Miracle-dz/UniFAR-FTUs huggingface dataset:
Obesity in Late-Life as a Protective Factor Against Dementia and Dementia-Related Mortality

Objective We estimated the conversion from cognitively normal to mild cognitive impairment (MCI) to probable dementia and death for underweight, normal, overweight, and obese older adults, where the timing of examinations is associated with the severity of dementia. Methods We analyzed six waves of the National Health and Aging Trends Study (NHATS). Body mass (BMI) was computed from height and weight. Multi-state survival models (MSMs) examined misclassification probability, time-to-event ratios, and cognitive decline. Results Participants (n = 6078) were 77 years old, 62% had overweight and/or obese BMI. After adjusting for the effects of cardiometabolic factors, age, sex, and race, obesity was protective against developing dementia (aHR=.44; 95%CI [.29-.67]) and dementia-related mortality (aHR=.63; 95%CI [.42-.95]). Discussion We found a negative relationship between obesity and dementia and dementia-related mortality, a finding that has been underreported in the literature. The continuing obesity epidemic might complicate the diagnosis and treatment of dementia.
Variable selection for response surface modeling using R-splines

Motivated by an application with a moderate number of potential explanatory variables, this paper explores a technique for variable and model selection using R-splines. R-splines are a recently proposed extension to thin plate splines with a modiication to the roughness penalty that allows for a reduced polynomial component to be t. The key model selection idea is a two-stage approach. First, the important explanatory variables are identiied using a speciic type of R-spline. Then these variables are used to t diierent R-spline models from which the most desirable is chosen. This new method is then compared to all subset regressions by leaps and bounds and regression trees. An application of the methodology is also discussed.
Smoothed analysis of belief propagation and minimum-cost flow algorithms

Abstract: Algorithms that have good worst-case performance are not always the ones that perform best in practice. The smoothed analysis framework is a way of analyzing algorithms that usually matches practical performance of these algorithms much better than worst-case analysis. In this thesis we apply smoothed analysis to two classes of algorithms: minimum-cost flow algorithms and belief propagation algorithms. The minimum-cost flow problem is the problem of sending a prescribed amount of flow through a network in the cheapest possible way. It is very well known, and over the last half a century many algorithms have been developed to solve it. We analyze three of these algorithms (the successive shortest path algorithm, the minimum-mean cycle canceling algorithm, and the network simplex algorithm) in the framework of smoothed analysis and show lower and upper bounds on their smoothed running-times. The belief propagation algorithm is a message-passing algorithm for solving probabilistic inference problems. Because of its simplicity, it is very popular in practice. However, its theoretical behavior is not well understood. To obtain a better theoretical understanding of the belief propagation algorithm, we apply it to several well-studied optimization problems. We analyze under which conditions the belief propagation algorithm converges to the correct solution and we analyze its smoothed running-time.
Bivariate Unspanned Stochastic Volatility Models

In Backwell [2015], a simple unspanned stochastic volatility (USV) model with two factors is constructed. This contradicts Proposition 3 in Collin-Dufresne and Goldstein [2002], where they assert that such bivariate USV models do not exist. In this note, we uncover the error in their proof, and identify the precise category of bivariate USV models that does in fact exist.
Tide–surge interaction in the Persian Gulf, Strait of Hormuz and the Gulf of Oman

The extent and magnitude of surges produced along a coast due to a tropical cyclone not only depends on the magnitude of the meteorological forcing (McRobie et al., 2005; Irish et al., 2008; Wang et al., 2008), but also depends on the period and phase of the tide at that location. The worst disasters occur when a large surge coincides with high tide (Murty et al., 1986; Flather, 1994). The magnitude of surges can only be predicted accurately if the interactions between tides and surges are taken into account (Sindhu et al., 2013). The tide–surge interaction has been shown to be stronger in straits compared with other coastal regions (Zhang et al., 2010). Therefore, as a first step towards analysing the combined effect of tides and surges, it is useful to develop a method which extracts surge and tide simultaneously. This method should be efficiently validated with locally available tide gauge records. Surge formation during the passage of a storm has two causes: the first is the wind stress associated with the storm; the second is the increase of sea-surface height due to the inverted barometer effect. As the atmospheric pressure decreases, the sea level responds by hydrostatically rising. Generally, a 1hPa decrease in atmospheric pressure causes sea level to rise by about 1cm (Picot et al., 2008). The term ‘surge’ implies a net meteorological contribution to sea level (see immediately above). Otherwise, we refer to ‘residual’, which may contain surge and tide–surge interaction (Horsburgh et al., 2007). In this work, the impact of surge-tides on sea-level variations during the passage of tropical storms has been investigated. The simultaneous impact of tide and surge has been investigated using tide gauge data for times that coincide with the passage of storms. We then used a method to separate the surges caused by cyclones from tides in the Persian Gulf, Strait of Hormuz and the Gulf of Oman, and to estimate their impact on total sea level along the north coast of Iran. The main objective of the present paper is to describe the characteristics of surges and tides in this region.
Multiple Memory Subsystems: Reconsidering Memory in the Mind and Brain

The multiple-memory-systems framework—that distinct types of memory are supported by distinct brain systems—has guided learning and memory research for decades. However, recent work challenges the one-to-one mapping between brain structures and memory types central to this taxonomy, with key memory-related structures supporting multiple functions across substructures. Here we integrate cross-species findings in the hippocampus, striatum, and amygdala to propose an updated framework of multiple memory subsystems (MMSS). We provide evidence for two organizational principles of the MMSS theory: First, opposing memory representations are colocated in the same brain structures; second, parallel memory representations are supported by distinct structures. We discuss why this burgeoning framework has the potential to provide a useful revision of classic theories of long-term memory, what evidence is needed to further validate the framework, and how this novel perspective on memory organization may guide future research.
A hybrid transfer learning model for crude oil price forecasting

Most of the existing models for oil price forecasting only use the data in the forecasted time series. This study proposes a hybrid transfer learning model (HTLM) for crude oil price forecasting. We ﬁrst selectively transfer some re-lated time series in the source domain to assist in modeling the target time series by using a transfer learning technique, and then construct the forecasting model using the analog complexing (AC) method. Further, we introduce a genetic algorithm to ﬁnd the optimal match between two important parameters in HTLM. Finally, we use two main crude oil price time series—the West Texas Intermediate (WTI) and the Brent crude oil spot prices—for empirical analysis. Our results show the eﬀectiveness and superiority of the proposed model compared with existing models.
Department of Economics Working Paper Series Existence of Nash Networks in One-way Flow Models Existence of Nash Networks in One-way Flow Models

This paper addresses the existence of Nash networks for the one-way flow model of Bala and Goyal (2000) in a number of different settings. We provide conditions for the existence of Nash networks in models where costs and values of links are heterogeneous and players obtain resources from others through the directed path between them. We find that costs of establishing links play a vital role in the existence of Nash networks. Next we examine the existence of Nash networks when there are congestion effects in the model.
A Memristor-Based Learning Engine for Synaptic Trace-Based Online Learning

The memristor has been extensively used to facilitate the synaptic online learning of brain-inspired spiking neural networks (SNNs). However, the current memristor-based work can not support the widely used yet sophisticated trace-based learning rules, including the trace-based Spike-Timing-Dependent Plasticity (STDP) and the Bayesian Confidence Propagation Neural Network (BCPNN) learning rules. This paper proposes a learning engine to implement trace-based online learning, consisting of memristor-based blocks and analog computing blocks. The memristor is used to mimic the synaptic trace dynamics by exploiting the nonlinear physical property of the device. The analog computing blocks are used for the addition, multiplication, logarithmic and integral operations. By organizing these building blocks, a reconfigurable learning engine is architected and realized to simulate the STDP and BCPNN online learning rules, using memristors and 180 nm analog CMOS technology. The results show that the proposed learning engine can achieve energy consumption of 10.61 pJ and 51.49 pJ per synaptic update for the STDP and BCPNN learning rules, respectively, with a 147.03× and 93.61× reduction compared to the 180 nm ASIC counterparts, and also a 9.39× and 5.63× reduction compared to the 40 nm ASIC counterparts. Compared with the state-of-the-art work of Loihi and eBrainII, the learning engine can reduce the energy per synaptic update by 11.31× and 13.13× for trace-based STDP and BCPNN learning rules, respectively.
Assessment of Diffuse Optical Tomography Image Reconstruction Methods Using a Photon Transport Model

Introduction: Imaging of tissue with nearinfrared DOT is emerging as a practicable method to map hemoglobin concentrations within tissue for breast cancer detection. The accurate recovery of images by using numerical modeling requires an effective image reconstruction method. We illustrate a comparison between two widely used reconstruction methods (Born approximation [1,2] and NIRFAST [3,4]) using finite element modeling in COMSOL for 3D forward data generation. Results: A comparison between two DOT image reconstruction methods is presented in the following two graphs and table:
A Brief Review of the Amati Relation for GRBs

Gamma-ray bursts (GRBs) are the most powerful explosions in the universe. Alt-hough the exact mechanism behind these explosions remains elusive, GRBs hold great promise as cosmological probes for two main reasons: they have been observed up to very high redshift (z > 9), and their gamma-ray emission is unencumbered by any intervening dust. Several GRB energy and luminosity indicators have been discovered. These indicators correlate an observable quantity, like the intrinsic peak energy, Ep,i, in the spectrum of a burst to an unobservable parameter like the equivalent isotropic energy, Eiso, or the isotropic peak luminosity, Lp,iso. This paper provides a brief review of one of these energy and luminosity indicators, the Amati relation, and discusses its potential use as a cosmological probe.
THE GROSSER ALETSCHGLETSCHER DYNAMICS: FROM A “MINIMAL MODEL” TO A STOCHASTIC EQUATION

Mountain glaciers manifest oscillations at different time-scales. Apart from synchronous reaction to lasting changes, there is asynchronism between climatic forcing and observed anomalies of the glaciers. Based on general theories on the laws of temporal dynamics relating to massive inertial objects, the observed interannual changes of glacier length could result from the accumulation of small anomalies in the heat/water fluxes. Despite the fact that the original model of the dynamics of mountain glaciers is deterministically based on the physical law of conservation of water mass, the model of length change is interpreted as stochastic; from this perspective, it is the Langevin equation that incorporates the action of temperature anomalies and precipitation like random white noise. The process is analogous to Brownian motion. Under these conditions, the Grosser Aletschgletscher (selected as an example) is represented by a system undergoing a random walk. It was shown that the possible range of variability covers the observed interval of length fluctuations.
A trainable graph combination scheme for belief propagation

Many problems in computer vision can be viewed as marginalization calculations in a probability distribution. Belief propagation is well suited for such calculations, but it suffers from several major limitations. Exact solutions are generally NP hard to compute, and approximate schemes can be difficult to set up and computationally expensive, and do not necessarily converge. Furthermore, parameter estimation is tedious, and precise tests of accuracy are not easily constructed. In this thesis, I propose a new approach to belief propagation that is both highly accurate and efficient. A given Markov random field is approximated by multiple sets of singly-connected graphs. Exact belief propagation on each set is then combined to yield a final set of marginal values. I demonstrate the approach in an image segmentation test case where optimal solutions are available from the maximum flow algorithm. The non-linear combination of information from each set of graphs improves greatly on the performance of each set alone and yields accuracy approaching the optimal. The graph combination scheme can additionally learn and optimize its parameters. This eliminates the manual selection of such parameters, which becomes increasingly difficult and inaccurate as the complexity of the problem grows. I demonstrate a learning scheme for a boundary-based image segmentation technique that combines the dual approaches of region and boundary segmentation. The system is trained on a set of pre-segmented images and yields convincing results while requiring only a fraction of the effort normally involved in setting up such a system.
Model development and comparison of different heuristics for production planning in large volume softwood sawmills

ABSTRACT This study investigates the production planning at softwood sawmills dealing with the co-production of several products. The problem is formulated as a mixed-integer program and applied to a real-size case study. For comparison purposes, five different heuristics are used for comparison with optimization in different settings and for which the loss in performance is investigated. A sensitivity analysis is performed for the initial parameters of storage values of products and raw materials. In addition, the importance of parameters like variable costs, available raw materials, and minimal cutting quantity is investigated. Furthermore, several different planning time horizons are compared for varying periods. In general, it can be concluded that the real-life inspired heuristics are worse than the optimization results and more sensitive to the parameters. When applying the multi-period model in a rolling planning horizon, the best heuristic outperforms it owing to the end effects included in the model.
Using dual stable isotopes method for nitrate sources identification in Cao-E River Basin, Eastern China

Excess nitrate (NO3 −) of water is a worldwide environmental problem. Therefore, identifying the sources and analyzing respective contribution rates are of great importance for improving water quality. The current study was carried out to identify the potential sources of NO3 − pollution in Cao-E River basin, in Eastern China. Surface water samples were collected during the dry season and wet season. Multiple hydrochemical indices, dual NO3 − isotopes (δ15N–NO3 − and δ18O–NO3 −) and a Bayesian model (stable isotope analysis in R, MixSIAR) were applied to identify NO3 − sources and estimate the proportional contributions of multiple NO3 − sources. During the sampling period, nitrification was a dominant nitrogen transformation process in the study area. The results of the NO3 − isotopes suggested that manure and sewage (M&S), soil nitrogen (SN) and nitrogen fertilizer (NF) were the major contributors to NO3 −. Moreover, the results obtained from the MixSIAR model showed that the proportional contributions of atmospheric deposition (AD), NF, M&S and SN to NO3 − were 2.82, 15.45, 44.25, 37.47% and 3.14, 23.39, 31.78, 41.69% in the dry and wet season, respectively. This study provided evidence to further understand the sources, transport, and transformation of N in Cao-E River basin, which deepens the understanding of the management of N contaminant.
Characterization of sub-long graphs of arbitrary rank

The rank of a graph is defined to be the rank of its adjacency matrix. A connected graph G of rank r is said to be sub-long if it has the second largest diameter, that is , among all connected graphs of rank r. In this paper, we characterize the sub-long graphs of rank r, for an arbitrary positive integer .
Ecological Momentary Assessment and Personalized Networks in Cognitive Bias Modification Studies on Addiction: Advances and Challenges

Adding cognitive bias modification (CBM) to treatment as usual for alcohol use disorders has been found to reduce relapse rates. However, CBM has not yielded effects as a stand-alone intervention. One possible reason may be that this is due to CBM effects being underpinned by inferential rather than associative mental mechanisms. This change in perspective has led to a proposed improved version of CBM: Inference-based ABC training. In ABC training, participants learn to relate the antecedents (A) of their addiction behavior to alternative behaviors (B) and to their expected consequences (C) in relation to their long-term goals. Mechanisms triggering and maintaining addiction, such as those targeted during ABC training, can differ between people. Ecological Momentary Assessment (EMA) and derived personalized statistics, including models depicting relationships between variables (i.e., personalized networks), are therefore promising tools to help to optimally personalize this training. In this paper, we (1) explain the theoretical background and first implementations of ABC training; (2) present novel approaches to personalize treatment based on EMA; (3) propose ways forward to integrate improved CBM approaches and EMA to potentially advance addiction treatment; and (4) discuss promises and challenges of these proposed new approaches.
Kinematic and Kinetic Comparisons of Arm Slot Position Between High School and Professional Pitchers

Background: Throwing arm kinetics differ in pitchers at varying arm slot (AS) positions (frontal-plane arm position at ball release relative to the vertical axis). Purpose: To determine how kinematic and kinetic values differ between professional and high school pitchers with varying AS positions, and whether these differences are similarly observed in both populations. Methods: High school (n = 130) and professional (n = 288) pitchers threw 8 to 12 fastballs under 3-dimensional motion capture technology. Pitchers in each cohort were subdivided based on mean AS position at ball release: AS1 (least degree of AS: most overhand throwing styles), AS2 (intermediate degree of AS: three-quarter throwing styles), or AS3 (greatest degree of AS: most sidearm throwing styles). Kinetic and kinematic parameters were compared between groups. Study Design: Controlled laboratory study. Results: High school pitchers had a more overhand AS at ball release (50° ± 11°) compared with professional pitchers (58° ± 14°) (P < .001). In both cohorts, AS1 pitchers had significantly greater shoulder abduction (high school, P <0.001; professional, P <0.0001) and lateral trunk flexion (high school, P < 0.001; professional, P <0.0001) at ball release compared with AS3 pitchers. Professional pitchers with an AS3 position had significantly delayed timing of maximum upper trunk angular velocity compared with AS1 pitchers (64% ± 7% vs 57% ± 7% of pitch time, respectively; P < .0001). A significant positive correlation between AS and elbow flexion torque was found in high school pitchers (P = .002; β = 0.28), and a significant negative correlation between AS and elbow varus torque (P < .001; β = –0.22) and shoulder internal rotation torque (P < .001; β = –0.20) was noted in professional pitchers. Conclusion: AS position was related to shoulder abduction and trunk lateral tilt. Professional and high school pitchers with varying AS positions did not experience similar changes in throwing arm kinetics. Clinical Relevance: In professional pitchers, the earlier onset of maximum upper trunk angular velocity with overhand throwing style may reflect inappropriate pelvis-trunk timing separation, a parameter implicated in upper extremity injury, and the negative correlation between AS and elbow varus and shoulder internal rotation torque suggests that both excessive and minimal AS positions have negative implications.
Splitting up force

This paper proposes to split Rizzi’s (1997) ForceP into two distinct projections: Illocutionary Force (ILL) and Clause Type (CT). The proposal is meant to capture the cross-linguistic properties of discourse particles at the discourse level (they modify the illocutionary force by turning it into a more specific force reflecting the speaker’s intentions/attitude, Jacobs 1986, 1991), and also their syntactic restrictions. Specifically, each particle can occur in certain clause types, but not in others, and they are only licensed in clauses with ‘root properties’. In order to account for these facts, we will adopt a feature valuation mechanism along the lines of Pesetsky & Torrego (2007).
Issue Communication Network Dynamics in Connective Action: The Role of Non-Political Influencers and Regular Users

Social media influencers (SMIs) have prevailed in online communication networks and can play key roles in connective action. However, how influencers connectively drive activism communication network is less clear. This study conceptualizes issue influencers as users who emerge as influential nodes in shaping the communication network in a contentious political issue niche. A new typology of influencers is proposed based on both issue and platform influence. Drawing on connective action theory and SMI studies, this study employed a Stochastic Actor-Oriented Model to examine how structural (endogenous) processes and influencer-level (exogenous) characteristics explain the communication network dynamics among issue influencers based on 200,000 Weibo posts from approximately 150,000 unique users about a gender policy discussion in China. The results show reciprocity, closure triplets, and influencer-level attributes including issue stance and influencer status significantly drive the longitudinal activism communication network. This study suggests that, when political influencers are absent, non-political influencers and regular users can effectively lead a contentious communication network on a platform with increasing censorship and suppression.
Integral identity for a class of ill-posed problems generated by a parabolic equation

Abstract The goal of the present study is to derive an integral identity for a class of ill-posed problems generated by a parabolic equation. The obtained result enables us to reduce the original ill-posed problem directly to the first-kind Fredholm equation with translation kernel. Various real world applications to a different fields of knowledge, including ecology and finances, are presented.