Miracle-dz/UniFAR-FTUs huggingface dataset:
PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry Generation

Formal verse poetry imposes strict constraints on the meter and rhyme scheme of poems. Most prior work on generating this type of poetry uses existing poems for supervision, which are difficult to obtain for most languages and poetic forms. In this work, we propose an unsupervised approach to generate poems following any given meter and rhyme scheme, without requiring any poetic text for training. Our method works by splitting a regular, non-poetic corpus into phrases, prepending control codes that describe the length and end rhyme of each phrase, and training a transformer language model in the augmented corpus. During inference, we build control codes for the desired meter and rhyme scheme, and condition our language model on them to generate formal verse poetry. Experiments in Spanish and Basque show that our approach is able to generate valid poems, which are often comparable in quality to those written by humans.
Spatio-Temporal Modeling for Flash Memory Channels Using Conditional Generative Nets

Modeling spatio-temporal read voltages with complex distortions arising from the write and read mechanisms in flash memory devices is essential for the design of signal processing and coding algorithms. In this work, we propose a data-driven approach to modeling NAND flash memory read voltages in both space and time using conditional generative networks. This generative flash modeling (GFM) method reconstructs read voltages from an individual memory cell based on the program levels of the cell and its surrounding cells, as well as the time stamp. We evaluate the model over a range of time stamps using the cell read voltage distributions, the cell level error rates, and the relative frequency of errors for patterns most susceptible to inter-cell interference (ICI) effects. Experimental results demonstrate that the model accurately captures the complex spatial and temporal features of the flash memory channel.
Heterogeneous causal effects of neighbourhood policing in New York City with staggered adoption of the policy

In New York City, neighbourhood policing was adopted at the police precinct level over the years 2015–2018, and it is of interest to both (1) evaluate the impact of the policy, and (2) understand what types of communities are most impacted by the policy, raising questions of heterogeneous treatment effects. We develop novel statistical approaches that are robust to unmeasured confounding bias to study the causal effect of policies implemented at the community level. We find that neighbourhood policing decreases discretionary arrests in certain areas of the city, but has little effect on crime or racial disparities in arrest rates.
Balanced Graph Structure Learning for Multivariate Time Series Forecasting

Accurate forecasting of multivariate time series is an extensively studied subject in finance, transportation, and computer science. Fully mining the correlation and causation between the variables in a multivariate time series exhibits noticeable results in improving the performance of a time series model. Recently, some models have explored the dependencies between variables through end-to-end graph structure learning without the need for predefined graphs. However, current models do not incorporate the trade-off between efficiency and flexibility and lack the guidance of domain knowledge in the design of graph structure learning algorithms. This paper alleviates the above issues by proposing Balanced Graph Structure Learning for Forecasting (BGSLF), a novel deep learning model that joins graph structure learning and forecasting. Technically, BGSLF leverages the spatial information into convolutional operations and extracts temporal dynamics using the diffusion convolutional recurrent network. The proposed framework balance the trade-off between efficiency and flexibility by introducing Multi-Graph Generation Network (MGN) and Graph Selection Module. In addition, a method named Smooth Sparse Unit (SSU) is designed to sparse the learned graph structures, which conforms to the sparse spatial correlations in the real world. Extensive experiments on four real-world datasets demonstrate that our model achieves state-of-the-art performances with minor trainable parameters. Code will be made publicly available.
PowerGraph: Using neural networks and principal components to determine multivariate statistical power trade-offs

Statistical power estimation for studies with multiple model parameters is inherently a multivariate problem. Power for individual parameters of interest cannot be reliably estimated univariately since correlation and variance explained relative to one parameter will impact the power for another parameter, all usual univariate considerations being equal. Explicit solutions in such cases, especially for models with many parameters, are either impractical or impossible to solve, leaving researchers to the prevailing method of simulating power. However, the point estimates for a vector of model parameters are uncertain, and the impact of inaccuracy is unknown. In such cases, sensitivity analysis is recommended such that multiple combinations of possible observable parameter vectors are simulated to understand power trade-offs. A limitation to this approach is that it is computationally expensive to generate sufficient sensitivity combinations to accurately map the power trade-off function in increasingly high-dimensional spaces for the models that social scientists estimate. This paper explores the efficient estimation and graphing of statistical power for a study over varying model parameter combinations. We propose a simple and generalizable machine learning inspired solution to cut the computational cost to less than 10% of the brute force method while providing F1 scores above 90%. We further motivate the impact of transfer learning in learning power manifolds across varying distributions.
Insights into Improving Performance of Niobium Superconducting Transmon Qubit Devices through Advanced Microscopy

Superconducting thin ﬁlms of niobium have been extensively employed in transmon qubit architectures. Although these architectures have demonstrated remarkable improvements in recent years, further improvements in performance through materials engineering will aid in large-scale deployment. Here, we use information retrieved from electron microscopy to conduct a detailed assessment of defects and inhomogeneities on the atomic and nanoscale that may potentially induce quantum decoherence in transmon qubit test devices. In the niobium thin ﬁlm, we observe the presence of localized strain at the metal/substrate and grain boundaries, which may amplify interactions between two-level systems and impose limits on T 1 and T 2 relaxation times. Additionally, we observe the presence of a surface oxide with varying stoichiometry and bond distances, which can generate a broad two-level system noise spectrum. Finally, a similarly disordered and rough interface is observed between Nb and the Si substrate. We propose that this interface can also degrade the overall superconducting properties. Based on these ﬁndings, we propose solutions to eliminate these potentially problematic imperfections in future quantum devices.
Optische Qualitätsprüfung von Batterieseparatoren

Zusammenfassung Die Anforderungen an die Sicherheit von Lithium-Ionen-Zellen rücken durch deren Einsatz in Elektrofahrzeugen zunehmend in den Vordergrund. Als Basis für die Zellsicherheit müssen typische Produktionsfehler in Batterieseparatoren zuverlässig erkannt werden. Die Bildverarbeitung stellt hierfür eine geeignete Methode dar. Nachfolgend wird ein System zur Qualitätskontrolle beschrieben, das eine Detektion dieser Fehler ermöglicht. Die Besonderheit ist dabei die Unterscheidung auftretender Defektarten untereinander und die Abgrenzung von nicht qualitätsrelevanten optischen Effekten mittels eines expertenbasierten Ansatzes zur Fehlerklassifikation.
Gsm-Based System for School Children Transportation Safety Enhancement

Millions of children need to commute between homes to school every day. Safer transportation of school children has been a critical issue as it is often observed that, kids find themselves locked in the school bus at the bus stop after going to school, they miss the bus, or ride the wrong bus with no way to track them. This project intends to find yet another solution to solve this problem by developing a bus safety system that will control the entry and exit of students from the buses through an energy efficient methodology. The proposed system will control the entry and exit of students to and from the bus using RFID (Radio Frequency Identification) and GSM technologies to ensure the entering and exiting of all students to and from the school bus in a safer manner. The process does not require any additional action by the student and drivers. The system will do all the process and allow the student to be tracked while entering and leaving the bus and If all the students were wearing seat belts mean, it will allow bus driver to start the bus for safety precaution. If the bus journey is successful from the source to destination, it will send an SMS to the management to inform its departure and arrival.
Advertising cooperative phenotype through costly signals facilitates collective action

Around the world, people engage in practices that involve self-inflicted pain and apparently wasted resources. Researchers theorized that these practices help stabilize within-group cooperation by assorting individuals committed to collective action. While this proposition was previously studied using existing religious practices, we provide a controlled framework for an experimental investigation of various predictions derived from this theory. We recruited 372 university students in the Czech Republic who were randomly assigned into either a high-cost or low-cost condition and then chose to play a public goods game (PGG) either in a group that wastes money to signal commitment to high contributions in the game or to play in the group without such signals. We predicted that cooperators would assort in the high-cost revealed group and that, despite these costs, they would contribute more to the common pool and earn larger individual rewards over five iterations of PGG compared with the concealed group and participants in the low-cost condition. The results showed that the assortment of cooperators was more effective in the high-cost condition and translated into larger contributions of the remaining endowment to the common pool, but participants in the low-cost revealed group earned the most. We conclude that costly signals can serve as an imperfect assorting mechanism, but the size of the costs needs to be carefully balanced with potential benefits to be profitable.
Human-supervised clustering of multidimensional data using crowdsourcing

Clustering is a central task in many data analysis applications. However, there is no universally accepted metric to decide the occurrence of clusters. Ultimately, we have to resort to a consensus between experts. The problem is amplified with high-dimensional datasets where classical distances become uninformative and the ability of humans to fully apprehend the distribution of the data is challenged. In this paper, we design a mobile human-computing game as a tool to query human perception for the multidimensional data clustering problem. We propose two clustering algorithms that partially or entirely rely on aggregated human answers and report the results of two experiments conducted on synthetic and real-world datasets. We show that our methods perform on par or better than the most popular automated clustering algorithms. Our results suggest that hybrid systems leveraging annotations of partial datasets collected through crowdsourcing platforms can be an efficient strategy to capture the collective wisdom for solving abstract computational problems.
Exploring the need for external input through the prism of social, material and sensation seeking input

External input is any kind of physical stimulation created by an individual's surroundings that can be detected by the senses. The present research established a novel conceptualization of this construct by investigating it in relation to the needs for material, social and sensation seeking input, and by testing whether these needs predict psychological functioning during long- and short-term input deprivation. It was established that the three needs constitute different dimensions of an overarching construct (i.e. need for external input). The research also suggested that the needs for social and sensation seeking input are negatively linked to people's experiences of long-term input deprivation (i.e. COVID-19 restrictions), and that the need for material input may negatively predict the experiences of short-term input deprivation (i.e. sitting in a chair without doing anything else but thinking). Overall, this research indicates that the needs for social, material and sensation seeking input may have fundamental implications for experiences and actions in a range of different contexts.
Building Back Better with Poems

During the 2020–21 school year, remote instruction due to COVID-19 significantly limited children’s access to school-based social interactions. As schools return to in-person instruction, we ask: Can poetry and metaphor be used to develop theory of mind (ToM)/reflective functioning and emotional literacy in the early elementary setting? This article documents the use of poetry in a pandemic pod with four children ages four to eight years, demonstrating the relationship between children’s developing understanding and use of metaphor and their emotional literacy skills. Although the psychological significance of ToM is well documented, the field of education rarely focuses on the role school environments play in supporting this essential building block of learning. Our findings support the use of content-rich instruction and the significant role parents and teachers play in supporting the emotional development of children.
Blend in or stand out: social anxiety levels shape information-sharing strategies

Although living in social groups provides many benefits for group members, such groups also serve as a setting for social competition over rank and influence. Evolutionary accounts suggest that social anxiety plays a role in regulating in-group conflict, as individuals who are concerned about social threat may choose to defer to others to maintain the hierarchical status quo. Here, we examine how social anxiety levels are related to the advice-giving style an individual adopts: a competitive influence-seeking strategy or a defensive blend-in strategy. We begin by demonstrating that similarity to others drives activity in the brain's valuation system, even during a competitive advice-taking task. Then, in three behavioural experiments, we show that social anxiety levels are related to the tendency to give advice resembling the advice given by rival advisers and to refrain from status-seeking behaviour. Social anxiety was also associated with negative social comparisons with rival advisers. Our findings highlight the role of competing social goals in shaping information sharing.
Clinical decision support in cardiovascular medicine

ABSTRACT Despite considerable progress in tackling cardiovascular disease over the past 50 years, many gaps in the quality of care for cardiovascular disease remain. Multiple missed opportunities have been identified at every step in the prevention and treatment of cardiovascular disease, such as failure to make risk factor modifications, failure to diagnose cardiovascular disease, and failure to use proper evidence based treatments. With the digital transformation of medicine and advances in health information technology, clinical decision support (CDS) tools offer promise to enhance the efficiency and effectiveness of delivery of cardiovascular care. However, to date, the promise of CDS delivering scalable and sustained value for patient care in clinical practice has not been realized. This article reviews the evidence on key emerging questions around the development, implementation, and regulation of CDS with a focus on cardiovascular disease. It first reviews evidence on the effectiveness of CDS on healthcare process and clinical outcomes related to cardiovascular disease and design features associated with CDS effectiveness. It then reviews the barriers encountered during implementation of CDS in cardiovascular care, with a focus on unintended consequences and strategies to promote successful implementation. Finally, it reviews the legal and regulatory environment of CDS with specific examples for cardiovascular disease.
One step further: advancing lesson study practice through collaborative inquiry school-university partnerships

ABSTRACT This study offers insights into a professional graduate programme developed at the University of Fukui (Japan) that used the lesson study approach to elevate the application of field-based reflective practise for teachers and school leaders. This paper first details the programme’s structure and its school-university collaborative inquiry partnership model developed to build a sustainable platform where practices and research are integrated as teachers inquire about school-based issues. The impact of the programme and its lesson study approach is then assessed by qualitatively analysing the longitudinal reflection reports written by three graduate students enrolled as: new entrant teachers, in-service teachers, and school leaders. This study concludes by highlighting that the Fukui PGP brings a more significant commitment to HEI and the BoE and extends the application of lesson study for school changes and reforms. Internationally, it demonstrates a new systematic structure that works in institutional environments and settings where lesson study is practised to make changes in the organization. It fosters a more meaningful and critical lens to the traditional practice of lesson study.
History of subjectivity in dispositif: changing arrangements of Vietnamese teachers’ meeting through lesson study for learning community

ABSTRACT This study examined research–practice partnerships (RPP) as a place where teachers (re)shape their discourses in a distinctive working arrangement through our case in Vietnam. In particular, it explored the concept of subjectivity in dispositifs, focusing on a heterogeneous ensemble of discursive and nondiscursive arrangements of collective activities formulating its members’ patterns of thought, practice, and identity. We present the historical roots of dispositif in Vietnamese schools and locate a new one in our RPP. There are two categories of dispositif in organizations from the Vietnamese public sector: assembly and gathering. We added a model of Lesson Study for Learning Community (LSLC) as a dispositif from Japan. The assembly is an authorized place for the members to convey information in a hierarchical order, while the gathering represents occasions for discussing issues in an egalitarian manner. The arrangements of discursive and socio-material conditions of the LSLC allowed the teachers to engage in equal and free discussions relying on their own emotions. Our case suggests that exploring dispositif of schools could improve the design of teacher professional learning through RPP.
Parasitic effects of parallel-plate modes in planar antennas fed by conductor-backed coplanar waveguides

This paper presents a parametric study of microstrip antennas fed by conductor-backed coplanar waveguides in order to avoid excessive power lost in parallel-plate modes (PPMs). The calculation of power lost in PPMs and radiated power compared to return loss measurements permits to deduce general rules to limit spurious effects due to PPMs.
How to Approach Ambiguous Queries in Conversational Search: A Survey of Techniques, Approaches, Tools, and Challenges

The advent of recent Natural Language Processing technology has led human and machine interactions more toward conversation. In Conversational Search Systems (CSS) like chatbots and Virtual Personal Assistants such as Apple’s Siri, Amazon Alexa, Microsoft’s Cortana, and Google Assistant, both user and device have a limited platform to communicate through chatting or voice. In the information-seeking process, often users do not know how to properly describe their information need in a machine understandable language. Consequently, it is hard for the assistant agent to predict the user’s intent and yield relevant results by only relying on the original query. Studies have shown many unsatisfactory results can be enhanced with the benefit of CSS, which can dig deeper into the user’s query to reveal the real need. This survey intends to provide a comprehensive and comparative overview of ambiguous query clarification task in the context of conversational search technology. We investigate different approaches, their evaluation methods, and future work. We also address the importance of understanding a query for retrieving the most relevant document(s) and satisfying user’s need by predicting their potential request. This work provides an overview of characteristics of ambiguous queries and contributes to better understanding of the existing technologies and challenges in CSS focus on disambiguation of unclear queries from various dimensions.
Open innovation programmes related to data and AI: How do the entrepreneurial orientations of startups align with the objectives of public funders?

Abstract Open innovation programmes related to data and artificial intelligence have interested European policy-makers as a means of supporting startups and small and medium-sized enterprises to succeed in the digital economy. We discuss the objectives behind the typical service offerings of such programmes and propose a case for exploring how they align with the motivations of individual companies who are targeted by these initiatives. Using a qualitative analysis of 50 startup applications from the Data Market Services Accelerator programme, we find that applicants wrote most frequently about fundraising, acceleration and data skills. A smaller number of startups expressed interest in services related to standardization or legal guidance on General Data Protection Regulation and intellectual property rights, which are some of the ongoing priority areas for the European Commission. We discuss how the value propositions of these less desired offerings can be amplified by appealing the existing business motivations of data-driven startups.
circRNF20 aggravates the malignancy of retinoblastoma depending on the regulation of miR-132-3p/PAX6 axis

Abstract Circular RNAs (circRNAs) serve as essential players in diverse human cancers, including retinoblastoma (RB). In this study, the function of circRNA Ring Finger Protein 20 (circRNF20) in RB progression was investigated. Quantitative real-time polymerase chain reaction, western blot assay or immunohistochemistry assay was performed to determine the expression of circRNF20, miR-132-3p and Paired Box 6 (PAX6). Dual-luciferase reporter assay, RNA immunoprecipitation assay and RNA pull-down assay were utilized to verify the relationships among circRNF20, miR-132-3p and PAX6. In vivo experiment was done for circRNF20 function in tumor formation. It was found that ircRNF20 level was increased in RB tissues and linked to advanced tumor, nodes, metastases (TNM) stage and poor overall survival rate. Deficiency of circRNF20 suppressed cell proliferation, migration and invasion and induced apoptosis in vitro, as well as blocked tumor growth in vivo. circRNF20 directly targeted miR-132-3p and miR-132-3p overexpression inhibited RB cell progression. PAX6 was the target gene of miR-132-3p. Moreover, miR-132-3p inhibition or PAX6 overexpression reversed circRNF20 deficiency-mediated effects on RB cell malignant behaviors. In addition, exosomal circRNF20 was able to promote RB cell progression. Thus, we concluded that circRNF20 served as an oncogene in RB progression through the circRNF20/miR-132-3p/PAX6 pathway.