlying semi-narrow definition for press freedom elaborated in the paper. In addition, any of the indices seems suitable to be used interchangeability in empirical research. Limitations and future research directions are further discussed.
Overall Behavioural Index (OBI) For Measuring Segregation

Segregation, defined as the degree of separation between two or more population groups, helps to understand a complex social environment and subsequently provides a basis for public policy intervention. To measure segregation, past works often propose indexes that are criticized for being over-simplified and over-reduced. In other words, these indexes use the highly aggregated information to measure segregation. In this paper, we propose three novel indexes to measure segregation, namely: (i) Individual Segregation Index (ISI), (ii) Individual Inclination Index (III), and (iii) Overall Behavioural Index (OBI). The ISI index measures individuals' segregation, and the III index reports the individuals' inclination towards other population groups. The OBI index, calculated using both III and ISI index, is non-simplified and not only recognizes individuals' connectivity behaviour but group's connectivity behavioural distribution as well. By considering commonly used Freeman's segregation and homophily index as baseline indexes, we compare the OBI index on real call data records (CDR) dataset of Estonia to show the effectiveness of the proposed indexes.
Improving Label Quality by Jointly Modeling Items and Annotators

We propose a fully Bayesian framework for learning ground truth labels from noisy annotators. Our framework ensures scalability by factoring a generative, Bayesian soft clustering model over label distributions into the classic David and Skene joint annotator-data model. Earlier research along these lines has neither fully incorporated label distributions nor explored clustering by annotators only or data only. Our framework incorporates all of these properties as: (1) a graphical model designed to provide better ground truth estimates of annotator responses as input to \emph{any} black box supervised learning algorithm, and (2) a standalone neural model whose internal structure captures many of the properties of the graphical model. We conduct supervised learning experiments using both models and compare them to the performance of one baseline and a state-of-the-art model.
Learning the Preferences of Uncertain Humans with Inverse Decision Theory

Existing observational approaches for learning human preferences, such as inverse reinforcement learning, usually make strong assumptions about the observability of the human’s environment. However, in reality, people make many important decisions under uncertainty. To better understand preference learning in these cases, we study the setting of inverse decision theory (IDT), a previously proposed framework where a human is observed making non-sequential binary decisions under uncertainty. In IDT, the human’s preferences are conveyed through their loss function, which expresses a tradeoff between different types of mistakes. We give the ﬁrst statistical analysis of IDT, providing conditions necessary to identify these preferences and characterizing the sample complexity—the number of decisions that must be observed to learn the tradeoff the human is making to a desired precision. Interestingly, we show that it is actually easier to identify preferences when the decision problem is more uncertain. Furthermore, uncertain decision problems allow us to relax the unrealistic assumption that the human is an optimal decision maker but still identify their exact preferences; we give sample complexities in this suboptimal case as well. Our analysis contradicts the intuition that partial observability should make preference learning more difﬁcult. It also provides a ﬁrst step towards understanding and improving preference learning methods for uncertain and suboptimal humans.
A Generic Distributed Clustering Framework for Massive Data

In this paper, we introduce a novel Generic distributEd clustEring frameworK (GEEK) beyond $k$-means clustering to process massive amounts of data. To deal with different data types, GEEK first converts data in the original feature space into a unified format of buckets; then, we design a new Seeding method based on simILar bucKets (SILK) to determine initial seeds. Compared with state-of-the-art seeding methods such as $k$-means++ and its variants, SILK can automatically identify the number of initial seeds based on the closeness of shared data objects in similar buckets instead of pre-specifying $k$. Thus, its time complexity is independent of $k$. With these well-selected initial seeds, GEEK only needs a one-pass data assignment to get the final clusters. We implement GEEK on a distributed CPU-GPU platform for large-scale clustering. We evaluate the performance of GEEK over five large-scale real-life datasets and show that GEEK can deal with massive data of different types and is comparable to (or even better than) many state-of-the-art customized GPU-based methods, especially in large $k$ values.
Sample Efficient Social Navigation Using Inverse Reinforcement Learning

In this paper, we present an algorithm to efficiently learn socially-compliant navigation policies from observations of human trajectories. As mobile robots come to inhabit and traffic social spaces, they must account for social cues and behave in a socially compliant manner. We focus on learning such cues from examples. We describe an inverse reinforcement learning based algorithm which learns from human trajectory observations without knowing their specific actions. We increase the sample-efficiency of our approach over alternative methods by leveraging the notion of a replay buffer (found in many off-policy reinforcement learning methods) to eliminate the additional sample complexity associated with inverse reinforcement learning. We evaluate our method by training agents using publicly available pedestrian motion data sets and compare it to related methods. We show that our approach yields better performance while also decreasing training time and sample complexity.
The connection between star formation and supermassive black hole activity in the local Universe

We present a study of the active galactic nucleus (AGN) activity in the local Universe (I < 0.33) and its correlation with the host galaxy properties, derived from an Sloan Digital Sky Survey (SDSS DR8) sample with spectroscopic star-formation rate (SFR) and stellar mass (M∗) determination. To quantify the level of AGN activity we used X-ray information from the XMM-Newton Serendipitous Source Catalogue (3XMM DR8). Applying multiwavelength AGN selection criteria (optical BPT-diagrams, X-ray/optical ratio etc) we found that 24% of the detected sources are efficiently-accreting AGN with moderate-to-highX-ray luminosity, which are twice as likely to be hosted by star-forminggalaxies than by quiescent ones. The distribution of the specific Black Hole accretion rate (sBHAR, _sBHAR) shows that nuclear activity in local, non-AGN dominated galaxies peaks at very low accretion rates (−4 . log_sBHAR . −3) in all stellar mass ranges. However, we observe systematically larger values of sBHAR for galaxies with active star-formation than for quiescent ones, as well as an increase of the mean _sBHAR with SFR for both star-forming and quiescent galaxies. These findings confirm the decreased level of AGN activity with cosmic time and are consistent with a scenario where both star-formation and AGN activity are fuelled by a common gas reservoir.
Multidimensional Linear and Nonlinear Partial Integro-Differential Equation in Bessel Potential Spaces with Applications in Option Pricing

The purpose of this paper is to analyze solutions of a non-local nonlinear partial integrodifferential equation (PIDE) in multidimensional spaces. Such class of PIDE often arises in financial modeling. We employ the theory of abstract semilinear parabolic equations in order to prove existence and uniqueness of solutions in the scale of Bessel potential spaces. We consider a wide class of Lévy measures satisfying suitable growth conditions near the origin and infinity. The novelty of the paper is the generalization of already known results in the one space dimension to the multidimensional case. We consider Black–Scholes models for option pricing on underlying assets following a Lévy stochastic process with jumps. As an application to option pricing in the one-dimensional space, we consider a general shift function arising from a nonlinear option pricing model taking into account a large trader stock-trading strategy. We prove existence and uniqueness of a solution to the nonlinear PIDE in which the shift function may depend on a prescribed large investor stock-trading strategy function.
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis

Because it determines a center-outward ordering of observations in $\mathbb{R}^d$ with $d\geq 2$, the concept of statistical depth permits to define quantiles and ranks for multivariate data and use them for various statistical tasks (e.g. inference, hypothesis testing). Whereas many depth functions have been proposed \textit{ad-hoc} in the literature since the seminal contribution of \cite{Tukey75}, not all of them possess the properties desirable to emulate the notion of quantile function for univariate probability distributions. In this paper, we propose an extension of the \textit{integrated rank-weighted} statistical depth (IRW depth in abbreviated form) originally introduced in \cite{IRW}, modified in order to satisfy the property of \textit{affine-invariance}, fulfilling thus all the four key axioms listed in the nomenclature elaborated by \cite{ZuoS00a}. The variant we propose, referred to as the Affine-Invariant IRW depth (AI-IRW in short), involves the covariance/precision matrices of the (supposedly square integrable) $d$-dimensional random vector $X$ under study, in order to take into account the directions along which $X$ is most variable to assign a depth value to any point $x\in \mathbb{R}^d$. The accuracy of the sampling version of the AI-IRW depth is investigated from a nonasymptotic perspective. Namely, a concentration result for the statistical counterpart of the AI-IRW depth is proved. Beyond the theoretical analysis carried out, applications to anomaly detection are considered and numerical results are displayed, providing strong empirical evidence of the relevance of the depth function we propose here.
Rapid Discovery of Stable Materials by Coordinate-free Coarse Graining

A fundamental challenge in materials science pertains to elucidating the relationship between stoichiometry, stability, structure, and property. Recent advances have shown that machine learning can be used to learn such relationships, allowing the stability and functional properties of materials to be accurately predicted. However, most of these approaches use atomic coordinates as input and are thus bottlenecked by crystal structure identiﬁcation when investigating novel materials. Our approach solves this bottleneck by coarse-graining the inﬁnite search space of atomic coordinates into a combinatorially enumerable search space. The key idea is to use Wyckoﬀ representations – coordinate-free sets of symmetry-related positions in a crystal – as the input to a machine learning model. Our model demonstrates exceptionally high precision in discovering new stable materials, identifying 1,558 materials that lie below the known convex hull of previously calculated materials from just 5,675 ab-initio calculations. Our approach opens up fundamental advances in computational materials discovery.