Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrathin Films of Silver by Magnetron Sputtering

The interest in ultrathin silver (Ag) films has increased due to their high surface plasmon resonance for coatings of only a few nm. Low roughness ultrathin films of 1 to 9 nm have been deposited on different substrates, such as polyethylene terephthalate (PET) and optical glass, using radio frequency (RF) magnetron sputtering. Films show good surface plasmon resonance up to 7 nm thickness, as revealed by the ultraviolet-visible (UV-Vis) spectra. The roughness of the films, investigated by Atomic Force Microscopy (AFM) and Scanning Electron Microscopy (SEM), is small, and one can conclude that depositions are smooth and homogeneous. The bandgap values decrease with film thickness. The refractive index of the films, calculated from ellipsometry measurements, leads to values of under 1 visible domain, with minima in the wavelength range of 400–600 nm. The results are useful for obtaining lower roughness ultrathin Ag films with good surface plasmon resonance for photonic applications.
A Systematic Literature Review of User Experience Evaluation Scales for Human-Robot Collaboration

In the last decade, the field of Human-Robot Collaboration (HRC) has received much attention from both research institutions and industries. Robot technologies are in fact deployed in many different areas (e.g., industrial processes, people assistance) to support an effective collaboration between humans and robots. In this transdisciplinary context, User eXperience (UX) has inevitably to be considered to achieve an effective HRC, namely to allow the robots to better respond to the users’ needs and thus improve the interaction quality. The present paper reviews the evaluation scales used in HRC scenarios, focusing on the application context and evaluated aspects. In particular, a systematic review was conducted based on the following questions: (RQ1) which evaluation scales are adopted within the HRI scenario with collaborative tasks? , and (RQ2) how the UX and user satisfaction are assessed? The records analysis highlighted that the UX aspects are not sufficiently examined in the current HRC design practice, particularly in the industrial field. This is most likely due to a lack of standardized scales. To respond to this recognized need, a set of dimensions to be considered in a new UX evaluation scale were proposed.
Research on the Sustainable Design of Commercial Street Space Based on Importance Performance Analysis

Street commerce is the basic unit of urban structure and an important part of people’s life. With the continuous advancement of online commerce, people’s consumption habits and consumption patterns have begun to change. (1) Background: During the epidemic, the global business environment changed dramatically, and traditional street business has also been hit hard, leaving a lot of idle space. The question is how to solve the problem of activating the commercial street space after its decay, and realize its sustainable development. (2) Methods: This paper takes the commercial space of Nonglin Street as the object, constructs the relevant factors affecting the commercial space of the street by using the theory of scenes, and collects the data by means of a field investigation and questionnaire. According to a weight analysis and satisfaction analysis, the advantages and disadvantages of the commerce in Nonglin Street are understood. (3) Results: The importance of the three variables of the appearance, comfort and diversity of the commercial space in Nonglin Street is very high and the satisfaction is low. (4) Conclusions: Nonglin Street need to increase diversified forms of business, open block space, integrate regional cultural symbols, and improve the management model of street commerce.
Dengue Meteorological Determinants during Epidemic and Non-Epidemic Periods in Taiwan

The identification of the key factors influencing dengue occurrence is critical for a successful response to the outbreak. It was interesting to consider possible differences in meteorological factors affecting dengue incidence during epidemic and non-epidemic periods. In this study, the overall correlation between weekly dengue incidence rates and meteorological variables were conducted in southern Taiwan (Tainan and Kaohsiung cities) from 2007 to 2017. The lagged-time Poisson regression analysis based on generalized estimating equation (GEE) was also performed. This study found that the best-fitting Poisson models with the smallest QICu values to characterize the relationships between dengue fever cases and meteorological factors in Tainan (QICu = −8.49 × 10−3) and Kaohsiung (−3116.30) for epidemic periods, respectively. During dengue epidemics, the maximum temperature with 2-month lag (β = 0.8400, p < 0.001) and minimum temperature with 5-month lag (0.3832, p < 0.001). During non-epidemic periods, the minimum temperature with 3-month lag (0.1737, p < 0.001) and mean temperature with 2-month lag (2.6743, p < 0.001) had a positive effect on dengue incidence in Tainan and Kaohsiung, respectively.
Correlation between Eye Movements and Asthenopia: A Prospective Observational Study

Purpose: To analyze the correlation between eye movements and asthenopia so as to explore the possibility of using eye-tracking techniques for objective assessment of asthenopia. Methods: This prospective observational study used the computer visual syndrome questionnaire to assess the severity of asthenopia in 93 enrolled college students (age 20–30) who complained about asthenopia. Binocular accommodation and eye movements during the reading task were also examined. The correlations between questionnaire score and accommodation examination results and eye movement parameters were analyzed. Differences in eye movement parameters between the first and last reading paragraphs were compared. The trends in eye movement changes over time were observed. Results: About 81.7% of the subjects suffered from computer visual syndrome. Computer visual syndrome questionnaire total score was positively correlated with positive relative accommodation (p < 0.05). In the first reading paragraph, double vision was positively correlated with unknown saccades (all p < 0.05). Difficulty focusing at close range was positively correlated with total fixation duration, total visit duration, and reading speed (all p < 0.05). Feeling that sight was worsening was positively correlated with regressive saccades (p < 0.05). However, visual impairment symptoms were not significantly correlated with any accommodative function. In a total 20 min reading, significantly reduced eye movement parameters were: total fixation duration, fixation count, total visit duration, visit count, fixation duration mean, and reading speed (all p < 0.01). The eye movement parameters that were significantly increased were: visit duration mean and unknown saccades (all p < 0.001). Conclusion: Eye tracking could be used as an effective assessment for asthenopia. Among the various eye movement parameters, a decrease in fixation duration and counts may be one of the potential indicators related to asthenopia.
The Influence of Multiple Mechanical Recycling of Particleboards on Their Selected Mechanical and Physical Properties

This is a bridge between circular economy issues and wood-based panels technology, especially particleboards. Because these composites contain a significant amount of non-wood raw material (10–12% thermoset resin, high hardness laminates, among others), their mechanical recycling leads to an uncontrollable reduction in produced particle size. This problem can be especially significant since the particleboards can be intended for multiple recycling due to the shortening of their service life. This research aimed to produce particles in the cycle of multiple re-milling particleboards and evaluate the selected properties of the produced particles and particleboards. Thus, the response to the following scientific problem can be given: what factors qualitatively and quantitatively influence the properties of the particleboards produced by multi-re-milled particles? The novelty of this research is the approach to recycling the raw materials from particleboards in fully controlled conditions, providing the characterization of produced particles and producing particleboards with close-to-industrial parameters, and, finally, evaluating the features of produced particleboards in the light of raw materials used. The results confirmed that subsequent mechanical recycling of particleboards, where the other panels are made entirely of second-milling particles, leads to an unprofitable and unacceptable reduction in the mechanical properties of the panels. The physical parameters, such as thickness swelling and water absorption, are improved, but this can be the result of increased content of chemical ingredients, which negatively influence the hygienic features of panels (emission of formaldehyde and total volatile organic compounds—TVOC). Further research should be directed towards estimating the optimal addition of mechanically recycled particles to particleboard production.
Integrating AI, automation and multiscale simulations for end-to-end design of phase-separating proteins

Liquid-liquid phase separation (LLPS) is a fundamental cellular process that is 1 driven by self-assembly of intrinsically disordered proteins (IDPs), protein-RNA 2 complexes, or other bio-molecular systems which can form liquid droplets. Many 3 natural materials including silk, elastin, and gels are a result of LLPS and thus 4 rational design of such phase-separating peptides can have transformative im-5 pact, from designing new biologically inspired materials (e.g., clothing) to self-6 compartmentalized drug-delivery systems for biomedical applications. However, 7 given the intrisinc complexity in the rules governing LLPS, rational design of LLPS 8 undergoing peptides remains challenging. We posit that automation, foundation 9 models integrated with reinforcement learning approaches and multiscale molecular 10 simulations can drive the design of novel peptides that undergo LLPS. We describe 11 our progress towards the goal of end-to-end design of phase separating peptides 12 by summarizing current work at the Argonne National Laboratory’s Advanced 13 Photon Source 8ID-I beamline, where a robotic set up in the laboratory is enabled 14 via simulation and extensive testing of such bio-materials. Together, our approach 15 enables the design of novel bio-materials that can undergo phase separation under 16 diverse physiological conditions. 17
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints

Graphical structures estimated by causal learning algorithms from time series data 1 can provide highly misleading causal information if the causal timescale of the 2 generating process fails to match the measurement timescale of the data. Existing 3 algorithms provide limited resources to respond to this challenge, and so researchers 4 must either use models that they know are likely misleading, or else forego causal 5 learning entirely. Existing methods face up-to-four distinct shortfalls, as they might 6 a ) require that the di ff erence between causal and measurement timescales is known; 7 b ) only handle very small number of random variables when the timescale di ff erence 8 is unknown; c ) only apply to pairs of variables (albeit with fewer assumptions about 9 prior knowledge); or d ) be unable to find a solution given statistical noise in the data. 10 This paper aims to address these challenges. We present an algorithm that combines 11 constraint programming with both theoretical insights into the problem structure 12 and prior information about admissible causal interactions to achieve speed up of 13 multiple orders of magnitude. The resulting system scales to significantly larger 14 sets of random variables ( > 100) without knowledge of the timescale di ff erence 15 while maintaining theoretical guarantees. This method is also robust to edge 16 misidentification and can use parametric connection strengths, while optionally 17 finding the optimal among many possible solutions. 18
SPARQL-QA enters the QALD challenge

The large public knowledge bases available today empower the community with an invaluable amount of information covering multiple domains. Unfortunately, accessing these data sources can be diﬃcult without the proper knowledge of the KB structure and a query language, such as SPARQL. Question Answering over Knowledge Bases (KBQA) proposes to solve this problem by allowing users to pose questions in natural language to extract the desired information from the KBs, ab-stracting them from any technical complexity. In this context, the QALD challenges propose to evaluate and encourage the development of KBQA systems. In this paper, we describe sparql-qa , a system for question answering over KBs that have been submitted to the QALD-10 challenge. Our method uses Neural Machine Translation and Named Entity Recognition tasks that complement each other to create SPARQL queries. The experiments were performed using the Wikidata QALD-10 benchmark.
Controlling Neural Network Smoothness for Algorithmic Neural Reasoning

The modelling framework of neural algorithmic reasoning [1] postulates that a 1 continuous neural network may learn to emulate the discrete reasoning steps of 2 a symbolic algorithm. The purpose of this study is to investigate the underlying 3 hypothesis in the most simple conceivable scenario – the addition of real numbers. 4 We find that two layer neural networks fail to learn the structure of this task and 5 that growing the network’s width leads to a complex division of input space. This 6 behaviour can be emulated with Gaussian processes using radial basis function ker-7 nels of decreasing length scale. Classical results establish an equivalence between 8 Gaussian processes and infinitely wide neural networks. We demonstrate a tight 9 link between the scaling of a network weights’ standard deviation and its effective 10 length scale on a sinusoidal regression problem, suggesting simple modifications to 11 control the smoothness of the function learned by a neural network. This provides 12 a partial remedy to the brittleness of neural network predictions. We validate this 13 further in the setting of adversarial examples where we demonstrate the gains in 14 robustness that our modification achieves on a standard classification problem of 15 handwritten digit recognition. In conclusion, we show inherent problems of neural 16 networks emulating even simple algorithmic tasks which, however, may be partially 17 improved with smoothness priors inspired by Gaussian processes. 18
Dual PI3K d / c Inhibitor Duvelisib Prevents Development of Neuropathic Pain in Model of Paclitaxel-Induced Peripheral Neuropathy

The development of painful paclitaxel-induced peripheral neuropathy (PIPN) represents a major dose-limiting side effect of paclitaxel chemotherapy. Here we report a promising effect of duvelisib (Copiktra), a novel FDA-approved PI3K d / c isoform-specific inhibitor, in preventing paclitaxel-induced pain-like behavior and pronociceptive signaling in DRGs and spinal cord dorsal horn (SCDH) in rat and mouse model of PIPN. Duvelisib blocked the development of mechanical hyperalgesia in both males and females. Moreover, duvelisib prevented paclitaxel-induced sensitization of TRPV1 receptors, and increased PI3K/ Akt signaling in small-diameter DRG neurons and an increase of CD68 1 cells within DRGs. Specific optogenetic stimulation of inhibitory neurons combined with patch-clamp recording revealed that duvelisib inhibited paclitaxel-induced weakening of inhibitory, mainly glycinergic control on SCDH excitatory neurons. Enhanced excitatory and reduced inhibitory neurotransmission in the SCDH following PIPN was also alleviated by duvelisib application. In summary, duvelisib showed a promising ability to prevent neuropathic pain in PIPN. The potential use of our findings in human medicine may be augmented by the fact that duvelisib is an FDA-approved drug with known side effects.
Semi-Supervised Group Emotion Recognition Based on Contrastive Learning

The performance of all learning-based group emotion recognition (GER) methods depends on the number of labeled samples. Although there are lots of group emotion images available on the Internet, labeling them manually is a labor-intensive and cost-expensive process. For this reason, datasets for GER are usually small in size, which limits the performance of GER. Considering labeling manually is challenging, using limited labeled images and a large number of unlabeled images in the network training is a potential way to improve the performance of GER. In this work, we propose a semi-supervised group emotion recognition framework based on contrastive learning to learn efficient features from both labeled and unlabeled images. In the proposed method, the unlabeled images are used to pretrain the backbone by a contrastive learning method, and the labeled images are used to fine-tune the network. The unlabeled images are then given pseudo-labels by the fine-tuned network and used for further training. In order to alleviate the uncertainty of the given pseudo-labels, we propose a Weight Cross-Entropy Loss (WCE-Loss) to suppress the influence of the samples with unreliable pseudo-labels in the training process. Experiment results on three prominent benchmark datasets for GER show the effectiveness of the proposed framework and its superiority compared with other competitive state-of-the-art methods.
Ranking of Empirical Evapotranspiration Models in Different Climate Zones of Pakistan

Accurate estimation of evapotranspiration (ET) is vital for water resource development, planning and management, particularly in the present global warming context. A large number of empirical ET models have been developed for estimating ET. The main limitations of this method are that it requires several meteorological variables and an extensive data span to comprehend the ET pattern accurately, which is not available in most developing countries. The efficiency of 30 empirical ET models has been evaluated in this study to rank them for Pakistan to facilitate the selection of suitable models according to data availability. Princeton Global Meteorological Forcing daily climate data with a 0.25° × 0.25° resolution for 1948–2016 were utilized. The ET estimated using Penman–Monteith (PM) was considered as the reference. Multi-criteria group decision making (MCGDM) was used to rank the models for Pakistan. The results showed the temperature-based Hamon as the best model for most of Pakistan, followed by Hargreaves–Samani and Penman models. Hamon also showed the best performance in terms of different statistical metrics used in the study with a mean bias (PBias) of −50.2%, mean error (ME) of −1.62 mm and correlation coefficient (R2) of 0.65. Ivan showed the best performance among the humidity-based models, Irmak-RS and Ritch among the radiation-based models and Penman among the mass transfer-based models. Northern Pakistan was the most heterogeneous region in the relative performance of different ET models.
Exploring the Link between ADHD and Obesity: A Focus on Temperament

Multiple studies support the relationship between ADHD and overweight/obesity in youth. Different mechanisms may be involved, such as temperamental and psychopathological factors. The aim of this study was to test the hypothesis that specific temperamental and psychopathological dimensions could mediate the relationship between ADHD and obesity. The sample included 100 children and adolescents (78 males and 22 females; age range 6 to 18 years; mean age 9.90 ± 2.5 years). The assessment procedure included Conners’ Parent Rating Scale—Long (CPRS-R:L) as the inclusion criterion for ADHD diagnosis, the Child Behavior Checklist (CBCL), a dimensional measure for psychopathology, and the Junior Temperament and Character Inventory, which describes four temperamental dimensions: novelty seeking (NS), harm avoidance (HA), reward dependence (RD), and persistence (P). While in the whole ADHD sample, the highest scores were found in NS and the lowest in P, ADHD with overweight/obesity, compared to ADHD with normal weight, showed higher HA and RD, lower NS, and higher CBCL Internalizing scores. These findings suggest that ADHD youth with overweight/obesity present specific temperamental and psychopathological features compared to those without overweight/obesity. If confirmed in larger samples, using a control group without ADHD, these temperamental and psychological features may be helpful for an earlier recognition of ADHD patients at higher risk for obesity, and may represent possible targets for temperament-based preventive interventions and tailored treatment programs. These features should be included in the routine assessment of children and adolescents with ADHD and/or are overweight/obese.
High-Content RNAi Phenotypic Screening Unveils the Involvement of Human Ubiquitin-Related Enzymes in Late Cytokinesis

CEP55 is a central regulator of late cytokinesis and is overexpressed in numerous cancers. Its post-translationally controlled recruitment to the midbody is crucial to the structural coordination of the abscission sequence. Our recent evidence that CEP55 contains two ubiquitin-binding domains was the first structural and functional link between ubiquitin signaling and ESCRT-mediated severing of the intercellular bridge. So far, high-content screens focusing on cytokinesis have used multinucleation as the endpoint readout. Here, we report an automated image-based detection method of intercellular bridges, which we applied to further our understanding of late cytokinetic signaling by performing an RNAi screen of ubiquitin ligases and deubiquitinases. A secondary validation confirmed four candidate genes, i.e., LNX2, NEURL, UCHL1 and RNF157, whose downregulation variably affects interconnected phenotypes related to CEP55 and its UBDs, as follows: decreased recruitment of CEP55 to the midbody, increased number of midbody remnants per cell, and increased frequency of intercellular bridges or multinucleation events. This brings into question the Notch-dependent or independent contributions of LNX2 and NEURL proteins to late cytokinesis. Similarly, the role of UCHL1 in autophagy could link its function with the fate of midbody remnants. Beyond the biological interest, this high-content screening approach could also be used to isolate anticancer drugs that act by impairing cytokinesis and CEP55 functions.