Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite Mixture Models

Finite mixture models provide a natural way of modeling continuous or discrete outcomes that are observed from populations consisting of a finite number of homogeneous subpopulations. Applications of finite mixture models are abundant in the social and behavioral sciences, biological and environmental sciences, engineering and finance. Such models have a natural representation of heterogeneity in a finite, usually small, number of latent classes, each of which may be regarded as a type. More generally, the finite mixture model can be shown to approximate any unknown distribution under suitable regularity conditions. The Stata package -fmm- implements a maximum likelihood estimator for a class of finite mixture models. In this talk, I will begin by introducing finite mixture models using a number of examples and discuss issues of estimation, testing and model selection. I will then describe estimation using fmm, calculations of predictions, marginal effects, and posterior class probabilities, and illustrate these using examples from econometrics and finance.
Continuous Neurophysiologic Data Accurately Predict Mood and Energy in the Elderly

The elderly have an elevated risk of clinical depression because of isolation from family and friends and a reticence to report their emotional states. The present study explored whether data from a commercial neuroscience platform could predict low mood and low energy in members of a retirement community. Neurophysiologic data were collected continuously for three weeks at 1Hz and averaged into hourly and daily measures, while mood and energy were captured with self-reports. Two neurophysiologic measures averaged over a day predicted low mood and low energy with 68% and 75% accuracy. Principal components analysis showed that neurologic variables were statistically associated with mood and energy two days in advance. Applying machine learning to hourly data classified low mood and low energy with 99% and 98% accuracy. Two-day lagged hourly neurophysiologic data predicted low mood and low energy with 98% and 96% accuracy. This study demonstrates that continuous measurement of neurophysiologic variables may be an effective way to reduce the incidence of mood disorders in vulnerable people by identifying when interventions are needed.
Effect of Flat Cell Size, Transplant Age, and Production Site on Growth and Yield of Pepper Transplants

‘Yolo Wonder L’ pepper (Capsicum annuum L.) transplants produced in two locations (Florida and Kentucky) and in flats of five cell sizes were compared for fruit productivity in Kentucky. Transplants grown in large cells in both locations produced greater early yields than those from small cells, but did not produce greater total yields. Transplants grown in Speedling cell size 175 (39.5 cm3) possessed greater height, leaf area, and dry weight at field-setting and produced greater early fruit yields than did plants grown in smaller cells. Transplants grown in Speedling trays in Kentucky, but fertilized differently than those grown in Florida, produced greater early and total yields than Speedling transplants grown in Florida. Sixty-day-old seedlings transplanted into the field produced significantly larger early yields than did those that were 30, 40, or 50 days old.
Asset based wellbeing of poverty of artisanal fish farmers in delta state, nigeria

This research carried out in Delta State, Nigeria, focuses on the empirical application of Principal Component Analysis (PCA), an asset based measure of wellbeing which can be used to assess level of poverty among households in rural areas of developing countries. The study employed questionnaire-based household survey data collection methods. The final wealth index was derived using data collected from 430 artisanal fishing households in riverine Delta State communities. Data on 16 variables measuring multiple aspects of household wealth status were used to extract the set of principal components utilized in the construction of the index. Two key statistical tests, the KMO and Bartlett’s tests, showed the appropriateness of the data for PCA. Results revealed that five major factors influence the wealth status and hence the wellbeing of households: home infrastructure, energy sources, durable home assets, water sources and mobility. Therefore, it is suggested that any efforts to improve the wellbeing of farm households in the study area as well as in other regions with similar socio- economic settings should consider these factors as entry point to poverty alleviation.
Quantum stochastics and information : statistics, filtering, and control : University of Nottingham, UK, 15-22 July 2006

Quantum stochastic calculus has become an indispensable tool in modern quantum physics, its effectiveness being illustrated by recent developments in quantum control which place the calculus at the heart of the theory. Quantum statistics is rapidly taking shape as an intrinsically quantum counterpart to classical statistics, motivated by advances in quantum engineering and the need for better statistical inference tools for quantum systems.This volume contains a selection of regular research articles and reviews by leading researchers in quantum control, quantum statistics, quantum probability and quantum information. The selection offers a unified view of recent trends in quantum stochastics, highlighting the common mathematical language of Hilbert space operators, and the deep connections between classical and quantum stochastic phenomena.
Numerical studies of Casimir interactions

We study numerically the Casimir interaction between dielectrics in both two and three dimensions. We demonstrate how sparse matrix factorizations enable one to study torsional interactions in three dimensions. In two dimensions we study the full cross-over between non-retarded and retarded interactions as a function of separation. We use constrained factorizations in order to measure the interaction of a particle with a rough dielectric surface and compare with a scaling argument.
The impact of surface heterogeneity on the diurnal cycle of deep convection

Despite some recent improvements, major deficiencies remain in model simulations using parameterised convection in capturing both the phase and amplitude of the daily cycle of precipitation in tropical regions. The difficulties are particularly acute in regions of heterogeneous surface conditions, since the simulations need not only to respond appropriately to the local forcing from surface fluxes but also to capture the interactions with near‐surface mesoscale circulations. Here, we examine such a situation by means of idealised cloud‐resolving simulations of deep convection over a heterogeneous surface, performed using the cloud‐resolving simulation known as the Met Office NERC Cloud model (MONC). In these simulations, we show that precipitation forms preferentially over dry and warm patches (“DRY”) compared with wet and cold patches (“WET”), with the peak precipitation rates differing by a factor of approximately 4. The initiation of precipitation occurs approximately 1.5 hr earlier in the DRY patches compared with the WET patches. Moreover, within the WET and DRY patches there are marked differences in the spatial distribution of the precipitation. These cloud‐resolving simulations are then used as a benchmark to assess the behaviour of simulations using parameterised convection, performed using the idealised configuration of the Met Office Unified Model (MetUM). The MetUM simulations produce a response with some qualitative similarities to the cloud‐resolving simulations. In particular, although the simulations with parameterised convection initiate precipitation too early, they are capable of capturing the relative amounts of daily‐mean precipitation in the DRY and WET patches. We propose that the cloud‐resolving simulations could be used further to investigate the impact of fully interactive surface schemes and as benchmark simulations to evaluate new parameterisation schemes.
Tailoring the magnetic properties of cobalt antidot arrays by varying the pore size and degree of disorder

Magnetic properties of Co antidot arrays with different hole sizes fabricated by a template-assisted method have been studied by means of first-order reversal curves (FORCs) and micromagnetic simulations. Hysteresis curves show a significant increase of the coercivity of the antidot arrays, as compared with their parent continuous film, which depends on the hole size introduced in the Co thin film. This effect is related to the reversibility of the magnetic domains during magnetization reversal, since due to the appearance of pores, domains may become trapped between them. On the other hand, micromagnetic simulations performed on a perfect hexagonal lattice, when compared with those made on our disordered system taken from the scanning microscope images, reveal that the presence of defects in the antidot lattice affects its magnetic properties. Finally, FORCs show that there is greater interaction attributed to domain–domain interaction.
Spatiotemporal Attention-Based Pedestrian Trajectory Prediction Considering Traffic-Actor Interaction

Accurate pedestrian trajectory prediction is significant and challenging for traffic-actor protection and intelligent driving. However, most of the existing methods focus on pedestrian-pedestrian interaction ignoring the pedestrian-vehicle interaction and containing much ineffective interaction. To tackle the problem, this paper proposes a spatiotemporal LSTM attention network (STLAN) for pedestrian trajectory prediction based on a spatiotemporal dual attention module (STDAM). First, a pedestrian social force model is established to analyze the effective pedestrian-pedestrian interaction and pedestrian-vehicle interaction in mixed traffic scene. Second, a pedestrian trajectory prediction network architecture is constructed to integrate temporal and spatial information. By temporal attention STDAM captures underlying dependencies between time steps in individual pedestrian, assigning different weights to the input sequence of it. By spatial attention STDAM extracts latent interaction between traffic actors, assigning different weights to every traffic actor. In addition, random noise is introduced to simulate the uncertainty of pedestrian trajectory. Experimental results on a real-world traffic actors dataset show that STLAN outperforms several mainstream methods. Specifically, STLAN achieves 0.17/0.11 ADE/FDE and 3.79/0.38 AMD/AMV in the representative pedestrian-pedestrian interaction scene, with 0.15/0.17 ADE/FDE and 2.36/0.21 AMD/AMV in pedestrian-vehicle interaction scene. Furthermore, ablation experiments validate the effectiveness of STDAM in improving trajectory prediction accuracy.
Prospects and challenges of optical switching technologies for intra data center networks

This paper explores how optical switching technologies can innovate future intra data center networks. The effectiveness of applying large-port-count optical switches is clarified by comparing the available scales and performances of electrical and optical switching networks. The two core technologies needed to make it happen are presented: large-port-count optical switches that have pay-as-you-grow scalability and a simple and fast switch control procedure. Different from the present multi-tier electrical switch network, single-tier large-port-count optical switches and their parallel configurations are shown to greatly simplify the network. Their combination offers low blocking rates and low latency even though the connection set-up is performed in a completely distributed and asynchronous manner where network wide synchronization and routing control are unneeded. The design and performance verification of our control network are detailed, and experiments on a control network emulation based on field programmable gate arrays are presented. The connection set-up latency of around 20 µs is verified within a floor (200 m) using our recently developed silicon photonic switch and tunable filter devices. The devices rely on the thermo-optic effect, but if we apply the electro-optic effect and adopt a simplified connection set-up procedure, the set-up latency is reduced to around 7 µs. The direction discussed here will pave the way for achieving bandwidth abundant and energy efficient data center networks in the future.
Estimates of persistent inward current in human motor neurons during postural sway.

Persistent inward current (PIC) plays a critical role in setting the gain of spinal motor neurons. In humans, most estimates of PIC are made from plantarflexor or dorsiflexor motor units in a seated position. This seated and static posture negates the task-dependent nature of the monoaminergic drive and afferent inhibition that modulate PIC activation. Our purpose was to estimate PIC during both the conventional seated posture and in a more functionally-relevant anterior postural sway. We hypothesized that ∆F estimates of PIC would be greater during standing compared to sitting. Soleus motor neuron PIC was estimated via the paired motor unit (PMU) technique. For each motor unit pair, ∆F estimates of PIC were made during isometric ramps in plantarflexion force while seated (conventional approach) and during standing anterior postural sway (new approach). Baseline reciprocal inhibition (RI) was also measured in each posture using the post-stimulus time histogram (PSTH) technique. ∆F estimates during standing postural sway were not different (2.64±0.95pps, p=0.098) from seated PIC estimates (3.15±1.45pps) measured from the same motor unit pair. Similarly, reciprocal inhibition at the onset of each task was the same in standing (-0.60±0.32, p=0.301) and seated (-0.86±0.82) postures. PMU recordings made during standing postural sway met all assumptions which underlay the PMU technique, including rate modulation ≥ 0.5pps(3.11±1.90pps), rate-rate correlation r ≥ 0.7(0.84 ± 0.13), and time between reference and test unit recruitment ≥1s(1.83±0.81s). This study presents a novel, functionally-relevant standing method for investigating PIC in humans.
Dietary Fiber in Poultry Nutrition in the Light of Past, Present, and Future Research Perspective: A Review

Dietary fibers (DF) largely represent carbohydrate polymers of plant origin which are able to escape endogenous enzymatic digestion in the small intestine of non-ruminant hosts like poultry. Traditionally, DF was considered as nutrient diluent, and as an anti-nutritional factor. Dietary fibers, however, have been shown to positively influence digestive system, immune function, microbiota, and also poultry behavior. After the ban of antibiotics and antibiotic growth promoters in many countries, nutritional strategies to meet the genetic potential of poultry breeds have been extensively investigated. Furthermore, increase use of unconventional or alternative feed resources to reduce the feed cost and human food competition have made the DF topic more interesting as such products are generally rich in fiber; therefore can alter the poultry performance. Thus, to produce poultry sustainably and eco-friendly, DF has to be carefully managed in poultry which further requires sound knowledge on feed formulation, source of fiber, type, subtype, form, inclusion rate, and other managerial aspects like exogenous enzyme supplementation. To sum up, this review paper has critically analyzed the dietary fiber related issues including posi-tive and negative sides of DF in modern day’s poultry nutrition. Finally, gaps in previous researches have been also identified and future direction has been suggested to better understand the topic considering therapeutic role of DF in poultry health.
4-5-Day-Period Variation and Low-Level Dry Air Observed in the Equatorial Western Pacific during the

A significant 4-5-day-period variation was observed in surface and aerological data obtained at the equator, 156E during November 1992. The time-height structure of the variation is determined by use of spectral and composite analysis. The 4-5-day period variation can be basically explained by westward-propagating mixed Rossby-gravity wave disturbances. However, the structure of the circulation was significantly distorted by the presence of a tropical depression. Further, two distinctive events of extremely dry air in the lower troposphere were observed. The vertical profiles of specific humidity as well as the equivalent potential temperature had distinct minima at about the 3 km level. The trajectory analysis revealed that the dry-air parcels were brought from a subtropical area by tropical disturbances, especially by the intense meridional flow associated with 4-5-day period disturbances. These events of dry-air intrusion are detected in the precipitable water data derived from SSM/I satellite imageries and the zonal scale of the intrusion is estimated to be about 1000 km. Convection was strongly suppressed over the dry air, and the latter was maintained to about 4 days due to very weak vertical mixing.
-1-Quantitative Analysis of Consumption Influencing Factors in Smart Retail Scenarios Based on Structural Equation Modeling

: Taking Beijing as an example, we explore the paths and effects of several key factors on urban residents' consumption in smart retail scenarios. The study measured business identification (BI), marketing perception (MP), product experience (PE), environmental experience (EE) and purchase intention (PI). A structural equation model of the relationship between the five latent variables and their influences was constructed based on the measurement of the five influences of purchase intention (PI). In addition, the six observed variables of payment form, type, quality, logistics and delivery, shop environment and discount are very important in influencing purchase intention. Accordingly, the problems associated with smart retailing at this stage of development are identified and methodological recommendations are made. These include improving consumers' commercial acceptance of smart retail, optimising the product mix, strengthening the marketing management of smart retail and improving the quality of smart retail services.
Generalized representation of local relationships for few-shot incremental learning

Few-shot learning is a booming research area in computer vision that aims to recognize and grasp novel concepts by learning from a limited number of known samples. Existing literature in few-shot learning only focuses to recognize novel categories while neglecting the understanding of base knowledge. This paper introduces the concept of local relationship learning and proposes a generalized representation method for local relationships to address the two major research problems of the few-shot incremental learning tasks, i.e. , inferior inter-class distinguishability and diﬀicult generalization of incremental categories. To enhance the distinguishability in the incremental process, this paper first adopts the local spatial relationship to regularize the incremental representation ability. To alleviate the inductive biases caused by the lack of data in the incremental process, this paper proposes a spatial generalization prototype generation algorithm, which uses the distribution characteristics to quickly generate virtual prototypes and promote the effective representation of samples. Benefiting from the meta-learning training mechanism, this paper proposes a joint locality and generalization awareness incremental learning framework, which effectively alleviates catastrophic forgetting and diﬀiculties in distinguishability by combining the local representation of the base category and the fast generalization constraint of the incremental category. Experimental results have demonstrated that the proposed method achieves state-of-the-art results on few-shot incremental learning tasks.
Q A ] 22 J an 1 99 6 Jaeger ’ s Higman-Sims state model and the B 2 spider

Two related approaches to defining quantum topological invariants are skein relations and state models. One importan t example of the former is the Kauffman polynomial, while an important class of the latter is the class of checkerboard st te models. Given two numbers or indeterminates Q andd, the Kauffman polynomial is a function on link projections on the 2sphere defined axiomatically by the rules:
BTT_A_380027 129..140

Inflammatory bowel diseases are considered immune disorders with a complex genetic architecture involving constantly changing endogenous and exogenous factors. The rapid evolution of genomic technologies and the emergence of newly discovered molecular actors are compelling the research community to reevaluate the knowledge and molecular processes. The human intestinal tract contains intestinal human microbiota consisting of commensal, pathogenic, and symbiotic strains leading to immune responses that can contribute and lead to both systemic and intestinal disorders including IBD. In this review, we attempted to highlight some updates of the new IBD features related to genomics, microbiota, new emerging therapies and some major established IBD risk factors.
Working Paper Series Evaluating market risk from leveraged derivative exposures

Market participants use leveraged derivatives to gain access to equity market exposure through broker banks. Leverage and interconnectedness via overlapping portfolios of dealer banks can amplify adverse market movements, potentially causing sizeable losses. I propose a model, based on granular data, to simulate losses from a banks’ trading book in case of an adverse market scenario. Following a move in asset prices, banks mark their positions and issue margin calls; some (leveraged) counterparties fail to pay their margins, forcing banks to liquidate their positions causing a pressure on asset prices due to market impact. The impact is amplified because of the leverage and when counterparties are exposed to multiple banks over the same underlying. I employ the model to assess current capital and margin rules in covering risks from broker’s exposure to highly leveraged clients.
Association of left ventricular global area strain derived from resting 3D speckle-tracking echocardiography and exercise capacity in individuals undergoing treadmill exercise test

Background: Left ventricular (LV) global area strain (GAS) is a novel index derived from resting 3D speckle-tracking echocardiography (STE), and its clinical significance has rarely been studied. We examined the association of LV GAS and exercise capacity in a health check-up population. Methods: We recruited 94 symptom-free participants (52.2 ± 11.7 years, 62.8% male) without substantial structural heart disease or coronary heart diseases who were undergoing a routine health examination. All participants underwent resting echocardiography and symptom-limited treadmill exercise test according to the Bruce protocol. Four strain parameters were obtained from the analysis, namely 3D GAS (GAS3d), global longitudinal strain, global circumferential strain, and global radial strain. Results: After multivariate analysis for factors of exercise time, we observed a significant association in LV GAS3d (P < 0.001). We divided participants into preserved and impaired exercise capacity groups according to the cutoff value of 8 metabolic equivalent of tasks. LV GAS3d (OR 1.24, 95% CI 1.10-1.39, P < 0.001) was an independent predictor of impaired exercise capacity and the optimal cut-off value was -19.96% at a sensitivity of 77.8% and at a specificity of 92.1%. LV GAS3d could improve the discriminatory power of exercise capacity in individuals with early mitral filling velocity to average mitral annulus velocity ratio (E/e') ≥ 8. Conclusions: LV GAS3d was significantly associated with exercise time and exhibited incremental predictive value on E/e' for exercise capacity in participants undergoing treadmill exercise test.