Miracle-dz/UniFAR-FTUs huggingface dataset:
Primary care chaplaincy: an intervention for complex presentation

Abstract Aim To determine the responsiveness of primary care chaplaincy (PCC) to the current variety of presenting symptoms seen in primary care. This was done with a focus on complex and undifferentiated illness. Background Current presentations to primary care are often complex, undifferentiated and display risk factors for social isolation and loneliness. These are frequently associated with loss of well-being and spiritual issues. PCC provides holistic care for such patients but its efficacy is unknown in presentations representative of such issues. There is therefore a need to assess the characteristics of those attending PCC. The effectiveness of PCC relative to the type and number of presenting symptoms should also be analysed whilst evaluating impact on GP workload. Methods This was a retrospective observational study based on routinely collected data. In total, 164 patients attended PCC; 75 were co-prescribed antidepressants (AD) and 89 were not (No-AD). Pre- and post-PCC well-being was assessed by the Warwick–Edinburgh mental well-being score. Presenting issue(s) data were collected on a separate questionnaire. GP appointment utilisation was measured for three months pre- and post-PCC. Findings Those displaying undifferentiated illness and risk factors for social isolation and loneliness accessed PCC. PCC (No-AD) was associated with a clinically meaningful and statistically significant improvement in well-being in all presenting issues. This effect was maintained in those with multiple presenting issues. PCC was associated with a reduction in GP appointment utilisation in those not co-prescribed AD.
Corneal confocal microscopy: Neurologic disease biomarker in Friedreich ataxia

Friedreich ataxia (FRDA), an autosomal recessive neurodegenerative disease caused by mutations in the gene encoding for the mitochondrial protein frataxin, is characterized by ataxia and gait instability, immobility, and eventual death. We evaluated corneal confocal microscopy (CCM) quantification of corneal nerve morphology as a novel, noninvasive, in vivo quantitative imaging biomarker for the severity of neurological manifestations in FRDA.
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http://affective-meld.github. io.
Dynamic Contributions to a Public Project: The Impact of Rising Marginal Benefit and Completion Benefits

Many public projects are funded in a dynamic manner in which contributors are able to make gradual increases in contributions and condition additional contributions on the cooperation of others. This study presents results from experiments in which subjects with an initial fixed endowment make contributions to a public project gradually over a series of rounds. A 2 × 2 experimental design is used to examine the effectiveness of multiple thresholds that once crossed increase the marginal benefit of a contribution to the public project and the existence of a completion benefit upon project completion. Results reveal that when the multiple threshold design is combined with a completion benefit, overall contributions and project completion rates increase relative to other treatments. Without the presence of a completion benefit, contributions in the multiple threshold design are not significantly different from a constant marginal benefit design. In addition, completion benefits are shown to strongly encourage additional cooperation and project completion. Finally, projects are more likely to be completed when substantial contributions occur in the early rounds.
Can alternative epitope mapping approaches increase the impact of B‐cell epitopes in food allergy diagnostics?

In vitro allergy diagnostics are currently based on the detection of specific IgE binding on intact allergens or a mixture thereof. This approach has drawbacks as it may yield false‐negative and/or false‐positive results. Thus, we reviewed the impact of known B‐cell epitopes of food allergens to predict transience or persistence, tolerance or allergy and the severity of an allergic reaction and to examine new epitope mapping strategies meant to improve serum‐based allergy diagnostics. Recent epitope mapping approaches have been worthwhile in epitope identification and may increase the specificity of allergy diagnostics by using epitopes predominately recognized by allergic patients in some cases. However, these approaches did not lead to discrimination between clinically relevant and irrelevant epitopes so far, since the polyclonal serum IgE‐binding epitope spectrum seems to be too individual, independent of the disease status of the patients. New epitope mapping strategies are necessary to overcome these obstacles. The use of patient‐derived monoclonal antibodies instead of patient sera for functional characterization of clinically relevant and irrelevant epitope combinations, distinguished by their ability to induce degranulation, might be a promising approach to gain more insight into the allergic reaction and to improve serum‐based allergy diagnostics.
A 4-Channel 12-Bit High-Voltage Radiation-Hardened Digital-to-Analog Converter for Low Orbit Satellite Applications

This paper presents a circuit design and an implementation of a four-channel 12-bit digital-to-analog converter (DAC) with high-voltage operation and radiation-tolerant attribute using a specific CSMC H8312 0.5-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> Bi-CMOS technology to achieve the functionality across a wide-temperature range from −55 °C to 125 °C. In this paper, an R-2R resistor network is adopted in the DAC to provide necessary resistors matching which improves the DAC precision and linearity with both the global common centroid and local common centroid layout. Therefore, no additional, complicated digital calibration or laser-trimming are needed in this design. The experimental and measurement results show that the maximum frequency of the single-chip four-channel 12-bit R-2R ladder high-voltage radiation-tolerant DAC is 100 kHz, and the designed DAC achieves the maximum value of differential non-linearity of 0.18 LSB, and the maximum value of integral non-linearity of −0.53 LSB at 125 °C, which is close to the optimal DAC performance. The performance of the proposed DAC keeps constant over the whole temperature range from −55 °C to 125 °C. Furthermore, an enhanced radiation-hardened design has been demonstrated by utilizing a radiation chamber experimental setup. The fabricated radiation-tolerant DAC chipset occupies a die area of 7 mm <inline-formula> <tex-math notation="LaTeX">$\times7$ </tex-math></inline-formula> mm in total including pads (core active area of 4 mm <inline-formula> <tex-math notation="LaTeX">$\times5$ </tex-math></inline-formula> mm excluding pads) and consumes less than 525 mW, output voltage ranges from −10 to +10 V.
Energy-Delay Tradeoff for Dynamic Offloading in Mobile-Edge Computing System With Energy Harvesting Devices

Mobile-edge computing (MEC) has aroused significant attention for its performance to accelerate application's operation and enrich user's experience. With the increasing development of green computing, energy harvesting (EH) is considered as an available technology to capture energy from circumambient environment to supply extra energy for mobile devices. In this paper, we propose an online dynamic tasks assignment scheduling to investigate the tradeoff between energy consumption and execution delay for an MEC system with EH capability. We formulate it into an average weighted sum of energy consumption and execution delay minimization problem of mobile device with the stability of buffer queues and battery level as constraints. Based on the Lyapunov optimization method, we obtain the optimal scheduling about the CPU-cycle frequencies of mobile device and transmit power for data transmission. Besides, the dynamic online tasks offloading strategy is developed to modify the data backlogs of queues. The performance analysis shows the stability of the battery energy level and the tradeoff between energy consumption and execution delay. Moreover, the MEC system with EH devices and task buffers implements the high energy efficient and low latency communications. The performance of the proposed online algorithm is validated with extensive trace-driven simulations.
PAC Battling Bandits in the Plackett-Luce Model

We introduce the probably approximately correct (PAC) \emph{Battling-Bandit} problem with the Plackett-Luce (PL) subset choice model--an online learning framework where at each trial the learner chooses a subset of $k$ arms from a fixed set of $n$ arms, and subsequently observes a stochastic feedback indicating preference information of the items in the chosen subset, e.g., the most preferred item or ranking of the top $m$ most preferred items etc. The objective is to identify a near-best item in the underlying PL model with high confidence. This generalizes the well-studied PAC \emph{Dueling-Bandit} problem over $n$ arms, which aims to recover the \emph{best-arm} from pairwise preference information, and is known to require $O(\frac{n}{\epsilon^2} \ln \frac{1}{\delta})$ sample complexity \citep{Busa_pl,Busa_top}. We study the sample complexity of this problem under various feedback models: (1) Winner of the subset (WI), and (2) Ranking of top-$m$ items (TR) for $2\le m \le k$. We show, surprisingly, that with winner information (WI) feedback over subsets of size $2 \leq k \leq n$, the best achievable sample complexity is still $O\left( \frac{n}{\epsilon^2} \ln \frac{1}{\delta}\right)$, independent of $k$, and the same as that in the Dueling Bandit setting ($k=2$). For the more general top-$m$ ranking (TR) feedback model, we show a significantly smaller lower bound on sample complexity of $\Omega\bigg( \frac{n}{m\epsilon^2} \ln \frac{1}{\delta}\bigg)$, which suggests a multiplicative reduction by a factor ${m}$ owing to the additional information revealed from preferences among $m$ items instead of just $1$. We also propose two algorithms for the PAC problem with the TR feedback model with optimal (upto logarithmic factors) sample complexity guarantees, establishing the increase in statistical efficiency from exploiting rank-ordered feedback.
Multimodal Feature Level Fusion based on Particle Swarm Optimization with Deep Transfer Learning

There are several biometric-based systems which rely on a single biometric modality, most of them focus on face, iris or fingerprint. Despite the good accuracies obtained with single modalities, these systems are more susceptible to attacks, i.e, spoofing attacks, and noises of all kinds, especially in non-cooperative (in-the-wild) environments. Since non-cooperative environments are becoming more and more common, new approaches involving multi-modal biometrics have received more attention. One challenge in multimodal biometric systems is how to integrate the data from different modalities. Initially, we propose a deep transfer learning optimized from a model trained for face recognition achieving outstanding representation for only iris modality. Our feature level fusion by means of features selection targets the use of the Particle Swarm Optimization (PSO) for such aims. In our pool, we have the proposed iris fine-tuned representation and a periocular one from previous work of us. We compare this approach for fusion in feature level against three basic function rules for matching at score level: sum, multi, and min. Results are reported for iris and periocular region (NICE.II competition database) and also in an open-world scenario. The experiments in the NICE.II competition databases showed that our transfer learning representation for iris modality achieved a new state-of-the-art, i.e., decidability of 2.22 and 14.56% of EER. We also yielded a new state-of-the-art result when the fusion at feature level by PSO is done on periocular and iris modalities, i.e., decidability of 3.45 and 5.55% of EER.
Hiding Contextual Information for Defending a Global Attacker

As wireless sensor networks are mostly deployed in harsh and hostile environments, lack of human maintenance, it has become a severe challenge to offer security or privacy service, especially when facing a powerful attacker. For defending a global attacker, dummy message injection methods are employed by every node in WSN to enhance location privacy, which leads to significant overhead. Based on random walk model, we propose a novel scheme Chess-Board Alternation (CBA) which partition WSN into two sets, and each set operates active in an alternating fashion. We also prove CBA’s outstanding performance, including delivery time, energy consumption, and privacy theoretically and experimentally. In comparison with existing approaches, CBA can not only achieve perfect privacy but also decrease energy cost by 50% and the transmission latency from the source to the sink by more than 40%.
Specific learning disability

Specific learning disabilities (SLD) have an impact on discrete areas of academic function. They result in a level of difficulty that is unexpected when considered in the context of a student's overall intellectual and academic competence. A common example is dyslexia, an SLD that affects the acquisition of literacy. Medical professionals have an important role to play in the evaluation and management of SLD. The medical role is founded on the fact that these conditions are biologically based and have the potential to cause significant harm for the developing child. Even if details of biological causation are not possible to demonstrate for individual children, medical professionals may, nevertheless, make an important judgement call regarding the likelihood of aetiology. When learning problems are medically based, it is clearly unreasonable to expect children to do more than they are able and to learn faster than is reasonable. Advocacy around these biological presumptions and their implications is a major contribution to the well‐being of children who have SLD.
First Results Solving Arbitrarily Structured Maximum Independent Set Problems Using Quantum Annealing

Commercial quantum processing units (QPUs) such as those made by D-Wave Systems are being increasingly used for solving complex combinatorial optimization problems. In this paper, we review a canonical NP-hard problem, the Maximum Independent Set (MIS) problem. We show how to map MIS problems to quadratic unconstrained binary optimization (QUBO) problems, and use a D-Wave 2000Q QPU to solve them. We compare the results from the D-Wave system to classical algorithms such as simulated thermal annealing and the graphical networks package NetworkX. To our knowledge, these are the first results of experiments involving arbitrarily-structured MIS inputs using a D-Wave QPU. We find that the QPU can be used as a heuristic optimizer for randomly generated inputs, but due to physical control errors, can be outperformed by simulated thermal annealing.
Supervised Classification of Multisensor Remotely Sensed Images Using a Deep Learning Framework

In this paper, we present a convolutional neural network (CNN)-based method to efficiently combine information from multisensor remotely sensed images for pixel-wise semantic classification. The CNN features obtained from multiple spectral bands are fused at the initial layers of deep neural networks as opposed to final layers. The early fusion architecture has fewer parameters and thereby reduces the computational time and GPU memory during training and inference. We also propose a composite fusion architecture that fuses features throughout the network. The methods were validated on four different datasets: ISPRS Potsdam, Vaihingen, IEEE Zeebruges and Sentinel-1, Sentinel-2 dataset. For the Sentinel-1,-2 datasets, we obtain the ground truth labels for three classes from OpenStreetMap. Results on all the images show early fusion, specifically after layer three of the network, achieves results similar to or better than a decision level fusion mechanism. The performance of the proposed architecture is also on par with the state-of-the-art results.
A Hybrid Grammar-Based Genetic Programming for Symbolic Regression Problems

Genetic Programming (GP) is an important technique in evolutionary computing. There has been extensive research and great achievement in GP and its variants. Grammar-based genetic programming (GGP) is one of the most promising ones. We propose here a hybrid approach of GGP with Evolution Strategies (ES). GGP is used to evolve the structure of the models while ES searches for the numerical coefficients in order to improve the overall performance when solving symbolic regression problems. Computational experiments conducted on a set of test-cases reveal that the proposed hybrid approach achieved a good performance when compared to other methods from the literature.
Nephroprotective effect of cilostazol and verapamil against thioacetamide-induced toxicity in rats may involve Nrf2/HO-1/NQO-1 signaling pathway

Abstract Cilostazol and verapamil are widely used cardiovascular drugs, explored a beneficial effect on different organs-induced toxicities. We investigated whether the Nrf2 (nuclear erythroid factor 2) and its downstream pathway are involved in the protective role of these drugs against TAA-induced renal damage. Renal biomarkers (creatinine and urea) and histopathology were observed. Antioxidant and oxidant indicators; superoxide dismutase (SOD), reduced glutathione (GSH), malondialdehyde (MDA) and total nitrite (NO) were also measured. Antioxidant markers like; Nrf2/hemoxegenase-1 (HO-1) and NADPH quinone oxidoreductase-1 (NQO-1) expressions were determined by ELISA and immunohistochemistry. Cilostazol and verapamil pretreatment improved serum creatinine and urea elevation. Examined drugs also have an ameliorative effect on TAA-induced elevation in MDA and NO activities and antioxidant enzymes; SOD and GSH. Additionally, the pretreated drugs significantly up-regulated Nrf2/HO-1/NQO-1 expression levels. In conclusion, cilostazol and verapamil exerted their protective effects partially via a Nrf2/HO-1/NQO-1 activation pathway with anti-oxidant roles.
An Adaptive Periodic-Disturbance Observer for Periodic-Disturbance Suppression

Repetitive operations are widely conducted by automatic machines in industry. Periodic disturbances induced by the repetitive operations must be compensated to achieve precise functioning. In this paper, a periodic-disturbance observer (PDOB) based on the disturbance observer (DOB) structure is proposed. The PDOB compensates a periodic disturbance including the fundamental wave and harmonics by using a time-delay element. Furthermore, an adaptive PDOB is proposed for the compensation of frequency-varying periodic disturbances. An adaptive notch filter is used in the adaptive PDOB to estimate the fundamental frequency of the periodic disturbance. Simulations compare the proposed methods with a repetitive controller and the DOB. Practical performances are validated in experiments using a multiaxis manipulator. The proposal provides a new framework based on the DOB structure to design controllers using a time-delay element.
T-Shaped Patterned Microstrip Line for Noninvasive Continuous Glucose Sensing

In this letter, a microstrip line (MLIN)-based glucose sensor with significant improvement in sensitivity over sensors of the same category is proposed for noninvasive glucose monitoring. It has a T-shaped patterned signal line using the main field of an MLIN for sensing. Glucose under monitoring serves as the substrate of the MLIN. The pattern in the MLIN is designed for enhancing the field interaction in the substrate for improving the sensitivity of the sensor. It achieves much higher sensitivity than that of the state-of-the-art MLIN-based sensors for the same concentrations and comparable sensitivity to resonance-based microstrip sensors with improved robustness. The improvement in sensitivity is validated based on experiments using 0.9% sodium chloride with different glucose concentrations. The robustness is analyzed. The proposed structure is promising for noninvasive continuous glucose monitoring.
Use of social media by new library professionals: Outcomes from a UK survey

This research explores the connection between social media and independent continuing professional development, focusing on new professionals within the UK and Ireland who graduated between 2010 and 2015, based on Ruddock’s definition (see Appendix 1). Two online surveys were conducted to ascertain why continuing professional development networks have been created and why new professionals use them. The first survey was completed by 100 new professionals; the second was completed by four continuing professional development network founders. This paper explores the relationship between the uptake of continuing professional development and social media usage by new professionals and highlights the reasons social media are used by new professionals for continuing professional development which include cost, variety of continuing professional development options, and removal of boundaries, e.g. geographical, hierarchical. The survey also found that network founders used social media to promote continuing professional development and engage with users.
A Further Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al.

Lam et al. (2018) respond to a commentary of their paper entitled ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ Lam et al. (2017). While Lam et al. (2018) have now provided the recommended quality control metrics for their paper, problems remain. Specifically, Lam et al. (2018) do not dispute that the results of their multi-trait analysis of genome-wide association study (MTAG) analysis has produced a phenotype with a genetic correlation of one with three measures of education, but do claim the associations found are specific to the trait of cognitive ability. In this brief paper, it is empirically demonstrated that the phenotype derived by Lam et al. (2017) is more genetically similar to education than cognitive ability. In addition, it is shown that of the genome-wide significant loci identified by Lam et al. (2017) are loci that are associated with education rather than with cognitive ability.