Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Data using Monte Carlo Dropout

For many analytical problems the challenge is to handle huge amounts of available data. However, there are data science application areas where collecting information is difficult and costly, e.g., in the study of geological phenomena, rare diseases, faults in complex systems, insurance frauds, etc. In many such cases, generators of synthetic data with the same statistical and predictive properties as the actual data allow efficient simulations and development of tools and applications. In this work, we propose the incorporation of Monte Carlo Dropout method within Autoencoder (MCD-AE) and Variational Autoencoder (MCD-VAE) as efficient generators of synthetic data sets. As the Variational Autoencoder (VAE) is one of the most popular generator techniques, we explore its similarities and differences to the proposed methods. We compare the generated data sets with the original data based on statistical properties, structural similarity, and predictive similarity. The results obtained show a strong similarity between the results of VAE, MCD-VAE and MCD-AE; however, the proposed methods are faster and can generate values similar to specific selected initial instances.
Blockage detection in networks: The area reconstruction method†

In this note we present a reconstructive algorithm for solving the cross-sectional pipe area from boundary measurements in a tree network with one inaccessbile end. This is equivalent to reconstructing the first order perturbation to a wave equation on a quantum graph from boundary measurements at all network ends except one. The method presented here is based on a time reversal boundary control method originally presented by Sondhi and Gopinath for one dimensional problems and later by Oksanen to higher dimensional manifolds. The algorithm is local, so is applicable to complicated networks if we are interested only in a part isomorphic to a tree. Moreover the numerical implementation requires only one matrix inversion or least squares minimization per discretization point in the physical network. We present a theoretical solution existence proof, a step-by-step algorithm, and a numerical implementation applied to two numerical experiments.
Quantifying and Correlating Rhythm Formants in Speech

The objective of the present study is exploratory: to introduce and apply a new theory of speech rhythm zones or rhythm formants (R-formants). R-formants are zones of high magnitude frequencies in the low frequency (LF) long-term spectrum (LTS), rather like formants in the short-term spectra of vowels and consonants. After an illustration of the method, an R-formant analysis is made of non-elicited extracts from public speeches. The LF-LTS of three domains, the amplitude modulated (AM) absolute (rectified) signal, the amplitude envelope modulation (AEM) and frequency modulation (FM, F0, 'pitch') of the signal are compared. The first two correlate well, but the third does not correlate consistently with the other two, presumably due to variability of tone, pitch accent and intonation. Consequently, only the LF LTS of the absolute speech signal is used in the empirical analysis. An informal discussion of the relation between R-formant patterns and utterance structure and a selection of pragmatic variables over the same utterances showed some trends for R-formant functionality and thus useful directions for future research.
Improved lower bound on the family complexity of Legendre sequences

In this paper we study a family of binary Legendre sequences and its family complexity. Family complexity is a pseudorandomness measure introduced by Ahlswede et. ~al.~in 2003. A lower bound on the family complexity of a family based on the Legendre symbol of polynomials over a finite field was given by Gyarmati in 2015. In this article we improve the bound given by Gyarmati on family complexity of binary Legendre sequences. The bound depends on the Lambert W function and the number of elements in a finite field belonging to its proper subfield. Moreover, we present a fast method for calculating the bound.
A Reinforcement Learning Framework for Sequencing Multi-Robot Behaviors

Given a list of behaviors and associated parameterized controllers for solving different individual tasks, we study the problem of selecting an optimal sequence of coordinated behaviors in multi-robot systems for completing a given mission, which could not be handled by any single behavior. In addition, we are interested in the case where partial information of the underlying mission is unknown, therefore, the robots must cooperatively learn this information through their course of actions. Such problem can be formulated as an optimal decision problem in multi-robot systems, however, it is in general intractable due to modeling imperfections and the curse of dimensionality of the decision variables. To circumvent these issues, we first consider an alternate formulation of the original problem through introducing a sequence of behaviors' switching times. Our main contribution is then to propose a novel reinforcement learning based method, that combines Q-learning and online gradient descent, for solving this reformulated problem. In particular, the optimal sequence of the robots' behaviors is found by using Q-learning while the optimal parameters of the associated controllers are obtained through an online gradient descent method. Finally, to illustrate the effectiveness of our proposed method we implement it on a team of differential-drive robots for solving two different missions, namely, convoy protection and object manipulation.
Prediction and Communication Co-Design for Ultra-Reliable and Low-Latency Communications

Ultra-reliable and low-latency communications (URLLC) are considered as one of three new application scenarios in the fifth generation cellular networks. In this work, we aim to reduce the user experienced delay through prediction and communication co-design, where each mobile device predicts its future states and sends them to a data center in advance. Since predictions are not error-free, we consider prediction errors and packet losses in communications when evaluating the reliability of the system. Then, we formulate an optimization problem that maximizes the number of URLLC services supported by the system by optimizing time and frequency resources and the prediction horizon. Simulation results verify the effectiveness of the proposed method, and show that the tradeoff between user experienced delay and reliability can be improved significantly via prediction and communication co-design. Furthermore, we carried out an experiment on the remote control in a virtual factory, and validated our concept on prediction and communication co-design with the practical mobility data generated by a real tactile device.
VeREFINE: Integrating Object Pose Verification with Iterative Physics-guided Refinement

Precise object pose estimation for robotics applications and augmented reality relies on final refinement and verification steps. However, interactions between objects and interactions with the supporting structures in the observed scene are typically not considered. In this work, we propose to integrate scene-level hypotheses verification with object-level object pose refinement guided by physics simulation. This allows the physical plausibility of individual object pose estimates and the stability of the estimated scene to be consider in a unified search-based optimization. The proposed method is able to adapt to scenes of multiple objects and efficiently focuses on refining the most promising object poses in multi-hypotheses scenarios. We call this integrated approach VeREFINE and evaluate it on two datasets with varying scene complexity. The generality of the approach is shown by using two different pose estimators and two different baseline refiners. Results show improvements over all baselines and on all datasets with the inclusion of our proposed VeREFINE approach.
Cooperation-Aware Lane Change Control in Dense Traffic

This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of human drivers. This paper especially focuses on heavy traffic where vehicles cannot change lane without cooperating with other drivers. In this case, classical robust controls may not apply since there is no "safe" area to merge to. That said, modeling complex and interactive human behaviors is non-trivial from the perspective of control engineers. We propose a mathematical control framework based on Model Predictive Control (MPC) encompassing a state-of-the-art Recurrent Neural network (RNN) architecture. In particular, RNN predicts interactive motions of human drivers in response to potential actions of the autonomous vehicle, which are then be systematically evaluated in safety constraints. We also propose a real-time heuristic algorithm to find locally optimal control inputs. Finally, quantitative and qualitative analysis on simulation studies are presented, showing a strong potential of the proposed framework.
Estimation of expected value of function of i.i.d. Bernoulli random variables

We estimate the expected value of certain function $f:\{-1,1\}^{n}\to\mathbb{R}$. For example, with computer assistance, we show that if $\Delta$ is the Laplacian of the Cayley graph of $(\mathbb{Z}/15\mathbb{Z})\times(\mathbb{Z}/15\mathbb{Z})$ and $D$ is a diagonal $225\times 225$ matrix with entries chosen independently and uniformly from $\{-1,1\}$, then the expected value of the normalized trace of $(2I+D-\Delta)^{-1}$ is between $0.2006$ and $0.2030$.
Lead-Lag-Shaped Interactive Force Estimation by Equivalent Output Injection of Sliding-Mode

Estimation of interactive forces, which are mostly unavailable for a direct measurement on the interface between the system and its environment, is an essential task in various motion control applications. This paper proposes an interactive force estimation method, based on the well-known equivalent output injection of the second-order sliding mode. The equivalent output injection is used to obtain a dynamic quantity, which is first not appropriately shaped in the frequency domain, but appears as a matched external disturbance that encompasses interactive forces. Afterwards, a universal lead-lag shaper, which depends on the inherent dynamics of the motion control system coupled with environment, is used to extract an interactive force quantity. Once identified, the lead-lag shaper can be applied to the given system structure. An experimental case study with valve-controlled hydraulic cylinder and dynamic load is demonstrated. An accurate estimation of the interactive force, in comparison to the reference measurement, is shown.
Hyperbolic hybrid waves and optical topological transitions in few-layer anisotropic metasurfaces

A comprehensive analysis of hybrid TM-TE polarized surface electromagnetic waves supported by different few-layer anisotropic metasurfaces is presented. A generalized 4$\times$4 T-matrix formalism for arbitrary anisotropic 2D layers is developed, from which the general relations for the surface waves dispersions and scattering coefficients are deduced. Using this formalism and the effective conductivity approach, the dispersions and iso-frequency contours (IFCs) topology of the surface waves in various hybrid uniaxial metasurfaces are studied. The existence of hyperbolic plasmon-exciton polaritons in plasmon-exciton hybrids and hyperbolic acoustic waves with strong confinement in both out-of-plane and in-plane directions in uniaxial plasmonic bilayers are predicted. In plasmonic uniaxial metasurfaces on metal films, the elliptic and hyperbolic backward surface waves with negative group velocity are predicted and additional topological transitions in both elliptic and hyperbolic IFCs of the hybrid surface waves are revealed. Ultrathin twisted uniaxial plasmonic bilayers are proposed as systems with the IFCs topological transitions highly sensitive to the layers twist. The developed formalism may become a useful tool in the calculation of multifunctional few-layer metasurfaces or van der Waals heterostructures based on 2D materials with in-plane anisotropy, where the TM-TE polarization mixing must be considered. The predicted effects may open new horizons in the development and applications of planar optical technologies.
RRT2.0 for Fast and Optimal Kinodynamic Sampling-Based Motion Planning

We present RRT2.0: a simple yet efficient tree-based planner for asymptotically-optimal motion planning with kinodynamic constraints. RRT2.0 uses forward propagation and does not rely on the availability of a two-point boundary-value solver. The latter is a limiting requirement for some kinodynamic planners that are asymptotically optimal. The proposed approach improves upon a technique by Hauser and Zhou (2016), who explore an augmented state space with an additional coordinate, which endows every point in space with its cost-to-come value. Importantly, our optimality proofs require a milder and easily-verifiable set of assumptions on the problem and system: Lipschitz-continuity of the cost function and the dynamics. In particular, we prove that for any system satisfying those assumptions, any trajectory having a piecewise-constant control function and positive clearance from obstacles can be approximated arbitrarily well by a trajectory found by RRT2.0. We provide experimental results demonstrating on a couple of robot models with kinodynamic constraints that RRT2.0 outperforms the existing alternatives in practice.
Survey on Terahertz Nanocommunication and Networking: A Top-Down Perspective

Recent developments in nanotechnology herald nanometer-sized devices expected to bring light to a number of groundbreaking applications. Communication with and among nanodevices will be needed for unlocking the full potential of such applications. As the traditional communication approaches cannot be directly applied in nanocommunication, several alternative paradigms have emerged. Among them, electromagnetic nanocommunication in the terahertz (THz) frequency band is particularly promising, mainly due to the breakthrough of novel materials such as graphene. For this reason, numerous research efforts are nowadays targeting THz band nanocommunication and consequently nanonetworking. As it is expected that these trends will continue in the future, we see it beneficial to summarize the current status in these research domains. In this survey, we therefore aim to provide an overview of the current THz nanocommunication and nanonetworking research. Specifically, we discuss the applications envisioned to be supported by nanonetworks operating in the THz band, together with the requirements such applications pose on the underlying nanonetworks. Subsequently, we provide an overview of the current contributions on the different layers of the protocol stack, as well as the available channel models and experimentation tools. Finally, we identify a number of open research challenges and outline several future research directions.
Eating Planets for Lunch and Dinner: Signatures of Planet Consumption by Evolving Stars

Exoplanets have been observed around stars at all stages of stellar evolution, in many cases orbiting in configurations that will eventually lead to the planets being engulfed or consumed by their host stars, such as hot Jupiters or ultrashort period planets. Furthermore, objects such as polluted white dwarfs provide strong evidence that the consumption of planets by stars is a common phenomenon. This consumption causes several significant changes in the stellar properties, such as changes to the stellar spin, luminosity, chemical composition, or mass-loss processes. Here, we explore this wide variety of effects for a comprehensive range of stellar and planetary masses and stages of stellar evolution, from the main sequence over red giants to white dwarfs. We determine that planet consumption can cause transient luminosity features that last on the order of centuries to millennia, and that the post-consumption stellar spins can often reach breakup speeds. Furthermore, stellar mass loss can be caused by this spin-up, as well as through surface grazing interactions, leading to to the formation of unusual planetary nebula shapes or collimated stellar gas ejections. Our results highlight several observable stellar features by which the presence or previous existence of a planet around a given star can be deduced. This will provide future observational campaigns with the tools to better constrain exoplanet demographics, as well as planetary formation and evolution histories.
Learning First-Order Symbolic Planning Representations from Plain Graphs

One of the main obstacles for developing flexible AI system is the split between data-based learners and model-based solvers. Solvers such as classical planners are very flexible and can deal with a variety of problem instances and goals but require first-order symbolic models. Data-based learners, on the other hand, are robust but do not produce such representations. In this work we address this split by showing how the first-order symbolic representations that are used by planners can be learned from non-symbolic representations alone given by a number of observed system trajectories organized as graphs. The observations can be arbitrary, including raw images. What it is required is that two observations are different iff they proceed from different states. The representation learning problem is formulated as the problem of inferring the simplest planning instances over a common first-order domain that can generate the structures of the observed graphs. A slightly richer version of the problem is also considered where actions are also observed and the graphs are labeled. The problem is expressed and solved via a SAT formulation that is shown to produce first-order representations for domains like Gripper, Blocks, and Hanoi. The work suggests that the target symbolic representations for planning encode the structure of the observed state space, not the observations themselves, as assumed in deep learning approaches.
Augmented Data Science: Towards Industrialization and Democratization of Data Science

Conversion of raw data into insights and knowledge requires substantial amounts of effort from data scientists. Despite breathtaking advances in Machine Learning (ML) and Artificial Intelligence (AI), data scientists still spend the majority of their effort in understanding and then preparing the raw data for ML/AI. The effort is often manual and ad hoc, and requires some level of domain knowledge. The complexity of the effort increases dramatically when data diversity, both in form and context, increases. In this paper, we introduce our solution, Augmented Data Science (ADS), towards addressing this "human bottleneck" in creating value from diverse datasets. ADS is a data-driven approach and relies on statistics and ML to extract insights from any data set in a domain-agnostic way to facilitate the data science process. Key features of ADS are the replacement of rudimentary data exploration and processing steps with automation and the augmentation of data scientist judgment with automatically-generated insights. We present building blocks of our end-to-end solution and provide a case study to exemplify its capabilities.
Counseling Clients with HIV Disease: Assessment, Intervention, and Prevention

Counseling interventions are a proven and powerful way to help individuals with HIV cope with the enormous changes in their lives wrought by the disease. Proposing an innovative conceptual model for HIV clinical work, this well-organized and comprehensive guide provides a framework for assessing clients' psychosocial concerns and implementing interventions to facilitate adjustment; reviews medical and neurocognitive aspects of HIV disease progression; explores the psychotherapeutic context of HIV clinical work; and addresses risk reduction and prevention. Author Mary Ann Hoffman facilitates assessment with a conceptual model delineating the psychosocial characteristics of HIV disease and the unique life situation of people with HIV, and emphasizing the need to discuss difficult issues such as sexuality, sexual practices, and drug use. Next, interventions to enhance adaptation and alleviate emotional distress are introduced, covering broad areas including emotional reactions and coping; changes in life roles, including employment; risk-reduction; spirituality; and death and dying. The book also examines the unique psychotherapeutic context of HIV clinical work, citing the need to rethink the typical goals and desired outcomes of therapy, and explores perspectives on prevention from both the individual and community standpoint. An exhaustive synthesis of clinical material and empirical date, Counseling Clients with HIV Disease is essential reading for mental health professionals, including counselors, social workers, psychologists, psychiatrists, and psychiatric nurses.
Niche Differentiation of Aerobic and Anaerobic Ammonia Oxidizers in a High Latitude Deep Oxygen Minimum Zone

To elucidate the potential for nitrification and denitrification processes in a high latitude deep oxygen minimum zone (OMZ) we determined the abundance and community composition of the main microbial players in the aerobic and anaerobic (anammox) ammonium oxidation and denitrification processes in the Gulf of Alaska throughout the water column. Within the dominant bacterial groups, Flavobacterales, Rhodobacterales, Actinomarinales, and SAR86 were more abundant in epipelagic waters and decreased with depth, whereas SAR11, SAR324, Marinimicrobia, and Thiomicrospirales increased their contribution to the bacterial community with depth. Nitrosopumilaceae also increased with depth and dominated the OMZ and bathypelagic archaeal communities. Euryarchaeota Marine Group II exhibited an opposite depth pattern to Nitrosopumilaceae, whereas Marine Group III and Woesearchaeota were more abundant in the bathypelagic realm. Candidatus Brocadia contributed 70–100% of the anammox bacterial community throughout the water column. Archaeal ammonia oxidizers (AOA) dominated the microbial community involved in the nitrogen cycle. Two AOA ecotypes, the high ammonia (HAC) and low ammonia (LAC)-AOA, characterized by distinct genes for aerobic ammonia oxidation (amoA) and for denitrification (nirK), exhibited a distinct distribution pattern related to depth and ammonia concentrations. HAC-AOA dominated in epipelagic (80.5 ± 28.3% of total AOA) oxygenated and ammonia-rich waters, and LAC-AOA dominated in the OMZ (90.9 ± 5.1%) and bathypelagic waters (85.5 ± 13.5%), characterized by lower oxygen and ammonia concentrations. Bacterial denitrifiers (3.7 ± 6.9 bacterial nirK gene mL−1) and anaerobic ammonia oxidizers (78 ± 322 anammox 16S rRNA genes L−1) were low in abundance under the oxygen conditions in the Gulf of Alaska throughout the water column. The widespread distribution of bacterial denitrifiers and anaerobic ammonia oxidizers in low abundances reveals a reservoir of genetic and metabolic potential ready to colonize the environment under the predicted increase of OMZs in the ocean. Taken together, our results reinforce the niche partitioning of archaeal ammonia oxidizers based on their distinct metabolic characteristics resulting in the dominance of LAC-AOA in a high latitude deep OMZ. Considering the different ecological roles and functions of the two archaeal ecotypes, the expansion of the zones dominated by the LAC-ecotype might have implications for the nitrogen cycle in the future ocean.