Opportunistic Interference Mitigation Achieves Optimal Degrees-of-Freedom in Wireless Multi-cell Uplink Networks

We introduce an opportunistic interference mitigation (OIM) protocol, where a user scheduling strategy is utilized in $K$-cell uplink networks with time-invariant channel coefficients and base stations (BSs) having $M$ antennas. Each BS opportunistically selects a set of users who generate the minimum interference to the other BSs. Two OIM protocols are shown according to the number $S$ of simultaneously transmitting users per cell: opportunistic interference nulling (OIN) and opportunistic interference alignment (OIA). Then, their performance is analyzed in terms of degrees-of-freedom (DoFs). As our main result, it is shown that $KM$ DoFs are achievable under the OIN protocol with $M$ selected users per cell, if the total number $N$ of users in a cell scales at least as $\text{SNR}^{(K-1)M}$. Similarly, it turns out that the OIA scheme with $S$($<M$) selected users achieves $KS$ DoFs, if $N$ scales faster than $\text{SNR}^{(K-1)S}$. These results indicate that there exists a trade-off between the achievable DoFs and the minimum required $N$. By deriving the corresponding upper bound on the DoFs, it is shown that the OIN scheme is DoF optimal. Finally, numerical evaluation, a two-step scheduling method, and the extension to multi-carrier scenarios are shown.Comment: 18 pages, 3 figures, Submitted to IEEE Transactions on Communication

A Novel Generator with Auxiliary Branch for Improving GAN Performance

The generator in the generative adversarial network (GAN) learns image generation in a coarse-to-fine manner in which earlier layers learn the overall structure of the image and the latter ones refine the details. To propagate the coarse information well, recent works usually build their generators by stacking up multiple residual blocks. Although the residual block can produce a high-quality image as well as be trained stably, it often impedes the information flow in the network. To alleviate this problem, this brief introduces a novel generator architecture that produces the image by combining features obtained through two different branches: the main and auxiliary branches. The goal of the main branch is to produce the image by passing through the multiple residual blocks, whereas the auxiliary branch is to convey the coarse information in the earlier layer to the later one. To combine the features in the main and auxiliary branches successfully, we also propose a gated feature fusion module that controls the information flow in those branches. To prove the superiority of the proposed method, this brief provides extensive experiments using various standard datasets including CIFAR-10, CIFAR-100, LSUN, CelebA-HQ, AFHQ, and tiny-ImageNet. Furthermore, we conducted various ablation studies to demonstrate the generalization ability of the proposed method. Quantitative evaluations prove that the proposed method exhibits impressive GAN performance in terms of Inception score (IS) and Frechet inception distance (FID). For instance, the proposed method boosts the FID and IS scores on the tiny-ImageNet dataset from 35.13 to 25.00 and 20.23 to 25.57, respectively

TRACI Impact Assessment of Transportation Manufacturing Nexus in the U.S.: A Supply Chain-Linked Cradle-to-Gate LCA

Sustainable transportation is an inevitable component of sustainable development intitiatives for mitigating the climate change impacts and stabilizing the rising carbon emissions thus global temperature. In this context, comprehensive analysis of the environmental impact of transportation can play a critical role towards quantifying the midpoint environmental and human health related impacts associated with the transportation activities triggered by manufacturing sectors. This study traces the life cycle impact of the U.S. transportation and manufacturing sectors’ nexus using Tool for the Reduction and Assessment of Chemicals and Other Environmental Impacts (TRACI) in the context of the Economic Input-Output Life Cycle Assessment (EIO-LCA) framework considering the following midpoint impact categories: ‘global warming’, ‘particulate matter’, ‘eutrophication’, ‘acidification’, and ‘smog air’. Both direct (onsite) and indirect (supply chain) industries’ relationships with transportation industry are considered as the main scope. Results indicated that top ten contributor manufacturing sectors accounted for over 55% total environmental impacts on each impact category. Additionally, based on the decomposition analysis, food manufacturing sector was found to be the major contributor to smog air with an approximate share of 21% in the entire supply chain. Automobile related manufacturing sectors also have significant impact on all five life cycle impact categories that the environmental impact of transportation is higher than on-site (direct) impact. Overall decomposition analysis of 53 manufacturing sector indicated that the environmental impact of transportation has severe effects on ‘smog air’, ‘eutrophication’ and ‘acidification’ with a share of 16.4%, 10.5%, and 6.0%, respectively. When we consider the average percentage share of transportation related environmental impact on the entire supply chain, U.S manufacturing sectors have a negative impact with a share of 18.8% of ‘smog air’, 16.8% for ‘eutrophication’, and 8.1% for ‘acidification’

Role of fairness, accountability, and transparency in algorithmic affordance

© 2019 Elsevier Ltd As algorithm-based services increase, social topics such as fairness, transparency, and accountability (FAT) must be addressed. This study conceptualizes such issues and examines how they influence the use and adoption of algorithm services. In particular, we investigate how trust is related to such issues and how trust influences the user experience of algorithm services. A multi-mixed method was used by integrating interpretive methods and surveys. The overall results show the heuristic role of fairness, accountability, and transparency, regarding their fundamental links to trust. Despite the importance of algorithms, no single testable definition has been observed. We reconstructed the understandings of algorithm and its affordance with user perception, invariant properties, and contextuality. The study concludes by arguing that algorithmic affordance offers a distinctive perspective on the conceptualization of algorithmic process. Individuals’ perceptions of FAT and how they actually perceive them are important topics for further study

Contextualizing privacy on health-related use of information technology

© 2019 Elsevier Ltd Privacy amid rapid digitalization of medical records is a critical ingredient to the success of electronic-based health service. This paper explores the potential roles of privacy attitudes concerning medical data, based on a large set of a national sample data (n = 2638) from the U.S. Health Information National Trend Survey. We examine the ways in which privacy concern and confidence are (a) mediated through one\u27s interest in sharing information with health professionals and (b) moderated by one\u27s medical condition and the reliance on Internet. Evidence from this study provides insights into the factors shaping health-related engagement in information technologies, helping us argue that privacy is a key predictor. Discussion offers interpretations of how people\u27s perceived need of medical data will mediate privacy concern, contextualizing the affordances of health technologies in future algorithmic applications

Object-Aware Impedance Control for Human-Robot Collaborative Task with Online Object Parameter Estimation

Physical human-robot interactions (pHRIs) can improve robot autonomy and reduce physical demands on humans. In this paper, we consider a collaborative task with a considerably long object and no prior knowledge of the object's parameters. An integrated control framework with an online object parameter estimator and a Cartesian object-aware impedance controller is proposed to realize complicated scenarios. During the transportation task, the object parameters are estimated online while a robot and human lift an object. The perturbation motion is incorporated into the null space of the desired trajectory to enhance the estimator accuracy. An object-aware impedance controller is designed using the real-time estimation results to effectively transmit the intended human motion to the robot through the object. Experimental demonstrations of collaborative tasks, including object transportation and assembly tasks, are implemented to show the effectiveness of our proposed method.Comment: 11 pages, 5 figures, for associated video, see https://youtu.be/bGH6GAFlRgA?si=wXj_SRzEE8BYoV2

Can One Achieve Multiuser Diversity in Uplink Multi-Cell Networks?

We introduce a distributed opportunistic scheduling (DOS) strategy, based on two pre-determined thresholds, for uplink $K$-cell networks with time-invariant channel coefficients. Each base station (BS) opportunistically selects a mobile station (MS) who has a large signal strength of the desired channel link among a set of MSs generating a sufficiently small interference to other BSs. Then, performance on the achievable throughput scaling law is analyzed. As our main result, it is shown that the achievable sum-rate scales as $K\log(\text{SNR}\log N)$ in a high signal-to-noise ratio (SNR) regime, if the total number of users in a cell, $N$, scales faster than $\text{SNR}^{\frac{K-1}{1-\epsilon}}$ for a constant $\epsilon\in(0,1)$. This result indicates that the proposed scheme achieves the multiuser diversity gain as well as the degrees-of-freedom gain even under multi-cell environments. Simulation results show that the DOS provides a better sum-rate throughput over conventional schemes.Comment: 11 pages, 3 figures, 2 tables, to appear in IEEE Transactions on Communication