Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Co-Investment Network Learning for Financial Assets

Most recent works model the market structure of the stock market as a correlation network of the stocks. They apply pre-defined patterns to extract correlation information from the time series of stocks. Without considering the influences of the evolving market structure to the market index trends, these methods hardly obtain the market structure models which are compatible with the market principles. Advancements in deep learning have shown their incredible modeling capacity on various finance-related tasks. However, the learned inner parameters, which capture the essence of the finance time series, are not further exploited about their representation in the financial fields. In this work, we model the financial market structure as a deep co-investment network and propose a Deep Co-investment Network Learning (DeepCNL) method. DeepCNL automatically learns deep co-investment patterns between any pairwise stocks, where the rise-fall trends of the market index are used for distance supervision. The learned inner parameters of the trained DeepCNL, which encodes the temporal dynamics of deep co-investment patterns, are used to build the co-investment network between the stocks as the investment structure of the corresponding market. We verify the effectiveness of DeepCNL on the real-world stock data and compare it with the existing methods on several financial tasks. The experimental results show that DeepCNL not only has the ability to better reflect the stock market structure that is consistent with widely-acknowledged financial principles but also is more capable to approximate the investment activities which lead to the stock performance reported in the real news or research reports than other alternatives.
Detecting clinical and simulated dissociative identity disorder with the Test of Memory Malingering.

OBJECTIVE Few studies have assessed malingering in individuals with complex trauma and dissociation. This is concerning because these individuals' severe and ranging symptoms are associated with elevations on some, but not all, validity scales that detect symptom exaggeration. Dissociative individuals may experience dissociative amnesia, yet no study to date has examined how to distinguish clinical from malingered amnesia with dissociative samples. The current study examined whether the Test of Memory Malingering (TOMM) can accurately distinguish patients with clinically diagnosed dissociative identity disorder (DID) and simulators coached to imitate DID. METHOD Utility statistics classify individuals' TOMM scores as suggestive of clinical or simulated DID. TOMM scores from 31 patients diagnosed with DID via structured interviews were compared to those of 74 coached DID simulators. RESULTS Discriminant analyses found scores from TOMM Trials 1 and 2 and total TOMM scores accurately classified clinical or simulated DID group status. In addition, TOMM Trial 1 demonstrated high specificity (87%) and positive predictive power (94%), as well as moderate sensitivity (78%), negative predictive power (63%), and overall diagnostic power (81%). Despite exposure to DID-specific information, simulators were not able to accurately feign the DID group's TOMM scores, which is inconsistent with the iatrogenic/sociocultural model of DID. CONCLUSION The TOMM shows promise as useful in clinical and forensic contexts to detect memory malingering among DID simulators without sacrificing specificity. Accurate distinction between genuine and feigned complex trauma-related symptoms, including dissociative memory, is integral to the accurate diagnosis of traumatized populations. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Independent chondrogenic potential of canine bone marrow-derived mesenchymal stem cells in monolayer expansion cultures decreases in a passage-dependent pattern

Although chondroinductive growth factors are considered necessary for chondrogenesis of bone marrow-derived mesenchymal stem cells (BMSC), independent and spontaneous chondrogenesis has been previously demonstrated in adult horses, bovine calves and adult human BMSC. Surprisingly, adult canine BMSC under similar culture conditions previously failed to demonstrate chondrogenesis. The present study evaluated independent chondrogenic potential of BMSC sourced from three young dogs in the absence of known chondroinductive factors. BMSC were culture expanded in 10% DMEM up to third passage (P3). At each passage, the phenotype of BMSC was evaluated by RT-PCR gel electrophoresis and qPCR. BMSC exhibited a chondrogenic phenotype in the absence of dexamethasone and TGF-β1 as verified by the expression of Sox-9, type II collagen and aggrecan. Sox-9 was significantly downregulated (P<0.05) from P1−P3 compared to P0 while type II and X collagen, and aggrecan were significantly downregulated at P3 compared to P0. There was a significant (P<0.01) negative correlation between passaging and Sox-9, type II collagen and aggrecan gene expression. These results indicate that independent chondrogenic potential and phenotype retention of BMSC decreases in a passage-dependent pattern. Therefore, caution should be exercised for future experiments evaluating the chondrogenic potential of BMSC after extensive expansion cultures in 10% DMEM.
Cutting Edge: TGF-β and Phosphatidylinositol 3-Kinase Signals Modulate Distinct Metabolism of Regulatory T Cell Subsets

Murine Foxp3+ regulatory T cells (Tregs) differentiated in vitro (induced Tregs [iTregs]) in the presence of anti-inflammatory cytokine TGF-β rely predominantly upon lipid oxidation to fuel mitochondrial oxidative phosphorylation. Foxp3 expression underlies this metabolic preference, as it suppresses glycolysis and drives oxidative phosphorylation. In this study, we show that in contrast to iTregs, thymic-derived Tregs (tTregs), engage in glycolysis and glutaminolysis at levels comparable to effector T cells despite maintained Foxp3 expression. Interestingly, exposure of tTregs to the anti-inflammatory cytokine TGF-β represses PI3K-mediated mTOR signaling, inhibits glucose transporter and Hk2 expression, and reprograms their metabolism to favor oxidative phosphorylation. Conversely, replicating the effects of inflammation via elevation of PI3K signaling has minimal effects on tTregs but dramatically enhances the glycolysis of normally oxidative iTregs, resulting in reduction of Foxp3 expression. Collectively, these findings suggest both extrinsic and intrinsic factors govern the unique metabolic signature of Treg subsets.
Safe Navigation With Human Instructions in Complex Scenes

In this letter, we present a robotic navigation algorithm with natural language interfaces that enables a robot to safely walk through a changing environment with moving persons by following human instructions such as “go to the restaurant and keep away from people. ” We first classify human instructions into three types: goal, constraints, and uninformative phrases. Next, we provide grounding in a dynamic manner for the extracted goal and constraint items along with the navigation process to deal with target objects that are too far away for sensor observation and the appearance of moving obstacles such as humans. In particular, for a goal phrase (e.g., “go to the restaurant”), we ground it to a location in a predefined semantic map and treat it as a goal for a global motion planner, which plans a collision-free path in the workspace for the robot to follow. For a constraint phrase (e.g., “keep away from people”), we dynamically add the corresponding constraint into a local planner by adjusting the values of a local costmap according to the results returned by the object detection module. The updated costmap is then used to compute a local collision avoidance control for the safe navigation of the robot. By combining natural language processing, motion planning, and computer vision, our developed system can successfully follow natural language navigation instructions to achieve navigation tasks in both simulated and real-world scenarios. Videos are available at https://sites.google. com/view/snhi.
Influence of swine leukocyte antigen haplotype on serum antibody titers against swine erysipelas vaccine and reproductive and meat production traits of SLA-defined selectively bred Duroc pigs

We investigated possible associations of SLA class II haplotypes with serum antibody titers against a swine erysipelas vaccine, reproductive and meat production traits using a population of selective breeding Duroc pigs. In the selective breeding Duroc pigs, four SLA class II-DRB1 and -DQB1 alleles were assigned by using PCR-sequence specific primer technique. Low-resolution haplotype (Lr)-0.30 and/or Lr-0.13 were deduced from the SLA class II alleles in the population of SLA-defined Duroc pigs. SLA-homozygous piglets with the Lr-0.30 haplotype had relatively lower serum antibody titers against the vaccine compared to those with Lr-0.13. In contrast, there were no statistically significant differences in reproductive performance between the SLA-defined pigs with two SLA class II haplotypes. Weaning and rearing rates until the body weight of 105 kg was reached in homozygous piglets with Lr-0.30 were significantly lower than those in homozygous piglets with Lr-0.13. The SLA-defined pigs had lower birth and weaning weights, body weights at 60 days of age, and daily weight gains than non-selective breeding Duroc pigs. Furthermore, the SLA-defined pigs had slightly lower back fat thickness compared to the non-selective breeding pigs. The rib eye areas of homozygous or heterozygous pigs with Lr-0.13 were larger than those of homozygous pigs with Lr-0.30 and non-selective breeding pigs. These data suggested that SLA haplotypes had the potential as useful genetic markers for selective breeding in the population of SLA-defined Duroc pigs.
Transformational leadership and customer service: A moderated mediation model of negative affectivity and emotion regulation

Integrating theories from leadership, emotion management, affectivity, and customer service, this study examines how transformational leadership leads to favourable customer intentions via the mediation of service employees' emotion regulation, job satisfaction, and their service performance and via the moderation of employee negative affectivity. Results obtained from data of 204 matched sets of managers, service employees, and customers show that the effect of transformational leadership on amplification of pleasant emotions was conditioned on service employees' negative affectivity. Employee service performance partially mediated the effect of job satisfaction on customer outcomes. Finally, overall results reveal that transformational leadership and amplification of pleasant emotions were more strongly related to the customer outcomes, as mediated through the intervening variables in the model, when negative affectivity was high than when negative affectivity was low. Results have implications for how service workers with negative affectivity can manage their emotions to achieve effective service outcomes through interactions with a leader, how the effect of transformational leadership can be bounded, and how transformational leadership and emotion regulation are relevant to customer service.
A Compound LAMS-MOODLE Environment to Support Collaborative Project-Based Learning: A Case Study with the Group Investigation Method.

Collaborative project-based learning is well established as a component of several courses in higher education, since it seems to motivate students and make them active in the learning process. Collaborative Project-Based Learning methods are demanded so that tutors become able to intervene and guide the students in flexible ways: by encouraging them to develop independent solutions and also by keeping their efforts and activities targeted towards the lesson goals. Students, on the other hand, need to develop important skills in searching and analyzing information as well as in communication and time management. In this paper, we propose the design of a collaboration script, following the “Group Investigation method”, to support the tutors and students of a collaborative project-based course on ‘DataBases’. We implemented this script using a compound e-learning collaborative environment based on MOODLE and LAMS, which have provided tutors with several tools and methods to involve in the learning process. The evaluation of the students’ projects and the comparison with the corresponding projects of the previous academic year showed a better level of collaboration and performance of the students but also proved that the learning environment offered the tutors a more efficient way to guide their students in Collaborative Project-Based Learning.
Mathematics and Science Self-Efficacy Resources as the Predictor of Academic Success

Article History: Received 11.12. 2017 Received in revised form 20.03. 2018 Accepted 10.04. 2018 Available online 24.05. 2018 The basic aim of the study was to determine the power of mathematics and science self-efficacy resources as the predictors of academic success. The relational scanning model was used in the study, and the study was conducted with 492 students who were studying at sixth and seventh grades of a state school. The mathematics and science self-efficacy resources scales were used as the data collection tool. In addition, the school reports of the semester before the study was conducted were used to determine the academic success levels of the students. The Pearson moments multiplication correlation technique and multiple linear regression analysis were used in the analysis of the data. According to the results, there were significant relations between the mathematics and science selfefficacy resources and the academic success levels at high, medium and low levels. The personal experiences, indirect experiences and social persuasions, which have significant effects on academic success both for mathematics and for science, explained nearly 60% of the change in academic success. The physiological situations towards mathematics did not have any significant effects on academic success. According to the findings, some recommendations have also been made in the study. © 2018 IOJES. All rights reserved1
MiCT : Mixed 3 D / 2 D Convolutional Tube for Human Action Recognition

Human actions in videos are three-dimensional (3D) signals. Recent attempts use 3D convolutional neural networks (CNNs) to explore spatio-temporal information for human action recognition. Though promising, 3D CNNs have not achieved high performanceon on this task with respect to their well-established two-dimensional (2D) counterparts for visual recognition in still images. We argue that the high training complexity of spatio-temporal fusion and the huge memory cost of 3D convolution hinder current 3D CNNs, which stack 3D convolutions layer by layer, by outputting deeper feature maps that are crucial for high-level tasks. We thus propose a Mixed Convolutional Tube (MiCT) that integrates 2D CNNs with the 3D convolution module to generate deeper and more informative feature maps, while reducing training complexity in each round of spatio-temporal fusion. A new end-to-end trainable deep 3D network, MiCTNet, is also proposed based on the MiCT to better explore spatio-temporal information in human actions. Evaluations on three well-known benchmark datasets (UCF101, Sport1M and HMDB-51) show that the proposed MiCT-Net significantly outperforms the original 3D CNNs. Compared with state-of-the-art approaches for action recognition on UCF101 and HMDB51, our MiCT-Net yields the best performance.
Electroencephalographic Denoising and Classification by Using Power Spectrum Density Based Independent Component Analysis and Common Spatial Pattern

With the participation of 12 volunteers, the off-line application of independent component analysis for automatic artefacts removal based on power spectral density is investigated. By using the “range” values of the power spectra of the independent components within the frequency range of 2 to 8 Hz along with the integral values of the independent components in the range of 8 to 30 Hz, artificial independent components are automatically marked and removed. The artefact-free electroencephalographic signal is further classified using the method of common spatial pattern. It is found that the modification of the conventional common spatial pattern can result in a higher imagery task classification.
Job Mobility Networks and Endogenous Labor Markets

I introduce a novel method to identify endogenous labor markets revealed by job mobility flows. The estimation is based on tools from network analysis. Firms are in the same market if they have similar links to other firms and not because they are located in the same region. I compare endogenous markets to geographically separated markets. I analyze employment spillovers following a large local labor demand shock as well as mobility responses to import competition from China and Eastern Europe.
Contested paradigms of ‘viability’ in redistributive land reform: perspectives from southern Africa

‘Viability’ is a key term in debates about land redistribution in southern African and beyond. It is often used to connote ‘successful’ and ‘sustainable’– but what is meant by viability in relation to land reform, and how have particular conceptions of viability informed state policies and planning approaches over time? How have such notions influenced the contested politics of land and agriculture? In southern Africa policy debates have tended to focus narrowly on farm productivity and economic returns, and an implicit normative model is the large-scale commercial farm. Through a review of land reform experiences in South Africa, Namibia and Zimbabwe, this paper critically interrogates this influential but under-examined notion. It examines contrasting framings of viability derived from neo-classical economics, new institutional economics, livelihoods approaches (both developmentalist and welfarist), radical political economy and Marxism, and their influence in southern Africa. Through a discussion of alternative framings of viability, the paper aims to help shift policy debates away from a narrow, technocratic economism, a perspective often backed by powerful interests, towards a more plural view, one more compatible with small-scale, farming-based livelihoods.
Emotional competencies of the sport coach: A qualitative approach within a physical activity programme for multiple sclerosis patients.

Multiple sclerosis (MS) is characterised by functional impairments (fatigue, motor weakness, spasticity, poor balance) and limitations in social functioning. Accordingly, people with MS are more affected by sedentarity and not enough aware of their physical capacities. Finally, those behaviours lead to physical deconditioning and general poor health in this population. To encourage physical activity with MS people, a specific coaching programme whereby MS patient are individually supported by a sport coach has been launched at the University Hospital Center of Liège. In this context this study aimed to(1) describe the intervention of the coach ;(2) link emotional competencies and behaviors of the coach ; (3) propose recommendations to improve MS patient’s physical activity management. Behaviors, attitudes, values, representations and emotional competencies of seven coaches were analyzed focusing on both visible (audio and video recording) and invisible (questionnaires and interviews) variables. Results show that effective coaching to MS patient implies a high support and cooperation level of the coach and a highly specific approach characterized by a systematic appraisal of patient’s behaviors revealing his/her current psychological and physical states. Recommendations the coach education are discussed with an emphasis on social and emotional skills.
Agronomic Performance and Genotype × Environment Interaction of Herbicide-Resistant Maize Varieties in Eastern Africa

Striga hermonthica (Del.) Benth. and Striga asiatica (L.) Kuntze severely affect maize ( Zea mays L.) pro - duction in sub-Saharan Africa. A single Striga plant produces a large number of seeds that form a bank of viable but dormant seed in the soil until they get a chemical signal from suitable maize host roots. Imidazolinone-resistant (I r) open-pollinated maize varieties ( opVs) developed for Striga control were tested in diverse environments in four countries of eastern Africa in 2004. The objective of the study was to assess the agronomic performance of I r maize and genotypeenvironment interactions
Mesoscopic Facial Geometry Inference Using Deep Neural Networks

We present a learning-based approach for synthesizing facial geometry at medium and fine scales from diffusely-lit facial texture maps. When applied to an image sequence, the synthesized detail is temporally coherent. Unlike current state-of-the-art methods [17, 5], which assume "dark is deep", our model is trained with measured facial detail collected using polarized gradient illumination in a Light Stage [20]. This enables us to produce plausible facial detail across the entire face, including where previous approaches may incorrectly interpret dark features as concavities such as at moles, hair stubble, and occluded pores. Instead of directly inferring 3D geometry, we propose to encode fine details in high-resolution displacement maps which are learned through a hybrid network adopting the state-of-the-art image-to-image translation network [29] and super resolution network [43]. To effectively capture geometric detail at both mid- and high frequencies, we factorize the learning into two separate sub-networks, enabling the full range of facial detail to be modeled. Results from our learning-based approach compare favorably with a high-quality active facial scanhening technique, and require only a single passive lighting condition without a complex scanning setup.