Miracle-dz/UniFAR-FTUs huggingface dataset:
Increased extra-axial cerebrospinal fluid and benign external hydrocephalus in early autism

Previous studies have reported that some children with Autism Spectrum Disorder (ASD) below the age of 3-years-old exhibit increased extra-axial cerebrospinal fluid (EA-CSF) volumes, suggesting that transient early CSF circulation abnormalities may characterize a unique etiology in a sub-group of ASD children. Specifically, this sub-group was proposed to exhibit an EA-CSF to total brain volume (TBV) ratio >0.14. It is unknown whether such findings may correspond to an early radiological diagnosis of Benign external hydrocephalus (BEH), which is defined as an enlargement of CSF volumes within the subarachnoid space (SAS) during the first two years of life. Here we examined a retrospective clinical sample extracted from the National Autism Database of Israel. In 2020, ∼14% of ASD children in the database (n= 102) had completed clinical brain MRI scans at different ages before or after ASD diagnosis. Scans of 83 ASD children fulfilled quality control criteria and were compared to those of 53 age and sex matched typicality developing (TD) children who were referred to the same clinical center for similar clinical reasons. EA-CSF volume and total brain volume (TBV) were quantified from T2-weighted scans using manual labeling and compared across groups. The results revealed that ASD children scanned before the age of 2-years-old (i.e., before ASD diagnosis) exhibited significantly larger EA-CSF volumes when compared to controls (t(49)=2.89, p=0.006, Cohen’s d=0.82). No significant differences were apparent across groups at older ages. Of those scanned before the age of 2-years-old, ∼16% of the ASD children, and none of the control children, exhibited EA-CSF/TBV ratios >0.14, thereby replicating previous findings from prospective research samples. In addition, retrospective examination of the neuroradiology assessments revealed a BEH prevalence of 33% in the ASD children scanned before the age of 2-years-old, with 80% of these children exhibiting EA-CSF/TBV ratios >0.11. These findings reveal a clear relationship between quantitative EA-CSF volume analyses and qualitative BEH radiological findings, which characterize a considerable sub-group of ASD children. Accumulating evidence, therefore, suggests that BEH diagnosis in conjunction with a high EA-CSF/TBV ratio by the age of 2-years-old may be indicative of a specific ASD etiology that warrants further research and may offer unique opportunities for intervention.
Robust Discrete Choice Model for Travel Behavior Prediction With Data Uncertainties

Discrete choice models (DCMs) are the canonical methods for travel behavior modeling and prediction. However, in many scenarios, the collected data for DCMs are subject to measurement errors. Previous studies on measurement errors mostly focus on"better estimating model parameters"with training data. In this study, we focus on"better predicting new samples' behavior"when there are measurement errors in testing data. To this end, we propose a robust discrete choice model framework that is able to account for data uncertainties in both features and labels. The model is based on robust optimization theory that minimizes the worst-case loss over a set of uncertainty data scenarios. Specifically, for feature uncertainties, we assume that the $\ell_p$-norm of the measurement errors in features is smaller than a pre-established threshold. We model label uncertainties by limiting the number of mislabeled choices to at most $\Gamma$. Based on these assumptions, we derive a tractable robust counterpart for robust-feature and robust-label DCM models. The derived robust-feature binary logit (BNL) and the robust-label multinomial logit (MNL) models are exact. However, the formulation for the robust-feature MNL model is an approximation of the exact robust optimization problem. The proposed models are validated in a binary choice data set and a multinomial choice data set, respectively. Results show that the robust models (both features and labels) can outperform the conventional BNL and MNL models in prediction accuracy and log-likelihood. We show that the robustness works like"regularization"and thus has better generalizability.
The cosmic string in Horndeski-like gravity

Scalar-tensor theories have shown promise in many sectors of cosmology. However, recent constraints from the speed of gravitational waves have put severe limits on the breadth of models such classes of theories can realize. In this work, we explore the possibility of a string-like Horndeski Lagrangian that is equipped with two dilaton fields. This is an interesting low-energy effective string theory that has a healthy general relativity limit. The evolution of a two-dilation coupled cosmology is not well-known in the literature. We explore the tensor perturbations in order to assess the behavior of the model again the speed of gravitational wave constraint. Our main result is that this model exhibits of a class of cosmological theories that is consistent with this observational constraint.
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning

Recently, Heterogeneous Federated Learning (HtFL) has attracted attention due to its ability to support heterogeneous models and data. To reduce the high communication cost of transmitting model parameters, a major challenge in HtFL, prototype-based HtFL methods are proposed to solely share class representatives, a.k. a, prototypes, among heterogeneous clients while maintaining the privacy of clients’ models. However, these prototypes are naively aggregated into global prototypes on the server using weighted averaging, resulting in suboptimal global knowledge which negatively impacts the performance of clients. To overcome this challenge, we introduce a novel HtFL approach called FedTGP, which leverages our Adaptive-margin-enhanced Contrastive Learning (ACL) to learn Trainable Global Prototypes (TGP) on the server. By incorporating ACL, our approach enhances prototype separability while preserving semantic meaning. Extensive experiments with twelve heterogeneous models demonstrate that our FedTGP surpasses state-of-the-art methods by up to 9.08% in accuracy while maintaining the communication and privacy advantages of prototype-based HtFL. Our code is available at https://github.com/TsingZ0/FedTGP.
Geodesic Interpolation of Frame-Wise Speaker Embeddings for the Diarization of Meeting Scenarios

We propose a modified teacher-student training for the extraction of frame-wise speaker embeddings that allows for an effective diarization of meeting scenarios containing partially overlapping speech. To this end, a geodesic distance loss is used that enforces the embeddings computed from regions with two active speakers to lie on the shortest path on a sphere between the points given by the d-vectors of each of the active speakers. Using those frame-wise speaker embeddings in clustering-based diarization outperforms segment-level clustering-based diarization systems such as VBx and Spectral Clustering. By extending our approach to a mixture-model-based diarization, the performance can be further improved, approaching the diarization error rates of diarization systems that use a dedicated overlap detection, and outperforming these systems when also employing an additional overlap detection.
Human as AI Mentor: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving

Despite significant progress in autonomous vehicles (AVs), the development of driving policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully explored. In this paper, we propose an enhanced human-in-the-loop reinforcement learning method, termed the Human as AI mentor-based deep reinforcement learning (HAIM-DRL) framework, which facilitates safe and efficient autonomous driving in mixed traffic platoon. Drawing inspiration from the human learning process, we first introduce an innovative learning paradigm that effectively injects human intelligence into AI, termed Human as AI mentor (HAIM). In this paradigm, the human expert serves as a mentor to the AI agent. While allowing the agent to sufficiently explore uncertain environments, the human expert can take control in dangerous situations and demonstrate correct actions to avoid potential accidents. On the other hand, the agent could be guided to minimize traffic flow disturbance, thereby optimizing traffic flow efficiency. In detail, HAIM-DRL leverages data collected from free exploration and partial human demonstrations as its two training sources. Remarkably, we circumvent the intricate process of manually designing reward functions; instead, we directly derive proxy state-action values from partial human demonstrations to guide the agents' policy learning. Additionally, we employ a minimal intervention technique to reduce the human mentor's cognitive load. Comparative results show that HAIM-DRL outperforms traditional methods in driving safety, sampling efficiency, mitigation of traffic flow disturbance, and generalizability to unseen traffic scenarios. The code and demo videos for this paper can be accessed at: https://zilin-huang.github. io/HAIM-DRL-website/
WORKING PAPERS: EXPANDED BASIC EDUCATION PROGRAM The Child Friendly Schools Movement and Impacts on Children’s Learning: Practical Applications in Cambodia

This research paper is the first of two studies that are being funded through a national program to support Child Friendly Schools (CFS) in Cambodia. The study’s primary expectation was that program interventions would lead to significant and positive changes in critical and creative thinking skills among target groups relative to the comparison group in non-program sites. Conclusions in this regard were based on analysis of mean differences between comparison groups and effect sizes on locally designed testing instruments. Interventions consisted primarily of changes in the physical learning environment and teacher practice following a child rights approach to school development. The study employed a cluster sampling technique in which class was identified as the primary sampling unit. Sampling was not random but was based on equivalency matching of sampling units. Research results tentatively suggest that expectations relating to positive impact on higher order thinking skills among children in CFS classrooms are being fulfilled. In all cases, total mean scores among experimental group children were higher than those for control groups of comparable composition and that these differences were statistically significant with a very low probability of error (p<.01). As importantly, effect sizes indicated that the differences in total mean scores between comparison groups were moderate to large. Nevertheless, researchers stopped short of widely generalizing these conclusions due to the pre-experimental nature of the research design. Child Friendly Schools Movement & Impacts on Children’s Learning: Cambodia
COGNITIVE REHABILITATION IN MULTIPLE SCLEROSIS

Background/aims To explore the feasibility and efficacy of computerised, home-based cognitive rehabilitation in patients with multiple sclerosis using neuropsychological assessment and structural and functional MRI techniques. Materials, methods or case report 38 patients with MS and evidence of cognitive impairment on the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) test battery were enrolled and randomly assigned to undergo computerised cognitive rehabilitation (n=19) for six weeks or to a control condition (n=19). All patients underwent MRI at baseline (T1) and post-intervention (T2). Changes in cortical activations were explored using an n-back fMRI paradigm. Results Compared to baseline, patients in the treatment group showed significant improvement in the Brief Visuospatial Memory Test at T2 (p=0.035) and exhibited altered cortical fMRI activations compared to controls. Conclusion Our preliminary data support the hypothesis that home-based, computerised cognitive rehabilitation is a feasible and effective approach to improving cognitive performance in patients with MS and may reflect underlying changes in brain activations. BICAMS is a sensitive and easy to administer tool for identifying cognitive impairment in MS in the outpatient setting. Identification of cognitive impairment may have important treatment implications in the future.
IMPACT OF THE USE OF RENEWABLE MATERIALS ON THE ECO-EFFICIENCY OF MANUFACTURING PROCESSES

The use of renewable materials has attracted interest from a wide range of manufacturing industries looking to reduce their environmental and carbon footprints. As such, the development and use of bio-polymers has been largely driven by their perceived environmental benefits over conventional polymers. However, often these environmental claims, when challenged, are lacking in substance. One reason for this is the lack of quality data for all lifecycle stages. This applies to the manufacturing stages of packaging, otherwise known as ‘packaging conversion’, where for certain product/production types, a reduction in energy consumption of 25-30% from lower processing temperatures can be offset by an increase in pressure, cycle times and reject rates. The ambiguity of the overall environmental benefit achieved during this stage of the lifecycle, when this is the main driver for their use, highlights the need for a clearer understanding of impact such materials have on the manufacturing processes.
Assessment Assessment of benthic and coral community structure in an inshore reef in Balok, Pahang, Malaysia

Abstract. Hanapiah MFM, Saad S, Ahmad Z, Yusof MH, Khodzori MFA. Assessment of benthic and coral community structure in an inshore reef in Balok, Pahang, Malaysia. Biodiversitas 20: 872-877. Inshore water reef closer to human activities are often neglected since they are not protected by marine park management. Little is known on how this reef responses to challenging environment in terms of abundance, diversity and benthic community composition. This study provides a quantitative assessment on the benthic community composition at 5 reef sites in Balok, Pahang, Malaysia and observed the ecological adaptation in the reef community towards turbid water environment. Balok reef has 39% overall coral cover with very low macroalgae abundance (4%). A total of 28 coral genera from 12 families have been recorded with Porites the most dominant genus in the coral assemblages. Most reef sites in Balok are categorized under conservation class 1 (CC1), which indicated that resilient and survival of the reef rely on stress-tolerators taxa. The data presented here showed that Balok reef is highly resilient towards constant exposure to high sedimentation and wave action.
RTL Test Pattern Generation for High Quality Loosely Deterministic BIST

High quality Built-In Self Test (BIST) needs to efficiently tackle the coverage of random-pattern-resistant (r.p.r) defects. Several techniques have been proposed to cover r.p.r faults at logic level, namely, weighted pseudo-random and mixed-mode. In mixed-mode test pattern generation (TPG) techniques, deterministic tests are added to pseudo-random vectors to detect r.p.r faults. Recently, a RTL mixed-mode TPG technique has been proposed to cover r.p.r defects, the mask-based BIST technique. The purpose of this paper is to present mask-based BIST TPG improvements, namely in two areas: RTL estimation of the test length to be used for each mask, in order to reach high Defects Coverage (DC), and the identification of an optimum mask for each set of nested RTL conditions. Results are used to predict the number of customized vectors for each mask of one ITC'99 benchmark module.
NON MOTOR SYMPTOMS IN PATIENTS WITH PARKINSON’S DISEASE – AN OBSERVATIONAL STUDY

Background: In multiple studies around the globe, non-motor symptoms (NMS) have been identified as a source of immense disability in patients with Parkinson’s disease (PD). The study was aimed to assess the Non Motor Symptoms in patients with Parkinson’s Disease and its impact on Quality of Life. Materials and Methods: Observational, Cross sectional Study, single center, one point in time evaluation study in a tertiary care hospital in Parkinson’s Disease patients fulfilling the criteria of the UK-PDSBB. Epidemiology, medication, and the incidence of non-motor symptoms will be statistically analysed. Result: In total, 40 PD subjects, including 24 males (60%) and 16 females (40%). The mean age of all patients was 55.93 ± 9.29, mean UPDRS III score was 36.08 15.20, Mean duration of PD symptoms was 4.6 ± 2.84 years. 12 (30%) patients were in stage 1, 6 (15%) were in stage 1.5, 2(5%) were in stage 2, 3 (8%) were in stage 2.5, 12 (30%) were in stage 3 and 5(13%) were in stage 4. The mean total NMSS score was 20.55 (SD: 17.285). Higher scores were noted in the Miscellaneous sleep/fatigue, mood/cognition, gastrointestinal and urinary domains. There was a significant correlation between NMSS and disease duration and H&Y staging, PDQ-39 SI, disease duration, PDQ-39 SI. The PDQ-39 SI score strongly correlated with that of NMSS. When evaluating the correlation between PDQ-39 SI and the different NMSS dimensions, significant correlations from low to high were observed in urinary, gastrointestinal, miscellaneous, mood and sleep/fatigue. A stepwise multiple linear regression analysis with Hr-QoL as the dependent variable was used to evaluate the independent effects of the potential factors including age, disease duration, H&Y staging, NMSS, and UPDRS III on Hr-QoL. Conclusion: The whole model explained 75% of the variance of the PDQ-39. NMSS explained more of the variability in Hr-QoL than UPDRS III (57.8% vs. 17.2% respectively). NMS has strong, independent association with lower Hr-QoL, as evaluated by the PDQ-39.
Does Innovation Converge

This brief examines convergence of innovation using patent data from two - digit manufacturing industries in 32 countries between 1976 and 2006. Patenting rates converge over time (patenting growth is faster when patents are initially lower), including within countries (across industries) and within industries (across countries). The quality (citations and citations per patent) and efficiency (patents per worker) of innovation also exhibit convergence. Convergence is widespread across all countries and industries, and in all time periods. Patent convergence is stronger where financial development, international financial integration, and institutional quality are higher, and under the policies supportive of financial liberalization. These factors contribute to both within country and within industry convergence. The results highlight the importance of financial and institutional environment for patenting, and ultimately for productivity and economic growth
UAE: Universal Anatomical Embedding on Multi-modality Medical Images

Identifying specific anatomical structures (\textit{e.g.}, lesions or landmarks) in medical images plays a fundamental role in medical image analysis. Exemplar-based landmark detection methods are receiving increasing attention since they can detect arbitrary anatomical points in inference while do not need landmark annotations in training. They use self-supervised learning to acquire a discriminative embedding for each voxel within the image. These approaches can identify corresponding landmarks through nearest neighbor matching and has demonstrated promising results across various tasks. However, current methods still face challenges in: (1) differentiating voxels with similar appearance but different semantic meanings (\textit{e.g.}, two adjacent structures without clear borders); (2) matching voxels with similar semantics but markedly different appearance (\textit{e.g.}, the same vessel before and after contrast injection); and (3) cross-modality matching (\textit{e.g.}, CT-MRI landmark-based registration). To overcome these challenges, we propose universal anatomical embedding (UAE), which is a unified framework designed to learn appearance, semantic, and cross-modality anatomical embeddings. Specifically, UAE incorporates three key innovations: (1) semantic embedding learning with prototypical contrastive loss; (2) a fixed-point-based matching strategy; and (3) an iterative approach for cross-modality embedding learning. We thoroughly evaluated UAE across intra- and inter-modality tasks, including one-shot landmark detection, lesion tracking on longitudinal CT scans, and CT-MRI affine/rigid registration with varying field of view. Our results suggest that UAE outperforms state-of-the-art methods, offering a robust and versatile approach for landmark based medical image analysis tasks. Code and trained models are available at: \href{https://shorturl.at/bgsB3}
A review of geospatial-based urban growth models and modelling initiatives

Abstract The way cities are evolving and expanding has been a challenging issue and several urban growth models were proposed. Most of these models have common objectives but they differ in terms of implementation or theoretical assumptions. Urban growth modelling projects that simulate urban spatial change, across the globe, have been reviewed in this study with a view to providing reference for future urban growth modelling development. The mathematical concepts of the modelling techniques were critically examined for holistic understanding of their key components and also to provide a broad description of the existing urban growth modelling works, which has always been ignored in the literature.
Numerical evaluation of flow regime changes induced by the Three Gorges Dam in the Middle Yangtze

The full operation of the Three Gorges Dam (TGD) has altered downstream natural flow regime. Flow regime changes have resulted in profound influences on the utility of water resources and hence a large area with a riparian ecosystem including China's two largest freshwater lakes in the Middle Yangtze. Because of complicated flow regimes in this large-scale river–lake system, the TGD's impacts on flow regimes are highly heterogeneous and require to be carefully addressed. To better understand them, we estimated water level and discharge changes solely induced by the TGD from 2006 to 2011 using a hydrodynamic model that facilitates the separation of the TGD's contribution to flow regimes. Results indicated that water regulation of the TGD caused profound impacts on the flow regimes of the Middle Yangtze. In the impoundment period from mid-September to October, rapid and significant decline of the water discharge downstream the TGD produced a prolonged dry season that occurred around 10 days earlier than before. Our analysis elucidated a pattern of recent changes in the flow regimes caused by the TGD. The findings are useful for addressing the TGD-induced environmental issues, optimizing the TGD's operation, and generating adaptive management strategy for the complex river–lake ecosystem.