Miracle-dz/UniFAR-FTUs huggingface dataset:
NOMA-Enhanced Terrestrial and Aerial IoT Networks With Partial CSI

This article investigates a nonorthogonal multiple access (NOMA)-enhanced Internet of Things (IoT) network. In order to provide connectivity, a novel cluster strategy is proposed, where multiple devices can be served simultaneously. Two potential scenarios are investigated: 1) NOMA-enhanced terrestrial IoT networks and 2) NOMA-enhanced aerial IoT networks. We utilize stochastic geometry tools to model the spatial randomness of both terrestrial and aerial devices. New channel statistics are derived for both terrestrial and aerial devices. The exact and the asymptotic expressions in terms of coverage probability are derived. In order to obtain further engineering insights, short-packet communication scenarios are investigated. From our analysis, we show that the performance of NOMA-enhanced IoT networks is capable of outperforming OMA-enhanced IoT networks. Moreover, based on simulation results, there exists an optimal value of the transmit power that maximizes the coverage probability.
Deep Reinforcement Learning for Complex Manipulation Tasks with Sparse Feedback

Learning optimal policies from sparse feedback is a known challenge in reinforcement learning. Hindsight Experience Replay (HER) is a multi-goal reinforcement learning algorithm that comes to solve such tasks. The algorithm treats every failure as a success for an alternative (virtual) goal that has been achieved in the episode and then generalizes from that virtual goal to real goals. HER has known flaws and is limited to relatively simple tasks. In this thesis, we present three algorithms based on the existing HER algorithm that improves its performances. First, we prioritize virtual goals from which the agent will learn more valuable information. We call this property the \textit{instructiveness} of the virtual goal and define it by a heuristic measure, which expresses how well the agent will be able to generalize from that virtual goal to actual goals. Secondly, we designed a filtering process that detects and removes misleading samples that may induce bias throughout the learning process. Lastly, we enable the learning of complex, sequential, tasks using a form of curriculum learning combined with HER. We call this algorithm \textit{Curriculum HER}. To test our algorithms, we built three challenging manipulation environments with sparse reward functions. Each environment has three levels of complexity. Our empirical results show vast improvement in the final success rate and sample efficiency when compared to the original HER algorithm.
Resolving the induction problem: Can we state with complete confidence via induction that the sun rises forever?

Induction is a form of reasoning from the particular example to the general rule. However, establishing the truth of a general proposition is problematic, because it is always possible that a conflicting observation to occur. This problem is known as the induction problem. The sunrise problem is a quintessential example of the induction problem, which was first introduced by Laplace (1814). However, in Laplace's solution, a zero probability was assigned to the proposition that the sun will rise forever, regardless of the number of observations made. Therefore, it has often been stated that complete confidence regarding a general proposition can never be attained via induction. In this study, we attempted to overcome this skepticism by using a recently developed theoretically consistent procedure. The findings demonstrate that through induction, one can rationally gain complete confidence in propositions based on scientific theory.
Reverse Prevention Sampling for Misinformation Mitigation in Social Networks

In this work, we consider misinformation propagating through a social network and study the problem of its prevention. In this problem, a "bad" campaign starts propagating from a set of seed nodes in the network and we use the notion of a limiting (or "good") campaign to counteract the effect of misinformation. The goal is to identify a set of $k$ users that need to be convinced to adopt the limiting campaign so as to minimize the number of people that adopt the "bad" campaign at the end of both propagation processes. This work presents \emph{RPS} (Reverse Prevention Sampling), an algorithm that provides a scalable solution to the misinformation mitigation problem. Our theoretical analysis shows that \emph{RPS} runs in $O((k + l)(n + m)(\frac{1}{1 - \gamma}) \log n / \epsilon^2 )$ expected time and returns a $(1 - 1/e - \epsilon)$-approximate solution with at least $1 - n^{-l}$ probability (where $\gamma$ is a typically small network parameter and $l$ is a confidence parameter). The time complexity of \emph{RPS} substantially improves upon the previously best-known algorithms that run in time $\Omega(m n k \cdot POLY(\epsilon^{-1}))$. We experimentally evaluate \emph{RPS} on large datasets and show that it outperforms the state-of-the-art solution by several orders of magnitude in terms of running time. This demonstrates that misinformation mitigation can be made practical while still offering strong theoretical guarantees.
Game of Ages

We consider a distributed IoT network, where each node wants to minimize its age of information and there is a cost to make any transmission. A collision model is considered, where any transmission is successful from a node to a common monitor if no other node transmits in the same slot. There is no explicit communication/coordination between any two nodes. Under this distributed competition model, the objective of this paper is to find a distributed transmission strategy for each node that converges to an equilibrium that depends on the past observations seen by each node. A simple update strategy is shown to converge to an equilibrium, that is in fact a Nash equilibrium for a suitable utility function, that captures all the right tradeoffs for each node. In addition, the price of anarchy for the utility function is shown to approach unity as the number of nodes grows large.
Supply Network Formation and Fragility

We model the production of complex goods in a large supply network. Each firm sources several essential inputs through relationships with other firms. Individual supply relationships are at risk of idiosyncratic failure, which threatens to disrupt production. To protect against this, firms multisource inputs and strategically invest to make relationships stronger, trading off the cost of investment against the benefits of increased robustness. A supply network is called fragile if aggregate output is very sensitive to small aggregate shocks. We show that supply networks of intermediate productivity are fragile in equilibrium, even though this is always inefficient. The endogenous configuration of supply networks provides a new channel for the powerful amplification of shocks. (JEL D21, G31, L14)
Reproducible Bootstrap Aggregating

Heterogeneity between training and testing data degrades reproducibility of a well-trained predictive algorithm. In modern applications, how to deploy a trained algorithm in a different domain is becoming an urgent question raised by many domain scientists. In this paper, we propose a reproducible bootstrap aggregating (Rbagging) method coupled with a new algorithm, the iterative nearest neighbor sampler (INNs), effectively drawing bootstrap samples from training data to mimic the distribution of the test data. Rbagging is a general ensemble framework that can be applied to most classifiers. We further propose Rbagging+ to effectively detect anomalous samples in the testing data. Our theoretical results show that the resamples based on Rbagging have the same distribution as the testing data. Moreover, under suitable assumptions, we further provide a general bound to control the test excess risk of the ensemble classifiers. The proposed method is compared with several other popular domain adaptation methods via extensive simulation studies and real applications including medical diagnosis and imaging classifications.
Feedback Channel Communication with Low Precision Arithmetic

The problem of communicating over an additive white Gaussian noise channel with feedback, using low precision arithmetic, is considered. The Schalkwijk-Kailath (SK) scheme is known to achieve an error probability that decays double exponentially in the number of interaction rounds, for any rate below channel capacity. However, SK is also known to suffer from numerical issues. In this work we propose a new, modified scheme termed Zoom-in SK (ZSK), which breaks the SK protocol into several stages. Each stage comprises several SK iterations followed by a synchronized zoom step. The zoom-in allows the receiver and transmitter to keep the scheme's parameters relatively large such that low precision arithmetic can be used. We prove that the new scheme achieves approximately the same error probability as SK while not suffering from numerical issues. We further verify our results in simulation and compare ZSK to the original SK scheme.
Bionic Sea Urchin Robot with Foldable Telescopic Actuator

This paper presents a couple of interesting ideas: a telescopic actuator design and a bio-inspired sea urchin robot. The ”spines” of the sea urchin robot consist of fourteen telescopic actuators equally distributed over it’s spherical body. The telescopic actuation system integrates linked 3D printed rack articulations that are locked in all axes if the pinion moves the links in a forward direction, creating a solid and rigid rack. Thus, the robot is able to propel and move by extending its spines. On the other hand, if the pinion moves the rigid articulations backward, these are unlocked and can be folded in a minimal space, enabling the bionic sea urchin robot to hide its spines inside its constrained spherical-body and be able to roll. Simulations and experiments are presented from both, the sea urchin robotic prototype and different scales of the telescopic actuation system.
Noncooperative Precoding for Massive MIMO HetNets: SILNR Maximization Precoding

Massive multi-input multiple-out (MIMO) is a key ingredient in improving the spectral efficiencies for next-generation cellular systems. Thanks to the channel reciprocity, in time-division-duplexing mode, each base station (BS) can acquire local channel state information at the transmitter (CSIT) for a set of users possibly located in adjacent cells. When the small cell BSs equipped with not-so-many antennas are densely deployed with marcrocells, a simple noncooperative MIMO precoding technique using local CSIT fails to achieve high spectral efficiency because of strong inter-cell-interference (ICI). In this paper, we present a novel noncooperative massive MIMO precoding technique called signal-to-interference-plus-leakage-plus-noise-ratio (SILNR) maximization precoding. The key idea of the proposed precoding is to jointly find a scheduled user set per cell, the beamforming vectors for the users, and the allocated power by simultaneously mitigating both inter-user-interference (IUI) and ICI leakage power using local CSIT. To accomplish this, we present a low-complexity algorithm that finds a local-optimal solution of the maximization problem for a lower bound of the sum spectral efficiency, i.e., a non-convex optimization problem. By system-level-simulations, we show that the proposed precoding method considerably outperforms the existing noncooperative precoding techniques in terms of the ergodic spectral efficiencies and rate distributions per user.
Universal Differential Equations for Scientific Machine Learning

In the context of science, the well-known adage “a picture is worth a thousand words” might well be “a model is worth a thousand datasets. ” Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A central challenge is reconciling data that is at odds with simplified models without requiring "big data". In this work demonstrate how a mathematical object, which we denote universal differential equations (UDEs), can be utilized as a theoretical underpinning to a diverse array of problems in scientific machine learning to yield efficient algorithms and generalized approaches. The UDE model augments scientific models with machine-learnable structures for scientifically-based learning. We show how UDEs can be utilized to discover previously unknown governing equations, accurately extrapolate beyond the original data, and accelerate model simulation, all in a time and data-efficient manner. This advance is coupled with open-source software that allows for training UDEs which incorporate physical constraints, delayed interactions, implicitly-defined events, and intrinsic stochasticity in the model. Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating the training of physics-informed neural networks and large-eddy simulations, can all be transformed into UDE training problems that are efficiently solved by a single software methodology.
Attentionanatomy: A Unified Framework for Whole-Body Organs at Risk Segmentation Using Multiple Partially Annotated Datasets

Organs-at-risk (OAR) delineation in computed tomography (CT) is an important step in Radiation Therapy (RT) planning. Recently, deep learning based methods for OAR delineation have been proposed and applied in clinical practice for separate regions of the human body (head and neck, thorax, and abdomen). However, there are few researches regarding the end-to-end whole-body OARs delineation because the existing datasets are mostly partially or incompletely annotated for such task. In this paper, our proposed end-to-end convolutional neural network model, called AttentionAnatomy, can be jointly trained with three partially annotated datasets, segmenting OARs from whole body. Our main contributions are: 1) an attention module implicitly guided by body region label to modulate the segmentation branch output; 2) a prediction re-calibration operation, exploiting prior information of the input images, to handle partial-annotation(HPA) problem; 3) a new hybrid loss function combining batch Dice loss and spatially balanced focal loss to alleviate the organ size imbalance problem. Experimental results of our proposed framework presented significant improvements in both Sørensen-Dice coefficient (DSC) and 95% Hausdorff distance compared to the baseline model.
Inference of a Dynamic Aging-related Biological Subnetwork via Network Propagation

Gene expression (GE)data capture valuable condition-specific information (“condition” can mean a biological process, disease stage, age, patient, etc.)However, GE analyses ignore physical interactions between gene products, i.e., proteins. Because proteins function by interacting with each other, and because biological networks (BNs)capture these interactions, BN analyses are promising. However, current BN data fail to capture condition-specific information. Recently, GE and BN data have been integrated using network propagation (NP)to infer condition-specific BNs. However, existing NP-based studies result in a static condition-specific subnetwork, even though cellular processes are dynamic. A dynamic process of our interest is human aging. We use prominent existing NP methods in a new task of inferring a dynamic rather than static condition-specific (aging-related)subnetwork. Then, we study evolution of network structure with age – we identify proteins whose network positions significantly change with age and predict them as new aging-related candidates. We validate the predictions via e.g., functional enrichment analyses and literature search. Dynamic network inference via NP yields higher prediction quality than the only existing method for inferring a dynamic aging-related BN, which does not use NP. Our data and code are available at https://nd.edu/~cone/dynetinf.
Deciphering the Lyman α blob 1 with deep MUSE observations

Context. Lyman α blobs (LABs) are large-scale radio-quiet Lyman α (Lyα) nebula at high-z that occur predominantly in overdense proto-cluster regions. In particular, there is the prototypical SSA22a-LAB1 at z = 3.1, which has become an observational reference for LABs across the electromagnetic spectrum. Aims. We want to understand the powering mechanisms that drive the LAB so that we may gain empirical insights into the galaxy-formation processes within a rare dense environment at high-z. Thus, we need to infer the distribution, the dynamics, and the ionisation state of LAB 1’s Lyα emitting gas. Methods. LAB 1 was observed for 17.2 h with the VLT/MUSE integral-field spectrograph. We produced optimally extracted narrow band images, in Lyαλ1216, He IIλ1640, and we tried to detect C IVλ1549 emission. By utilising a moment-based analysis, we mapped the kinematics and the line profile characteristics of the blob. We also linked the inferences from the line profile analysis to previous results from imaging polarimetry. Results. We map Lyα emission from the blob down to surface-brightness limits of ≈6 × 10−19 erg s−1 cm−2 arcsec−2. At this depth, we reveal a bridge between LAB 1 and its northern neighbour LAB 8, as well as a shell-like filament towards the south of LAB 1. The complexity and morphology of the Lyα profile vary strongly throughout the blob. Despite the complexity, we find a coherent large-scale east-west velocity gradient of ∼1000 km s−1 that is aligned perpendicular to the major axis of the blob. Moreover, we observe a negative correlation of Lyα polarisation fraction with Lyα line width and a positive correlation with absolute line-of-sight velocity. Finally, we reveal He II emission in three distinct regions within the blob, however, we can only provide upper limits for C IV. Conclusions. Various gas excitation mechanisms are at play in LAB 1: ionising radiation and feedback effects dominate near the embedded galaxies, while Lyα scattering contributes at larger distances. However, He II/Lyα ratios combined with upper limits on C IV/Lyα are not able to discriminate between active galactic nucleus ionisation and feedback- driven shocks. The alignment of the angular momentum vector parallel to the morphological principal axis appears to be at odds with the predicted norm for high-mass halos, but this most likely reflects that LAB 1 resides at a node of multiple intersecting filaments of the cosmic web. LAB 1 can thus be thought of as a progenitor of a present-day massive elliptical within a galaxy cluster.
Decisiveness of Stochastic Systems and its Application to Hybrid Models

In [ABM07], Abdulla et al. introduced the concept of decisiveness, an interesting tool for lifting good properties of finite Markov chains to denumerable ones. Later, this concept was extended to more general stochastic transition systems (STSs), allowing the design of various verification algorithms for large classes of (infinite) STSs. We further improve the understanding and utility of decisiveness in two ways. First, we provide a general criterion for proving decisiveness of general STSs. This criterion, which is very natural but whose proof is rather technical, (strictly) generalizes all known criteria from the literature. Second, we focus on stochastic hybrid systems (SHSs), a stochastic extension of hybrid systems. We establish the decisiveness of a large class of SHSs and, under a few classical hypotheses from mathematical logic, we show how to decide reachability problems in this class, even though they are undecidable for general SHSs. This provides a decidable stochastic extension of o-minimal hybrid systems. [ABM07] Parosh A. Abdulla, Noomene Ben Henda, and Richard Mayr. 2007. Decisive Markov Chains. Log. Methods Comput. Sci. 3, 4 (2007).
Wyner-Ziv reconciliation for key exchange based on Ring-LWE

We consider a key encapsulation mechanism (KEM) based on ring-LWE where reconciliation is performed on an $N$-dimensional lattice using Wyner-Ziv coding. More precisely, we consider Barnes-Wall lattices and use Micciancio and Nicolosi's bounded distance decoder with polynomial complexity $\mathcal{O}(N log(N)^2)$. We show that in the asymptotic regime for large $N$, the achievable key rate is $\Theta(log N)$ bits per dimension, while the error probability $P_e \approx \mathcal{O}(e^{-N\epsilon})$. Unlike previous works, our scheme does not require a dither.
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders

Causal discovery from data aﬀected by latent confounders is an important and diﬃcult challenge. Causal functional model-based approaches have not been used to present variables whose relationships are aﬀected by latent confounders, while some constraint-based methods can present them. This paper proposes a causal functional model-based method called repetitive causal discovery (RCD) to discover the causal structure of observed variables aﬀected by latent confounders. RCD repeats inferring the causal directions between a small number of observed variables and determines whether the relationships are aﬀected by latent confounders. RCD ﬁnally produces a causal graph where a bi-directed arrow indicates the pair of variables that have the same latent confounders, and a directed arrow indicates the causal direction of a pair of variables that are not aﬀected by the same latent confounder. The results of experimental validation using simulated data and real-world data conﬁrmed that RCD is eﬀective in identifying latent confounders and causal directions be-tween observed variables.
Linear and Range Counting under Metric-based Local Differential Privacy

Local differential privacy (LDP) enables private data sharing and analytics without the need for a trusted data collector. Error-optimal primitives (for, e.g., estimating means and item frequencies) under LDP have been well studied. For analytical tasks such as range queries, however, the best known error bound is dependent on the domain size of private data, which is potentially prohibitive. This deficiency is inherent as LDP protects the same level of indistinguishability between any pair of private data values for each data downer.In this paper, we utilize an extension of -LDP called MetricLDP or E-LDP, where a metric E defines heterogeneous privacy guarantees for different pairs of private data values and thus provides a more flexible knob than does to relax LDP and tune utility-privacy trade-offs. We show that, under such privacy relaxations, for analytical workloads such as linear counting, multi-dimensional range counting queries, and quantile queries, we can achieve significant gains in utility. In particular, for range queries under E-LDP where the metric E is the L1distance function scaled by , we design mechanisms with errors independent on the domain sizes; instead, their errors depend on the metric E, which specifies in what granularity the private data is protected. We believe that the primitives we design for ELDP will be useful in developing mechanisms for other analytical tasks, and encourage the adoption of LDP in practice.