Miracle-dz/UniFAR-FTUs huggingface dataset:
STRUCTURE MATTER S: HOW ORGANIZATIONAL CHARA CTERISTICS AFFECT MILITARY EFFORTS

OF DISSERTATION STRUCTURE MATTERS: HOW ORGANIZATIONAL CHARACTERISTICS AFFECT MILITARY EFFORTS Military organizations develop a unique set of practices and procedures in response to their particular political, economic, and social circumstances. The characteristics of these organizations shape standardized behaviors, methods of training personnel, and the degree of stratification within their bureaucratic hierarchies. This study examines how organizational characteristics influence battlefield effectiveness, patterns of alliance formation, and the security of United Nations peacekeepers. Chapter 2 evaluates how differences in personnel sophistication and bureaucratic stratification influence battlefield efficacy. A military may devote substantial resources to develop war plans and procure advanced technology, but these assets are of limited consequence in the absence of personnel and a bureaucratic configuration capable of translating political aims into military actions. Using battle-level data from the First World War, I find that military organizations with stratified bureaucratic hierarchies and relatively sophisticated personnel are significantly more effective on the battlefield. Chapter 3 examines how characteristics of military organizations influence the likelihood of alliance formation. Previous literature argues that a cooperative relationship is essential for an alliance to form, but allied states must also coordinate military activities in order to operate as a cohesive unit. Recognizing the extensive interplay between cooperation and coordination, I contend that alliances form when states share common interests and have military organizations capable of coordinating actions. Through an analysis of alliance formation from 1816-2007, I find that states with similar military organizations are significantly more likely to create security alliances. Chapter 4 investigates how organizational traits of United Nations peacekeeping coalitions influence the frequency and magnitude of deliberate attacks on peacekeepers. Peacekeeping missions occur in unstable conflict environments, so effective collaboration among peacekeepers is critical to achieve mandated objectives and protect UN personnel drawn from harm. Operating as a cohesive unit presents a considerable challenge for peacekeeping forces because they are ad hoc coalitions of contingents from a variety of organizational cultures and professional backgrounds. Using annual data of UN peacekeeping operations from 1990-2013, I find that peacekeeping coalitions sharing similar organizational structures suffer fatalities at a significantly lower rate and magnitude.
Diffusion Tensor Magnetic Resonance Imaging Applications to Neurological Disease

Diffusion tensor magnetic resonance (DTMR) imaging is a novel imaging technique capable of indirectly measuring the cellular environment of cerebral tissues. Its use for the clinical assessment of neurological health is increasing, yet the relation between the DTMR metrics and the integrity, type, severity, and salvageability of damaged tissue is still an area of active research. The work presented in this dissertation aims to elucidate underlying cellular architecture of damaged tissues from patterns in DTMR metrics. Experiments involving neonatal hydrocephalus, neonatal hypoxiaischemia, and adult acute stroke models were performed wherein DTMR metrics were compared between disease and control specimens, as well as with histological and electron microscopy analyses. Initial investigations in an experimental neonatal hydrocephalus model showed significant differences of DTMR metrics between in vivo subjects and controls in the white, but not gray, matter structures. Imaging resolution was improved in a subsequent study of a neonatal hypoxia-ischemia model and the results compared to microscopic histological assays. Decreases in the apparent diffusion coefficient and increases in T2 values in the gray matter correlated to the formation of brain edema, whereas decreases in the fractional anisotropy of diffusion within the white matter corresponded to demyelination as observed by histology. Furthermore, the efficacy of a novel drug treatment for neonatal hypoxia-ischemia was tested. DTMR metrics of the gray and white matter tissue appeared normal after treatment, and histology confirmed that the tissue was preserved. However, the DTMR imaging resolution and brain volume coverage remained the limiting factors in a complete brain imaging analysis that could
A Spontaneous Driver Emotion Facial Expression (DEFE) Dataset for Intelligent Vehicles

In this paper, we introduce a new dataset, the driver emotion facial expression (DEFE) dataset, for driver spontaneous emotions analysis. The dataset includes facial expression recordings from 60 participants during driving. After watching a selected video-audio clip to elicit a specific emotion, each participant completed the driving tasks in the same driving scenario and rated their emotional responses during the driving processes from the aspects of dimensional emotion and discrete emotion. We also conducted classification experiments to recognize the scales of arousal, valence, dominance, as well as the emotion category and intensity to establish baseline results for the proposed dataset. Besides, this paper compared and discussed the differences in facial expressions between driving and non-driving scenarios. The results show that there were significant differences in AUs (Action Units) presence of facial expressions between driving and non-driving scenarios, indicating that human emotional expressions in driving scenarios were different from other life scenarios. Therefore, publishing a human emotion dataset specifically for the driver is necessary for traffic safety improvement. The proposed dataset will be publicly available so that researchers worldwide can use it to develop and examine their driver emotion analysis methods. To the best of our knowledge, this is currently the only public driver facial expression dataset.
Distributed Adaptive Newton Methods with Globally Superlinear Convergence

This paper considers the distributed optimization problem over a network where the global objective is to optimize a sum of local functions using only local computation and communication. Since the existing algorithms either adopt a linear consensus mechanism, which converges at best linearly, or assume that each node starts sufficiently close to an optimal solution, they cannot achieve globally superlinear convergence. To break through the linear consensus rate, we propose a finite-time set-consensus method, and then incorporate it into Polyak's adaptive Newton method, leading to our distributed adaptive Newton algorithm (DAN). To avoid transmitting local Hessians, we adopt a low-rank approximation idea to compress the Hessian and design a communication-efficient DAN-LA. Then, the size of transmitted messages in DAN-LA is reduced to $O(p)$ per iteration, where $p$ is the dimension of decision vectors and is the same as the first-order methods. We show that DAN and DAN-LA can globally achieve quadratic and superlinear convergence rates, respectively. Numerical experiments on logistic regression problems are finally conducted to show the advantages over existing methods.
Analyzing planar galactic halo distributions with fuzzy/cold dark matter models

We perform a numerical comparison between the fuzzy dark matter model and the cold dark matter model, focusing on formation of satellite galaxy planes around massive galaxies. Such galactic dynamics with controlled initial subhalo configurations are investigated using GADGET2 for the cold dark matter and PyUltraLight for the fuzzy dark matter, respectively. We demonstrate that satellite galaxies in the fuzzy dark matter side have a tendency to form more flattened and corotating satellite systems than in the cold dark matter side mainly due to the dissipation by the gravitational cooling effect of the fuzzy dark matter. Our simulations with the fuzzy dark matter typically show the minor-to-major axis ratio c/a of the satellite galaxy planes to be 0.21 ∼ 0.30; this well matches the current observed value for the Milky Way.
Implicit dynamic analysis using an isogeometric Reissner–Mindlin shell formulation

In isogeometric analysis, identical basis functions are used for geometrical representation and analysis. In this work, non‐uniform rational basis splines basis functions are applied in an isoparametric approach. An isogeometric Reissner–Mindlin shell formulation for implicit dynamic calculations using the Galerkin method is presented. A consistent as well as a lumped matrix formulation is implemented. The suitability of the developed shell formulation for natural frequency analysis is demonstrated by a numerical example. In a second set of examples, transient problems of plane and curved geometries undergoing large deformations in combination with nonlinear material behavior are investigated. Via a zero‐thickness stress algorithm for arbitrary material models, a J2‐plasticity constitutive law is implemented. In the numerical examples, the effectiveness, robustness, and superior accuracy of a continuous interpolation method of the shell director vector is compared with experimental results and alternative numerical approaches. Copyright © 2016 John Wiley & Sons, Ltd.
Equivalence relations and $L^p$ distances between time series

We introduce a general framework for defining equivalence and measuring distances between time series, and a first concrete method for doing so. We prove the existence of equivalence relations on the space of time series, such that the quotient spaces can be equipped with a metrizable topology. We illustrate algorithmically how to calculate such distances among a collection of time series, and perform clustering analysis based on these distances. We apply these insights to analyse the recent bushfires in NSW, Australia. There, we introduce a new method to analyse time series in a cross-contextual setting.
Generalizable Neuro-symbolic Systems for Commonsense Question Answering

This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks. Different methods for integrating neural language models and knowledge graphs are discussed. The situations in which this combination is most appropriate are characterized, including quantitative evaluation and qualitative error analysis on a variety of commonsense question answering benchmark datasets.
Linear analytical approach to dispersive, external dissipative, and intrinsic dissipative couplings in optomechanical systems

We present a theoretical study of optomechanical systems in which the mechanical resonator modulates both the resonant frequency (dispersive coupling) and the decay rates (dissipative coupling) of the optical cavity. We extend the generic dispersive framework to a more general case in which the dissipative coupling is split between its external and intrinsic contribution. We report a complete analysis of the influence of each kind of optical losses (intrinsic and external) on the three coupling mechanisms and highlight the interest of each external decay rate regime. The basic tools to experimentally identify the three couplings and their relative influence on the optical response are presented. We demonstrate the general expression of the optical spring effect and optomechanical damping. Comparison between experimental measurements in photonic crystal systems from the literature and our theoretical modal yields good agreement.
Autoencoders

An autoencoder is a specific type of a neural network, which is mainlydesigned to encode the input into a compressed and meaningful representation, andthen decode it back such that the reconstructed input is similar as possible to theoriginal one. This chapter surveys the different types of autoencoders that are mainlyused today. It also describes various applications and use-cases of autoencoders.
Learning Cost Functions for Optimal Transport

Inverse optimal transport (OT) refers to the problem of learning the cost function for OT from observed transport plan or its samples. In this paper, we derive an unconstrained convex optimization formulation of the inverse OT problem, which can be further augmented by any customizable regularization. We provide a comprehensive characterization of the properties of inverse OT, including uniqueness of solutions. We also develop two numerical algorithms, one is a fast matrix scaling method based on the Sinkhorn-Knopp algorithm for discrete OT, and the other one is a learning based algorithm that parameterizes the cost function as a deep neural network for continuous OT. The novel framework proposed in the work avoids repeatedly solving a forward OT in each iteration which has been a thorny computational bottleneck for the bi-level optimization in existing inverse OT approaches. Numerical results demonstrate promising efficiency and accuracy advantages of the proposed algorithms over existing state-of-the-art methods.
Learning What to Defer for Maximum Independent Sets

Hyperparameter. Every hyperparameter was optimized on a per graph type basis and used across all sizes within each graph type. Throughout every experiment, the policy and the value networks were parameterized by graph convolutional network with 4 layers and 128 hidden dimensions. Every instance of the model was trained for 20 000 updates of proximal policy optimization (Schulman et al., 2017), based on the Adam optimizer with a learning rate of 0.0001. The validation dataset was used for choosing the best performing model while using 10 samples for evaluating the performance. Reward was not decayed throughout the episodes of the Markov decision process. Gradient norms were clipped by a value of 0.5. We further provide details specific to each type of datasets in Table 5. For the compared baselines, we used the default hyperparameters provided in the respective codes.
Parallel and Distributed Systems Report Series Tribler : A social-based Peer-to-Peer system

Most current P2P file sharing systems treat their users as anonymous, unrelated entities, and completely disregard any social relationships between them. However, social phenomena such as friendship and the existence of communities of users with similar tastes may be well exploited in such systems, to increase their usability and performance. In this paper we present a novel social-based P2P file-sharing paradigm that exploits social phenomena by maintaining social networks and using these in content discovery, content recommendation, and downloading. Based on this paradigm’s first class concepts such as taste groups, friends, and friends-of-friends, we have designed and implemented the Tribler P2P filesharing system as a set of extensions to Bittorrent. We present and discuss the design of Tribler, and we show evidence that Tribler enables fast, trusted content discovery and recommendation at a low additional overhead, and a significant improvement in download performance. Wp 1 http://Tribler.org/ Pouwelse et al. Wp Tribler: A social-based Peer-to-Peer systemWp PDS
CLINICAL AND SEROLOGICAL PREDICTORS OF POST COVID-19 CONDITION: FINDINGS FROM A CANADIAN PROSPECTIVE COHORT STUDY

Introduction: More than three years into the pandemic, there is persisting uncertainty as to the etiology, biomarkers, and risk factors of Post COVID-19 Condition (PCC). Serological research data remain a largely untapped resource. Few studies have investigated the potential relationships between post-acute serology and PCC, while accounting for clinical covariates. Methods: We compared clinical and serological predictors among COVID-19 survivors with (n=102 cases) and without (n=122 controls) persistent symptoms [≥] 12 weeks post-infection. We selected four primary serological predictors (anti-nucleocapsid (N), anti-Spike, and anti-receptor binding domain (RBD) IgG titres, and neutralization efficiency), and specified clinical covariates a priori. Results: Similar proportions of PCC-cases (66.7%, n=68) and infected-controls (71.3%, n=87) tested positive for anti-N IgG. More cases tested positive for anti-Spike (94.1%, n=96) and anti-RBD (95.1%, n=97) IgG, as compared with controls (anti-Spike: 89.3%, n=109; anti-RBD: 84.4%, n=103). Similar trends were observed among unvaccinated participants. Effects of IgG titres on PCC status were non-significant in univariate and multivariate analyses. Adjusting for age and sex, PCC-cases were more likely to be efficient neutralizers (OR 2.2, 95% CI 1.11 - 4.49), and odds was further increased among cases to report deterioration in quality of life (OR 3.4, 95% CI 1.64 - 7.31). Clinical covariates found to be significantly related to PCC included obesity (OR 2.3, p=0.02), number of months post COVID-19 (OR 1.1, p<0.01), allergies (OR 1.8, p=0.04), and need for medical support (OR 4.1, p<0.01). Conclusion: Despite past COVID-19 infection, approximately one third of PCC-cases and infected-controls were seronegative for anti-N IgG. Findings suggest higher neutralization efficiency among cases as compared with controls, and that this relationship is stronger among cases with more severe PCC. Cases also required more medical support for COVID-19 symptoms, and described complex, ongoing health sequelae. More data from larger cohorts are needed to substantiate results, permit subgroup analyses of IgG titres, and explore for differences between clusters of PCC symptoms. Future assessment of IgG subtypes may also elucidate new findings.
QASMBench: A Low-level QASM Benchmark Suite for NISQ Evaluation and Simulation

The rapid development of quantum computing in the NISQ era urgently demands a low-level benchmark suite for conveniently evaluating and verifying the properties of selective prototype hardware, the efficiency of different assemblers, optimizers and schedulers, the robustness of distinct error correction technologies, and the performance of various quantum simulators on classical computers. In this paper, we fill this gap by proposing a low-level, light-weighted, and easy-to-use benchmark suite called QASMBench based on the OpenQASM assembly representation. It collects commonly seen quantum algorithms and routines from a variety of domains including chemistry, simulation, linear algebra, searching, optimization, quantum arithmetic, machine learning, fault tolerance, cryptography, etc. QASMBench trades-off between generality and usability. It covers the number of qubits ranging from 2 to 60K, and the circuit depth from 4 to 12M, while keeping most of the benchmarks with qubits less than 16 so they can be directly verified on contemporary public-available cloud quantum machines. QASMBench is available at this https URL.
QASMBench: A Low-Leveluantum Benchmark Suite for NISQ Evaluation and Simulation

The rapid development of quantum computing (QC) in the NISQ era urgently demands a low-level benchmark suite and insightful evaluation metrics for characterizing the properties of prototype NISQ devices, the eiciency of QC programming compilers, schedulers and assemblers, and the capability of quantum system simulators in a classical computer. In this work, we ill this gap by proposing a low-level, easy-to-use benchmark suite called QASMBench based on the OpenQASM assembly representation. It consolidates commonly used quantum routines and kernels from a variety of domains including chemistry, simulation, linear algebra, searching, optimization, arithmetic, machine learning, fault tolerance, cryptography, etc., trading-of between generality and usability. To analyze these kernels in terms of NISQ device execution, in addition to circuit width and depth, we propose four circuit metrics including gate density, retention lifespan, measurement density, and entanglement variance, to extract more insights about the execution eiciency, the susceptibility to NISQ error, and the potential gain from machine-speciic optimizations. Applications in QASMBench can be launched and veriied on several NISQ platforms, including IBM-Q, Rigetti, IonQ and Quantinuum. For evaluation, we measure the execution idelity of a subset of QASMBench applications on 12 IBM-Q machines through density matrix state tomography, comprising 25K circuit evaluations. We also compare the idelity of executions among the IBM-Q machines, the IonQ QPU and the Rigetti Aspen M-1 system. QASMBench is released at: http://github.com/pnnl/QASMBench.
Analysis of economic and educational spillover effects in PEPFAR countries

The United States Presidents Emergency Plan for AIDS Relief (PEPFAR) has been credited with saving millions lives and helping to change the trajectory of the global human immunodeficiency virus (HIV) epidemic. This study assesses whether PEPFAR has had impacts beyond health by examining changes in five economic and educational outcomes in PEPFAR countries: the gross domestic product (GDP) per capita growth rate; the share of girls and share of boys, respectively, who are out of school; and female and male employment rates. We constructed a panel data set for 157 low- and middle-income countries between 1990 and 2018 to estimate the macroeconomic impacts of PEPFAR. Our PEPFAR group included 90 countries that had received PEPFAR support over the period. Our comparison group included 67 low- and middle-income countries that had not received any PEPFAR support or had received minimal PEPFAR support (<$1M or <$.05 per capita) between 2004 and 2018. We used differences in differences (DID) methods to estimate the program impacts on the five economic and educational outcome measures. This study finds that PEPFAR is associated with increases in the GDP per capita growth rate and educational outcomes. In some models, we find that PEPFAR is associated with reductions in male and female employment. However, these effects appear to be due to trends in the comparison group countries rather than programmatic impacts of PEPFAR. We show that these impacts are most pronounced in COP countries receiving the highest levels of PEPFAR investment.
Clustering of Shoulder Movement Patterns using K-means Algorithm Based on the Shoulder Range of Motion

Background: Categorization in medicine is used to enhance understanding of a disease or syndrome and apply it to treatment and is based on human clinical experience or theory. Cluster analysis using the K-means algorithm is an unsupervised machine learning method that classifies clusters based on numerical data Purpose: The purpose of this study was to classify subjects into clusters using K-means algorithm based on shoulder range of motion (ROM) and identify the characteristics of the clusters. Methods: 551 data samples measured in the 5th Size Korea Anthropometric Survey (2003~2004) were used. Clustering was performed using the K-means algorithm, and the appropriate number of clusters was determined using the elbow curve and silhouette score. The characteristics of the clusters were analyzed by comparing the average values of shoulder ROM in the clusters. Results: The appropriate number of classifications of clusters according to the shoulder ROM was 8. Clusters 1 and 5 had the lowest flexion range, and clusters 7 and 8 had low internal rotation and shoulder horizontal adduction ranges. Clusters 2 and 6 exhibited the highest flexion and overall high flexibility. Clusters 3 and 4 showed moderate flexion ranges but low horizontal adduction ranges. Shoulder movement patterns were classified into a total of 8 clusters according to the shoulder ROM. Conclusion: Based on this clustering system, it was possible to identify the pattern of shoulder movement in ordinary people, and it could be used as basic data to identify and treat diseases or syndromes according to the pattern.