Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental study of optimal energy weighting in energy-resolved CT using a CZT detector

Recent advances in energy-resolved CT can potentially improve contrast-to-noise ratio (CNR), which could subsequently reduce dose in conventional and dedicated breast CT. Two methods have been proposed for optimal energy weighting: weighting the energy- bin data prior to log normalization (projection- based weighting) and weighting the energy-bin data after log normalization (image-based weighting). Previous studies suggested that optimal projection-based and image-based energy weighting provide similar CNR improvements for energy resolved CT compared to photon-counting or conventional energy-integrating CT. This study experimentally investigated the improvement in CNR of projection-based and image-based weighted images relative to photon counting for six different energy-bin combinations using a bench top system with a CZT detector. The results showed CNR values ranged between 0.85 and 1.01 for the projection- based weighted images and between 0.91 and 1.43 for the image- based weighted images, relative to the CNR for the photon-counting image. The range of CNR values demonstrates the effects of energy-bin selection on CNR for a particular energy weighting scheme. The non-ideal spectral response of the CZT detector caused spectral tailing, which appears to generally reduce the CNR for the projection-based weighted images. Image-based weighting increased CNR in five of the six bin combinations despite the non-ideal spectral effects.
Impact of Embedded Deep Learning Optimizations for Inference in Wireless IoT Use Cases

Wireless internet-of-things devices typically transmit data to high-end computing platforms such as edge devices or cloud devices for further processing. However, processing the data at the edge or even on the constrained embedded devices using neural network can eliminate the need for high-throughput links and provides several benefits in terms of latency, reliability, privacy and energy consumption. In this article, we quantify how efficient neural networks can run on embedded devices for three typical wireless use cases. To this end, we give an overview of different optimizations and strategies for embedded deep learning inference on constrained devices. Next, we quantify the performance impact of optimized neural networks for edge and embedded inference, which perform up to 2.5x and 20x faster and consume 20x less energy at the cost of less than 2 percent accuracy difference for classification models. Although most published oversized models cannot run on typical embedded devices, with optimizations, we achieve efficient embedded inference and mitigate the need for raw data transmissions and thus preserve privacy. Finally, we discuss trends found in embedded deep learning use cases and present insights between design- and run-time metrics to predict model memory, storage and energy consumption together with model inference time.
Subgradient-Push Is of the Optimal Convergence Rate

The push-sum based subgradient is an important method for distributed convex optimization over unbalanced directed graphs, which is known to converge at a rate of $O\left( {\ln t/\sqrt t } \right)$. This paper shows that the subgradient-push algorithm actually converges at a rate of $O\left( {1/\sqrt t } \right)$, which is the same as that of the single-agent subgradient and thus optimal. The proposed tool for analyzing push-sum based algorithms is of independent interest.
On the Role of Regularization in Direct Data-Driven LQR Control

The linear quadratic regulator (LQR) problem is a cornerstone of control theory and a widely studied benchmark problem. When a system model is not available, the conventional approach to LQR design is indirect, i.e., based on a model identified from data. Recently a suite of direct data-driven LQR design approaches has surfaced by-passing explicit system identification (SysID) and based on ideas from subspace methods and behavioral systems theory. In either approach, the data underlying the design can be taken at face value (certainty-equivalence) or the design is robustified to account for noise. An emerging topic in direct data-driven LQR design is to regularize the optimal control objective to account for implicit SysID (in a least-square or low-rank sense) or to promote robust stability. These regularized formulations are flexible, computationally attractive, and theoretically certifiable; they can interpolate between direct vs. indirect and certainty-equivalent vs. robust approaches; and they can be blended resulting in remarkable empirical performance. This manuscript reviews and compares different approaches to regularized direct data-driven LQR.
A lagged dependent variable, autocorrelated disturbances, and unit root tests - peculiar OLS bias properties - a pedagogical note

The paper provides applied econometricians with a useful insight into the interaction between lagged dependent variables and autocorrelated disturbances. More specifically, the paper explains heuristically why, how and when the bias of the OLS estimator of the coefficient of a lagged dependent variable can be smaller when the disturbances are autocorrelated than when they are NID. It also explains why and how the powers and sizes of some of the unit root tests are distorted by AR(1) and MA(1) disturbances. The results should be of interest to applied econometricians using vector autoregressive or error-correction models as well.
TWLBR: Multi-Human Through-Wall Localization and Behavior Recognition Based on MIMO Radar

Human localization and behavior recognition (HLBR) is an important research topic in wireless sensing and computer vision. In existing work, most of the approaches using sensors such as cameras and mmWave radar cannot solve the wall occlusion problem, while the approaches using Wi-Fi can penetrate the wall but cannot locate human precisely due to its bandwidth limitation. All these limit the application of HLBR in reality. In this paper, we propose TWLBR, a real-time detection system for inferring the localization and behavior of human behind brick walls from radar heatmaps. In this system, we design a multiple-input multiple-output (MIMO) radar in the frequency range of 1–2 GHz and a multi-feature fusion network based on a 3D convolutional neural network (CNN) and a transformer. The network takes four radar heatmaps with background removal as input and outputs the location and behavior of the target humans. Our experiments show that TWLBR can locate and recognize the behavior of target humans behind a 24 cm brick wall with a localization accuracy of 6.2 cm and a behavior recognition accuracy of 96.37%, which is better than existing methods.
Solving Least Squares Problems on Partially Ordered Sets

We study a general class of least-squares problems structured according to a partially ordered set (poset). This is a fundamental optimization problem underlying the design of structured controllers on directed acyclic graphs or posets. We show that the optimality conditions of this problem yield a structured linear system, with sparsity pattern determined by a derived poset known as the poset of intervals. In general, this system could be relatively dense, and thus standard sparse linear algebra techniques may fail to provide significant reduction in computational complexity. Nonetheless, for a broad class of posets called multitrees identified in [1] we show that performing elimination according to an order defined by the poset intervals progressively decouples variables, reducing the arithmetic complexity of solving the problem.
Lidar SLAM Comparison in a Featureless Tunnel Environment

Simultaneous Localization and Mapping (SLAM) algorithm consists a vital part of decision-making process of autonomous robot platforms. Many lidar-based SLAM methods have been proposed for indoor and urban environments. However, a few studies are reported in a featureless tunnel environment. In this paper we consider recent lidar SLAM frameworks and test their performance in a tunnel environment. Our dataset is collected by a four-wheeled ground vehicle that is equipped with a lidar sensor used for mapping and feature detection and an IMU sensor used for odometry tracking information. The performance of seven different lidar SLAM algorithms is tested and as a result, in corridor environment LIO-SAM and SC-LIO-SAM frameworks show the lowest trajectory and point cloud error, respectively. On the other hand, LIO-SAM and FAST-LIO2 displays the best trajectory accuracy in the tunnel environment with addition of artificial landmarks and without them, respectively. The results obtained during the performance of seven different lidar SLAM algorithms can contribute to the development of a SLAM framework in a featureless tunnel environment.
Uncertain uncertainty in data-driven stochastic optimization: towards structured ambiguity sets

Ambiguity sets of probability distributions are a prominent tool to hedge against distributional uncertainty in stochastic optimization. The aim of this paper is to build tight Wasserstein ambiguity sets for data-driven optimization problems. The method exploits independence between the distribution components to introduce structure in the ambiguity sets and speed up their shrinkage with the number of collected samples. Tractable reformulations of the stochastic optimization problems are derived for costs that are expressed as sums or products of functions that depend only on the individual distribution components. The statistical benefits of the approach are theoretically analyzed for compactly supported distributions and demonstrated in a numerical example.
Multiple Noise Suppression Based on Simultaneous Learning and Independent Estimation of Amplitude and Phase of Noise

This study aims to suppress noise by taking advantage of the fact that the target sound components are concentrated in the spatial direct current (DC) bin when microphone array outputs are transformed into a spatiotemporal two-dimensional spectrum. For this purpose, the spatial DC components of the noise spectrum are estimated and subtracted from the observed spectrum in the spatial DC bin to suppress the noise. The amplitude and phase of the spatial DC components of noise are estimated using two independent neural networks (NNs) in which both amplitude and phase information are used simultaneously to consider the mutual dependency between the amplitude and phase. The NNs are learned by assuming the presence of two noise sources. Computer experiments show that approximately 20 dB of noise suppression can be achieved for the cases with one, two, and three noise sources.
Distributed Resource Allocation via ADMM over Digraphs

In this paper, we solve the resource allocation problem over a network of agents, with edges as communication links that can be unidirectional. The goal is to minimize the sum of allocation cost functions subject to a coupling constraint in a distributed way by using the finite-time consensus-based alternating direction method of multipliers (ADMM) technique. In contrast to the existing gradient descent (GD) based distributed algorithms, our approach can be applied to non-differentiable cost functions. Also, the proposed algorithm is initialization-free and converges at a rate of $\mathcal{O}\left( {1/k} \right)$, where k is the optimization iteration counter. The fast convergence performance related to iteration counter k compared to state-of-the-art GD based algorithms is shown via a simulation example.
Determining the medical and biological safety of meat-containing polycomponent products based on regional raw materials

This paper reports medical and biological studies of semi­finished meat product and meat­containing semi­smoked sausage on laboratory rats. The purpose of the study was to investigate the effect of the developed products on the dynamics of live weight, the state of internal organs and tissues, general and biochemical parameters of the blood of animals when they are introduced into the standard diet in the amount of 30 %. The studies were conducted on 30 white nonlinear rats weighing 145–150 g, of which one control and two experimental groups were formed, according to the principle of the method of analog groups.It is proved that the introduction of meat­containing multicomponent products with a high protein content into the diet of rats in the amount of 30 % of the standard diet contributes to the intensification of animal growth processes. It was confirmed that the increase in live body weight of rats after 21 days of the experiment amounted to 33–38 %, which is larger than that in control by 69–90 %. No negative impact on the state of the internal organs of rats from the consumption of the developed products was detected.It was found that the inclusion of meat­containing multicomponent products in the diet of rats contributes to an increase in erythropoiesis by 12.66 % compared to the starting data. The inclusion of products in the diet does not significantly change the content of leukocytes and platelets while increasing blood saturation with hemoglobin by 45.83–58.33 % higher compared to control animals.The introduction into the diet of laboratory rats of meat­containing multicomponent products has an anabolic effect and contributes to an increase in the concentration of hemoglobin by 42.12 %, total protein by 4.79 %, creatinine by 19.68 %. In laboratory rats, there is a decrease in glucose by an average of 8.17 %, which indicates an intensification of processes in muscle tissue. Enhanced protein synthesis due to increased catabolism leads to an increase in bilirubin concentration by 19.12–21.97 % compared to control rats. Medical and biological studies of meat­containing multicomponent products can be applied in practice to confirm their safety
HCN in Bok Globules: A Good Tracer of Collapsing Cores

We have conducted an HCN (J = 1–0) survey of dense cores in a sample of 24 star-forming and quiescent Bok globules. HCN emission was detected toward 11 of 13 globules containing embedded point sources, with no HCN detection among 11 starless globules. As in other dark clouds, the J = 1–0 hyperfine line intensity ratios vary from globule to globule and also with position toward the same globule, suggesting the presence of either a scattering envelope surrounding the core or a complex density structure. We find that the J = 1–0 transition of the HCN molecule can be used effectively to search for early stages of star formation in small molecular clouds: in the sample of star-forming cores, for three (of five) sources mapped, there is good spatial coincidence (better than 6″) between the position of the peak integrated HCN emission and the location of the associated embedded source. Furthermore, this transition is well correlated with the young stellar object (YSO) class, detecting preferentially class I YSOs. Our results indicate that detecting strong (>1 K km s-1) HCN emission from a molecular cloud core seems to imply the presence of an embedded protostar and thus, indirectly, that of a collapsing core.
Improving Scalability of 6G Network Automation with Distributed Deep Q-Networks

In recent years, owing to the architectural evolution of 6G towards decentralization, distributed intelligence is being studied extensively for 6G network automation. Distributed intelligence, based on Reinforcement Learning (RL), particularly Q-Learning (QL), has been proposed as a potential direction. The distributed framework consists of independent QL agents, attempting to reach their own individual objectives. The agents need to learn using a sufficient number of training steps before they converge to the optimal performance. After convergence, they can take reliable management actions. However, the scalability of QL could be severely hindered, particularly in the convergence time - when the number of QL agents increases. To overcome the scalability issue of QL, in this paper, we explore the potentials of the Deep Q-Network (DQN) algorithm, a function approximation-based method. Results show that DQN outperforms QL by at least 37% in terms of convergence time. In addition, we highlight that DQN is prone to divergence, which, if solved, could rapidly advance distributed intelligence for 6G.
Germano identity-based subgrid-scale modeling: A brief survey of variations on a fertile theme

It has now been over 20 years since the introduction of the Germano identity. Mostly, the identity has been applied to closures for the subgrid-scale fluxes required in large eddy simulations in the bulk of turbulent flows. However, the basic ideas underlying the Germano identity can be applied in various other contexts. In recent years a number of such generalizations have been developed, and several of these are surveyed in this paper. The survey is based on an interpretation of the Germano identity stating that the sum of resolved and modeled contributions to basic quantities of intrinsic physical interest must be independent of filter scale. The focus of this survey is on the conceptual bases of the various generalizations and their common features, as a way of pointing to possible further extensions.
Technology Prospects for Data-Intensive Computing

For many decades, progress in computing hardware has been closely associated with CMOS logic density, performance, and cost. As such, slowdown in 2-D scaling, frequency saturation in CPUs, and increased cost of design and chip fabrication for advanced technology nodes since the early 2000s have led to concerns about how semiconductor technology may evolve in the future. However, the last two decades have also witnessed a parallel development in the application landscape: the advent of big data and consequent rise of data-intensive computing, using techniques such as machine learning. In this article, we advance the idea that data-intensive computing would further cement semiconductor technology as a foundational technology with multidimensional pathways for growth. Continued progress of semiconductor technology in this new context would require the adoption of a system-centric perspective to holistically harness logic, memory, and packaging resources. After examining the performance metrics for data-intensive computing, we present the historical trends for general-purpose graphics processing unit (GPGPU) as a representative data-intensive computing hardware. Thereon, we estimate the values of the key data-intensive computing parameters for the next decade, and our projections may serve as a precursor for a dedicated technology roadmap. By analyzing the compiled data, we identify and discuss specific opportunities and challenges for data-intensive computing hardware technology.
A Cloud-based IoMT Data Sharing Scheme with Conditional Anonymous Source Authentication

As a rapidly growing subset of the Internet of Thing (IoT), the cloud-based Internet of Medical Thing (IoMT) has been widely applied in remote healthcare industries, which allows the physicians to monitor patients' body parameters remotely to offer continuous and timely healthcare. These healthcare parameters usually contain sensitive information, such as heart rates, glucose levels and etc., and the exposure of them may pose serious threats to the patients' health and lives. To guarantee security and privacy, many IoMT data sharing schemes have been proposed. However, most of these schemes either exhibit a one-to-one data sharing structure or fail to protect the patients' privacy. Since the data usually needs to be shared to different physicians, patients may want to be assisted without revealing their identities. To meet these requirements in healthcare systems, we propose a multi-receiver secure healthcare data sharing scheme, in which the patients are allowed to share their IoMT data to multiple physicians simultaneously for a multidisciplinary treatment, and the conditional anonymity is achieved where data source authentication is provided without revealing the patient's identity. When the patient health condition is abnormal, the hospital can correctly and quickly trace the patient's identity and inform him/her immediately. Our scheme is formally proved to achieve multiple security properties including confidentiality, unforgeability and anonymity. Simulation results demonstrate that the proposed scheme is efficient and practical.
System-Level Recurrent State Estimators for Affine Systems Subject to Data Losses Modeled by Automata

This paper proposes a robust output feedback state estimator for uncertain/bounded-error affine systems subject to data losses modeled by an automaton. Specifically, by introducing a novel property known as recurrent recovery, where the estimation errors are required to be recurrent to some minimum recovery levels at each node of the data loss automata, we design a robust estimator design that guarantees that the state estimation errors remain bounded in a recurrent manner despite worst-case realizations of process and sensor noise/uncertainties in addition to missing data. Our design can directly deal with infinite-horizon missing data specifications modeled by automata by recasting the problem into multiple finite-horizon problems of varying lengths, which results in an optimization-based approach with only a finite number of constraints. Moreover, our design is built upon system-level parameterization and for this purpose, we propose a novel affine output feedback strategy that also contributes to the literature of finite-horizon optimal control.
Design and Evaluation of an Experimental Visible Light Communications Platform

Visible Light Communication (VLC) based systems are proving to be a promising candidate for the development of 6G communication networks. However, new developments in devices and materials need to be done to deal with the limitations and drawbacks of VLC. In the meantime, the work and the experiments done in this paper show that it’s possible to implement a light based communication system in non-controlled environments, getting positive SNR values while having several noise and interference sources such as ambient and artificial lightning. All of this within the context of designing and implementing an experimental VLC platform for academic research such as, but not limited to, the implementation of VLC technologies for underground mining facilities.
Numerical solution of 3D unsteady nonlinear inverse problem of estimating surface heat flux for cylindrical geometry

A numerical algorithm is proposed for solving a three-dimensional unsteady nonlinear inverse heat conduction problem of estimating the boundary conditions at the heated surface of a solid. In this study, the geometrical form of the solid is a finite hollow half-cylinder. As additional information, needed to solve the inverse problem under study, we use both temperature measurements at the internal surface (case of the use of thermo-couples or an infrared camera) inside the half-cylinder (case of the use of thermo-couples) and those at the heated surface (case of the use of an infrared camera). The iterative regularization method is used to build the numerical algorithm. The method is based on the residual functional minimization by using the unconstrained conjugate gradient method with the regularizing discrepancy principle as a stopping criterion of the iterative processes. The unknown function of three variables is sought for as a grid function.