Miracle-dz/UniFAR-FTUs huggingface dataset:
The Acute Effects of Caffeinated Black Coffee on Cognition and Mood in Healthy Young and Older Adults

Cognitive and mood benefits of coffee are often attributed to caffeine. However, emerging evidence indicates behavioural effects of non-caffeine components within coffee, suggesting the potential for direct or synergistic effects of these compounds when consumed with caffeine in regular brewed coffee. The current randomised, placebo-controlled, double-blind, counterbalanced-crossover study compared the effects of regular coffee, decaffeinated coffee, and placebo on measures of cognition and mood. Age and sex effects were explored by comparing responses of older (61–80 years, N = 30) and young (20–34 years, N = 29) males and females. Computerised measures of episodic memory, working memory, attention, and subjective state were completed at baseline and 30 min post-drink. Regular coffee produced the expected effects of decreased reaction time and increased alertness when compared to placebo. When compared to decaffeinated coffee, increased digit vigilance accuracy and decreased tiredness and headache ratings were observed. Decaffeinated coffee also increased alertness when compared to placebo. Higher jittery ratings following regular coffee in young females and older males represented the only interaction of sex and age with treatment. These findings suggest behavioural activity of coffee beyond its caffeine content, raising issues with the use of decaffeinated coffee as a placebo and highlighting the need for further research into its psychoactive effects.
Deciphering moral intuition: How agents, deeds, and consequences influence moral judgment

Moral evaluations occur quickly following heuristic-like intuitive processes without effortful deliberation. There are several competing explanations for this. The ADC-model predicts that moral judgment consists in concurrent evaluations of three different intuitive components: the character of a person (Agent-component, A); their actions (Deed-component, D); and the consequences brought about in the situation (Consequences-component, C). Thereby, it explains the intuitive appeal of precepts from three dominant moral theories (virtue ethics, deontology, and consequentialism), and flexible yet stable nature of moral judgment. Insistence on single-component explanations has led to many centuries of debate as to which moral precepts and theories best describe (or should guide) moral evaluation. This study consists of two large-scale experiments and provides a first empirical investigation of predictions yielded by the ADC model. We use vignettes describing different moral situations in which all components of the model are varied simultaneously. Experiment 1 (within-subject design) shows that positive descriptions of the A-, D-, and C-components of moral intuition lead to more positive moral judgments in a situation with low-stakes. Also, interaction effects between the components were discovered. Experiment 2 further investigates these results in a between-subject design. We found that the effects of the A-, D-, and C-components vary in strength in a high-stakes situation. Moreover, sex, age, education, and social status had no effects. However, preferences for precepts in certain moral theories (PPIMT) partially moderated the effects of the A- and C-component. Future research on moral intuitions should consider the simultaneous three-component constitution of moral judgment.
A 600 °C TTL-Based 11-Stage Ring Oscillator in Bipolar Silicon Carbide Technology

Ring oscillators (ROs) are used to study the high-temperature characteristics of an in-house silicon carbide (SiC) technology. Design and successful operation of the in-house-fabricated 4H-SiC n-p-n bipolar transistors and TTL inverter-based 11-stage RO are reported from 25 °C to 600 °C. Non-monotonous temperature dependence was observed for the oscillator frequency; in the range of 25 °C to 300 °C, it increased with the temperature (1.33 MHz at 300 °C and <inline-formula> <tex-math notation="LaTeX">$V_{CC} =15$ </tex-math> </inline-formula> V), while it decreased in the range of 300 °C–600 °C. The oscillator output frequency and delay were also characterized over a wide range of supply voltage (10 to 20 V). The noise margins of the TTL inverter were also measured; noise margin low (<inline-formula> <tex-math notation="LaTeX">$\text {NM}_{L}$ </tex-math> </inline-formula>) decreases with the temperature, whereas noise margin high (<inline-formula> <tex-math notation="LaTeX">$\text {NM}_{H}$ </tex-math> </inline-formula>) increases with the temperature. The measured power-delay product (<inline-formula> <tex-math notation="LaTeX">${P}_{D} \cdot {T}_{P}$ </tex-math> </inline-formula>) of the TTL inverter and 11-stage RO was ≈ 4.5 and ≈285 nJ, respectively, at <inline-formula> <tex-math notation="LaTeX">${V}_{\text {CC}} =15$ </tex-math> </inline-formula> V. Reliability testing indicated that the RO frequency of oscillation decreased 16% after HT characterization.
On Regularization and Robustness of Deep Neural Networks

In this work, we study the connection between regularization and robustness of deep neural networks by viewing them as elements of a reproducing kernel Hilbert space (RKHS) of functions and by regularizing them using the RKHS norm. Even though this norm cannot be computed, we consider various approximations based on upper and lower bounds. These approximations lead to new strategies for regularization, but also to existing ones such as spectral norm penalties or constraints, gradient penalties, or adversarial training. Besides, the kernel framework allows us to obtain margin-based bounds on adversarial generalization. We show that our new algorithms lead to empirical benefits for learning on small datasets and learning adversarially robust models. We also discuss implications of our regularization framework for learning implicit generative models.
Multifocal cerebral infarction as the first manifestation of occult malignancy: Case series of trousseau's syndrome and literature review

Ischemic stroke as an initial presentation of malignancy is extremely rare and the underlying etiology is often ignored. The aim of this study is to outline the clues to occult malignancy in patients presenting with cerebral infarction initially. The clinical characteristics of total 19 patients with Trousseau's Syndrome presenting with cerebral infarction initially were analyzed. Among those patients, no conventional vascular risk factors were detected in 68% (13/19) of patients, and infarction occurring in multiple vascular distributions was found in 84% (16/19). Blood test showed thrombophilia in 79% (15/19) of patients with significantly elevated D-dimer, disseminated intravascular coagulopathy (DIC) in 59% (11/19), and elevated levels of tumor makers in 47% (9/19). The prognosis of the 19 patients was poor, with 68% (13/19) of patients undergoing a relapse of stroke in short interval, and 84% (16/19) being reportedly to die in 6 months. In patients, who developed unexplained recurrent brain infarction involving multiple arterial territory, with laboratory evidence suggesting hypercoagulability (higher level of D-dimer, or DIC), Trousseau's Syndrome should be considered, and investigation for an occult malignancy was required.
Improved Smart Power Socket for Monitoring and Controlling Electrical Home Appliances

The recent increase in electricity tariff and the introduction of feed-in tariff from renewable resources have increased the interest of energy consumers, such as those in commercial and residential buildings, in reducing their energy usage. This paper proposes a smart power socket and central control system that utilizes the Zigbee communication protocol to control energy usage. The system is designed such that smart sockets wirelessly provide the necessary data to a central controller. Then, the system analyzes the data to generate control commands to turn the devices attached to the smart socket ON or OFF. Experimental results show that the proposed smart socket can correctly read the power consumption of wirelessly connected devices from up to 18 m away without loss of data. The central controller can effectively control multiple sockets on the basis of a scheduled user program code. A 24-min implementation of the proposed energy management algorithm shows a reduction of 0.811 kWmin (0.0134 kWh) in energy usage after the use of the smart sockets as load controllers. Thus, the proposed smart socket system can be fully utilized in a home energy management system with a proper scheduling algorithm.
Architectural evaluation of node: server partitioning for people counting

The Internet of Things has changed the range of applications for cameras requiring them to be easily deployed for a variety of scenarios indoor and outdoor, while achieving high performance in processing. As a result, future projections emphasise the need for battery operated smart cameras, capable of complex image processing tasks that also communicate within one another, and the server. Based on these considerations, we evaluate in-node and node -- server configurations of image processing tasks to provide an insight of how tasks partitioning affects the overall energy consumption. The two main energy components taken in consideration for their influence in the total energy consumption are processing and communication energy. The results from the people counting scenario proved that processing background modelling, subtraction and segmentation in-node while transferring the remaining tasks to the server results in the most energy efficient configuration, optimising both processing and communication energy. In addition, the inclusion of data reduction techniques such as data aggregation and compression not always resulted in lower energy consumption as generally assumed, and the final optimal partition did not include data reduction.
Hierarchical Spatial Pyramid Pooling for Fine-Grained Vehicle Classification

In this paper, we proposed a hierarchical spatial pyramid pooling mechanism for improving fine-grained vehicle classification problems. Our proposed method created by removing the last layer of convolutional neural network (CNN) classifier, attaching the hierarchical spatial pyramid pooling at the end of the network, and fine-tune the CNN classifier. Our hierarchical spatial pyramid pooling consists of two independent spatial pyramid pooling that pooled the features from two different layers in the original CNN classifier. We modified ResNet50 and AlexNet CNN classifier using our proposed method and test it on fine-grained vehicle classification dataset. Unlike part-based classifier, which very popular method to tackle fine-grained classification problems, our method does not require any pre-processing or post-processing step in order to do the classification tasks. Experiments on BoxCars fine-grained vehicle classification dataset shows that our proposed method can increase the overall accuracy of the classifier and some of the results achieve state-of-the-art performance.
Performance Evaluations of Rain Microphysical Retrieval Using Gpm Dual-Wavelength Radar by Way of Comparison With the Self-Consistent Numerical Method

The primary concern of this paper is to evaluate the accuracy of drop size distribution (DSD) retrievals for the rain region using global precipitation measurement (GPM) dual-frequency precipitation radar (DPR). Direct comparison between ground and spaceborne radar might answer the question, but unfortunately, this is a difficult and challenging task that can result in largely unmeasurable uncertainty. In this paper, the self-consistent numerical method (SNM) was used to estimate microphysical parameters along rain profiles. The assessment of the SNM performance characterizes its accuracy in the microphysics retrieval. It follows that the profile data set used for the evaluation plays an important role in this process. The ideal solution would be to use real conditions. For this purpose, a new approach for providing vertical profiles closer to the natural vertical variability of rainfall microphysical characteristics and its global vertical structure can be derived from the observations of NASA dual-frequency, dual-polarization, Doppler radar (D3R)—a matched-beam, <inline-formula> <tex-math notation="LaTeX">$\textit {Ku}$ </tex-math></inline-formula>- and <inline-formula> <tex-math notation="LaTeX">$\textit {Ka}$ </tex-math></inline-formula>-band dual-frequency radar that is similar to the precipitation radar (DPR) on board the GPM core satellite. The profile data set was generated using reflectivity measurements collected in RHI mode, after correcting them for attenuation using the polarimetric measurements of the <inline-formula> <tex-math notation="LaTeX">$\textit {Ku}$ </tex-math></inline-formula>-band. With this data set, the behavior of SNM is evaluated in the presence of the inaccuracy introduced by taking the constant value of <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> and the measurement errors. The entire version 4 of the GPM DPR precipitation products obtained with data collected from September 1, 2014 to February 28, 2017 was used. In the presence of bright-band features, the rain region of matched scans, for which measurements were simultaneously collected by <inline-formula> <tex-math notation="LaTeX">$\textit {Ku}$ </tex-math></inline-formula>- and <inline-formula> <tex-math notation="LaTeX">$\textit {Ka}$ </tex-math></inline-formula>-band radars, was employed to retrieve DSD parameters with SNM and GPM algorithms. A direct comparison between the two procedures was performed in terms of merit factors. The results show a significant difference between the performance of the two methods by not taking into consideration the SNM procedures that, instead, are being performed by the GPM algorithms.
Nonnegative Matrix Factorization Regularized with Trust Relationships for Solving Cold-Start Problem in Recommender Systems

Matrix factorization has shown as an effective collaborative filtering approach to build a successful recommender system. However, these systems have poor performance while facing cold-start users (items). To tackle this issue, in this paper, a social regularization method called TrustANLF is proposed which combines the social network information of users in a nonnegative matrix factorization framework. The proposed method integrates multiple information sources such as user-item ratings and trust statements to reduce the cold-start and data sparsity issues. Moreover, the alternating direction method is used to improve the convergence speed and reduce the computational cost. To evaluate the proposed method, several experiments are performed on two real-world datasets. The results report the effectiveness of the proposed method compared to several state-of-the-art methods.
Describing a Knowledge Base

We aim to automatically generate natural language descriptions about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new table position self-attention to capture the inter-dependencies among related slots. For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we propose a KB reconstruction based metric by extracting a KB from the generation output and comparing it with the input KB. We also create a new data set which includes 106,216 pairs of structured KBs and their corresponding natural language descriptions for two distinct entity types. Experiments show that our approach significantly outperforms state-of-the-art methods. The reconstructed KB achieves 68.8% - 72.6% F-score.
Separation of PCG signal from Mixture of Speech and PCG Signals with Genetic Algorithm-Based Filter Banks

The aim of the paper is to separate the phonocardiographic (PCG) signal from the mixture of PCG and speech signals. Therefore, genetic algorithm (GA) based filter-banks approach has been used to separate the signal. In this proposed technique, speech signal was modified and then subtracted from mixed signal to obtain the PCG signal. The modification in the speech was performed by modifying the short-time Fourier transform magnitude response. The magnitude response was further decomposed into nine filter-banks, each of bandwidth 50 Hz, upto 450 Hz. The magnitude components in each filter-band were varied with the weights of GA. The phase component was not modified. Extracted PCG signals were evaluated using Mel-frequency cepstral coefficients (MFCCs) based Mahalanobis distance measure and perceptual evaluation of speech quality. It is observed that GA performs well to extract PCG signal form mixture, with population size 30, number of weights 10 and the number of iterations less than 80. The proposed technique shows better accuracy than FastICA, however, the main limitation of the GA-based filter banks is the time complexity arising due to the involvement of iterative behavior.
752-MHz Modulation Bandwidth of High-Speed Blue Micro Light-Emitting Diodes

In this paper, the optical frequency response of gallium nitride-based blue micro-LEDs with different quantum well numbers in the active region is presented. Single quantum well (SQW) blue micro-LEDs have about 1.6 times higher modulation bandwidth (<inline-formula> <tex-math notation="LaTeX">$f_{-3\,\text {dB}}$ </tex-math></inline-formula>) by comparison with triple quantum well blue micro-LEDs. The larger bandwidth enhancement of the SQW micro-LEDs can be attributed to more carriers stored in a quantum well leading to higher recombination rate and lower recombination lifetime. The high <inline-formula> <tex-math notation="LaTeX">$f_{-3\,\text {dB}}$ </tex-math></inline-formula> up to 752 MHz is demonstrated in the SQW micro-LED with a light-emitting area of <inline-formula> <tex-math notation="LaTeX">$10\,\,\mu \text {m} \times 10\,\,\mu \text{m}$ </tex-math></inline-formula>. It suggests that micro-LEDs designed with fewer quantum well numbers may have the potential for the application of visible light communication (VLC) technology.
Students’ Experience of Participation in a Discipline—A Longitudinal Study of Computer Science and IT Engineering Students

This article concludes a longitudinal study with the broader aim to explore learner development as a long-term, social process. One goal has been to inform the endeavours of improving student engagement, retention, as well as under-representation of certain demographics in computing. Students of two computer science--related study programmes (CS/IT) reflected on their engagement in their field of study at different times during the first three study years. Drawing on social identity theory, the focus has been to analyse and describe different ways in which the students experience participation in CS/IT, i.e., doing, thinking, and feeling, in relation to CS/IT, negotiated among different people. Insights into participation in CS/IT were used to discuss what it entails to fit in and become a computing professional. Phenomenographic analysis yields an outcome space that describes increasingly broad ways in which first-, second-, and third-year students experience participation in CS/IT. Two further outcome spaces provide nuanced insights into experiences that are of increasing relevance as the students advance in their studies, participation as problem solving, and problem solving for others. Participation as problem solving appears to be central in this learning environment and the students integrate such experiences into their histories of engagement in CS/IT. In study year 3, the students also reason about participation as problem solving for others that they encounter in the human computer interaction course. However, at that time several students perform a technical problem solver identity and reject such broader ways of participating in CS/IT.
The Effect of the Opioid Epidemic on Donation After Circulatory Death Transplantation Outcomes

Background. The opioid epidemic and the deaths of otherwise healthy individuals due to drug overdose in the United States has major implications for transplantation. The current extent and safety of utilization of liver and kidney grafts from donation after circulatory death (DCD) donors who died from opioid overdose is unknown. Methods. Using national data from 2006 to 2016, we estimated the cumulative incidence of graft failure for recipients of DCD grafts, comparing the risk among recipients of organs from donors who died of anoxic drug overdose and recipients of organs from donors who died of other causes. Results. One hundred seventy-nine (6.2%) of 2908 liver graft recipients and 944 (6.1%) of 15520 kidney graft recipients received grafts from donors who died of anoxic drug overdose. Grafts from anoxic drug overdose donors were less frequently used compared with other DCD grafts (liver, 25.9% versus 29.6%; 95% confidence interval [CI] for difference, −6.7% to −0.7%; kidney, 81.0% versus 84.7%; 95% CI for difference, −7.3% to −0.1%). However, the risk of graft failure at 5 years was similar for recipients of anoxic drug overdose donor grafts and recipients of other grafts (liver risk difference, 1.8%; 95% CI, −7.8% to 11.8%; kidney risk difference, −1.5%; 95% CI, −5.4% to 3.1%). Conclusions. In the context of the current opioid epidemic, utilization of anoxic drug overdose DCD donor grafts does not increase the risk of graft failure and may help to address waitlist demands.
Characterization of Genetic and Epigenetic Variation in Sperm and Red Blood Cells from Adult Hatchery and Natural-Origin Steelhead, Oncorhynchus mykiss

While the goal of most conservation hatchery programs is to produce fish that are genetically and phenotypically indistinguishable from the wild stocks they aim to restore, there is considerable evidence that salmon and steelhead reared in hatcheries differ from wild fish in phenotypic traits related to fitness. Some evidence suggests that these phenotypic differences have a genetic basis (e.g., domestication selection) but another likely mechanism that remains largely unexplored is that differences between hatchery and wild populations arise as a result of environmentally-induced heritable epigenetic change. As a first step toward understanding the potential contribution of these two possible mechanisms, we describe genetic and epigenetic variation in hatchery and natural-origin adult steelhead, Oncorhynchus mykiss, from the Methow River, WA. Our main objectives were to determine if hatchery and natural-origin fish could be distinguished genetically and whether differences in epigenetic programming (DNA methylation) in somatic and germ cells could be detected between the two groups. Genetic analysis of 72 fish using 936 SNPs generated by Restriction Site Associated DNA Sequencing (RAD-Seq) did not reveal differentiation between hatchery and natural-origin fish at a population level. We performed Reduced Representation Bisulfite Sequencing (RRBS) on a subset of 10 hatchery and 10 natural-origin fish and report the first genome-wide characterization of somatic (red blood cells (RBCs)) and germ line (sperm) derived DNA methylomes in a salmonid, from which we identified considerable tissue-specific methylation. We identified 85 differentially methylated regions (DMRs) in RBCs and 108 DMRs in sperm of steelhead reared for their first year in a hatchery environment compared to those reared in the wild. This work provides support that epigenetic mechanisms may serve as a link between hatchery rearing and adult phenotype in steelhead; furthermore, DMRs identified in germ cells (sperm) highlight the potential for these changes to be passed on to future generations.