Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Network Connectivity Based Mental Health Category Prediction from Rest-fMRI Data

One of the most significant health issues the world is now experiencing is mental health. Since it is challenging to measure, it fails to motivate behavioral change or other interventions targeted at improving it. The lack of such measurements discourages learning and behavior modification, which are critical for enhancing healthy habits. Neuroimaging promises to provide a window into mental health. The use of resting fMRI (rs-fMRI) models capable of categorizing mental health at the individual level has the potential to provide insights into how it impacts the brain. Here, we applied a deep learning approach to classify a mental health score using static functional network connectivity (FNC) derived from rs-fMRI data. Comparisons were made against traditional machine learning approaches to evaluate the model’s performance. The discriminative features present in each mental health category were analyzed for interpreting the deep learning model. The experiments resulted in a classification accuracy of 91%, 91%, 100%, and 100% for excellent, good, fair, and poor mental health classes. Results also highlighted the most salient brain network in the sFNC matrix for each mental health score classification.
Survey Methodology

In this article, we propose a Bernoulli­type bootstrap method that can easily handle multi­stage stratified designs where sampling fractions are large, provided simple random sampling without replacement is used at each stage. The method provides a set of replicate weights which yield consistent variance estimates for both smooth and non­smooth estimators. The method’s strength is in its simplicity. It can easily be extended to any number of stages without much complication. The main idea is to either keep or replace a sampling unit at each stage with preassigned probabilities, to construct the bootstrap sample. A limited simulation study is presented to evaluate performance and, as an illustration, we apply the method to the 1997 Japanese National Survey of Prices. 1. F. Funaoka, Professor, Faculty of Economics, Shinshu University, 3­1­1 Asahi, Matsumoto, Nagano, 390­8621, Japan; H. Saigo, Professor, School of Political Science and Economics, Waseda University, 1­6­1 Nishiwaseda Shinjuku, Tokyo, 169­8050, Japan; R.R. Sitter, Professor, Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, B.C., V5A 1S6, Canada; T. Toida, Associate Professor, Faculty of Social and Information Studies, Gunma University, 2­4 Aramakicho, Maebashi, Gunma 371­8510, Japan.
Input Impedance Modeling of Dual-Active-Bridge-Based Input-Series Output-Parallel Converters

Modular DC-DC converters with submodules in input-series output-parallel (ISOP) connection have attracted the interests of researchers and industry. Due to its modularity, the ISOP converter allows a high voltage conversion ratio with enhanced reliability. Considering the ISOP converter contains multiple submodules, the converter structure can be more complicated and challenge the converter modeling, which is important for control design and system integration. To solve this issue, in this paper the small-signal modeling of the ISOP converter with dual-active-bridge (DAB) submodules is presented. To validate the accuracy of the proposed small-signal model, a switching model of the DAB-based ISOP converter is built in a simulation and the input impedance of the switching model is measured and compared to the proposed small-signal model.
A Novel Technique to Mitigate the Overlap-Time Effect in Current Source Inverters

Generally, the current source inverter is considered more reliable than the voltage source inverter due to the presence of inductor as an energy storage element on the DC link, but a disruption in the inductor current could significantly risk its reliability. To guarantee the reliable operation of current source inverter, an overlap time must be added to the gate signals of its switches. This ensures the safe operation of current source inverter but results in increased total harmonic distortion and reduction of DC current utilization ratio. This paper proposes a new technique for reducing the effect of overlap time and improving the performance of pulse width modulated current source inverter. The proposed technique modifies the level-shifted sinusoidal pulse width modulation to mitigate the effect of overlap time. In this paper, the working principle of the proposed technique is presented in detail, and a mathematical relation between the level shifting and overlap time is established. Moreover, a function for the approximation of output AC current is also presented. Finally, results from computer simulations and experiments are presented which validate the effectiveness of the proposed technique.
Energy Aware Collaborative Machine Learning on Energy-Harvesting Devices

Performing machine learning tasks on low end devices enables the development of various smart applications. Especially, these low end devices are often equipped with ultra-low-power microcontroller units (MCUs) that have weak computation power and few memory resources. It is a more challenging work to put these machine learning tasks on those end devices powered by harvested ambient energy, which are often referred to as energy-harvesting (EH) devices, since the unstable ambient energy can lead to the execution failure of the machine learning tasks. This paper proposes an adaptive energy-aware design to coordinate multiple EH devices to accomplish multi-class classification computation. It also leverages the concept of the One-vs-All (OVA) strategy turning a multi-class classification into multiple binary classifications. The experimental results show our work performs better than the widely used round-robin policy and self-greedy policy in consideration of time and energy consumption.
A study on indoor positioning of an open-source drone using AR markers

When drones fly autonomously, GPS is basically used to establish navigation and safety functions. However, the above functions cannot be used in non-GPS environments such as indoor flight. This study focuses on indoor drone positioning using AR markers and aims to clarify its accuracy with a self-localization algorithm based on open-source platform. In the experimental evaluation, the drone flies autonomously by detecting AR markers with a drone-mounted camera, and its position coordinate data are collected to evaluate the accuracy of the self-localization. In particular, this paper evaluates the contribution of the position estimation by the AR markers to the self-localization computed by the flight controller, and shows that the self-localization accuracy is highly dependent on the position estimation by the AR markers, and its accuracy is about 1 to 2 cm.
Augmentation Enables One-Shot Generalization in Learning from Demonstration for Contact-Rich Manipulation

We introduce a Learning from Demonstration (LID) approach for contact-rich manipulation tasks, i.e., tasks in which the manipulandum's motion is constrained by contact with the environment. Our approach is motivated by the insight that even a large number of demonstrations will often not contain sufficient information to obtain a general policy for the task. To obtain general policies, our approach augments the information contained in a single demonstration. This autonomous augmentation is based on the insight that environmental constraints play a central role in generalization. We validate our approach in real-world experiments with mechanisms with multiple, interdependent articulations, including latch locks, chain locks, and drawers with handles. The extracted policies, obtained from a single augmented human demonstration, generalize to different mechanisms of the same type and in varying environmental settings.
A Low-Profile Broadband Quad-Polarization Reconfigurable Omnidirectional Antenna

A low-profile broadband quad-polarization reconfigurable omnidirectional antenna is presented. The quad-polarization antenna consists of a circular loop for horizontal polarization (HP) and a top-loaded monopole for vertical polarization (VP). The circular loop is composed of four arc dipoles excited by a broadband feeding network. The left-handed circular polarization (LHCP) is obtained by simultaneously feeding the circular loop and the top-loaded monopole with different phases. Two <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> g/2 phase shifters are introduced for the right-handed circular polarization (RHCP). The 12 p-i-n diodes are employed in the feeding network to realize the quad-polarization reconfiguration. It is shown that the bandwidth of the quad-polarization reconfigurable omnidirectional antenna overlapped for HP, VP, LHCP, and RHCP is about 28.6% (1.65–2.2 GHz) with good omnidirectional patterns. The total height of the antenna is about <inline-formula> <tex-math notation="LaTeX">$0.09\lambda _{0}$ </tex-math></inline-formula>.
Physics-based fluid simulation in Computer Graphics: Survey, research trends, and challenges

Physics-based fluid simulation has played an increasingly important role in the computer graphics community. Recent methods in this area have greatly improved in generating complex visual effects and also in computational efficiency. Novel techniques have emerged to deal with complex boundaries, multiphase fluids, gas-liquid interfaces, and fine details. In parallel, the combined use of machine learning, image processing, and fluid control technologies has brought many interesting and novel research perspectives. In this survey, we provide an introduction to theoretical concepts underpinning physics-based fluid simulation and their practical implementation with the aim to serve as a guide for both newcomer and seasoned researchers for exploring the physics-based fluid simulation field, with a focus on recent developments in the last decade. Driven by the distribution of recent publications in the field, we structure our survey to cover physical background, discretization approaches, computational methods that address scalability, fluid interactions with other materials and interfaces, and methods for expressive aspects of surface detail and control. From a practical perspective, we overview existing implementations available for the above methods.
Cloud Computing for Agent-based Traffic Management Systems

Increased traffic congestion and the associated pollution are forcing everyone in transportation to think about rapid changes in traffic processes and procedures [2]. This paper titled ―Cloud Computing for Agent-Based Traffic Management Systems‖ illustrates the use of autonomy, mobility, and adaptability of mobile agents to deal with dynamic vehicular traffic over the cloud environment [1]. Cloud computing can help such systems cope with the large amounts of storage and computing resources required to use traffic strategy agents and mass transport data effectively. This paper also reviews the history of the development of traffic control and management systems within the evolving computing paradigm and shows the state of traffic control and management systems based on mobile multi agent technology [3]. Intelligent transportation clouds could provide services such as decision support and a standard development environment for traffic management strategies. With mobile agent technology, an urban-traffic management system based on Agent-Based Distributed and Adaptive Platforms for Transportation Systems (ADAPTS) [3] is both feasible and effective. However, the large-scale use of mobile agents will lead to the emergence of a complex, powerful organization layer that requires enormous computing and power resources. To deal with this problem, the proposed system also illustrates an urban-traffic management system using intelligent traffic clouds.
Mapping Estimation for Discrete Optimal Transport Supplementary Material

Proof. First of all recall that a sum of jointly convex functions is jointly convex. Hence it is sufficient to show that the three terms of optimization problem (1) are jointly convex. We note f1(γ, T ) = 1 nsdt ‖T (Xs)− nsγXt‖2F , f2(γ) = λγ max(C) 〈γ,C〉F and f3(T ) = λT dsdt R(T ). f1 depends on both T and γ and controls the proximity between the transformation induced by T and the barycentric interpolation obtained from γ. f2 only depends on γ, it corresponds to the standard term minimized to solve the optimal transport problem. f3 regularizes T to ensure a better generalization. Note that f2 and f3 are by construction jointly convex in γ and T . We will show that the f1 is also jointly convex. Let g(γ, T ) = ‖T (Xs)− nsγXt‖F , we want to show that:
Adaptive network and spatial sampling

This paper describes recent developments in adaptive sampling strategies and introduces new variations on those strategies. Recent developments described included targeted random walk designs and adaptive web sampling. These designs are particularly suited for sampling in networks; for example, for finding a sample of people from a hidden human population by following social links from sample individuals to find additional members of the hidden population to add to the sample. Each of these designs can also be translated into spatial settings to produce flexible new spatial adaptive strategies for sampling unevenly distributed populations. Variations on these sampling strategies include versions in which the network or spatial links have unequal weights and are followed with unequal probabilities.
Effect of (cid:1) 2 -adrenoceptor agonists and other cAMP-elevating agents on inﬂammatory gene expression in human ASM cells: a role for protein kinase A

Effect of (cid:1) 2 -adrenoceptor agonists and other cAMP-elevating agents on inﬂammatory gene expression in human ASM cells: a role for protein kinase A. Am J Physiol Lung Cell Physiol 295: L505–L514, diseases such as asthma, airway smooth muscle (ASM) cells play a synthetic role by secreting inﬂammatory mediators such as granulocyte-mac-rophage colony-stimulating factor (GM-CSF), IL-6, or IL-8 and by expressing surface adhesion molecules, including ICAM-1. In the present study, PGE 2 , forskolin, and short-acting (salbutamol) and long-acting (salmeterol and formoterol) (cid:1) 2 -adrenoceptor agonists reduced the expression of ICAM-1 and the release of GM-CSF evoked by IL-1 (cid:1) in ASM cells. IL-1 (cid:1) -induced IL-8 release was also repressed by PGE 2 and forskolin, whereas the (cid:1) 2 -adrenoceptor agonists were ineffective. In each case, repression of these inﬂammatory indexes was prevented by adenoviral overexpression of PKI (cid:2) , a highly selective PKA inhibitor. These data indicate a PKA-dependent mechanism of repression and suggest that agents that elevate intracellular cAMP, and thereby activate PKA, may have a widespread anti-inﬂammatory effect in ASM cells. Since ICAM-1 and GM-CSF are highly NF- (cid:3) B-dependent genes, we used an adenoviral-delivered NF- (cid:3) B-dependent luciferase reporter to examine the effects of forskolin and the (cid:1) 2 adrenoceptor agonists on NF- (cid:3) B activation. There was no effect on luciferase activity measured in the presence of forskolin or (cid:1) 2 adrenoceptor agonists. This ﬁnding is consistent with the observation that IL-1 (cid:1) -induced expression of IL-6, a known NF- (cid:3) B-dependent gene in ASM, was also unaffected by (cid:1) 2 -adrenoceptor agonists, forskolin, PGE 2 , 8-bromo-cAMP, or rolipram. Collectively, these results indicate that repression of IL-1 (cid:1) -induced ICAM-1 expression and GM-CSF release by cAMP-elevating agents, including (cid:1) 2 -adre-noceptor agonists, may not occur through a generic effect on NF- (cid:3) B.
Match-Mode Autoregressive Method for Moving Source Depth Estimation in Shallow Water Waveguides

Source depth estimation is always a problem in underwater acoustic area, because depth estimation is a nonlinear problem. Traditional depth estimation methods use a vertical line array, which has disadvantage of poor mobility due to the size of sensor array. In order to estimate source depth with a horizontal line array, we propose a matched-mode depth estimation method based on autoregressive (AR) wavenumber estimation for a moving source in shallow water waveguides. First, we estimate the mode wavenumbers using the improved AR modal wavenumber spectrum. Second, according to the mode wavenumber estimation, we estimate the mode amplitudes by the wavenumber spectrum, which is obtained by generalized Hankel transform. Finally, we estimate source depth estimation by the peak of source depth function wherein the data mode best matches the replica mode that is calculated using a propagation model. Compared with synthetic aperture beamforming, the proposed method exhibits a better performance in source depth estimation under low signal-to-noise ratio or the small range span. The robustness of the proposed method is illustrated by simulating the performance in mismatched environment.
The Digital Divide in Healthcare: A Socio-Cultural Perspective of Digital Health Literacy

As the use of IT for health management increases, threats to exacerbate existing health inequalities increases. Through semi-structured expert interviews, this qualitative study aims to examine the Socio-Cultural characteristics associated with access to and use of technologies for health managing purposes and its relationship with health literacy. Inspired by Bourdieu, the paper uses a socio-cultural perspective to understand the choices and lifestyle of individuals, based on the underlying human mechanisms that may function as determinants of the (in)equality which digitalization of health services may entail. The study focuses on the following question: How do social factors create and reproduce (in)equality in behavioral skills, regarding digital health technologies? The study contributes with a multidimensional perspective on social factors that influences the use and acceptance of digital health technologies from the expert’s perspectives.
Providing Social Support May Be More Beneficial Than Receiving It

This study examines the relative contributions of giving versus receiving support to longevity in a sample of older married adults. Baseline indicators of giving and receiving support were used to predict mortality status over a 5-year period in the Changing Lives of Older Couples sample. Results from logistic regression analyses indicated that mortality was significantly reduced for individuals who reported providing instrumental support to friends, relatives, and neighbors, and individuals who reported providing emotional support to their spouse. Receiving support had no effect on mortality once giving support was taken into consideration. This pattern of findings was obtained after controlling for demographic, personality, health, mental health, and marital-relationship variables. These results have implications for understanding how social contact influences health and longevity.
Eye Movements Reveal the Spatiotemporal Dynamics of Visual Search

Given that attention precedes an eye movement to a target it becomes possible to use fixation sequences to probe the spatiotemporal dynamics of search Applying this method to a realistic search task, we found eye movements directed to the geometric centers of progressively smaller groups of objects rather than accurate fixations to individual objects in a display Such a binary search strategy is consistent with zoom-lens models positing an initially broad distribution of search, followed by a narrowing of this search region until only the target is selected We also interpret this oculomotor averaging behavior as evidence for an initially parallel search analysis that becomes increasingly serial as the search process converges on the target
Predicting Adoption Intention of Artificial Intelligence-A Study on ChatGPT

— The objective of this research is to study the factors that influence the adoption behaviour of ChatGPT among professionals in Bangladesh. This study investigates the adoption intention of ChatGPT by using the modified Unified Theory of Acceptance and Use of Technology (UTAUT) model. This model is used as a theoretical framework to analyse the many aspects that exert effect on the adoption process. The study was administered to a sample of 350 professional knowledge-workers using an online questionnaire. The data analysis was performed using PLS-SEM. The findings of the research provided a significant understanding of the determinants that influence the desire to adopt and the actual use of ChatGPT. The study highlights the significance of strengthening behavioral intentions, emphasizing performance benefits and building trust
The BERT Ranking Paradigm: Training Strategies Evaluated

This thesis researches the most recent paradigm in information retrieval, which applies the neural language representation model BERT to rank relevant passages out of a corpus. The research focuses on a re-ranker scheme that uses BM25 to pre-rank the corpus followed by BERT-based ranking, exploring better ﬁne-tuning methodology for a pre-trained BERT. This goal is pursued in two parts, in the ﬁrst, all methods rely on binary relevance labels, while the second part applies methods that rely on multiple relevance labels instead. Part one researches methods that apply training data enhancement and the application of inductive transfer learning methods. Part two researches the application of single class multi label methods, multi class multi label methods and label-based regression. In all parts, the methods were evaluated on the fully annotated Cranﬁeld dataset. This thesis demonstrates that applying inductive transfer learning with the Next Sentence Prediction task improves the baseline by presenting various methods to enrich the ﬁne-tuning data for diﬀerent levels of the BM25-BERT ranking pipeline. Also, this thesis demonstrates that application of a regression method results in above baseline performance. This indicates the superiority of this method over rule-based ﬁltering of classiﬁer results.
THE INTERDEPENDENCE OF FISCAL AND MONETARY POLICY

In this paper we study ﬁscal policy eﬀects and ﬁscal space for countries in a monetary union with diﬀerent levels of public debt. We develop a dynamic stochastic general equilibrium (DSGE) model of a two-country monetary union, calibrated to match characteristics of Spain and Germany, in which debt sustainability is endogenously determined a la Bi (2012) to shape the responses of the risk premium on public debt. Policy shocks change the market’s expectation about future primary surplus, producing a direct eﬀect on the sovereign risk premium and macroeconomic responses of the economy. In normal times, the costs of a government spending driven ﬁscal consolidation in the high-debt country are greatly diminished when the consolidation improves its debt sustainability prospects. Fiscal consolidations in both members of the monetary union decrease real interest rates and amplify the reduction in risk premium in the highly-indebted country, improving union-wide output in the long run, at the cost of lower output in the low-debt country in the short run. On the contrary, when monetary policy is constrained at the zero lower bound, the risk premium channel arising from the endogenous determination of debt sustainability becomes muted.