Miracle-dz/UniFAR-FTUs huggingface dataset:
A catalog of agent coordination patterns

This paper surveys the current state of the art in agentoriented software engineering, focusing on the area of coordinated multi-agent systems. In multi-agent systems, the interactions between the agents are crucial in determining the effectiveness of the system. Hence the adoption of an appropriate coordination mechanism is pivotal in the design of multi-agent system architectures. This paper does not focus on agent theory, rather on the development of an agent-oriented software engineering methodology, collaboration architectures and design patterns for collaboration. A catalog of coordination patterns inherent in multi-agent architectures is presented. Such patterns may be utilized in the architectural design stage of an agent-oriented software engineering methodology.
Microwave Characterization of Nickel

In recent years, nickel has found new applications in RF microfabricated fllters and MEMS switches, as it is proving to be a convenient structural material and suitable for realizing power-e-cient electrothermal buckle-beam microactuators. While nickel is becoming a material of choice for processing engineers, there is a serious issue of RF characterization at microwave frequencies. This paper investigates some of the issues associated with both measurement and modelling of nickel. It has been found that RF engineers are currently faced with the problem that there is insu-cient data available to undertake simulation designs with a high level of confldence at microwave frequencies.
A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery

Objective: State-of-the-art techniques for surgical data analysis report promising results for automated skill assessment and action recognition. The contributions of many of these techniques, however, are limited to study-specific data and validation metrics, making assessment of progress across the field extremely challenging. Methods: In this paper, we address two major problems for surgical data analysis: First, lack of uniform-shared datasets and benchmarks, and second, lack of consistent validation processes. We address the former by presenting the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), a public dataset that we have created to support comparative research benchmarking. JIGSAWS contains synchronized video and kinematic data from multiple performances of robotic surgical tasks by operators of varying skill. We address the latter by presenting a well-documented evaluation methodology and reporting results for six techniques for automated segmentation and classification of time-series data on JIGSAWS. These techniques comprise four temporal approaches for joint segmentation and classification: hidden Markov model, sparse hidden Markov model (HMM), Markov semi-Markov conditional random field, and skip-chain conditional random field; and two feature-based ones that aim to classify fixed segments: bag of spatiotemporal features and linear dynamical systems. Results: Most methods recognize gesture activities with approximately 80% overall accuracy under both leave-one-super-trial-out and leave-one-user-out cross-validation settings. Conclusion: Current methods show promising results on this shared dataset, but room for significant progress remains, particularly for consistent prediction of gesture activities across different surgeons. Significance: The results reported in this paper provide the first systematic and uniform evaluation of surgical activity recognition techniques on the benchmark database.
Extracorporeal photopheresis for chronic graft-versus-host disease: a systematic review and meta-analysis

Background The safety of extracorporeal photopheresis (ECP) in steroid-refractory chronic graft-versus-host disease (SR-cGVHD) has been explored in multiple studies but reported response rates (RR) vary significantly across studies. Methods We conducted a meta-analysis to assess the efficacy of ECP for SR-cGVHD. A search of electronic databases for studies published between 1984 and 2012 was conducted. End points included RR: complete response (CR), overall response rates (ORR), and organ-specific RR. The initial search generated 312 studies, of which 18 met the selection criteria (N=595). A random effects model was used for pooled rates. Results Pooled CR rates and ORR were 29% (confidence interval [CI], 19-42%) and 64% (CI, 65-82%), respectively. One-year overall survival was available for 4 studies only and was 49% (CI, 29-70%). The pooled RR for skin, liver, ocular, oral, lung, gastrointestinal and musculoskeletal SR-cGVHD was 74%, 68%, 60%, 72%, 48%, 53%, and 64%, respectively. There was a significant heterogeneity among studies due to differences in ECP schedules and duration. No significant differences in responses to ECP for pediatric and adult populations were found. Sensitivity analysis could not be undertaken due to a limited number of prospective studies. Conclusion ECP is an effective therapy for oral, skin, and liver SR-cGVHD, with modest activity in lung and gastrointestinal SR-cGVHD.
FDA Approval: Gemtuzumab Ozogamicin for the Treatment of Adults with Newly Diagnosed CD33-Positive Acute Myeloid Leukemia

On September 1, 2017, the FDA granted approval for gemtuzumab ozogamicin (Mylotarg; Pfizer Inc.) in combination with daunorubicin and cytarabine and as a monotherapy for the treatment of adult patients with newly diagnosed CD33-positive acute myeloid leukemia (AML). Gemtuzumab ozogamicin is a CD33-targeted antibody–drug conjugate joined to calicheamicin. Approval of gemtuzumab ozogamicin combination treatment was based on a randomized trial of 271 patients with newly diagnosed AML treated with daunorubicin and cytarabine with or without 3 mg/m2 fractionated gemtuzumab ozogamicin, which resulted in an event-free survival (EFS) of 13.6 months for gemtuzumab ozogamicin + daunorubicin and cytarabine and 8.8 months for daunorubicin and cytarabine alone [HR = 0.68 (95% confidence interval (CI), 0.51–0.91)]. Hemorrhage, prolonged thrombocytopenia, and veno-occlusive disease were serious toxicities that were more common in patients treated with gemtuzumab ozogamicin + daunorubicin and cytarabine. Approval of gemtuzumab ozogamicin monotherapy was based on a randomized trial of 237 patients with newly diagnosed AML treated without curative intent. Median overall survival (OS) was 4.9 months with gemtuzumab ozogamicin versus 3.6 months on best supportive care [HR = 0.69 (95% CI, 0.53–0.90)]. Adverse events were similar on both arms. Postapproval, several studies are required including evaluation of fractionated gemtuzumab ozogamicin pharmacokinetics, safety of combination gemtuzumab ozogamicin in the pediatric population, immunogenicity, and the effects of gemtuzumab ozogamicin on platelet function. Clin Cancer Res; 24(14); 3242–6. ©2018 AACR.
Spectrally resolved multi-channel contributions to the harmonic emission in N2

When generated in molecules, high-order harmonics can be emitted through different ionization channels. The coherent and ultrafast electron dynamics occurring in the ion during the generation process is directly imprinted in the harmonic signal, i.e. in its amplitude and spectral phase. In aligned N2 molecules, we find evidence for a fast variation of this phase as a function of the harmonic order when varying the driving laser intensity. Basing our analysis on a three-step model, we find that this phase variation is a signature of transitions from a single- to a multi-channel regime. In particular, we show that significant nuclear dynamics may occur in the ionization channels on the attosecond timescale, affecting both the amplitude and the phase of the harmonic signal.
Comprehensive Evaluation of Examination Quality Based on Fuzzy AHP

Examination is undoubtedly one of good means to evaluate what and how much the examinees have mastered. Effective evaluation of the quality of curriculum examination will not only contribute to scientifically testing the students’ mastery of the knowledge but also help instruct the teachers to set scientific tests. This paper establishes the evaluation indexes of curriculum quality, calculates the weight of them, and gives effective evaluation of the quality of course examination by using the fuzzy AHP comprehensive evaluation, which has practical values in improving the efficiency of teaching evaluation. The research for the examination quality evaluation system is to accurately reflect and describe the rationality of examination quality index system so as to improve the rationalization level of examination papers. DOI: http://dx.doi. org/10.11591/telkomnika.v11i9. 3295
Mnemonic Transmission, Social Contagion, and Emergence of Collective Memory: Influence of Emotional Valence, Group Structure, and Information Distribution

Social transmission of memory and its consequence on collective memory have generated enduring interdisciplinary interest because of their widespread significance in interpersonal, sociocultural, and political arenas. We tested the influence of 3 key factors—emotional salience of information, group structure, and information distribution—on mnemonic transmission, social contagion, and collective memory. Participants individually studied emotionally salient (negative or positive) and nonemotional (neutral) picture–word pairs that were completely shared, partially shared, or unshared within participant triads, and then completed 3 consecutive recalls in 1 of 3 conditions: individual–individual–individual (control), collaborative–collaborative (identical group; insular structure)–individual, and collaborative–collaborative (reconfigured group; diverse structure)–individual. Collaboration enhanced negative memories especially in insular group structure and especially for shared information, and promoted collective forgetting of positive memories. Diverse group structure reduced this negativity effect. Unequally distributed information led to social contagion that creates false memories; diverse structure propagated a greater variety of false memories whereas insular structure promoted confidence in false recognition and false collective memory. A simultaneous assessment of network structure, information distribution, and emotional valence breaks new ground to specify how network structure shapes the spread of negative memories and false memories, and the emergence of collective memory.
Do latent tree learning models identify meaningful structure in sentences?

Recent work on the problem of latent tree learning has made it possible to train neural networks that learn to both parse a sentence and use the resulting parse to interpret the sentence, all without exposure to ground-truth parse trees at training time. Surprisingly, these models often perform better at sentence understanding tasks than models that use parse trees from conventional parsers. This paper aims to investigate what these latent tree learning models learn. We replicate two such models in a shared codebase and find that (i) only one of these models outperforms conventional tree-structured models on sentence classification, (ii) its parsing strategies are not especially consistent across random restarts, (iii) the parses it produces tend to be shallower than standard Penn Treebank (PTB) parses, and (iv) they do not resemble those of PTB or any other semantic or syntactic formalism that the authors are aware of.
Data-driven iterative adaptive dynamic programming algorithm for approximate optimal control of unknown nonlinear systems

In this paper, we develop a data-driven iterative adaptive dynamic programming algorithm to learn offline the approximate optimal control of unknown discrete-time nonlinear systems. We do not use a model network to identify the unknown system, but utilize the available offline data to learn the approximate optimal control directly. First, the data-driven iterative adaptive dynamic programming algorithm is presented with a convergence analysis. Then, the error bounds for this algorithm are provided considering the approximation errors of function approximation structures. To implement the developed algorithm, two neural networks are used to approximate the state-action value function and the control policy. Finally, two simulation examples are given to demonstrate the effectiveness of the developed algorithm.
Predicting Frames

Kahneman and Tversky's (1979) "prospect theory" has demonstrated that the way in which a decision problem is formulated, or "framed," can have strong and predictable effects on the perceived attractiveness of the options it offers. At times, the relative attractiveness of two options may be reversed as the result of a reframing that should make no difference at all, according to traditional economic theories of choice. To predict people's behavior with the theory, one must be able to predict what frame they will impose on a particular problem. The seven studies reported here explored different ways of predicting frames, with results that were generally discouraging for the prediction of individuals' choices, generally encouraging for the prediction of group choices.
Learning to cluster in order to Transfer across domains and tasks

This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster. The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning. We begin by reducing categorical information to pairwise constraints, which only considers whether two instances belong to the same class or not. This similarity is category-agnostic and can be learned from data in the source domain using a similarity network. We then present two novel approaches for performing transfer learning using this similarity function. First, for unsupervised domain adaptation, we design a new loss function to regularize classification with a constrained clustering loss, hence learning a clustering network with the transferred similarity metric generating the training inputs. Second, for cross-task learning (i.e., unsupervised clustering with unseen categories), we propose a framework to reconstruct and estimate the number of semantic clusters, again using the clustering network. Since the similarity network is noisy, the key is to use a robust clustering algorithm, and we show that our formulation is more robust than the alternative constrained and unconstrained clustering approaches. Using this method, we first show state of the art results for the challenging cross-task problem, applied on Omniglot and ImageNet. Our results show that we can reconstruct semantic clusters with high accuracy. We then evaluate the performance of cross-domain transfer using images from the Office-31 and SVHN-MNIST tasks and present top accuracy on both datasets. Our approach doesn't explicitly deal with domain discrepancy. If we combine with a domain adaptation loss, it shows further improvement.
Nutrition strategies before and during ultra‐endurance event: A significant gap between science and practice

The purpose of this study was to investigate and assess the nutritional practices of open‐water swimmers, during the preparation period and the competition, as well as their implications on performance and anthropometric parameters according to their age. Twenty‐four (n = 24) men open‐water swimmers, twelve young (26.3 ± 4.9 years) and twelve older athletes (45.8 ± 9.7 years), participated in this study. Nutritional behavior—energy intake consumption, and anthropometric characteristics were evaluated prior to the Toroneos Gulf crossing (26 km). In addition, nutritional intakes during the race and anthropometrics after the race were also evaluated. Significant differences between the two groups were found for Toroneos Gulf crossing performance, BMI, and all variables of body composition at the preparation period (P < .05). The results involving energy consumption, in both preparation period and during the race, displayed significant differences between the two groups, while both groups exhibited significantly negative energy balances. During the race, significant differences were found also for the energy expenditure, relative energy intake, negative energy balance, relative consumption of carbohydrates per hour, and relative protein intake (P < .05). In both groups, lower intakes than the recommended were observed for macronutrient‐micronutrient elements at any time point. Significant correlations were found between selected variables of body composition, nutritional intakes during the preparation period and the competition, and athletes' performance. In summary, this study provides strong evidence about the malnutrition of ultra‐endurance swimmers prior and during their competition, with potential dangers for their health and performance as certain deficiencies in both macro‐ and micronutrient contents are present.
Explain Images with Multimodal Recurrent Neural Networks

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on three benchmark datasets (IAPR TC-12, Flickr 8K, and Flickr 30K). Our model outperforms the state-of-the-art generative method. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.
Sustainable Traffic Aware Duty-Cycle Adaptation in Harvested Multi-Hop Wireless Sensor Networks

Sustainable power management techniques in energy harvesting wireless sensors currently adapt the consumption of sensors to their harvesting rate within the limits of their battery residual energy, but regardless of the traffic profile. To provide a fairer distribution of the energy according to application needs, we propose a new sustainable traffic aware duty-cycle adaptation scheme (STADA) that takes into account the traffic load in addition to previous factors. We evaluate our protocol in the specific context of multi-hop IEEE 802.15. 4 beacon-enabled wireless sensor networks powered by solar energy. Simulations show that our solution outperforms traffic-unaware adaptation schemes while minimizing the variance of the quality of service provided to applications.
Structural Performance of FRP Composites in Fire

This paper reviews the experimental and modeling work that has been carried out concerning the behavior of fiber-reinforced polymer (FRP) composites in fire since the 1980s. The first part focuses on the thermophysical properties and temperature responses, while the second considers thermomechanical properties and responses, which are significantly affected by the thermal exposure of the FRP material. Furthermore, the application of deformation-based or strength-based failure criteria enables the prediction of time-to-failure. If the fire exposure time is less than the time-to-failure, the post-fire behavior is of interest and the post-fire properties and their potential recovery after cooling are particularly addressed. This fundamental understanding obtained as a result of both modeling and experimental work makes a reliable endurance design for FRP structures in fire possible.
Platelet-rich plasma to treat experimentally-induced skin wounds in animals: A systematic review and meta-analysis

The objective of the study was to review current literature to determine whether the topical application of platelet-rich plasma (PRP) promotes healing in experimentally-induced full-thickness skin wounds in animals. The hypothesis was that the adjunct of PRP has a positive effect on wound healing. An electronic search was carried out on the following databases: Web of Science, Cochrane Library, PubMed, Research Gate, Cochrane Wounds Group, Veterinary Information Network. No publication date nor language restrictions were applied. Randomised and not randomised controlled clinical trials comparing PRP with placebo or with other treatments were included. The reduction of open wound area in PRP-treated (test) wounds compared to control wounds was the primary outcome. Secondary outcomes were healing time and number of healed cases in test group compared to control. The following effect sizes were calculated: the Hedges’ g for continuous variables; the odds ratio for binary data. Eighteen controlled clinical trials were included in the qualitative and quantitative synthesis, with a total of 661 wounds. All studies were published in the period 2007–2016. Eight studies were carried out on rodent/lagomorph mammals and 10 on non-rodent/lagomorph mammals. In all included studies, control wounds underwent placebo or were left untreated. The PRP group showed a better healing performance than the control group in each outcome. The effect size was statistically significant considering the primary outcome and the overall aggregation of the three outcomes. The effect size, although in favour of the treatment with PRP, was not significant considering the healing time and the number of healings. The overall heterogeneity was mild or moderate. Five studies reported a high risk of selection bias. The publication bias was always mild or absent. The results support the hypothesis of the positive effects of the PRP when compared to control groups in the treatment of experimentally-induced full-thickness skin wounds in animals. PRP can therefore be considered an effective adjunctive therapy in stimulating second intention healing of acute wounds in healthy animals.
The effects of geometry and operational conditions on gas holdup, liquid circulation and mass transfer in an airlift reactor

In airlift reactors transport phenomena are achieved by pneumatic agitation and circulation occurs in a defined cyclic pattern through a loop. In the present work, the effect of geometrical relations on gas holdup and liquid velocity, and consequently on the gas-liquid mass transfer coefficient, was studied in a 6-liter airlift bioreactor with AD/AR = 0.63; AD, downcomer cross-sectional area, and AR, riser cross-sectional area. Measurements of the volumetric oxygen transfer coefficient (kLa) were taken in a water-air system using a modified sulfite oxidation method. Different conditions were examined by varying parameters such as superficial air velocity in the riser (UGR), bottom clearance (d1) and top clearance (d2). It was observed from the experimental results that d1 and d2 have a remarkable effect on kLa values. The effect is due to their influence on gas holdup and liquid velocity, consequently affecting kLa. Superficial air velocity in the riser (UGR) ranged from 0.0126 to 0.0440 m.s-1 and kLa varied between 40 to 250 h-1, whereas gas holdup (e) reached values up to 0.2. The volumetric oxygen transfer coefficient (kLa), gas holdup in the riser (eR) and downcomer (eD) and superficial liquid velocity in the riser (ULR) for all the geometrical relations were successfully correlated with dimensionless numbers, namely, the Sherwood number (Sh) and the Froude number (Fr) as well as with geometrical relations such as the bottom space ratio (B = d1/DD) and top space ratio (T = (d2 + DD)/DD).