Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-model ensembling of probabilistic streamflow forecasts: Role of predictor state space in skill evaluation

Seasonal streamflow forecasts contingent on climate information are essential for shortterm planning and for setting up contingency measures during extreme years. Recent research shows that operational climate forecasts obtained by combining different General Circulation Models (GCM) have improved predictability/skill in comparison to the predictability from single GCMs [Rajagopalan et al., 2002; Doblas-Reyes et al., 2005]. In this study, we present a new approach for developing multi-model ensembles that combines streamflow forecasts from various models by evaluating their performance from the predictor state space. Based on this, we show that any systematic errors in model prediction with reference to specific predictor conditions could be reduced by combining forecasts with multiple models and with climatology. The methodology is demonstrated by obtaining seasonal streamflow forecasts for the Neuse river basin by combining two low dimensional probabilistic streamflow forecasting models that uses SST conditions in tropical Pacific, North Atlantic and North Carolina Coast. Using Rank Probability Score (RPS) for evaluating the probabilistic streamflow forecasts developed contingent on SSTs, the methodology gives higher weights in drawing ensembles from a model that has better predictability under similar predictor conditions. The performance of the multimodel forecasts are compared with the individual model’s performance using various forecast verification measures such as anomaly correlation, root mean square error (RMSE), Rank Probability Skill Score (RPSS) and reliability diagrams. By developing multi-model ensembles for both leave-one out cross validated forecasts and adaptive forecasts using the proposed methodology, we show that evaluating the model performance from predictor state space is a better alternative in developing multi-model ensembles instead of combining model’s based on their predictability of the marginal distribution.
A Model of Employment Supports for Job Seekers with Intellectual Disabilities

Interviews with 16 employment consultants—triangulated with job seekers, family members, and supervisors— revealed a model of employment supports aligned with the elements described in the literature, although with an added emphasis on (a) building trust as a key element starting from day one; (b) a circular process converging on the job match; (c) and flexible intensity of supports. The model can be used for improving clarity in communication with employment consultants about effective employment support practices for assisting job seekers with intel­ lectual and developmental disabilities.
Feedback control for rotational movements in feature space

Synthesizing controllers for rotational movements in feature space is an open research problem and is particularly challenging because of the need to precisely regulate the character's global orientation, angular momentum and inertia. This paper presents feature‐based controllers for a wide variety of rotational movements, including cartwheels, dives and flips. We show that the controllers can be made robust to large external disturbances by using a time‐invariant control scheme. The generality of the control laws is demonstrated by providing examples of the flip controller with different apexes, the diving controller with different heights and styles, the cartwheel controller with different speeds and straddle widths, etc. The controllers do not rely on any input motion or offline optimization.
Role of Extracellular RNA and TLR3‐Trif Signaling in Myocardial Ischemia–Reperfusion Injury

Background Toll‐like receptor 3 (TLR3) was originally identified as the receptor for viral RNA and represents a major host antiviral defense mechanism. TLR3 may also recognize extracellular RNA (exRNA) released from injured tissues under certain stress conditions. However, a role for exRNA and TLR3 in the pathogenesis of myocardial ischemic injury has not been tested. This study examined the role of exRNA and TLR3 signaling in myocardial infarction (MI), apoptosis, inflammation, and cardiac dysfunction during ischemia‐reperfusion (I/R) injury. Methods and Results Wild‐type (WT), TLR3−/−, Trif−/−, and interferon (IFN) α/β receptor‐1 deficient (IFNAR1−/−) mice were subjected to 45 minutes of coronary artery occlusion and 24 hours of reperfusion. Compared with WT, TLR3−/− or Trif−/− mice had smaller MI and better preserved cardiac function. Surprisingly, unlike TLR(2/4)‐MyD88 signaling, lack of TLR3‐Trif signaling had no impact on myocardial cytokines or neutrophil recruitment after I/R, but myocardial apoptosis was significantly attenuated in Trif−/− mice. Deletion of the downstream IFNAR1 had no effect on infarct size. Importantly, hypoxia and I/R led to release of RNA including microRNA from injured cardiomyocytes and ischemic heart, respectively. Necrotic cardiomyocytes induced a robust and dose‐dependent cytokine response in cultured cardiomyocytes, which was markedly reduced by RNase but not DNase, and partially blocked in TLR3‐deficient cardiomyocytes. In vivo, RNase administration reduced serum RNA level, attenuated myocardial cytokine production, leukocytes infiltration and apoptosis, and conferred cardiac protection against I/R injury. Conclusion TLR3‐Trif signaling represents an injurious pathway during I/R. Extracellular RNA released during I/R may contribute to myocardial inflammation and infarction.
A Contract-based Framework for Integrated Demand Response Management in Smart Grids

Demand Response (DR) is considered a promising approach to cope with the increasing variability in power grids due to the penetration of renewable energy sources. However, it still remains a challenge to manage the aggregation of a large number of heterogeneous loads to achieve a desired response, especially at a fast time scale. In this paper, we present a system-level modeling and control design approach for DR management in smart grids, by following a contract-based methodology. Given a set of flexible DR loads, capable of adapting their power consumption upon external requests, we find a strategy for an aggregator to optimally track a DR requirement by combining the individual contributions of the DR components. In our framework, both the design requirements and the DR components' interfaces are specified by assume-guarantee contracts expressed using mixed-integer linear constraints. Contracts are used to formulate an optimal control problem, which is solved repeatedly over time, in a receding horizon fashion. We illustrate the effectiveness of our methodology for a set of DR components including fans and pumps, showing that it enables modular development of non-disruptive DR control schemes.
Downlink radio resource allocation with Carrier Aggregation in MIMO LTE-advanced systems

Long Term Evolution-Advanced (LTE-A) networks exploit the Carrier Aggregation (CA) technique to achieve a higher data rate by allowing user equipments (UEs) to simultaneously aggregate multiple component carriers (CCs). Moreover, MIMO technologies have become increasingly mature and been adopted as a default choice of the 4G standards. However, most of existing studies on resource allocation with carrier aggregation do not consider the MIMO capability of UEs. In this paper, we address the spectrum resource allocation problem with consideration of UEs' MIMO capability as well as modulation and coding schemes (MCSs) selection in carrier aggregation based LTE-A systems. We formulate the problem under both backlogged and finite queue traffic models as an optimization model, and prove its NP-hardness. As a result, a 1/2-approximation algorithm is proposed to find a suboptimal solution of resource allocation. Simulation results show that the proposed algorithm outperforms the existing schemes, and performs fairly close to the optimal solution under the small-scale scenarios.
Interaction between the antioxidant activity of curcumin and cholinergic system on memory retention in adult male Wistar rats

Objective(s): The cholinergic system plays an important role in learning and memory. This study investigated the effects of curcumin (turmeric extract) and the cholinergic system and their interaction on memory retention of passive avoidance learning in adult male Wistar rats. Materials and Methods: At first, an injection cannula was implanted in right ventricles of the animals. One week after the surgery, the animals were trained with a shuttle box set up. Post-training, injections were performed in all experiments. Administration of curcumin increased memory retention. Also administrations of nicotine and pilocarpine, the cholinergic receptor agonists, increased memory retention, while it is decreased by succinylcholine and scopolamine, the cholinergic receptor antagonists. Then co-administration of curcumin and cholinergic drugs were performed. Intraperitoneal and intracerebroventricular injections were applied for the curcumin and cholinergic drugs, respectively. Results: Co-administration of curcumin (45 mg/kg) with a low dose of nicotine (0.1 µg/rat) or pilocarpine (0.5 µg/rat) increased memory retention significantly. Effects of succinylcholine (0.01, 0.1 and 0.5 µg/rat) or scopolamine (0.1, 1 and 5 µg/rat) were attenuated by curcumin markedly (45 mg/kg). Conclusion: The results suggest that curcumin has a close interaction with cholinergic system in memory retention process.
Research on Semi-Automatic Construction of Domain Ontology Based on Machine Learning and Clustering Technique

In this paper we take the approach that constructed the ontology automatically, which attempted to take a method that extremely beneficial for the knowledge acquisition task was the integration of knowledge acquisition with machine learning techniques to increase the ontology construction effect, including domain concepts acquisition, taxonomy relation recognition, non-taxonomy relation recognition and ontology formalization description. This paper adopted an approach of non-dictionary Chinese word Segmentation techniques based on N-Gram to acquire domain candidate concepts, take the method based of NLP in the recognition of domain concept property relation, extracted subject, predicate and object of sentences. This triangle data can be treated as the triplet of data and object type property.
Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank

We present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers. We estimate polarity using topic-specific PageRank. Sentiment information is propagated from an initial seed lexicon through a joint graph representation of words and documents. We report improved classification accuracies across multiple domains for the base models and the maximum entropy model bootstrapped from the PageRank annotation.
Hierarchical survivability model for slotted all-optical packet switching networks

Network survivability is defined as the ability of a network to maintain an acceptable level of network performance in the presence of failures. Quantifying network survivability through pure network performance tends to be optimistic since it ignores the availability of the resources of the network at the time of failure. On the other hand, pure availability analysis tends to be too conservative since performance considerations are not taken into account. In this paper, a composite model to accurately measure the survivability of slotted multi-fiber optical packet switching networks is proposed. The end-to-end performance of multi-fiber slotted optical packet switched networks as well as the steady-state availability of the network are modeled. These two models are combined to construct a hierarchical network survivability model. The study shows that the use of a multiple-fiber network configuration provides additional network capacity and increases network survivability. Also, it shows that the use of wavelength conversion increases survivability by resolving the wavelength contention problem.
How good are we at detecting deception? A review of current techniques and theories

The task of discerning truth from untruth has long interested psychologists; however, methods for doing so accurately remain elusive. In this article, we provide an overview and evaluation of methods of detecting deception used in the laboratory and the field. We identify and discuss three broad approaches to detecting deception: measurement of non-verbal behaviour, verbal interview methods, and statement evaluation by humans and computers. Part of the problem in devising good methods for detecting deception is the absence of a sound understanding of deception in human lives. We thus consider three theories of deception – leakage, reality monitoring, and truth-default – and conclude that although promising, they do not yet provide an adequate foundation. We review 10 extant methods of detecting deception in the second part of the article, focusing at greatest length on the most widely used method in South Africa, the polygraph test of deception. Our conclusion is that non-verbal methods that work by inducing anxiety in interviewees are fundamentally flawed, and that we ought to move away from such methods. Alternate methods of detecting deception, including statement analysis, are considered, but ultimately our view is that there are currently no methods sufficiently accurate for practitioners to rely on. We suspect that a precondition for developing such measures is a coherent and validated theory.
Human detection in groups using a fast mean shift procedure

Detecting individual humans within groups becomes a non-trivial task when performing automatic visual surveillance in crowded scenes. This paper proposes a novel way to detect individual humans directly from the difference image using a fast variant of the mean shift mode seeking procedure and verifying the hypothesized configuration by a model-based approach. The method runs in real-time. Promising result are demonstrated for challenging image sequences.
Default Mode Network Maturation and Environmental Adversities During Childhood

Default mode network (DMN) plays a central role in cognition and brain disorders. It has been shown that adverse environmental conditions impact neurodevelopment, but how these conditions impact in DMN maturation is still poorly understood. This article reviews representative neuroimaging functional studies addressing the interactions between DMN development and environmental factors, focusing on early life adversities, a critical period for brain changes. Studies focused on this period of life offer a special challenge: to disentangle the neurodevelopmental connectivity changes from those related to environmental conditions. We first summarized the literature on DMN maturation, providing an overview of both typical and atypical development patterns in childhood and early adolescence. Afterward, we focused on DMN changes associated with chronic exposure to environmental adversities during childhood. This summary suggests that changes in DMN development could be a potential allostatic neural feature associated with an embodiment of environmental circumstances. Finally, we discuss about some key methodological issues that should be considered in paradigms addressing environmental adversities and open questions for future investigations.
An Examination of School Climate, Victimization, and Mental Health Problems Among Middle School Students Self-Identifying With Emotional and Behavioral Disorders

The purpose of the current study was to examine perceptions of school climate among students who self-identify as having an emotional and behavioral disorder (EBD) and their counterparts without disabilities on the Georgia Student Health Survey 2.0. Although research examining the link between perceptions of school climate and student outcomes has increased, no research has examined how students with disabilities, particularly those with EBD, perceive school climate, and the relation of those perceptions to outcomes including peer victimization and mental health problems. The current study found that in comparison with students without disabilities (SWOD), students with EBD (SWEBD) reported significantly lower perceptions of school climate and significantly higher rates of mental health problems and peer victimization. Perceptions of school climate and mental health were inversely associated with reports of victimization among both SWOD and SWEBD. We discuss how educational leaders can improve school climate for SWEBD through tiered systems of supports such as School-Wide Positive Behavior Interventions and Supports to support mental health and reduce peer victimization.
Paper Title: a Multi-resolution Approach for Infrared Vision-based Pedestrian Detection a Multi-resolution Approach for Infrared Vision-based Pedestrian Detection *

This paper presents the improvements of a system for pedestrian detection in infrared images. The system is based on a multi-resolution localization of warm symmetrical objects with specific size and aspect ratio; the multi-resolution approach allows to detect both close and far pedestrians. A match against a set of 3D models encoding human shape's morphological and thermal characteristics is used as a validation process to remove false positives. No temporal correlation, nor motion cues are used for the processing that is based on the analysis of single frames only.
Estimation of facial expression intensity from a sequence of binary face images

This paper presents a method of estimation of facial expression intensity from a sequence of binary facial images obtained from video. The binarization has been done using a neuro-visual model of figure ground segregation. The Local Binary Pattern (LBP) is taken as characteristic feature of a face with expression. This pattern gets evolved in the temporal domain over the sequence. The dynamics of the pattern, starting from a neutral face, is characterised by Hausdorff distance. Back Propagation (BP) Neural Networks are trained to estimate the expression intensity level of the basic expressions.
Advancing a Conceptual and Empirical Model of School Climate for School Staff in California

ABSTRACT School climate is increasingly intertwined with efforts to create safe schools and improve social-emotional learning and academic outcomes. While current definitions of school climate implicate the decisions and actions of school staff members, the majority of school climate research focuses on the perspectives of students. This study presents an adapted model of school climate that focuses on the experiences of school staff members, and is one of the first empirical studies to explore a statewide data set of responses from staff members in California public schools. Results suggest that there are meaningful differences in the experiences of school climate for different staff members and that these differences should guide the assessments of school climate and changes to policies and procedures that may influence climate.
Luttinger liquid superlattices

We calculate the correlation functions and the dc conductivity of Luttinger liquid superlattices, modelled by a repeated pattern of interacting and free Luttinger liquids. In a specific realization, where the interacting subsystem is a Hubbard chain, the system exhibits a rich phase diagram with four different phases: two metals and two compressible insulators. In general, we find that the effective low-energy description amalgamates features of both types of liquids in proportion to their spatial extent, suggesting the interesting possibility of `engineered' Luttinger liquids.
On-to-knowledge Methodology — Final Version On-to-knowledge Consortium

ion Levels and Size In our scenarios typically several people were involved in creating first draft versions of the ontology during early stages in the kick-off phase. Due to different backgrounds and mind models of the developers all of them had different abstraction levels. This made it difficult to merge them into a single coherent ontology. Typical problems rose from differing numbers of branches per node and more general from how deep and wide the ontology is at all. Many iterations in the ontology development process were necessary to align e.g. the three skills ontologies in the Swiss Life case study (cf. Section 3.8) and finally to merge them together. We stress that the ontology modelling process should start with a definition of the abstraction level, which is strongly dependent on the usage of the ontology. In the skills management scenario, we had to go down to a more precise level during the modelling process due to the high number of employees and the need to differentiate them for the intended project staffing functionality. First feedback from test users of the skills management system and the underlying ontology showed that the skills trees are too large for browsing. Most users prefer shallow trees with a list of concepts on each node on a more abstract level. The reasons for this are the high time consuming usage of the deep ontology concepts and the amount of possible concepts, which can be chosen. The latter reason is based on the integrated skills ontology of all domains or departments. A suggestion from the test users was to define user views on the ontology depending on the department where the user comes from. This would lead to much smaller skills trees and more usable ones in the according departments. Employees with broader skills can change from the department/domain view to the interdisciplinary view on the whole ontology. Though views are common to the database community, no concept for defining and using views on ontologies has been developed so far. As a summary, it is a challenging task to find the right balance between the requirements of different users of the skills management system. While some want to have a ontology as large as possible, the other ones feels comfortable with a small ontology. OTK/2002/D18/v1.0 26th September, 2002 47 On-To-Knowledge Methodology — Final Version
Mishima Yukio and the Homoeroticisation of the Emperor of Japan

This article aims to contextualise Mishima Yukio’s works and nationalist politics into a history of Japanese queer politics. Mishima Yukio is considered as one of the most prominent artists in modern Japan, famous for his homosexual-themed works and nationalist politics. This article explores the discursive relationships between Japanese national politics and homoeroticism through analysing Mishima Yukio’s works. It will review discourses about Mishima, his performance, works and sexuality; it points out how these discourses contain homophobic presumptions and overlook the wider social and historical contexts of Mishima’s works. The article will discuss Mishima’s far-right political discourses and representations in his famous political essays The Defense of Culture (Bunka bōuei ron) (1969) and The Manifesto of Anti-revolution (Han-kakumei ron) (1969) and the film Patriot (Yūkoku) (1966). This article points out that Mishima intertwined homoerotic ethos and Japanese national politics through the emperor, establishing a space for queer homoerotic desire in Japan’s public culture and politics. In conclusion, this article discusses how Mishima’s representation of the homoeroticised emperor prepared the ground to limit political discourses of queer politics in contemporary Japan.