Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning

Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their future performance on new questions based on their past response records. While existing deep learning knowledge tracing (DLKT) methods have significantly improved prediction accuracy and achieved state-of-the-art results, they often suffer from a lack of interpretability. To address this limitation, current approaches have explored incorporating psychological influences to achieve more explainable predictions, but they tend to overlook the potential influences of historical responses. In fact, understanding how models make predictions based on response influences can enhance the transparency and trustworthiness of the knowledge tracing process, presenting an opportunity for a new paradigm of interpretable KT. However, measuring unobservable response influences is challenging. In this paper, we resort to counterfactual reasoning that intervenes in each response to answer \textit{what if a student had answered a question incorrectly that he/she actually answered correctly, and vice versa}. Based on this, we propose RCKT, a novel response influence-based counterfactual knowledge tracing framework. RCKT generates response influences by comparing prediction outcomes from factual sequences and constructed counterfactual sequences after interventions. Additionally, we introduce maximization and inference techniques to leverage accumulated influences from different past responses, further improving the model's performance and credibility. Extensive experimental results demonstrate that our RCKT method outperforms state-of-the-art knowledge tracing methods on four datasets against six baselines, and provides credible interpretations of response influences.
Knowledge Graph Enhanced Aspect-Level Sentiment Analysis

In this paper, we propose a novel method to enhance sentiment analysis by addressing the challenge of context-specific word meanings. It combines the advantages of a BERT model with a knowledge graph based synonym data. This synergy leverages a dynamic attention mechanism to develop a knowledge-driven state vector. For classifying sentiments linked to specific aspects, the approach constructs a memory bank integrating positional data. The data are then analyzed using a DCGRU to pinpoint sentiment characteristics related to specific aspect terms. Experiments on three widely used datasets demonstrate the superior performance of our method in sentiment classification.
HIV Testing and Preexposure Prophylaxis Prescriptions Among U.S. Commercially Insured Transgender Men and Women, 2014 to 2021

BACKGROUND Transgender persons are disproportionately affected by HIV, but preexposure prophylaxis (PrEP) use has been low in this population. Clinical encounters for gender-affirming hormone therapy (GAHT) provide opportunities for HIV prevention. OBJECTIVE To estimate the number of commercially insured transgender women (TGW) and transgender men (TGM) in the United States and their use of HIV prevention services. DESIGN Retrospective analysis of secondary data. SETTING Merative MarketScan commercial databases from 2014 to 2021. PARTICIPANTS TGW and TGM, defined as those with transgender-related diagnoses and prescriptions for feminizing or masculinizing GAHT. MEASUREMENTS HIV testing and PrEP use. RESULTS A substantially increasing trend was observed in the prevalence of transgender-related diagnosis codes from 2014 to 2021 and in the proportion of persons who used GAHT. The increases were driven by persons aged 18 to 34 years. In 2021, among 10 613 TGW with a test for or a diagnosis of a sexually transmitted infection (STI) in the previous 12 months, 61.1% had an HIV test; among those, 20.2% were prescribed PrEP. Among 4184 TGM with STI risk, 48.3% had an HIV test; among those, 10.2% were prescribed PrEP. The prevalence of TGW and TGM who had a test for or a diagnosis of an STI, had an HIV test, and were prescribed PrEP increased substantially from 2014 to 2021. LIMITATION The findings represent only persons with commercial health insurance who sought health care services for GAHT. CONCLUSION It is important to identify transgender persons to monitor their receipt of HIV prevention services. Encounters for GAHT provide opportunities to offer HIV prevention and other prevention services. Many HIV prevention opportunities were likely missed at clinical encounters for GAHT. PRIMARY FUNDING SOURCE None.
A data-driven method for modelling dissipation rates in stratified turbulence

Abstract We present a deep probabilistic convolutional neural network (PCNN) model for predicting local values of small-scale mixing properties in stratified turbulent flows, namely the dissipation rates of turbulent kinetic energy and density variance, $\varepsilon$ and $\chi$. Inputs to the PCNN are vertical columns of velocity and density gradients, motivated by data typically available from microstructure profilers in the ocean. The architecture is designed to enable the model to capture several characteristic features of stratified turbulence, in particular the dependence of small-scale isotropy on the buoyancy Reynolds number $Re_b:=\varepsilon /(\nu N^2)$, where $\nu$ is the kinematic viscosity and $N$ is the background buoyancy frequency, the correlation between suitably locally averaged density gradients and turbulence intensity and the importance of capturing the tails of the probability distribution functions of values of dissipation. Empirically modified versions of commonly used isotropic models for $\varepsilon$ and $\chi$ that depend only on vertical derivatives of density and velocity are proposed based on the asymptotic regimes $Re_b\ll 1$ and $Re_b\gg 1$, and serve as an instructive benchmark for comparison with the data-driven approach. When trained and tested on a simulation of stratified decaying turbulence which accesses a range of turbulent regimes (associated with differing values of $Re_b$), the PCNN outperforms assumptions of isotropy significantly as $Re_b$ decreases, and additionally demonstrates improvements over the fitted empirical models. A differential sensitivity analysis of the PCNN facilitates a comparison with the theoretical models and provides a physical interpretation of the features enabling it to make improved predictions.
Treatment of central nervous system relapse in PLZF::RARA-positive acute promyelocytic leukemia by venetoclax combined with arubicin, cytarabine and intrathecal therapy: a case report

Abstract Objectives Patients suffering from refractory acute promyelocytic leukemia with central nervous system relapse often have a poor prognosis. Among these patients, those with PLZF::RARA rearrangement exhibit poor responses to all-trans retinoic acid and conventional chemotherapy. Venetoclax, a selective inhibitor of B-cell lymphoma-2, can cross the blood–brain barrier and has been widely applied to acute myeloid leukemia therapy recently. Case presentation A case of central nervous system relapse in a patient with acute promyelocytic leukemia harboring PLZF::RARA rearrangement was successfully treated with anthracycline cytotoxic chemotherapy and cytarabine in combination with venetoclax, resulting in complete remission. Liquid chromatography-tandem mass spectrometry revealed that the concentration of venetoclax in the cerebrospinal fluid (CSF) was approximately 1/1,000 of that in plasma. Following the first treatment course, the patient’s bone marrow sample tested negative for PLZF::RARA. After the third treatment course, abnormal promyelocytic leukemia cells in the CSF were not detected using flow cytometry, and the PLZF::RARA test in the CSF remained negative. Conclusions This case report highlights a new approach for the treatment of central nervous system relapse in patients with PLZF::RARA-positive acute promyelocytic leukemia.
Anti-invasive Activity of Lawsonia inermis Branch and Its Potential Target Protein

The methanolic extract from the branch of Lawsonia inermis was found to inhibit the invasion of HT1080 cells strongly. The extract and its EtOAc- and 1-BuOH-soluble fractions strongly inhibited the enzyme activities and the secretions of MMPs from HT1080 cells. We, therefore, tried to examine and estimate the active constituents among the constituents isolated. However, there are no active compound among the isolated compounds. Next, we tried to identify the selective binding protein of whole constituents of their extracts. As a result, ribosomal protein S18 (RPs S18, human), 60S ribosomal protein L18 isoform 1 (60S RPs L18 i1, human) and eukaryotic elongation factor 2 (eEF2, human) were detected as their selective binding and potential target proteins. To confirm that the proteins are target proteins or not, we examined the effects of their proteins on synthesis of MMPs using siRNA method. With the treatment of siRNA for RPs S18, 60S RPs L18 i1 and eEF2 proteins, the MMP-2 protein synthesis were downregulated in HT1080 cells. MMP-9 productions were also downregulated with the treatment of siRNA for RPs S18 and eEF2. From the results, we reached a conclusion that the extract was considered to have the potential to inhibit the production of MMPs proteins via inhibition of RPs S18 and eEF2 proteins, leading to inhibit the invasion in HT1080 cells.
Stress-induced c-fos expression in the medial prefrontal cortex of male rats differentially involves the main glial cell phenotypes

Stress poses a challenge to the body’s equilibrium and triggers a series of responses that enable organisms to adapt to stressful stimuli. The prefrontal cortex (PFC), particularly in acute stress conditions, undergoes significant physiological changes to cope with the demands associated with cellular activation. The proto-oncogene c-fos and its protein product c-Fos have long been utilized to investigate the effects of external factors on the central nervous system (CNS). While c-Fos expression has traditionally been attributed to neurons, emerging evidence suggests its potential expression in glial cells. In this study, our main objective was to explore the expression of c-Fos in glial cells and examine how acute stress influences these activity patterns. We conducted our experiments on male Wistar rats, subjecting them to acute stress and sacrificing them two hours after the stressor initiation. Using double-labelling fluorescent immunohistochemistry targeting c-Fos, along with markers such as GFAP, Iba-1, Olig2, NG2, and NeuN, we analyzed a series of 35 μm brain slices obtained from the medial PFC. Our findings compellingly demonstrate that c-Fos expression extends beyond neurons and is present in astrocytes, oligodendrocytes, microglia, and NG2 cells—the diverse population of glial cells. Moreover, we observed distinct regulation of c-Fos expression in response to stress across the different phenotypes. These results emphasize the importance of considering glial cells and their perspective in studies investigating brain activity, highlighting c-Fos as a response marker in glial cells. By shedding light on the differential regulation of c-Fos expression in response to stress, our study also contributes to the understanding of glial cell involvement in stress-related processes.
Configurations of Digital Participatory Budgeting

Participatory budgeting is a democratic innovation increasingly supported by digital platforms. Like any technology, participatory budgeting platforms are not value-free or politically neutral; their design, configuration, and deployment display assumptions and configure participant behaviour. To understand what kinds of configurations occur and what kinds of democratic values they hold, we studied 31 digital participatory budgeting cases in Spain, France, and Finland. These cases were all supported by the same technical platform, Decidim, allowing us to focus on the variations in their configurations. We examined the data from these cases and identified 25 different technical configurations and 15 participatory budgeting configurations. The configurations observed in our cases exhibit individual and community-centred assumptions about expected state-society interactions, as well as open vs managerial approaches to participatory budgeting. Based on these findings, we highlight a dilemma for civic technology designers: to what degree should platforms be open to configuration and customisation, and which political values should be enforced by platform design?
Understanding and Managing Freshwater Recreational Fisheries as Complex Adaptive Social-Ecological Systems

ABSTRACT The state of knowledge on the science and management of freshwater recreational fisheries is reviewed, with the objective of integrating insights from disparate fields such as fisheries science, environmental complexity theory, common-pool-resource theory, and resilience theory. First, freshwater recreational fisheries are characterized as complex adaptive social-ecological systems (SESs). Subsequently, two interrelated frameworks, drawing on the Ostrom framework for the analysis of SESs and adaptive management as key foundations, are presented. These frameworks are useful to structure the complexity and apprehend the various feedbacks and links inherent in any particular recreational fisheries system. Moreover, the frameworks help to identify operational management strategies in the face of substantial social-ecological uncertainty. It is concluded that to understand and manage freshwater recreational fisheries as complex adaptive SESs, a sustained shift from disciplinary to inter- and sometimes transdisciplinary research as well as a focus on flexible, adaptive and generally enabling rather than command-and-control type governance and management are needed. Understanding and managing recreational fisheries as complex adaptive SESs will benefit from an increasing focus on (i) managing social-ecological feedbacks and processes, (ii) managing critical slow variables that either drive the system or maintain it in potentially undesirable states, and (iii) managing and maintaining social and ecological diversity. It is hoped that the frameworks presented in this article may guide future interdisciplinary inquiry to manage for sustainability by building resilience.
Extreme in Every Way: Exceedingly Low Genetic Diversity in Snow Leopards Due to Persistently Small Population Size

Snow leopards (Panthera uncia) serve as an umbrella species whose conservation benefits their high-elevation Asian habitat. Their numbers are believed to be in decline due to numerous Anthropogenic threats; however, their conservation is hindered by numerous knowledge gaps. They are the least studied genetically of all big cat species and little is known about their historic population size and range, current population trends, or connectivity across their range. Here, we use whole genome sequencing data for 41 snow leopards (37 newly sequenced) to assess population connectivity, historic population size, and current levels of genetic diversity. Among our samples, we find evidence of a primary genetic divide between the northern and southern part of the range around the Dzungarian Basin and a secondary divide south of Kyrgyzstan around the Taklamakan Desert. However, we find evidence of gene flow, suggesting that barriers between these groups are permeable. Perhaps most noteworthy, we find that snow leopards have the lowest genetic diversity of any big cat species, likely due to a persistently small population size throughout their evolutionary history. Without a large population size or ample standing genetic variation to help buffer them from any forthcoming Anthropogenic challenges, snow leopard persistence may be more tenuous than currently appreciated.
Abundant microorganisms in soil are mainly controlled by stochastic processes, while bacteria and fungi differ in rare taxa

The assembly mode of microbial communities helps to explain the ecological processes of soil subsurface groups, and abundant and rare microorganisms have their own unique assembly patterns. In this paper, the effect of changing vegetation types on the assembly of bacteria and fungi with different abundances in soil was investigated by iCAMP in the Tianshan Mountains. The results showed that: (i) there were differences in the distribution of rare and abundant microbial communities among different vegetation types: the main trends were forests>shrubs > grasslands, and the α diversity and variability of rare microorganisms were greater than those of abundant taxa; (ii) abundant taxa were mainly affected by stochastic processes (mainly diffusion limitation), whereas among rare microbes, the main controlling process for bacteria was heterogeneous selection and for fungi was diffusion limitation; (ⅲ)soil carbon, nitrogen and temperature were important determinants driving bacterial community structure. Our results deepen the understanding of the various ecological processes involved in microbial community assembly, reveal the effects of environmental factors on abundant and rare microorganisms, and provide evidence for understanding the mechanisms of soil microbial community construction among different vegetation covers in arid zones. Importance The study compares the response of different vegetation types to soil microbial community processes in the arid zone and deepens the understanding of the mechanisms of soil microbial community composition at different abundances.
A Robotic Cane for Balance Maintenance Assistance

This paper proposes a robotic cane that helps users to stand and maintain their balance while standing and walking. The design is based on an inverted pendulum model, uses a smart omnidirectional wheel, and is linearized by the Lie algebra method (LAM) combined with a nonlinear disturbance observer that estimates the external force applied by the user. The hardware and mathematical model of the robotic cane are analyzed in detail. The hardware design is complete, and considers the weight and battery capacity. The performance of the controller is confirmed in three-dimensional simulations and in physical experiments. The robotic cane tolerates a large fluctuation angle from its equilibrium point. It also has a fall prevention function, which is among the most desirable functions of any walking assistance device, and responds quickly to a human force. The balance maintenance functions are as follows: self-balance, standing and walking assistance, fall prevention, and a mechatronic safety mode. In experimental measurements of the cane in use, the gyroscope sensor insignificant vibrations on the user's bodies, verifying the effectiveness of the robotic cane in daily life activities. The performance of LAM is also compared with that of the linear quadratic regulator.
On the Impact of Showing Evidence from Peers in Crowdsourced Truthfulness Assessments

Misinformation has been rapidly spreading online. The common approach to deal with it is deploying expert fact-checkers that follow forensic processes to identify the veracity of statements. Unfortunately, such an approach does not scale well. To deal with this, crowdsourcing has been looked at as an opportunity to complement the work done by trained journalists. In this paper, we look at the effect of presenting the crowd with evidence from others while judging the veracity of statements. We implement various variants of the judgment task design to understand if and how the presented evidence may or may not affect the way crowd workers judge truthfulness and their performance. Our results show that, in certain cases, the presented evidence and the way in which it is presented may mislead crowd workers who would otherwise be more accurate if judging independently from others. Those who make appropriate use of the provided evidence, however, can benefit from it and generate better judgments.
Correlation of EEG Responses Reflects Structural Similarity of Choruses in Popular Music

Music structure analysis is a core topic in Music Information Retrieval and could be advanced through the inclusion of new data modalities. In this study we consider neural correlates of music structure processing using popular music—specifically choruses of Bollywood songs— and the NMED-H electroencephalographic (EEG) dataset. Motivated by recent findings that listeners’ EEG responses correlate when hearing a shared music stimulus, we investigate whether responses correlate not only within single choruses but across pairs of chorus instances as well. We find statistically significant correlations within and across several chorus instances, suggesting that brain responses synchronize across structurally matched music segments even if they are not contextually or acoustically identical. Correlations were only occasionally higher within than across choruses. Our findings advance the state of the art of naturalistic music neuroscience, while also highlighting a novel approach for further studies of music structure analysis and audio understanding more broadly.
Human-AI Music Creation: Understanding the Perceptions and Experiences of Music Creators for Ethical and Productive Collaboration

Recently, there has been a surge in Artiﬁcial Intelligence (AI) tools that allow creators to develop melodies, har-monies, lyrics, and mixes with the touch of a button. The reception of and discussion on the use of these tools - and more broadly, any AI-based art creation tools - tend to be polarizing, with opinions ranging from enthusiasm about their potential to fear about how these tools will impact the livelihood and creativity of human creators. However, a more desirable future path is most likely somewhere in be-tween these two polar opposites where productive and ethical human-AI collaboration could happen through the use of these tools. To explore this possibility, we ﬁrst need to improve our understanding of how music creators perceive and utilize these types of tools in their creative process. We conducted case studies of a range of music creators to better understand their perception and usage of AI-based music creation tools. Through a thematic analysis of these cases, we identify the opportunities and challenges related to the use of AI for music creation from the perspective of the musicians and discuss the design implications for AI music tools.
Modeling and formal verification of the Fairisle ATM switch fabricusing MDGs

In this paper, we present several techniques for modeling and formal verification of the Fairisle asynchronous transfer mode (ATM) switch fabric using multiway decision graphs (MDGs). MDGs represent a new class of decision graphs which subsumes Bryant's reduced ordered binary decision diagrams (ROBDDs) while accommodating abstract sorts and uninterpreted function symbols. The ATM device we investigated is in use for real applications in the Cambridge University Fairisle network. We modeled and verified the switch fabric at three levels of abstraction: behavior, and register transfer level (RTL) and gate levels. In a first stage, we validated the high-level specification by checking specific safety properties that reflect the behavior of the fabric in its real operating environment. Using the intermediate abstract RTL model, we hierarchically completed the verification of the original gate-level implementation of the switch fabric against the behavioral specification. Since MDGs avoid model explosion induced by data values, this work demonstrates the effectiveness of MDG based verification as an extension of ROBDD-based approaches. All the verifications were carried out automatically in a reasonable amount of CPU time.