Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Control Abilities in Squirrel Monkeys (Saimiri sciureus)

Self-control has been studied extensively in both humans and nonhumans in relation to planning, goal-oriented behavior, and overall higher cognitive function. These investigations have resulted in a vast literature pool afflicted by differing definitions, procedural inconsistencies, and numerous paradigms that were thought to measure selfcontrol. I utilized a within subject design to address the question of what these existing tasks were actually studying and if they produced similar results. The present study tested squirrel monkeys on four tasks. Two of which were widely accepted self-control paradigms (food exchange and accumulation) that involved refraining from choosing a low-value reward in favor of a high-value reward. Importantly, these two rewards differed qualitatively (choosing a more preferred item) or quantitatively (choosing a larger amount of the same item) depending on the task/phase. The other two tasks were highly contested self-control paradigms (cylinder and A-not-B) that involved the inhibition of a prepotent motor response. All squirrel monkeys tested were capable of displaying self-control on all tasks, to some extent. However, the results indicate that not all self-control paradigms measure the same aspect of self-control, as individual performances varied across the tasks. Task type and previous experience with other selfcontrol tasks did not predict the variability in performance, but the qualitative or quantitative nature of the rewards did. Ultimately, this suggests that researchers should not compare the results from one task to that of another, especially if the nature of the reward differs. Finally, the present studies added to the existing literature pool of selfcontrol studies, specifically addressing the paucity of data for New World monkey
Insect Conservation

Understanding of ecosystem interactions and management has led to a major advance in the conservation of specialized insects. Conservation efforts in Europe mainly aim to preserve and manage ecosystems that contain putatively endangered biotic communities. However, as Thomas et al. show on page 80 of this issue (1), this approach may not yield the desired results—particularly in the case of comparatively specialized taxa such as butterflies and other insect groups.
Alleviating data sparsity for Twitter sentiment analysis Conference or Workshop Item

Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifiers from tweets data often faces the data sparsity problem partly due to the large variety of short and irregular forms introduced to tweets because of the 140-character limit. In this work we propose using two different sets of features to alleviate the data sparseness problem. One is the semantic feature set where we extract semantically hidden concepts from tweets and then incorporate them into classifier training through interpolation. Another is the sentiment-topic feature set where we extract latent topics and the associated topic sentiment from tweets, then augment the original feature space with these sentiment-topics. Experimental results on the Stanford Twitter Sentiment Dataset show that both feature sets outperform the baseline model using unigrams only. Moreover, using semantic features rivals the previously reported best result. Using sentimenttopic features achieves 86.3% sentiment classification accuracy, which outperforms existing approaches.
Qualitative and quantitative composition of the algal community in the water column of the Grlište reservoir ( Eastern Serbia )

The aim of this paper was to determine the diversity, density and biomass of algal populations in the water column of the Grlište reservoir, situated in Eastern Serbia. A total of 199 species were recorded during the study period, showing a well-diversified community. Temporal and spatial diversity patterns were investigated through seasonal variation in the abundance and biomass of taxa. Bacillariophyta, Chlorophyta and Cyanobacteria were considerably more abundant, in terms of both the number of taxa and the number of cells in the water column, than the remaining algal groups. The succession of these algal groups showed some similarities with Sommer’s PEG model.
A Randomized Trial of Enema Versus Polyethylene Glycol 3350 for Fecal Disimpaction in Children Presenting to an Emergency Department

Objective This study aimed to compare efficacy of enema versus polyethylene glycol (PEG) 3350 for pediatric fecal impaction treatment. Methods We conducted a prospective, randomized comparison of treatments of fecal impaction in children in a pediatric emergency department (ED). Treatment arms were a single milk and molasses enema in the ED or PEG 3350 for 3 days outpatient. Telephone follow-up was done on days 1, 3, and 5. The primary outcome was main symptom improvement. Additional outcomes were stool frequency, consistency, and ease of stool passage. Treatment failures (home enema, ED return, or hospital admission) were tracked. Results Seventy-nine subjects participated (39 PEG; 40 enema). At day 1, PEG subjects were less likely to have improved main symptom (odds ratio [OR], 0.3; 95% confidence interval [CI], 0.1–0.8) but no difference in other outcomes. Half (54%) in enema arm were reported as upset by ED therapy, whereas no children in PEG arm were upset (P < 0.05). At day 3, more patients in enema arm reported ideal stool consistency (74% vs 38%; P < 0.05). At day 5, no difference between groups was noted. Most treatment failures were in PEG arm (83%; P = 0.08). Conclusions This pilot study suggests that disimpaction by enema may be superior to PEG for immediate relief of symptoms. Larger trials are needed to assess any advantage.
Diagnosis of Human Prion Disease Using Real-Time Quaking-Induced Conversion Testing of Olfactory Mucosa and Cerebrospinal Fluid Samples

Importance Early and accurate in vivo diagnosis of Creutzfeldt-Jakob disease (CJD) is necessary for quickly distinguishing treatable from untreatable rapidly progressive dementias and for future therapeutic trials. This early diagnosis is becoming possible using the real-time quaking-induced conversion (RT-QuIC) seeding assay, which detects minute amounts of the disease-specific pathologic prion protein in cerebrospinal fluid (CSF) or olfactory mucosa (OM) samples. Objective To develop an algorithm for accurate and early diagnosis of CJD by using the RT-QuIC assay on CSF samples, OM samples, or both. Design, Setting, and Participants In this case-control study, samples of CSF and OM were collected from 86 patients with a clinical diagnosis of probable (n = 51), possible (n = 24), or suspected (n = 11) CJD and 104 negative control samples (54 CSF and 50 OM). The CSF and OM samples were analyzed using conventional RT-QuIC. The CSF samples underwent further testing using improved RT-QuIC conditions. In addition, the diagnostic performance of a novel, easy-to-use, gentle flocked swab for sampling of OM was evaluated. Data were collected from January 1 to June 30, 2015. Main Outcome and Measures Correlations between RT-QuIC results and the final diagnosis of recruited patients. Results Among the 86 patients (37 men [43%] and 49 women [57%]; mean [SD] age, 65.7 [11.5] years) included for analysis, all 61 patients with sporadic CJD had positive RT-QuIC findings using OM or CSF samples or both for an overall RT-QuIC diagnostic sensitivity of 100% (95% CI, 93%-100%). All patients with a final diagnosis of non–prion disease (71 CSF and 67 OM samples) had negative RT-QuIC findings for 100% specificity (95% CI, 94%-100%). Of 8 symptomatic patients with various mutations causing CJD or Gerstmann-Sträussler-Scheinker syndrome, 6 had positive and 2 had negative RT-QuIC findings for a sensitivity of 75% (95% CI, 36%-96%). Conclusions and Relevance A proposed diagnostic algorithm for sporadic CJD combines CSF and OM RT-QuIC testing to provide virtually 100% diagnostic sensitivity and specificity in the clinical phase of the disease.
Lasers in Manufacturing Conference 2015 Influence of temperature gradients on the part properties for the simultaneous laser beam melting of polymers

By Laser Beam Melting of polymers (LBM), parts with almost any geometry can be built directly out of CAD files without the need for additional tools. Thus, prototypes or parts in small series production can be generated within short times. Up to now, no multi-material parts have been built by LBM, which is a major limitation of the technology. To realize multi-material parts, new mechanisms for depositing different polymer powders as well as new irradiation strategies are needed, by which polymers with different melting temperatures can be warmed to their specific preheating temperatures and be molten simultaneously. This is achieved by simultaneous laser beam melting (SLBM). In the process, two different materials are deposited next to each other and preheated a few degrees below their melting temperatures by infrared emitters and laser radiation (λ = 10.60 μm), before in the last step the two preheated powders are molten simultaneously by an additional laser (λ = 1.94 μm). So far, multi-material tensile bars have been realized and analyzed regarding their boundary zone between both materials. The experiments showed that the temperature gradients in the boundary zone and along the building direction seem to be of great importance for the process stability and the resulting part properties. Therefore, a detailed analysis of the occurring temperature gradients during the process is needed to identify adequate process adjustments regarding the temperature controlling. To analyze the temperature gradients, thermocouples positioned inside the powder bed are used. By varying the temperature of the building platform, the influence of different temperature gradients on the resulting part properties is shown.
Numerical Modeling and Large – Scale Simulation of the Cardiac Electromechanics

Simulating the whole cardiac function represents a challenging task from the mathematical, numerical, and computational standpoints. This is mainly due to the fact that a full cardiac model should exploit the multiphysics nature of the problem, which is indeed comprised of several core models, namely electrophysiology, mechanics (both in its passive and active components), valve dynamics, and fluid dynamics. Each of these models is intrinsically complex and features a wide range of spatial and temporal scales along the heartbeat; these need to be suitably captured to correctly represent the mutual interactions of the heart components [6]. Notable examples are the study of blood flows in the heart [5, 7] and through the valves [3]. In this talk, we consider the mathematical and numerical modeling of the left ventricle by integrating state–of–the art models for the electrophysiology of the tissue, mechanical activation at the cellular level [1], and the passive mechanical response of the muscle, thus yielding a coupled electromechanical problem [4]. We consider its spatial approximation by means of the Finite Element method and we propose a fully coupled approach with an implicit scheme for its time discretization based on BDF formulas. We solve the corresponding discrete, large–scale problem by means of the GMRES method with a newly proposed physics–based preconditioner that exploits the coupling of the core models [2, 4]. We present and discuss numerical results – obtained in the high performance computing framework – for the electromechanical problem applied to patient–specific geometries of the left ventricle.
Evaluating credibility of interest reflection on Twitter

Purpose – The purpose of this article was to confirm whether users’ interests are reflected by tweeted Web pages, and to evaluate the credibility of interest reflection of tweeted Web pages. Design/methodology/approach – Interest reflection of Twitter is investigated based on the context of sharing behavior. A context-oriented approach is proposed to evaluate the interest reflection of tweeted Web pages based on machine learning. Some different distribution models of similarity are present, and infer whether tweeted Web pages reflect respective users’ interests by analyzing user access profiles. Findings – The analysis of browsing behaviors finds that many users partially hide their own concerns, hobbies and interests, and emphasize the concerns about social phenomenon. The extensive experimental results showed the context-oriented approach is effective on real net view data. Originality/value – As the first-of-its-kind study on evaluating the credibility of interest reflection on Twitter, extensive exper.
Overview of Validation

USEPA recently published the 2006 UV Disinfection Guidance Manual. Compared to the 2003 draft Guidance, the 2006 Guidance includes an updated section on UV reactor validation that reflects new developments in validation approaches and a modified approach for applying that validation data for defining pathogen inactivation credit. This paper describes what’s new in the 2006 guidance and describes recent developments in UV validation methods which will impact how UV validation will be applied in the future.
Predicting Order Variability in Inventory Decisions : A Model of Forecast Anchoring

Problem Definition: In this paper, we develop a forecast anchoring model that explains and predicts order variability behavior in a multi-period newsvendor problem. Our model assumes that people anchor on random point forecast (hence the name) and insufficiently adjust toward the profit-maximizing quantity. Academic/Practical Relevance: We show that this model is capable of predicting both the mean and variance of order quantities observed in experimental studies, and thus extends the existing literature that focus on predicting the mean order quantities only. It also helps identify possible behavioral causes that lead to excessive order variability (or the bullwhip effect) in supply chains. Methodology: We apply the generalized method of moments to fit the forecast anchoring model to an experimental data set, and then use these estimates to generate predictions for order decisions in a separate data set, as an out-of-sample test. As a further robustness check, we conduct our own experiment, which considers a unique setting with asymmetric two-point demand, and fit the model to its data as well. Results: Our model fits both the mean and variance of order quantities well across the different data sets. We also find a consistent pattern of subjects anchoring heavily on their demand forecast when the product profit margin is high. Moreover, our results show that the main cause of order variability at the individual level is random point forecasts, whereas the order variability at the aggregate level is driven by both random point forecasts and variable adjustment levels. Managerial Implications: Our study provides a new perspective for explaining and predicting order variability in inventory decisions. By providing better predictions, our model can help the upstream supply chain parties better anticipate the order variability from the downstream buyers and thus improve overall profitability.
SVM Multi-Classification of Induction Machine's bearings defects using Vibratory Analysis based on Empirical Mode Decomposition

The rolling defect is one of the main causes of dysfunctions in induction machines, resulting in a significant economic loss. The main purpose of this study is an automated diagnosis of bearing defects based on vibratory analysis. The work was carried out on the Case Western Reserve University database, where the signals were acquired in different modes and operating conditions (healthy and faulty cases, speed variations, load and defect severity variations). A vector of indicators known as vector form, composed of statistical and spectral indicators for optimal performance, is extracted from the original vibration signals via processed signals using Empirical Mode Decomposition (EMD). In the classification phase, the Multi-Class Support Vector Machine (SVM) was chosen. A comparison of the results with empirical modal decomposition using one and three relevant Intrinsic Mode Functions (IMFs) was performed; the obtained results show the effectiveness of this approach. Furthermore, a comparative study was achieved on two other types of classifiers, namely, Artificial Neural Network (ANN) and Neuro-Fuzzy Network (NFN). A better efficiency in terms of time and classification rate goes to the multi-class SVM compared to the ANN and the NFN.
Precise tuning of barnacle leg length to coastal wave action

Both spatial and temporal variation in environmental conditions can favour intraspecific plasticity in animal form. But how precise is such environmental modulation? Individual Balanus glandula Darwin, a common northeastern Pacific barnacle, produce longer feeding legs in still water than in moving water. We report here that, on the west coast of Vancouver Island, Canada, the magnitude and the precision of this phenotypic variation is impressive. First, the feeding legs of barnacles from protected bays were nearly twice as long (for the same body mass) as those from open ocean shores. Second, leg length varied surprisingly precisely with wave exposure: the average maximum velocities of breaking waves recorded in situ explained 95.6–99.5% of the variation in average leg length observed over a threefold range of wave exposure. The decline in leg length with increasing wave action was less than predicted due to simple scaling, perhaps due to changes in leg shape or material properties. Nonetheless, the precision of this relationship reveals a remarkably close coupling between growth environment and adult form, and suggests that between–population differences in barnacle leg length may be used for estimating differences in average wave exposure easily and accurately in studies of coastal ecology.
DRIFT ANALYSIS ON NEURAL NETWORK MODEL OF HEAT EXCHANGER FOULING

Neural Networks (NN) provide a good platform for modeling complex and poorly understood systems in many different fields. Due to the empirical nature of NN, it is typically valid only for small operating windows. As the process drifts, the prediction accuracy of such models deteriorates very much rendering the models unfit. An on-line mechanism to follow the drift in the process is necessary in order to retrain the NN models. Information Criteria have been reported to be used for the selection of relevant input variables and determination of optimal NN model structures. This paper proposes the use of information criteria for tracking the model prediction accuracy and provides an algorithm for retraining the model. A heat exchanger in a refinery Crude Preheat Train (CPT) has been used as a case study. The operational problems of heat exchangers in CPT are compounded by the varying nature of crude blends and the complex fouling phenomenon. Fouling develops slowly and therefore the drift in the process occurs on a slower scale. The performance of a NN fouling model, developed using industrial data is investigated for drift. Model performance at different operating conditions is evaluated and it has been shown that drifts do occur in the process. An algorithm for retraining NN model has been proposed.
Aquatic Physical Therapy for Children with Developmental Coordination Disorder: A Pilot Randomized Controlled Trial

ABSTRACT Aquatic therapy is an intervention for children with Developmental Coordination Disorder (DCD) that has not been investigated formally. This was a pilot randomized controlled trial to investigate the feasibility and preliminary effectiveness of an aquatic therapy program to improve motor skills of children with DCD. Thirteen children (mean age 7 years 1 month; 10 males) with DCD were randomly allocated to receive either six sessions of aquatic therapy (once weekly session of 30 min for 6–8 weeks) or to a wait-list (control group). The intervention and measures were demonstrated to be feasible, but barriers, such as limited appointment times and accessibility, were encountered. Analysis of covariance indicated that at posttest, mean scores on the Movement Assessment Battery were higher for children who received aquatic therapy compared to those on the wait-list (p = .057). Similar trends were noted on the physical competence portion of the Pictorial Scale of Perceived Competence and Social Acceptance (p = .058). Participation levels, as measured by a parent questionnaire, showed improvement for both groups. Potential facilitators and barriers to implementation of an aquatic therapy for children with DCD are discussed.
This is why ML-driven cluster scheduling remains widely impractical

Using learning algorithms in cluster schedulers has the potential to increase the effectiveness of a computing cluster, yet deployments are limited. Our analysis of a diverse set of workload traces from national labs and industry points to three challenges that have received little attention in the literature. First, lack of data diversity can adversely affect the design of prediction systems. We demonstrate how we detected limitations, such as impractical feature engineering, unreliable prediction performance, and inconspicuous overfitting in prior work. Second, workload changes can negatively affect predictor performance. For the first time, we quantify how accuracy degrades over time due to the non-stationarity that is common across most traces. We further examine the effectiveness of adaptive techniques, such as online training, at addressing this problem. Third, aiming for high prediction accuracy alone does not guarantee dramatically better end-to-end performance. Our experimental results indicate that the relationship between prediction error and end-to-end performance can be small for common cluster deployments. 1Contributed Equally. Acknowledgements: We thank the members and companies of the PDL Consortium (Alibaba Group, Amazon, Datrium, Dell EMC, Facebook, Google, Hewlett Packard Enterprise, Hitachi, IBM Research, Intel, Micron, Microsoft Research, NetApp, Oracle, Salesforce, Samsung, Seagate, Two Sigma, Veritas and Western Digital) for their interest, insights, feedback, and support. This work is supported in part by a National Defense Science and Engineering Graduate Fellowship.
Reconfigurable band-notched UWB-MIMO antenna

A miniaturized ultrawideband (UWB) multiple-input multiple-output (MIMO) antenna with reconfigurable band-notched characteristics is examined in this paper. The MIMO antennas exhibit a good impedance match over frequency band of 2.5 GHz-10.4 GHz, while exhibiting high isolation. The proposed MIMO antenna system consists of two complementary radiating patches and a common ground plane. Enhanced isolation is achieved with a ground structure consisting of a strip line and a slotted circular ring at the center. The decoupling structure provides isolation of more than 25 dB over whole UWB band spectrum. Moreover, the reconfigurable band-notch characteristic is achieved by introducing switches to quarter-wave slots in the main radiators. The notch is achieved for WLAN 5.5 GHz (5.15-5.825 GHz). The proposed antenna results suggests that the antenna design is suitable for UWB-MIMO applications.
Rural Areas and Gross Domestic Product in Some Countries of the Mediterranean Sea Basin

The transition from an agricultural productivist to a post productivist model has implied a change about the function and role of rural areas, that have an active part in reducing, through the multifunctionality, the social and economic exclusion in the countryside. This paper studies by a quantitative approach in some countries of the basin of the Mediterranean Sea which variables have been pivotal to improve the agricultural Gross Domestic Product (GDP) and per capita GDP over 8 years. The study has pointed out as there are relentless discrepancies among rural and urban areas in terms of income and its distribution. JEL Classification: Q10, R12