Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Contributory Pension Scheme and Welfare of Retired Civil Servants in Ebonyi State, Nigeria

The challenges pensioners experience in the non-contributory pension scheme before accessing their retirement benefits are enormous in Nigeria. Delays in the payment of pensions and gratuities, coupled with frustrations during screening exercises have negative implications on pensioners’ welfare. These challenges necessitated the introduction of contributory pension scheme in 2004. This study examined the influence of the non-contributory pension scheme on post retirement life of civil servants in Ebonyi State. Multi-stage sampling technique was used to administer questionnaire to 365 respondents. In-depth interviews were conducted with three key respondents. There was a significant association between monthly pension’s distribution of retirees’ and meeting their welfare needs. The majority of the respondents indicated that the pension provided under the non-contributory pension scheme was not adequate in meeting retirees’ welfare needs. Also, there was a significant association between monthly pensions and retirees’ household size. In-depth interviews revealed inadequate preparations for retirement by pensioners, delay in payment of retirement benefits, embezzlement of pension funds, corrupt practices among pension managers and governments’ unwillingness to review pensions as challenges confronting the non-contributory pension scheme. The provisions of the non-contributory pension scheme were inadequate in meeting retirees’ welfare needs in Ebonyi State. The study therefore recommended the adoption of the 2004 Pension scheme so as to overcome the challenges inherent in the management of non-contributory pension scheme in Ebonyi State, Nigeria. Nigeria. The results indicate that non-contributory pension scheme has adverse influence on the post retirement life of civil servants in Ebonyi State. There is a consensus that pensions provided by the scheme are grossly inadequate and face pervasive problems. In-depth interviews revealed inadequate preparations for retirement by pensioners, delay in payment of retirement benefits, embezzlement of pension funds, corrupt practices among pension managers, and governments’ unwillingness to review pensions
The 2–primary Hurewicz image of tmf

We determine the image of the 2-primary tmf-Hurewicz homomorphism, where tmf is the spectrum of topological modular forms. We do this by lifting elements of tmf_* to the homotopy groups of the generalized Moore spectrum M(8,v_1^8) using a modified form of the Adams spectral sequence and the tmf-resolution, and then proving the existence of a v_2^32-self map on M(8,v_1^8) to generate 192-periodic families in the stable homotopy groups of spheres.
Action-specific feature processing in the human visual cortex

Sensorimotor integration involves feedforward and reentrant processing of sensory input. Grasp-related motor activity precedes and is thought to influence visual object processing. Yet, while the importance of reentrant feedback is well established in perception, the top-down modulations for action and the neural circuits involved in this process have received less attention. Do action-specific intentions influence the processing of visual information in the human cortex? Using a cue-separation fMRI paradigm, we found that action-specific instruction (manual alignment vs. grasp) influences the cortical processing of object orientation several seconds after the object had been viewed. This influence occurred as early as in the primary visual cortex and extended to ventral and dorsal visual stream areas. Importantly, this modulation was unrelated to non-specific action planning. Further, the primary visual cortex showed stronger functional connectivity with frontal-parietal areas and the inferior temporal cortex during the delay following orientation processing for align than grasping movements, strengthening the idea of reentrant feedback from dorsal visual stream areas involved in action. To our knowledge, this is the first demonstration that intended manual actions have such an early, pervasive, and differential influence on the cortical processing of vision.
Gunrock 2.0: A User Adaptive Social Conversational System

Gunrock 2.0 is built on top of Gunrock with an emphasis on user adaptation. Gunrock 2.0 combines various neural natural language understanding modules, including named entity detection, linking, and dialog act prediction, to improve user understanding. Its dialog management is a hierarchical model that handles various topics, such as movies, music, and sports. The system-level dialog manager can handle question detection, acknowledgment, error handling, and additional functions, making downstream modules much easier to design and implement. The dialog manager also adapts its topic selection to accommodate different users' profile information, such as inferred gender and personality. The generation model is a mix of templates and neural generation models. Gunrock 2.0 is able to achieve an average rating of 3.73 at its latest build from May 29th to June 4th.
Differences among regions in environmental predictors of primate community similarity affect conclusions about community assembly

Abstract Understanding why ecological communities contain the species they do is a long-standing question in ecology. Two common mechanisms that affect the species found within communities are dispersal limitation and environmental filtering. Correctly identifying the relative influences of these mechanisms has important consequences for our understanding of community assembly. Here variable selection was used to identify the environmental variables that best predict tropical forest primate community similarity in four biogeographic regions: the Neotropics, Afrotropics, Madagascar and the island of Borneo in South-East Asia. The environmental variables included net primary productivity and altitude, as well as multiple temperature, precipitation and topsoil variables. Using the best environmental variables in each region, Mantel and partial Mantel tests were used to reanalyse data from a previously published study. The proportion of variance explained increased for each region. Despite increases, much of the variation remained unexplained for all regions (R2: Africa = 0.45, South America = 0.16, Madagascar = 0.28, Borneo = 0.10), likely due to different evolutionary and biogeographic histories within each region. Nonetheless, substantial variation among regions in the environmental variables that best predicted primate community similarity were documented. For example, none of the 14 environmental variables was included for all four regions, yet each variable was included for at least one region. Contrary to prior results, environmental filtering was an important assembly mechanism for primate communities in tropical forests worldwide. Geographic distance more strongly predicted African and South American communities whereas environmental distance more strongly predicted Malagasy and Bornean communities. These results suggest that dispersal limitation structures primate communities more strongly than environmental filtering in Africa and in South America whereas environmental filtering structures primate communities more strongly than dispersal limitation in Madagascar and Borneo. For communities defined by genera, environmental distance more strongly predicted primate communities than geographic distance in all four regions, which suggests that environmental filtering is a more influential assembly mechanism at the genus level. Therefore, a more nuanced consideration of environmental variables affects conclusions about the influences of environmental filtering and dispersal limitation on primate community structure.
Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response

During a disaster event, images shared on social media helps crisis managers gain situational awareness and assess incurred damages, among other response tasks. Recent advances in computer vision and deep neural networks have enabled the development of models for real-time image classification for a number of tasks, including detecting crisis incidents, filtering irrelevant images, classifying images into specific humanitarian categories, and assessing the severity of damage. Despite several efforts, past works mainly suffer from limited resources (i.e., labeled images) available to train more robust deep learning models. In this study, we propose new datasets for disaster type detection, and informativeness classification, and damage severity assessment. Moreover, we relabel existing publicly available datasets for new tasks. We identify exact- and near-duplicates to form non-overlapping data splits, and finally consolidate them to create larger datasets. In our extensive experiments, we benchmark several state-of-the-art deep learning models and achieve promising results. We release our datasets and models publicly, aiming to provide proper baselines as well as to spur further research in the crisis informatics community.
Shari’ah Issues in Short Selling and its Potential Implementation in Qatar Stock Exchange

Through surveying the literature to examine the impact of short selling on stock markets, we found that most journal articles and research papers are strongly in favor of short selling. The finance literature claims that short selling is important for a well-functioning financial market because it offers number of advantages which include efficient price discovery, better market liquidity, and enhance risk management. We showed that these claims are not accurate, and that these studies are attempting to promote the practice of short selling despite its negative impact. The practice of short selling is still a subject of debate especially among Shari’ah scholars where they are still divided on its legitimacy. The paper provided an insight into this debate by investigating various Shari’ah aspects of short selling, and we found that this financial tool does not comply with Shari’ah principles. Since Shari’ah scholars do not permit short selling, some institutions and hedge funds claim offering Shari’ah compliant short selling alternatives. In this paper, we outlined four approaches based on Ijarah, Salam, Arbun, and Wa’d structures as alternatives to conventional short selling. Based on our analysis of the mechanism of each structure, we found that all the structures involve unresolved Shari’ah issues. Malaysia claims offering short selling that complies with Shari’ah principles, we therefore discussed the short selling as applied in Malaysia to examine its compliance with Shari’ah principles. The Shari’ah Advisory Council (SAC) of Malaysian Securities Commission resolved that Securities Borrowing and Lending (SBL) should be aligned to Ijarah principles in order for SBL to comply with Shari’ah principles. The SAC used the principle of Istihsan based on Maslahah as an exception to the general principles of Ijarah to justify the use of short selling in its Islamic Capital Market. The principle of Istihsan based on ‘UrfKhas, that is, a customary practice accepted in economic activities, was also used by the SAC as a basis for the permissibility of SBL. We showed that there is no maslahah to the market or the society from applying short selling as claimed by the SAC. Moreover, such use to the concept of Istihsan based on Maslahah to legalize what serves the benefit of some groups could dampen the credibility of Islamic finance.
Short Communication: The effects of vermicompost and vermiwash from the medicinal plants, neem (Azadirachta indica) and lime (Citrus aurantifolia), on the growth parameters of lettuce in a hydroponic system

Jaikishun S, Hoosein A, Ansari AA. 2018. The effects of vermicompost and vermiwash from the medicinal plants, neem (Azadirachta indica) and lime (Citrus aurantifolia), on the growth parameters of lettuce in a hydroponic system. Nusantara Bioscience 10: 91-95. The farming community is confronting critical issues on the usage of chemicals and rapidly decreasing arable lands from salinity, flooding and drought. This study was designed to compare the effectiveness of vermiwash produced from medicinal plants, as a potential substitute for chemicals, and standard hydroponic solution in supporting the growth of lettuce in a hydroponic system, a soilless medium. Vermicompost and vermiwash were produced from Azadirachta indica, and Citrus aurantifolia using Eisenia fetida and physiochemical analyses done indicated the presence of adequate amount of nutrients for plant growth. Therefore, using vermiwash and vermicompost as a standard hydroponic solution is an efficient and effective method of growing more food for the world’s growing population in soilless medium, thereby reducing the dependency on land. Plants grown in the vermiwash and vermicompost in the hydroponic system had higher productivity than the one produced by commercial hydroponic solution.
Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players' and coordinated teams' behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).
Space Telescope and Optical Reverberation Mapping Project. XIII. An Atlas of UV and X-Ray Spectroscopic Signatures of the Disk Wind in NGC 5548

The unusual behavior of the spectral lines of NGC5548 during the STORM campaign demonstrated a missing piece in the structure of AGNs. For a two-month period in the middle of the campaign, the spectral lines showed a deficit in flux and a reduced response to the variations of the UV continuum. This was the first time that this behavior was unequivocally observed in an AGN. Our previous papers explained this as being due to a variable disk wind that acts as a shield and alters the SED. Here, we use Cloudy to create an atlas of photoionization models for a variety of disk winds, in order to study their effects on the SED. We show that the winds have three different cases: Case 1 winds are transparent, fully ionized, and have minimal effects on the intrinsic SED, although they can produce some line emission, especially He ii or FeKα. We propose that this is the situation in most of the AGNs. Case 2 winds, which have a He++–He+ ionization front, block part of the XUV continuum but transmit much of the Lyman continuum. They lead to the observed abnormal behavior. Case 3 winds have a H+ ionization front and block much of the Lyman continuum. The results show that the presence of these winds has important effects on the spectral lines of AGNs. They will thus have an effect on the measurements of the black hole mass and the geometry of the AGN. This atlas of spectral simulations can serve as a guide to future reverberation campaigns.
Palomino-Ochoa at SemEval-2020 Task 9: Robust System Based on Transformer for Code-Mixed Sentiment Classification

We present a transfer learning system to perform a mixed Spanish-English sentiment classification task. Our proposal uses the state-of-the-art language model BERT and embed it within a ULMFiT transfer learning pipeline. This combination allows us to predict the polarity detection of code-mixed (English-Spanish) tweets. Thus, among 29 submitted systems, our approach (referred to as dplominop) is ranked 4th on the Sentimix Spanglish test set of SemEval 2020 Task 9. In fact, our system yields the weighted-F1 score value of 0.755 which can be easily reproduced — the source code and implementation details are made available.
Multi-Channel Automatic Speech Recognition Using Deep Complex Unet

The front-end module in multi-channel automatic speech recognition (ASR) systems mainly use microphone array techniques to produce enhanced signals in noisy conditions with reverberation and echos. Recently, neural network (NN) based front-end has shown promising improvement over the conventional signal processing methods. In this paper, we propose to adopt the architecture of deep complex Unet (DCUnet) - a powerful complex-valued Unet-structured speech enhancement model - as the front-end of the multi-channel acoustic model, and integrate them in a multi-task learning (MTL) framework along with cascaded framework for comparison. Meanwhile, we investigate the proposed methods with several training strategies to improve the recognition accuracy on the 1000-hours real-world XiaoMi smart speaker data with echos. Experiments show that our proposed DCUnet-MTL method brings about 12.2% relative character error rate (CER) reduction compared with the traditional approach with array processing plus single-channel acoustic model. It also achieves superior performance than the recently proposed neural beamforming method.
Global view of axion stars with nearly Planck-scale decay constants

We show that axion stars formed from axions with nearly Planck-scale decay constants $f$ are unstable to decay, and are unlikely to have phenomenological consequences. More generally, we show how results at smaller $f$ cannot be naively extrapolated to $f=\mathcal{O}(M_P)$ as, contrary to conventional wisdom, gravity and special relativity can both become relevant in the same regime. We clarify the rate of decay by reviewing and extending previous work on oscillons and axion stars, which imply a fast decay rate even for so-called dilute states at large $f$.
Effects of Ballistic Resistance Training on Swimming Start

Ballistic training, also known as power training, is a form of strength training that entails an athlete lifting, accelerating and finally releasing a weight rather than slowly lowering it. It was first used among elite athletes who were trying to develop explosiveness to help them improve their speed. The purpose of this study was to determine whether ballistic resistance training would improvement the performance of swim starts for female students. Ten, grade 4 female students from a physical education program participated in the study. The students’ mean height was 1.77± 3.02 meters, with a mean age of 21.14±1.25 years. The subjects were divided equally into two groups. The experimental group completed 6 weeks of ballistic resistance training while the control group participated in a traditional training program. Statistical analysis of the results was carried out using SPSS software. The results showed that the experimental group had significantly higher power and faster swimming starts than the control group. Under the conditions of our study, an intervention using ballistic resistance training for 6 weeks had beneficial effects on performance levels of swimming starts and power.
Observation de la sémiotisation chez des adolescents pris en charge pour des troubles du raisonnement logico-mathématique

Lors de seances de reeducation orthophonique des troubles du raisonnement logico-mathematique, nous avons pu observer le rapport specifique des adolescents au signe linguistique. L'evocation du signifiant semble activer le fragment de la realite (le referent) mais ne permet pas de solliciter le signifie. A partir de ce constat, nous nous sommes interrogee sur les liens que l'on peut etablir entre les troubles du raisonnement logico-mathematique et l'elaboration du signifie. L'hypothese est lasuivante : des raisonnements encore ancres dans le « figuratif » empecheraient l'adolescent d'acceder au signifie en terme de concept. Lors de l'experimentation, nous avons teste 25 adolescents pris en charge en orthophonie pour des troubles du raisonnement logico-mathematique a l'aide d'un protocole compose de 5 epreuves : les 3 premieres visent l'evaluation des competences logiques a travers les epreuves piagetiennes issues du bilan ERLA (Exploration du Raisonnement et du Langage Associe) ; les 2 dernieres permettent d'apprecier les competences semiotiques a travers des tests definitoires extraits du PELEA (Protocole d'Evaluation du Langage Elabore de l'Adolescent). L'analyse des resultats a montre une correlation moyenne sur le versant quantitatif : 23 % des adolescents obtiennent des scores pathologiques, 38 % obtiennent des scores deficitaires. L'evaluation qualitative montre que les adolescents les plus en difficulte logique utilisent des strategies referentielles contrairement aux adolescents ayant construit les operations concretes, qui sont en mesure de definir sur un mode conceptuel. Deux profils d'adolescents apparaissent : ceux dont les difficultes semiotiques semblent resulter d'une alteration dans l'elaboration du signifie et ceux pour lesquels la tâche d'expression du signifie est entravee. Ces resultats s'accordent sur la difficulte de ces adolescents a partager des representations fondees sur des signifies perennes, difficulte susceptible de fragiliser leur position d'interlocuteur.
Domain Concretization From Examples: Addressing Missing Domain Knowledge Via Robust Planning

The assumption of complete domain knowledge is unwarranted for robot planning and decision-making in the real world. Incompleteness in domain knowledge may come from design flaws or arise from domain ramifications or qualifications. In such cases, traditional planning methods can produce highly undesirable behaviors. Addressing the planning problem under incomplete domain knowledge is challenging since the agent has no clue about what information is missing. This is a type of unknown unknowns, which differs significantly from partial observability, a type of known unknowns. In this work, we assume that the missing information is encoded in a set of examples or teacher demonstrations. We formulate the problem of domain concretization with these examples as an inverse problem to domain abstraction. Given a domain model provided initially, when the model does not conform with the teacher demonstrations, our method searches for a candidate model set that refines the initial model under a minimalistic and deterministic model assumption. For new problems, it generates a robust plan with the maximum probability of success under the set of candidate models. Together with a standard search formulation in the model-space, we propose a heuristic-based search method and also an online version of it to reduce the search time. We evaluated our approach with several International Planning Competition (IPC) domains and a simulated robotics domain where incompleteness was introduced by removing domain features from the complete models. Results show that our methods increase the success rate of planning without significantly impacting the plan cost.