Miracle-dz/UniFAR-FTUs huggingface dataset:
THE TOLL OF INEQUALITY

Abstract To take stock of the human toll resulting from racial inequality in the United States, we estimate the number of excess deaths that accumulated among African Americans over the twentieth century as a result of the enduring racial gap in mortality rates. We assemble a wide array of demographic and vital statistics data for all years since 1900 to calculate the number of Black deaths in each half-decade that occurred in excess of what would be projected if Blacks had experienced the same gender- and age-specific mortality rates as Whites. We estimate that there were almost 7.7 million excess deaths among African Americans from 1900 through 1999. Those deaths comprised over 40% of all African American deaths over the century. Excess deaths were highest in the early decades (peaking in 1925–1934), but the only period of sustained decline was 1935–1949. Subsequent reductions in excess deaths were relatively modest and unstable, and in the last decade of the century the percentage of Blacks’ deaths that were excess returned to levels as high as in the first decade. That trajectory is less positive than the trajectory for the racial gap in life expectancy over the century. Excess deaths fell disproportionately among the young in the early twentieth century, but in the succeeding decades they progressively hit harder among older African Americans, many of them in the prime of life when their economic and social pursuits were vital to their families and communities. Excess deaths were also especially heavy among Black women for most of the century. We conclude by discussing the social and policy implications of the excess deaths. We assess trends in the early twenty-first century as we consider the political challenges involved in tackling the continuing excess death toll.
Evaluation of U-Net CNN Approaches for Human Neck MRI Segmentation

The segmentation of neck muscles is useful for the diagnoses and planning of medical interventions for neck pain-related conditions such as whiplash and cervical dystonia. Neck muscles are tightly grouped, have similar appearance to each other and display large anatomical variability between subjects. They also exhibit low contrast with background organs in magnetic resonance (MR) images. These characteristics make the segmentation of neck muscles a challenging task. Due to the significant success of the U-Net architecture for deep learning-based segmentation, numerous versions of this approach have emerged for the task of medical image segmentation. This paper presents an evaluation of 10 U-Net CNN approaches, 6 direct (U-Net, CRF-Unet, A-Unet, MFP-Unet, R2Unet and U-Net++) and 4 modified (R2A-Unet, R2A-Unet++, PMS-Unet and MS-Unet). The modifications are inspired by recent multi-scale and multi-stream techniques for deep learning algorithms. T1 weighted axial MR images of the neck, at the distal end of the C3 vertebrae, from 45 subjects with real-time data augmentation were used in our evaluation of neck muscle segmentation approaches. The analysis of our numerical results indicates that the R2Unet architecture achieves the best accuracy.
Analysis of Age-Aware Slotted ALOHA

We present a steady-state analysis of threshold-ALOHA, a distributed age-aware modification of slotted ALOHA. Under threshold-ALOHA, each source remains passive until a certain age threshold $\Gamma$ is reached. Nodes whose ages exceed the threshold attempt transmission with a fixed probability $\tau$ in each slot. We study the scaling of the time average Age of Information (AoI) with the network size, n. We derive the distribution of the number of active users and establish that the policy converges into a slotted ALOHA with fewer participants, specifically, approximately one-fifth of all users under age-optimal parameter settings. We derive an expression for the average AoI and obtain that the optimal age threshold and transmission probability scale respectively as 2. 2n and 4. 69/n. We show that optimal AoI scales linearly as 1. 4169n, at nearly half the minimum slope achievable using slotted ALOHA, while the loss from the maximum achievable throughput of $e^{-1}$ remains below 1%.
GOODNESS-OF-FIT TESTS FOR MULTIVARIATE COPULA-BASED TIME SERIES MODELS

In recent years, stationary time series models based on copula functions became increasingly popular in econometrics to model nonlinear temporal and cross-sectional dependencies. Within these models, we consider the problem of testing the goodness-of-fit of the parametric form of the underlying copula. Our approach is based on a dependent multiplier bootstrap and it can be applied to any stationary, strongly mixing time series. The method extends recent i.i. d. results by Kojadinovic et al. (2011) and shares the same computational benefits compared to methods based on a parametric bootstrap. The finite-sample performance of our approach is investigated by Monte Carlo experiments for the case of copula-based Markovian time series models.
A 2.75-to-75.9TOPS/W Computing-in-Memory NN Processor Supporting Set-Associate Block-Wise Zero Skipping and Ping-Pong CIM with Simultaneous Computation and Weight Updating

Computing-in-memory (CIM) is an attractive approach for energy-efficient neural network (NN) processors, especially for low-power edge devices. Previous CIM chips [1] –[5] have demonstrated macro and system-level design enabling multi-bit operations and sparsity support. However, several challenges exist, as shown in Fig. 15.2. 1. First, though a previously proposed block-wise sparsity strategy [5] can power off ADCs, zeros still contributed to storage requirements, and power gating was not applied to computing resources. Second, on-chip SRAM CIM macros are not large enough to hold all weights. Updating weights between computing operations leads to significant performance loss. Finally, the limited sensing margin incurs poor accuracy for large NN models on practical datasets, such as ImageNet. The precision and power of the ADCs should be optimized and adjusted.
Fluctuations in entanglements of polymer liquids

A method for incorporating reptation ideas in a temporary network model is used to predict the fluctuations of entanglement number and monomer density between entanglements in entangled polymeric liquids. The dynamic variables are chosen to be the number of Kuhn steps and the position of the entanglements. A chain of fixed number of Kuhn steps in a bath that fixes the chemical potential conjugate to the number of entanglements is considered. The static equilibrium statistics of such a model can be calculated analytically. Since the dynamics of the model may also be simulated, these analytic expressions may be used to check the algorithm. Also, the damping function is calculated analytically from these distributions, as well as normal stresses following a step shear.
Benchmarking RDF Metadata Representations: Reification, Singleton Property and RDF*

RDF reification is a data modelling solution for writing RDF statements about RDF statements. A set of approaches exist in the literature to express statement-level metadata, or “reified” statements, in RDF. They have primarily been designed to attach additional contextual information to individual triples, such as provenance, spatio-temporal validity, or certainty. Practically, different methods have been developed to present such metadata while complying with the RDF standard and syntax. However, when effectively stored in triplestores, these various solutions produce diverse results in terms of querying performance, storage efficiency and usability. In this article, we analyse these metrics across three popular reification approaches: Standard Reification, Singleton Property and the recent RDF*. We publish our benchmark evaluation resources so that the community could expand and compare the study on different datasets, reification approaches and storage implementations.
The Impact of Bit Errors on Intra-session Network Coding with Heterogeneous Packet Lengths

While intra-session network coding has proven to be a promising packet-level forward error correction scheme, recent studies have shown that it can invoke increased packet sizes and excessive overhead. This problem is especially significant for traffic with heterogeneous packet lengths. Yet, most conducted studies emulate packet loss using length-independent random loss models, ignoring the overhead. In reality, larger packets have a higher probability to get corrupted, e.g., by failing a series of link layer cycling redundancy checks. To analyze the impact of this problem, the here presented study instead applies a bit error model to the encoded traffic, including the coding overhead. We will present formal equations to calculate the impact in terms of frame error rate and packet loss rate before and after decoding. Finally, a trace-based simulation highlights the implications for relevant real-world applications, namely QUIC web browsing, UFTP file transfer, Skype VoIP, and FFmpeg video streaming.
Delay-Aware Green Routing for Mobile-Sink-Based Wireless Sensor Networks

Mobile sinks were introduced in wireless sensor networks (WSNs) to mitigate the infamous hotspot problem. However, routing in mobile-sink-based WSNs requires frequent updation of sink location information to all the sensor nodes; which is an energy-expensive process for resource-constrained WSNs. Therefore, it is required to develop a green routing protocol that can minimize the energy overhead in sink location updation as well as reduce the data delivery delay. This article proposes a virtual-infrastructure-based delay-aware green routing protocol (DGRP) that creates multiple rings in the sensor field and limits the updation of mobile sink location information to the nodes belonging to the rings only. Simulation results show that DGRP outperforms existing routing protocols in terms of energy consumption and throughput. In addition to this, DGRP results in <inline-formula> <tex-math notation="LaTeX">$\approx 26$ </tex-math></inline-formula>%, <inline-formula> <tex-math notation="LaTeX">$\approx 39$ </tex-math></inline-formula>%, and <inline-formula> <tex-math notation="LaTeX">$\approx 35$ </tex-math></inline-formula>% improvement in data delivery delay for a varying number of sensor nodes, sink speeds, and network sizes, respectively, when compared with the state of the art.
Ammonia Volatilization from Surface‐Applied Urea: Effect of Hydrogen Ion Buffering Capacity

The influence of soil pH and soil cation exchange capacity on ammonia volatilization from surface applied ammoniacal nitrogen has been reported in previous studies. Since the hydrolysis of ureacontaining N fertilizers causes an increase in alkalinity, a soil's inherent H buffering capacity (defined as the soil's total acidity, comprised of exchangeable acidity plus non-exchangeable, titratable acidity), should exert a dominant influence on the maximum soil pH at the site of urea fertilizer application. The objective of this study was to demonstrate the importance of a soil's H buffering capacity in affecting NH3 volatilization from surface-applied urea. The H+ buffering capacity of two soils was increased by adding hydroxy-Al polymers to one soil, and weak and strong acid cation exchange resins to the other soil. Care was taken to keep cation exchange capacity and initial pH close to the same on amended and unamended (control) soils. Urea was surface-applied to amended and unamended soils and ammonia volatilization and soil surface pH were measured. The increase of H buffering capacity of soils was found to reduce soil surface pH and NH3 volatilization after application of urea. It is concluded from this work that H buffering capacity of a soil is a better indicator of NH3 loss potential than a soil's initial pH. Additional Index Words: N fertilizers, nitrogen loss, fertilizer ef-
Icosahedral quasicrystal in annealed ZnMgSc alloys

A new icosahedral quasicrystal has been found in Zn 85-x Mg x Sc 15 ( x = 3,4,5) alloys annealed at 922K for 5-100h. Electron diffraction and powder X-ray diffraction experiments indicate that this quasicrystal has a primitive icosahedral quasilattice (P type) and a six-dimensional lattice parameter a 6D =0.7115nm. The stoichiometric composition of the icosahedral quasicrystal was estimated to be close to Zn 80 Mg 5 Sc 15, since the icosahedral quasicrystal coexists in the x = 5 samples with small amounts of a MgZn 2 -type Laves phase and a Zn 17 Sc 3 -type cubic phase with lattice parameter a =1.3854nm. The latter is interpreted to be a 1/1-type approximant crystal of the icosahedral quasicrystal. The atomic cluster included in the Zn 17 Sc 3 -type cubic phase indicates a structural similarity between the Zn-Mg-Sc quasicrystal and the Cd-Yb (and Cd-Ca) icosahedral quasicrystals recently reported.
Nonlinear S-Parameters Inversion for Stroke Imaging

Stroke identification by means of microwave tomography requires a very accurate reconstruction of the dielectric properties inside patient’s head. This is possible when a precise measurement system is combined with a full nonlinear inversion method. In this article, the inversion of S-parameter data collected in a metallic chamber is performed with a nonlinear inversion strategy in Lebesgue spaces with nonconstant exponents. This is the first time that this kind of nonlinear S-parameter electromagnetic formulation has been applied to this problem. The inverse-scattering method incorporates a 2-D electromagnetic model of the imaging chamber based on a finite-element formulation, which has led to a complete redefinition of the solving procedure with respect to previous works. This allows a suitable description of the multistatic S-parameters due to the interactions between the incident radiation and the structure under test. The developed inversion procedure is first assessed by means of numerical simulations. The experimental results, obtained with a clinical microwave system prototype containing a liquid-filled 3-D SAM phantom with an inhomogeneity mimicking a hemorrhagic stroke, further prove the effectiveness of the proposed approach.
Preparation and Application of Composite Material as Sorbent for Oil Spills

Oil spills are a serious environmental, economic and social problem. The use of sorbents derived from different materials is considered to be the most effective method for their removal. In this study, we obtained novel sorbent materials based on natural products: textile material made of cotton and modified with chitosan crosslinked with glutaraldehyde and in situ obtained ZnO particles. These composite materials have good sorption properties and are easy and effective to use and they remove crude oil and oil products contaminants in water. Their reusability has also been investigated.
Multitask Learning for Video-based Surgical Skill Assessment

Surgical skill assessment (SSA) plays a vital role in medical systems for reducing intraoperative surgical errors and improving clinical outcomes. To ensure objective and efficient SSA, many automatic video-based SSA methods have been developed. In particular, various deep learning methods have been devised recently by utilising CNN or RNN-based networks for various skill assessment tasks (e.g., skill level prediction). While predicting overall skill levels and assessing detailed attribute-based scores are highly correlated, most existing studies deal with these two tasks separately, without fully exploiting different information sources encoded in a dataset. In contrast, we propose a novel end-to-end multitask learning framework to conduct skill level classification and attribute score regression jointly. Specifically, our network incorporates two branches for the two tasks, which share earlier layers for feature extraction and hold different prediction layers for specific targets. The shared feature extractor is optimised under the supervision of both tasks simultaneously, encouraging the model to consider information from different aspects and their relatedness to learn richer and more generalised features. In addition, since not every part of a surgical video contributes to skill assessment equally, we enhance an existing feature extractor I3D with a novel Spatio-Temporal & Channel Attention Module to emphasize important features. Experimental results on the public dataset JIGSAWS show that our proposed network outperforms state-of-the-art models on both skill classification and score regression tasks.
Pore Size Effects in Fischer Tropsch Synthesis over Cobalt-Supported Mesoporous Silicas

Abstract Pore size effects on Fischer Tropsch reaction rates and selectivities over cobalt catalysts were studied at atmospheric pressure using periodic (SBA-15 and MCM-41) and commercial mesoporous silicas as catalytic supports. The catalysts were characterized by nitrogen adsorption, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), and oxygen titration. Fischer Tropsch reaction rates were found much higher on cobalt catalysts with the pore diameter exceeding 30 A than on the narrow pore catalysts. A larger diameter of catalyst pores also led to significantly higher C 5+ selectivities. The catalytic effects were interpreted in terms of different cobalt particle size and reducibility in the wide pore and narrow pore silicas. XRD and XPS showed that the size of supported cobalt species strongly depended on the pore size, increasing with increases in catalyst pore diameter. TGA and oxygen titration indicated higher extent of overall reduction of cobalt species in wide pore supports. Lower reducibility of small cobalt particles is likely to be one of the reasons responsible for the lower Fischer Tropsch reaction rates and higher methane selectivities on narrow pore cobalt catalysts.
COVID-19 detection from chest X-Ray images using ensemble of CNN models

The year 2020 will certainly be remembered for the outbreak of COVID-19 pandemic. With the first case being reported in December 2019, the SARSCoV2 virus has proved to be one of the most deadly virus which has affected the human civilization. Relatively high contagious rate and asymptomatic patients also being carrier of the virus makes it more dangerous. The only way to get a control on the outbreak is rapid testing. With the present testing mechanisms being costly and time consuming the end of this pandemic doesn’t seems near. These challenges motivates us to come up with a system which can be effective in testing large population size and at the same time be less time consuming. We have proposed a Deep Convotuional Neural Network based ensemble architecture for extracting features from Chest X-Ray images and later classifying them into three categories namely- Community Acquired Pneumonia(CAP), Normal and COVID-19. We have shown that applying such technique can give better performance. Our ensemble network uses three pre-trained DCNN networks namely- NASNet, MobileNet and DenseNet. The low level features extracted from the three DCNN architectures are later concatenated and applied to a classifier for final classification. We have achieved an accuracy of 91.99% which is slightly better than the state of the art performances.
Distributed Dynamic Channel Allocation in 6G in-X Subnetworks for Industrial Automation

In this paper, we investigate dynamic channel se- lection in short-range Wireless Isochronous Real Time (WIRT) in-X subnetworks aimed at supporting fast closed-loop control with super-short communication cycle (below 0.1 ms) and extreme reliability (>99.999999%). We consider fully distributed approaches in which each subnetwork selects a channel group for transmission in order to guarantee the requirements based solely on its local sensing measurements without the possibility for exchange of information between subnetworks. We present three fully distributed schemes: $\epsilon$-greedy channel allocation, minimum SINR guarantee (minSINR) and Nearest Neighbor Conflict Avoidance (NNCA) based on measurements of the minimum SINR and interference power. We further apply a centralized graph coloring scheme as a baseline for evaluating performance of the proposed distributed algorithms. Performance evaluation considering subnetwork mobility and spatio-temporal correlated channel models shows that the dynamic allocation schemes results in significant performance improvement and a reduction in the bandwidth required for supporting such extreme connectivity by up to a factor larger than 2 relative to static channel assignment.
Randomized Stepped Frequency Radars Exploiting Block Sparsity of Extended Targets: A Theoretical Analysis

Randomized stepped frequency radar (RSFR) is very attractive for tasks under complex electromagnetic environment. Due to the synthetic high range resolution in RSFRs, a target usually occupies a series of range cells and is called an extended target. To reconstruct the range-Doppler information in a RSFR, previous studies based on sparse recovery mainly exploit the sparsity of the target scene but do not adequately address the extended-target characteristics, which exist in many practical applications. Block sparsity, which combines the sparsity and the target extension, better characterizes a priori knowledge of the target scene in a wideband RSFR. This paper studies the RSFR range-Doppler reconstruction problem using block sparse recovery. Particularly, we theoretically analyze the block coherence and spectral norm of the observation matrix in RSFR and build a bound on the parameters of the radar, under which the exact recovery of the range-Doppler information is guaranteed. Both simulation and field-experiment results demonstrate the superiority of the block sparse recovery over conventional sparse recovery in RSFRs.
Usability And Practicality of Speech Recording by Mobile Phones for Phonetic Analysis

High-quality speech recording is critical to phonetic analysis. However, when professional equipment or a sound-proof booth is not accessible, such as in random sampling or during the current pandemic period, is it reliable and valid to use non-professional devices to record speech data? We selected ten devices and examined the frequency range and signal-to-noise ratio (SNR) of speech data they recorded. We also compared recordings in a quiet room with noise at a moderate level. The results showed that all devices recorded a wide frequency range, which covered speech frequency well. But, their SNRs differed significantly. Environmental noise also appeared to affect recording quality. We then analyzed fine-grained phonetic parameters of data recorded in the quiet room, including suprasegmental, segmental and phonation-related parameters. F0 was found relatively consistent in the recordings from all devices, but certain differences were captured in F1, F2 and Center of Gravity (CoG). F3 as well as parameters relevant to phonation analysis, on the other hand, showed high variations. Therefore, our findings suggest that non-professional devices such as mobile phones are reliable substitutes of professional recorders, at least in prosodic analysis for general purposes. Cautions should be taken when values of F3 and phonation-related parameters are involved.