Miracle-dz/UniFAR-FTUs huggingface dataset:
Pediatric obesity phenotyping by magnetic resonance methods

Purpose of reviewAccurate measurement of adiposity in obese children is required for characterizing the condition's phenotype, severity, and treatment effects in vivo. Non-invasive and safe, magnetic resonance imaging and spectroscopy provide an important new approach for characterizing key aspects of pediatric obesity. This review focuses on recent advances in non-invasive magnetic resonance imaging and spectroscopy for quantifying total body and regional adiposity, mapping adipose tissue distribution, and evaluating selected metabolic disturbances in children. The aim is to provide an investigator-focused overview of magnetic resonance methods for use in the study of pediatric body composition and metabolism. Recent findingsWhole body axial images can be rapidly acquired on most clinical magnetic resonance imaging scanners. The images can then be semi-automatically segmented into subcutaneous, visceral, and intramuscular adipose tissue. Specific pediatric studies of errors related to slice gap and number are available. The acquisition of scans in healthy and premature infants is now feasible with recent technological advances. Spectroscopic, Dixon, and other approaches can be used to quantify the lipid content of liver, skeletal muscle, and other organs. Protocol selection is based on factors such as subject age and cost. Particular attention should be directed towards identification of landmarks in growth studies. Recent advances promise to reduce the requirement of subjects to remain motionless for relatively long periods. SummaryMagnetic resonance imaging and spectroscopy are safe, practical, and widely available methods for phenotyping adiposity in children that open new opportunities for metabolism and nutritional research.
Temperature-dependent optimization of cache leakage power dissipation

Leakage power consists of an increasing portion of the total power consumption for modern IC designs. Due to the strong inter-dependency between leakage and temperature, it becomes imperative to consider the thermal effects while optimizing the leakage power. In this paper, we present a temperature-dependent optimization methodology for on-chip caches. By integrating fast yet accurate coupled thermal-leakage simulations into an optimization flow, we are able to optimally tradeoff between the cache performance and leakage power while considering realistic on-chip temperature distribution. Our analysis indicates that for future memory intensive designs, the lack of chip temperature information can cause a significant error in the leakage power estimation, thus leading to non-optimal cache designs. Our results further imply that the optimization of cache performance and leakage power shall be attacked as part of the whole system design task in which chip-level floor planning and its thermal impacts are fully addressed.
Variational Inference for Policy Search in changing situations

Many policy search algorithms minimize the Kullback-Leibler (KL) divergence to a certain target distribution in order to fit their policy. The commonly used KL-divergence forces the resulting policy to be 'reward-attracted'. The policy tries to reproduce all positively rewarded experience while negative experience is neglected. However, the KL-divergence is not symmetric and we can also minimize the the reversed KL-divergence, which is typically used in variational inference. The policy now becomes 'cost-averse'. It tries to avoid reproducing any negatively-rewarded experience while maximizing exploration. Due to this 'cost-averseness' of the policy, Variational Inference for Policy Search (VIP) has several interesting properties. It requires no kernel-band with nor exploration rate, such settings are determined automatically by the inference. The algorithm meets the performance of state-of-the-art methods while being applicable to simultaneously learning in multiple situations. We concentrate on using VIP for policy search in robotics. We apply our algorithm to learn dynamic counterbalancing of different kinds of pushes with human-like 2-link and 4-link robots.
Improving transparency of virtual coupling for haptic interaction with human force observer

SUMMARY Relying solely on virtual springs and dampers, the transparency of standard virtual coupling suffers from the device-proxy coordination error when a large interaction force is engaged (e.g., contact tasks) and also from the unmodifiable inertias of the haptic device and the virtual proxy. To overcome these limitations, we propose a novel virtual coupling scheme, which, utilizing passive decomposition and a human force observer, can maintain the device-proxy coordination error even during contact tasks, while also allowing for scaling down (or up) the apparent inertia of the coordinated device-proxy system, thereby, substantially improving transparency of the standard virtual coupling. Experiments are performed to show the performance and passivity of the proposed virtual coupling. Minimum-possible passive inertia scaling is also theoretically established via some positive-real analysis.
Understanding Gender as a Fundamental Cause of Health: Simultaneous Linear Relationships between Gender, Mental Health, and Physical Health Over Time

Abstract Responding to calls to move beyond mental or physical health examinations of gender and health, and to systemically understand the relationships between mental health, physical health, and social identity locations, we explored the extent to which gender is a fundamental cause of health by estimating the relationships between gender, mental health, and physical health simultaneously. We analyzed nationally representative longitudinal data from Waves I and III of the American Changing Lives Survey, and estimated general and gender-specific structural equation models to better understand direct and indirect effects of the structural relationships between gender, mental health, and physical health over time. Our findings suggest mental health outcomes drive the likelihood of physical health outcomes in the case of women only, whereas for men, there is no direct nor significant relationship. However, we find persistent effects of anxiety on limited daily activities for men, but not for women. In conclusion, we discuss potential lessons learned from using such analytic methods and the implications of our findings as they relate to gender and health.
Translation of Chinese Xiehouyu (Sayings) and Relevance Theory

This study pioneers in applying the relevance theoretical framework developed by Gutt (1991, 2000a) and based on Sperber and Wilson's relevance theory (1986, 1995) to Chinese xiehouyu translations. A Chinese xiehouyu is an idiom or an enigmatic folk saying consisting of two parts. This study examines direct approaches and indirect approaches with or without the substitution of appropriate metaphors. It argues that while relevance theory is effective in both approaches, it is particularly important for indirect translation because there are various ways of translating a xiehouyu indirectly, depending upon what meaning aspects of the original the target audience will find relevant. A match-up metaphor in the target language may not always be available, but whenever possible the strategy of substituting metaphors should be utilised to maximise contextual effects and minimise the processing effort; this is probably the most effective way to achieve optimal relevance. As an instance of interpretive use, a translation's success depends on the achievement of optimal relevance. The translator has to pay attention to the kind and degree of interpretative resemblance the audience expects: that is to say, a translator should take the target audience's cognitive environment into account and choose the most applicable approach to guide the audience in achieving optimal relevance.
CVaR-Constrained Optimal Bidding of Electric Vehicle Aggregators in Day-Ahead and Real-Time Markets

An electric vehicle aggregator (EVA) that manages geographically dispersed electric vehicles offers an opportunity for the demand side to participate in electricity markets. This paper proposes an optimization model to determine the day-ahead inflexible bidding and real-time flexible bidding under market uncertainties. Based on the relationship between market price and bid price, the proposed optimal bidding model of EVA aims to minimize the conditional expectation of electricity purchase cost in two markets considering price volatility. Moreover, the penalty cost of the deviation between the bidding quantities is included to avoid large power variation and arbitrage. The conditional expectation optimization model is formulated as an expectation minimization problem with the conditional value-at-risk constraints. Based on the price data in the PJM market, simulation results verify that our model is a decision-making tool in electricity markets, which can help market players comprehend the variants of bid price, expected cost and probability of successful bidding.
Constraint-based mobile robot localization

Mobile Robot Localization is a fundamental problem to solve when navigating in an indoor structured environment This problem might be stated as a match ing problem between sensor observations and model fea tures of an a priori map of the environment Geometric constraints may be used to reduce the complexity of the matching process Appropiate modelling of the geomet ric information is required to deal with such constraints In this paper we estimate the mobile robot localization by means of a matching between segments obtained by a laser range nder mounted on the robot and model seg ments of an a priori map of the environment A prob abilistic method is used to represent the uncertainty and partiallity of the geometric information involved We give some experimental results in which we compare two alternative matching schemes
Doppler shift simulation of scattered HF signals during the Tromsø HF pumping experiment on 16 February 1996

Abstract. Comparisons between bistatic scatter measurements and simulation results during the Tromso HF pumping experiment on 16 February 1996 are made. Doppler measurements of an HF diagnostic signal scattered from the field-aligned irregularities (FAIs) in the auroral E-region were carried out on the London – Tromso – St. Petersburg path at 9410 kHz from 21:00 to 22:00 UT. The scattered signals were observed both from natural and artificial ionospheric irregularities located in the vicinity of Tromso. To simulate the Doppler frequency shifts, fd , of scattered signals, a radio channel model, named CONE, was developed. The model allows for ray tracing, group and phase paths, and Doppler frequency shift calculations. The calculated Doppler shifts were analyzed for dependence on the magnitude and direction of plasma velocities in the scattering volume. It was found that the velocity components in the north-south direction are crucial for explaining the Doppler frequency shifts of the scattered diagnostic signals. To simulate fd , real velocities obtained from the EISCAT UHF radar at an altitude of 278 km and from the digital all-sky imager during the experiment were employed. The simulation results of Doppler frequency shift variations with time are in reasonable agreement with the experimental Doppler shifts of scattered signals on the London – Tromso – St. Petersburg path. Key words. Ionosphere (active experiments; ionospheric irregularities) Radio science (ionospheric propagation)
Title Socially-optimal ISP-aware P 2 P Content Distribution via aPrimal-Dual Approach

Peer-to-peer (P2P) technology is popularly exploited to enable large-scale content distribution (e.g., live and ondemand video streaming) with low server costs. Most P2P protocols in place are network agnostic, where peers download content chunks from each other regardless of their ISP belonging, leading to significant amounts of inter-ISP traffic. It has been a daunting challenge how to design protocols that optimize the P2P content distribution topology, such that inter-ISP traffic is minimized while the dissemination performance is maximized, not to mention one that motivates peer’s voluntary ISP-aware peer selection. In this paper, we formulate a social welfare maximization framework for dynamical construction of the P2P content distribution topology taking into consideration both peers’ gain due to chunk downloading and inter-ISP traffic that is incurred. Given its nature of an assignment problem, we resort to a primal-dual framework to design the solution algorithm, which can be practically implemented as a set of distributed, interleaving auctions, where peers bid for bandwidth to download chunks at other peers considering the potential inter-ISP traffic as a cost factor. Such an auction-based mechanism encourages peers to download from neighbors with low network costs in between, in order to succeed in bandwidth acquisition for chunk downloading. We analyze and prove the social optimality achieved by the distributed auctions and verify the performance of our proposal using realistic emulation experiments with real P2P traffic.
MultiMQC: A Multilevel Message Queuing Cache Combining In-NIC and In-Kernel Memories

Message queuing systems that deliver messages from publishers to subscribers play an important role to collect data from IoT devices. Traditional message queuing systems have improved their performance in the context of transferring log data from publishers such as Web servers to subscribers that analyze the log data. In this case, both publishers and subscribers have been assumed to have enough buffer capacity and can transfer data as jumbo frame packets for high efficiency. In recent IoT applications, however, publishers are small sensors or edge devices with low-power processors and limited memory capacity. Vast numbers of such publishers produce relatively small packets. Such a lot of small messages significantly decrease the efficiency of conventional message queuing systems. To address this issue, a dedicated message queuing logic can be implemented on FPGA-based network interface card (FPGA NIC). However, a serious issue of such in-NIC approach is a limited memory capacity on the FPGA NIC. To handle message overflow of the in-NIC cache, in this paper, it is combined with a large in-kernel software cache. More specifically, we propose a multilevel message queuing cache combining in-NIC and in-kernel memories, called MultiMQC. The multilevel cache improves the read performance. Regarding the write performance, MultiMQC introduces a batch transfer that packs small incoming messages into a single batch. We implemented MultiMQC using NetFPGA-SUME board as in-NIC cache and Linux Netfilter framework as in-kernel cache. The experimental results demonstrate that the write throughput is increased in proportion to the batch size. When pull requests hit in the in-NIC cache, the read throughput reaches 95.8% of 10GbE line rate in four 10GbE interfaces.
Identification of Key Determinants of Satisfaction of Users of Electronic Banking Services

Abstract Under the conditions of increased competition, imperatives for banking institutions are focusing on the provision of better quality services, meeting the needs of clients and adjusting to their specific demands, as these are the conditions for achieving satisfaction. Satisfaction has a consequent effect on the realisation of communication, but also economic business goals of market players. In accordance with these facts, the significance of measuring the quality of services needs to be pointed out. In recent years, the tendency of increase in the number of bank clients who use electronic banking services has been perceived in the Republic of Serbia. Advantages of this modern service are numerous, and the ones that stand out among them are saving time, more favourable price, comfortable and fast transaction execution. The purpose of this paper is to examine the satisfaction of clients in relation to electronic banking services, i.e. to come to a conclusion which elements of service quality have an impact on satisfaction, as well as to assess the intensity of such impact. In order to obtain the necessary information, we have used the measures of descriptive statistical analysis, reliability analysis, correlation and regression analysis. The data have been collected using the survey method and the questionnaire distributed to respondents for the purpose of specific research has been created on the basis of previous research in the quality of service field. The results have shown that three dimensions of modified SERVQUAL model (security, ease of use and responsibility) have a statistically significant impact on satisfaction, while other factors (reliability, empathy and website content) have, in their joint effect, indicated a non-significant impact on satisfaction. The contribution of this paper is reflected in the fact that the identification of factors (reliability, empathy, website content) that have not achieved a significant impact in their joint effect on the satisfaction of clients with electronic banking can be a signal for the management of financial institutions to take action in order to reduce the perceived risk, maintain trust, provide training and development of employees, modernize website appearance.
Succinylated Octopamine Ascarosides and a New Pathway of Biogenic Amine Metabolism in Caenorhabditis elegans*

Background: Ascarosides play central roles regulating C. elegans behavior and development. Results: L1 larvae produce starvation-dependent ascarosides based on succinylated octopamine. Conclusion: Succinylation is an important pathway for metabolism of biogenic amines in C. elegans. Significance: Octopamine ascarosides connect neurotransmitter and ascaroside signaling in C. elegans. The ascarosides, small-molecule signals derived from combinatorial assembly of primary metabolism-derived building blocks, play a central role in Caenorhabditis elegans biology and regulate many aspects of development and behavior in this model organism as well as in other nematodes. Using HPLC-MS/MS-based targeted metabolomics, we identified novel ascarosides incorporating a side chain derived from succinylation of the neurotransmitter octopamine. These compounds, named osas#2, osas#9, and osas#10, are produced predominantly by L1 larvae, where they serve as part of a dispersal signal, whereas these ascarosides are largely absent from the metabolomes of other life stages. Investigating the biogenesis of these octopamine-derived ascarosides, we found that succinylation represents a previously unrecognized pathway of biogenic amine metabolism. At physiological concentrations, the neurotransmitters serotonin, dopamine, and octopamine are converted to a large extent into the corresponding succinates, in addition to the previously described acetates. Chemically, bimodal deactivation of biogenic amines via acetylation and succinylation parallels posttranslational modification of proteins via acetylation and succinylation of l-lysine. Our results reveal a small-molecule connection between neurotransmitter signaling and interorganismal regulation of behavior and suggest that ascaroside biosynthesis is based in part on co-option of degradative biochemical pathways.
Title On the Analysis of the Bifurcation Sets of Equilibrium Points inParameter Space

This paper addresses the problems of characterizing and estimating the bifurcation sets of equilibrium points in multi-parameter space of a class of nonlinear dynamical systems. Specifically, we investigate the sets of parameters that lead to saddle-node bifurcations and Hopf bifurcations at an equilibrium point of interest. First, a characterization of these sets is provided in terms of the zeros of some functions. Second, this characterization is exploited to estimate such sets through convex programming for the case of polynomial dynamical systems. In particular, two conditions are proposed for establishing whether a sublevel set of a given polynomial does not contain parameters that lead to bifurcations. By using these conditions, the largest of such sublevel sets can be estimated by solving an eigenvalue problem. Some numerical examples illustrate the proposed results.
AutoFE : Efficient and Robust Automated Feature Engineering by Hyunjoon Song

Feature engineering is the key to building highly successful machine learning models. We present AutoFE, a system designed to automate feature engineering. AutoFE generates a large set of new interpretable features by combining information in the original features. Given an augmented dataset, it discovers a set of features that significantly improves the performance of any traditional classification using an evolutionary algorithm. We demonstrate the effectiveness and robustness of our approach by conducting an extensive evaluation on 8 datasets and 5 different classification algorithms. We show that AutoFE can achieve an average improvement in predictive performance of 25.24% for all classification algorithms over their baseline performance obtained with the original features. Thesis Supervisor: Samuel Madden Title: Professor of Electrical Engineering and Computer Science
Exploring the use of volatile STT-RAM for energy efficient video processing

This paper explores the efficient use of STT-RAM in video processing system by choosing display processing as a case study. Through architectural analysis, we demonstrate that volatile STT-RAM rather than non-volatile STT-RAM is more appropriate for video processing system, as video data is mostly processed in streaming-style and relaxing the retention time can significantly reduce the write latency and energy of STT-RAM. Moreover, the error resilience of video display processing system is also evaluated to address the potential bit error rate increase when we relax the retention time of STT-RAM cells. Simulation results demonstrate that, the use of volatile STT-RAM and extra design techniques can significantly reduce the overall memory energy consumption to 38.24% with respect to that of baseline SRAM architecture, and video display processing system can tolerate up to 1×10-5 bit error rate without any visible image quality degradation.
An Iterative ‘Sudoku Style’ Approach to Subgraph-based Word Sense Disambiguation

We introduce an iterative approach to subgraph-based Word Sense Disambiguation (WSD). Inspired by the Sudoku puzzle, it significantly improves the precision and recall of disambiguation. We describe how conventional subgraph-based WSD treats the two steps of (1) subgraph construction and (2) disambiguation via graph centrality measures as ordered and atomic. Consequently, researchers tend to focus on improving either of these two steps individually, overlooking the fact that these steps can complement each other if they are allowed to interact in an iterative manner. We tested our iterative approach against the conventional approach for a range of well-known graph centrality measures and subgraph types, at the sentence and document level. The results demonstrated that an average performing WSD system which embraces the iterative approach, can easily compete with state-ofthe-art. This alone warrants further investigation.