Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions

Recent studies of the computational power of recurrent neural networks (RNNs) reveal a hierarchy of RNN architectures, given real-time and finite-precision assumptions. Here we study auto-regressive Transformers with linearised attention, a.k. a. linear Transformers (LTs) or Fast Weight Programmers (FWPs). LTs are special in the sense that they are equivalent to RNN-like sequence processors with a fixed-size state, while they can also be expressed as the now-popular self-attention networks. We show that many well-known results for the standard Transformer directly transfer to LTs/FWPs. Our formal language recognition experiments demonstrate how recently proposed FWP extensions such as recurrent FWPs and self-referential weight matrices successfully overcome certain limitations of the LT, e.g., allowing for generalisation on the parity problem. Our code is public.
From Pointwise to Powerhouse: Initialising Neural Networks with Generative Models

Traditional initialisation methods, e.g. He and Xavier, have been effective in avoiding the problem of vanishing or exploding gradients in neural networks. However, they only use simple pointwise distributions, which model one-dimensional variables. Moreover, they ignore most information about the architecture and disregard past training experiences. These limitations can be overcome by employing generative models for initialisation. In this paper, we introduce two groups of new initialisation methods. First, we locally initialise weight groups by employing variational autoencoders. Secondly, we globally initialise full weight sets by employing graph hypernetworks. We thoroughly evaluate the impact of the employed generative models on state-of-the-art neural networks in terms of accuracy, convergence speed and ensembling. Our results show that global initialisations result in higher accuracy and faster initial convergence speed. However, the implementation through graph hypernetworks leads to diminished ensemble performance on out of distribution data. To counteract, we propose a modification called noise graph hypernetwork, which encourages diversity in the produced ensemble members. Furthermore, our approach might be able to transfer learned knowledge to different image distributions. Our work provides insights into the potential, the trade-offs and possible modifications of these new initialisation methods.
Machine Learning Approaches for Fine-Grained Symptom Estimation in Schizophrenia: A Comprehensive Review

Schizophrenia is a severe yet treatable mental disorder, it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatment for each individual depends on the severity of the symptoms, therefore there is a need for accurate, personalised assessments. However, the process can be both time-consuming and subjective; hence, there is a motivation to explore automated methods that can offer consistent diagnosis and precise symptom assessments, thereby complementing the work of healthcare practitioners. Machine Learning has demonstrated impressive capabilities across numerous domains, including medicine; the use of Machine Learning in patient assessment holds great promise for healthcare professionals and patients alike, as it can lead to more consistent and accurate symptom estimation.This survey aims to review methodologies that utilise Machine Learning for diagnosis and assessment of schizophrenia. Contrary to previous reviews that primarily focused on binary classification, this work recognises the complexity of the condition and instead, offers an overview of Machine Learning methods designed for fine-grained symptom estimation. We cover multiple modalities, namely Medical Imaging, Electroencephalograms and Audio-Visual, as the illness symptoms can manifest themselves both in a patient's pathology and behaviour. Finally, we analyse the datasets and methodologies used in the studies and identify trends, gaps as well as opportunities for future research.
Accelerated linearized alternating direction method of multipliers with Nesterov extrapolation

The alternating direction method of multipliers (ADMMs) has found widespread use in solving separable convex optimization problems. In this paper, by employing Nesterov extrapolation technique, we propose two families of accelerated linearized ADMMsfor addressing two-block linearly constrained separable convex optimization problems where each block of the objective function exhibits a `nonsmooth' plus `smooth' composite structure. Our proposed accelerated linearized ADMMs extend two classical Nesterov acceleration methods designed for unconstrained composite optimization problems to linearly constrained problems. These methods are capable of achieving non-ergodic convergence rates of O(1/k^2) provided that one block of the objective function exhibits strong convexity and the gradients of smooth terms are Lipschitz continuous. We show that the proposed methods can reduce to accelerated linearized augmented Lagrangian methods (ALMs) with non-ergodic O(1/k2 ) convergence rates for solving one-block linearly constrained convex optimization problems. Furthermore, we explore hybrid versions of the proposed linearized accelerated ADMMs, which also demonstrate non-ergodic convergence rates of O(1/k2) under the same assumption. We also investigate inexact versions of the proposed methods. Under mild assumptions regarding error sequences, these methods still maintain O(1/k2) non-ergodic convergence rates. By choosing different extrapolation parameters, we explore the relationship between the proposed methods and existing accelerated methods.
(Uni)rational parametrizations of $\mathcal R_{g,2}$, $\mathcal R_{g,4}$ and $\mathcal R_{g,6}$ in low genera

The moduli space R_{g,2n} parametrizes double covers of smooth curves of genus g ramified at 2n points. We will prove the (uni)rationality of R_{g,2}, R_{g,4} and R_{g,6} in low genera.
The Influence Of Covid-19 On Patient Mobilization And Injury Attributes In The ICU: A Retrospective Analysis Of A Level II Trauma Center

The objectives of this study were to determine the effect of COVID-19 on physical therapy (PT) mobilization of traumatically-injured patients and to determine if mobilization affected patient course in the ICU. This retrospective study included patients who were admitted to the ICU of a level II trauma center. The patients were divided into two groups, i.e., those admitted before (n=378) and after (n=499) April 1, 2020 when Georgia's COVID-19 Shelter-in-place order was mandated. The two groups were contrasted on nominal and ratio variables using Chi-square and Student's t-tests. A secondary analysis focused specifically on the after COVID patients examined the extent to which mobilization (n=328) or lack of mobilization (n=171) influenced ICU outcomes (e.g., mortality, readmission). The two groups were contrasted on nominal and ratio variables using Chi-square and Student's -tests. The after COVID patients had higher injury severity as a greater proportion was classified as severely injured (i.e., >15 on Injury Severity Score) compared to the before COVID patients. After COVID patients also had greater cumulative number of comorbidities and experienced greater complications in the ICU. Despite this, there was no difference between patients in receiving a PT consultation or day-to-mobilization. Within the after COVID cohort, those that were mobilized were older, a higher proportion were female, they had greater Glasgow Coma Scale scores, had longer total hospital days, and a lesser mortality rate. Despite shifting patient injury attributes post-COVID-19, a communicable disease, mobilization care remained consistent and effective.
Digital Screener of Socio-Motor Agency Balancing Autonomy and Control

Dyadic social interactions evoke complex dynamics between two agents that while exchanging unequal levels of body autonomy and motor control, may find a fine balance to take turns and gradually build social rapport. To study the evolution of such complex interactions, we currently rely exclusively on subjective pencil and paper means. Here we complement this approach with objective biometrics of socio-motor behaviors conducive of socio-motor agency. Using a common clinical test as the backdrop of our study to probe social interactions between a child and a clinician, we demonstrate new ways to streamline the detection of social readiness potential in both typically developing and autistic children. We highlight differences between males and females and uncover a new data type amenable to generalize our results to any social settings. The new methods convert dyadic bodily biorhythmic activity into spike trains and demonstrates that in the context of dyadic behavioral analyses, they are well characterized by a continuous gamma process independent from corresponding binary spike rates. 24 We offer a new framework that combines stochastic analyses, nonlinear dynamics, and information theory, to facilitate scaling the screening and tracking of social interactions with applications to autism.
Discovering the Cognition behind Language: Financial Metaphor Analysis with MetaPro

Metaphors frequently appear in financial news headlines due to their ability to effectively convey complex financial concepts and market trends in a concise and memorable manner. Cognitive scientists have found that metaphors serve as the reflections of human cognition by means of concept mappings. In this work, we aim to analyze the metaphorical expressions and associated cognitive patterns employed by financial analysts in the headlines of financial analysis reports. Such an examination would enhance our comprehension of the cognitive state of financial analysts regarding various financial trends. We employ the latest computational metaphor processing tool, MetaPro to achieve this target by mining metaphors and cognitive patterns from 1,407,328 financial analyst report headlines, spanning the period from 14 February 2009 to 11 June 2020. We analyze the mined concept mappings by different time periods, and market movements, and deliver novel findings in these two dimensions.
Large Deﬂections in Thin Rectangular Plates Subjected to Uniform Load: Pitfalls in the Application of Analytical Methods

Thin, rectangular structural plates are used in a wide range of applications, including architectural sheet metal cladding, in which the magnitude of lateral deﬂection is much larger the material’s thickness. This study compares new experimental data with established algebraic models and assesses the inﬂuence of practicalities such as geometric tolerances and partial ﬁxity of connections. Errors and anomalies are identiﬁed in existing analytical formulas. It is also shown that standardised test methods measure only part of an imperfectly-ﬂat plate’s lateral movement, and that the actual displacement of plates in real structures can be several times greater than that inferred by a naive application of non-linear plate theory. Findings are presented as set of guidelines to help practising engineers create economical structures that will not deﬂect excessively when load is applied.
Cornerstone: A Collection of Scholarly Cornerstone: A Collection of Scholarly and Creative Works for Minnesota and Creative Works for Minnesota

Children with Autism Spectrum Disorder (ASD) are at an elevated risk of becoming lost in situations due to running away or wandering behaviors. When children with ASD become lost it may be difficult for them to effectively and efficiently seek help from community helpers in a safe manner due to communication deficits and poor social skills. This study aims to teach children with ASD how to identify when they are lost and how to seek help from police officers in the community by vocalizing the problem and using a communication card. Generalization probes were conducted with three children diagnosed with ASD before and after behavioral skills training (BST) to determine mastery of help seeking behaviors. Remedial BST and in situ trainings were used to increase generalization. Two of the three participants were able to independently seek help from a confederate law enforcement officer and one participant required a gestural prompt.
Assessing Impacts of Environmental Perturbations on Urban Biogenic Carbon Exchange in the Chicago Region

Carbon dioxide (CO2) quantification is critical for assessing city‐level carbon emissions and sustainable urban development. While urban vegetation has the potential to provide environmental benefits, such as heat and carbon mitigation, the CO2 exchange from biogenic sectors and its impact from the environmental perturbations are often overlooked. It is also challenging to simulate the plant functions in the complex urban terrain. This study presents a processed‐based modeling approach to assess the biogenic carbon fluxes from the vegetated areas over the Chicago Metropolitan Area (CMA) using the Weather Research and Forecast—Urban Biogenic Carbon exchange model. We investigate the change of CO2 sink power in CMA under heatwaves and irrigation. The results indicate that the vegetation plays a significant role in the city's carbon portfolio and the landscaping management has the potential to reduce carbon emissions significantly. Furthermore, based on the competing mechanisms in the biogenic carbon balance identified in this study, we develop a novel Environmental Benefit Score metrics framework to identify the vulnerability and mitigation measures associated with nature‐based solutions (NbS) within CMA. By using the generalized portable framework and our science‐policy confluence analysis presented in this study, global cities can maximize the effectiveness of NbS and accelerate carbon neutrality.
Reinforcement Learning for the Just-in-Time Job-Shop Scheduling Problem

This paper focuses on the optimization of job-shop scheduling in industrial production systems in the just-in-time configuration. The scheduling function plays a crucial role in determining the performance and competitiveness of a company, especially with the increasing demand for timely product delivery. The main challenge in job-shop scheduling is to find the optimal scheduling of multiple jobs on several machines, taking into account their due dates and processing times. The earliness-tardiness model adds another layer of complexity to the problem by considering the impact of delivering jobs early or late, incurring inventory costs or tardiness penalties, respectively. Due to the multiple constraints and objectives involved, finding the optimal solution is a challenging task. To tackle this problem, we in this paper investigate the use of recent advances in deep reinforcement learning techniques to optimize the earliness-tardiness cost in the job-shop. The experimental results showed that the proposed method performed at least as good as the state of the art in 65% of the studied instances, while strictly improving 10% of the same benchmark.
ECMER: Edge-Cloud Collaborative Personalized Multimodal Emotion Recognition Framework in the Internet of Vehicles

Real-time driver emotion recognition and timely risk warning can effectively reduce the incidence of traffic accidents. However, existing emotion recognition methods obtain emotion features from human physiological signals and are unsuitable for complex scenarios in the Internet of Vehicles (loV). Moreover, the existing methods in the IoV cannot fully use the resources of edge devices for mining the driver's personalities, resulting in limited accuracy. To address the problem, we propose a novel Edge-Cloud Collaborative Multimodal Emotion Recognition Framework (ECMER). The driver's facial expression and audio data are loaded to the edge for preliminary calculation, including coarse-grained facial expression recognition and driver's personality features extraction, which is uploaded to the cloud for cross-fusion. Specifically, a personality-coupled driver's emotion recognition method is proposed, and the Big Five Model is introduced from the psychological perspective. The facial expression features contained in images and audio features in videos are employed to calculate the driver's personality features, which are further fused with multimodal features. Subsequently, a hierarchical multi-granularity driver emotion recognition method is designed where the real-time coarsegran-ularity driver emotion recognition is conducted by edge devices to reduce the data transmission pressure and cloud computing load. The empirical results on real-world datasets demonstrate that the performance of driver emotion recognition under this architecture is improved.
Teacher! Please Light My Way: Effect of Providing Individual Feedback on Achievement in Social Studies Education

Feedback in education is messages that contain information about how close the student’s current behavior in the learning process is to the expected behavior, what deficiencies and mistakes exist, and how they can be eliminated, and messages that contain information about the last level reached. Feedback guides students’ learning and is an important component of assessment. In this study, the type of individual feedback was discussed. The research aims to determine the effect of the individual feedback provided to the students from the social studies course on their exam success. The research was conducted with three different social studies teachers and their six different students at three different socio-economic levels. After the teachers were given effective feedback training, two students in their classes were asked to give individual feedback on their lessons. At the end of the process, the exam scores of the students before receiving feedback and the exam scores after receiving feedback were compared according to the class averages. In addition, the opinions of the teachers in this process were also taken in the research. As a result of the research, it was determined that the exam scores of the students who received individual feedback increased and there was a higher change in their scores compared to the class average. According to the teachers, individual feedback increased the students’ interest in the lesson. Finally, teachers suggested to their colleagues that they should receive training on feedback and emphasized that every teacher should be good feedback literacy. Based on these results of the study, it is suggested that teachers should be trained about feedback and that their feedback literacy levels should be increased.
The Effect of Parental Vaccine Literacy Level and Attitudes on COVID19 Vaccination Decisions for Children Aged 5-11 Years: A Cross-sectional Study

Objective: The aim of this study was to determine the decision of parents of children aged 5-11 years to vaccinate their children against COVID-19 and to investigate the relationship between this decision and their vaccine literacy and attitudes. Materials and Methods: A descriptive and cross-sectional research design was implemented on 635 parents between April 2022 and December 2022. Data were collected using the COVID-19 Vaccine Literacy Scale, the Attitudes Towards the COVID-19 Vaccine Scale, and an Information Form. Results: A positive and significant relationship was found between the scores of the parents on the COVID-19 Vaccine Literacy Scale and the Attitudes Towards the COVID-19 Vaccine Scale. In the study, it was determined that the total score averages of the Attitudes Towards the COVID-19 Vaccine Scale were significantly higher for the parents who had had all childhood vaccinations for their children, who agreed to have their child vaccinated for the COVID-19 vaccine, who had had the COVID-19 vaccine themselves, and who stated that the vaccine should be mandatory for both adults and children. Conclusion: It was concluded that parents’ willingness to vaccinate their children routinely and their level of vaccine literacy about the COVID-19 vaccine positively influenced their attitude and decisions towards vaccinating their children against COVID-19.
Comparative Effects of Intra-Articular versus Intravenous Mesenchymal Stromal Cells Therapy in a Rat Model of Osteoarthritis by Destabilization of Medial Meniscus

Transplanted mesenchymal stromal cells (MSCs) exhibit a robust anti-inflammatory and homing capacity in response to high inflammatory signals, as observed in studies focused on rheumatic diseases that target articular cartilage (AC) health. However, AC degradation in osteoarthritis (OA) does not necessarily coincide with a highly inflammatory joint profile. Often, by the time patients seek medical attention, they already have damaged AC. In this study, we examined the therapeutic potential of a single bone marrow MSC transplant (2 × 106 cells/kgbw) through two different routes: intra-articular (MSCs-IAt) and intravenous (MSCs-IVt) in a preclinical model of low-grade inflammatory OA with an established AC degeneration. OA was induced through the destabilization of the medial meniscus (DMM) in female Wistar Kyoto rats. The animals received MSCs 9 weeks after surgery and were euthanized 4 and 12 weeks post-transplant. In vivo and ex vivo tracking of MSCs were analyzed via bioluminescence and imaging flow cytometry, respectively. Cytokine/chemokine modulation in serum and synovial fluid was measured using a multiplex panel. AC degeneration was quantified through histology, and hindlimb muscle balance was assessed with precision weighing. To our knowledge, we are the first group to show the in vivo (8 h) and ex vivo (12 h) homing of cells to the DMM–OA joint following MSCs-IVt. In the case of MSCs-IAt, the detection of cellular bioluminescence at the knee joint persisted for up to 1 week. Intriguingly, intra-articular saline injection (placebo-IAt) resulted in a worse prognosis of OA when compared to a non-invasive control (placebo-IVt) without joint injection. The systemic cytokines/chemokines profile exhibited a time-dependent variation between transplant routes, displaying a transient anti-inflammatory systemic response for both MSCs-IVt and MSCs-IAt. A single injection of MSCs, whether administered via the intra-articular or intravenous route, performed 9 weeks after DMM surgery, did not effectively inhibit AC degeneration when compared to a non-invasive control.
A History Lesson Designed with the Digital Storytelling Method: Kara Fatma Example

Due to the reflections of the changes and developments in science and technology in the field of education, students need educational opportunities that can meet their changing learning needs. Based on this need, the main purpose of this study is to evaluate the digital storytelling process in history teaching and the contribution of this method to history teaching through the views of students. To achieve this goal, a history lesson about “Kara Fatma”, one of the heroines of the National Struggle in the 12th Grade Turkish Republic History of Revolution and Kemalism course, was designed using the digital storytelling method. A seven-week-long study was planned. The qualitative research method was used in the study. The study group consisted of 15 volunteer 12th-grade students. A semi-structured interview form was used to determine the evaluations of the students about the digital storytelling process, and a checklist was employed to evaluate students’ digital stories. The study data were analyzed with content analysis. As a result, it was found that students only used internet sources during the evidence search process, their knowledge of history was enriched with digital storytelling, and that they had difficulty eliciting material for their stories from a huge stack of information. It was concluded that this application contributed to making history lessons fun and could contribute to the presentation of brief and concise, but not long and boring, information and the use of critical thinking skills in lessons. Thus, it has been understood that digital storytelling may contribute to the development of historical literacy in students.