Miracle-dz/UniFAR-FTUs huggingface dataset:
Arterial stiffness precedes hypertension and metabolic risks in youth: a review

Arterial stiffness is a strong predictor of cardiovascular events and all-cause mortality in middle-aged and old adults. Arterial stiffness has been limited to being an intermediate marker of atherosclerotic cardiovascular events in adolescents and young adult studies. The paucity of normative longitudinal data and repeated gold-standard assessments of arterial stiffness among the young population has occasioned a huge knowledge gap in its clinical utility. This review summarizes recent longitudinal evidence in a large adolescent population, supporting the value of arterial stiffness as a novel risk factor for hypertension, overweight/obesity and insulin resistance. Preventing or decreasing arterial stiffness during adolescence may confer cardiometabolic health benefits in later life, but further pathological and mechanistic research is needed. The review also offers suggestions for incorporating arterial stiffness measures into routine paediatric and young adult clinical practice.
Differentially Private Linear Bandits with Partial Distributed Feedback

In this paper, we study the problem of global reward maximization with only partial distributed feedback. This problem is motivated by several real-world applications (e.g., cellular network configuration, dynamic pricing, and policy selection) where an action taken by a central entity influences a large population that contributes to the global reward. However, collecting such reward feedback from the entire population not only incurs a prohibitively high cost, but often leads to privacy concerns. To tackle this problem, we consider differentially private distributed linear bandits, where only a subset of users from the population are selected (called clients) to participate in the learning process and the central server learns the global model from such partial feedback by iteratively aggregating these clients’ local feedback in a differentially private fashion. We then propose a unified algorithmic learning framework, called differentially private distributed phased elimination (DP-DPE), which can be naturally integrated with popular differential privacy (DP) models (including central DP, local DP, and shuffle DP). Furthermore, we prove that DP-DPE achieves both sublinear regret and sublinear communication cost. Interestingly, DP-DPE also achieves privacy protection “for free” in the sense that the additional cost due to privacy guarantees is a lower-order additive term. Finally, we conduct simulations to corroborate our theoretical results and demonstrate the effectiveness of DP-DPE.
Strong Spin-Orbit Torque Induced by the Intrinsic Spin Hall Effect in Cr1-xPtx

We report on a spin-orbit torque study of the spin current generation in Cr 1− x Pt x alloy, using the light 3 d ferromagnetic Co as the spin current detector. We find that the dampinglike spin-orbit torque of Cr 1− x Pt x /Co bilayers can be enhanced by tuning the Cr concentration in the Cr 1− x Pt x layer, with a maximal value of 0.31 at the optimal composition of Cr 0.2 Pt 0.8 . The mechanism and the efficiency of spin current generation in the Cr 1− x Pt x alloy can be fully understood by the characteristic trade-off between the intrinsic spin Hall conductivity of Pt and the spin carrier lifetime in the dirty limit. This suggests that Cr is simply as effective as other metals, oxides, and nitrides (e.g. Hf, Au, Pd, Cu, Ti, MgO, and Si 3 N 4 ) in enhancing the dampinglike spin-orbit torque generated by the spin Hall effect of a Pt host via strengthening the spin carrier scattering and that alloying Pt with Cr does not employ observable spin current generation via additional spin Hall effect, orbital Hall effect, or interfacial spin-orbit coupling effects. This work also establishes the low-resistivity Cr 0.2 Pt 0.8 as an energy-efficient spin-orbit torque provider for magnetic memory and computing technologies.
Metabolic dysfunction‐associated fatty liver disease (MAFLD) and advanced liver fibrosis among hemodialysis patients in a multiethnic urban population in Malaysia

Metabolic‐dysfunction‐associated fatty liver disease (MAFLD) and end stage kidney disease (ESKD) are complications of the metabolic syndrome. Our aim is to study the prevalence of MAFLD and advanced liver fibrosis and the associated factors among hemodialysis patients in a multiracial urban population in Malaysia.
Message testing in India for COVID-19 vaccine uptake: What appeal and what messenger are most persuasive?

ABSTRACT Few studies have examined the relationships between the different aspects of vaccination communication and vaccine attitudes. We aimed to evaluate the influence of three unique messaging appeal framings of vaccination from two types of messengers on COVID-19 vaccine acceptance in India. We surveyed 534 online participants in India using Amazon Mechanical Turk (MTurk) from December 2021 through January 2022. We assessed participants’ perception of three messaging appeals of vaccination – COVID-19 disease health outcomes, social norms related to vaccination, and economic impact of COVID-19 - from two messengers, healthcare providers (HCP) and peers. Using a multivariable multinomial logistic regression, we examined participants’ ad preference and vaccine hesitancy. Participants expressed a high level of approval for all of the ads, with >80% positive responses for all questions across ads. Overall ads delivered by health care workers were preferred by a majority of participants in our study (n = 381, 71.4%). Ad preference ranged from 3.6% (n = 19) social norm/peer ad to 32.4% (n = 173) health outcome/HCP ad and half of participants preferred the health outcome ad (n = 279, 52.3%). Additionally, vaccine hesitancy was not related to preference (p = .513): HCP vs. peer ads (p = .522); message type (p = .284). The results suggest that all three appeals tested were generally acceptable, as well as the two messenger types, although preference was for the health care provider messenger and health outcome appeal. Individuals are motivated and influenced by a multitude of factors, requiring vaccine messaging that is persuasive, salient, and induces contextually relevant action.
The association of potentially inappropriate medications, polypharmacy and anticholinergic burden with readmission and emergency room revisit after discharge: A hospital‐based retrospective cohort study

While certain drug‐use indicators are known to be associated with clinical outcomes, the relationship is unclear for some highly prevalent conditions in in patients aged ≥65 years. We examine correlations between 3 drug‐use indicators and postdischarge healthcare services use by older patients according to the presence of dementia, advanced age and frailty.
Use of Herbal Medicine Is Associated With Late-Stage Presentation in Tanzanian Patients With Cancer: A Survey to Assess the Utilization of and Reasons for the Use of Herbal Medicine

PURPOSE In Tanzania, patients with cancer present late with advanced stages. Among other reasons, the use of herbal medicine (HM) might contribute to delay in diagnosis and treatment. This study aims to understand the utilization of HM and the reasons for its' use. METHODS A semistructured 30-item questionnaire with closed- and open-ended questions was applied to a cohort of patients with cancer at Kilimanjaro Christian Medical Centre. Content analysis was performed for answers to open-ended questions. RESULTS Three hundred two patients were interviewed, 50.9% males and 49.1% females. The mean age was 64 years. 71.6% were in stages III/IV. 70.5% patients used HM and 67.1% started conventional medicine after stopping HM, 7.5% started HM after conventional medicine, and 24.9% combined both. Stage IV patients used HM as first line significantly more often in comparison: stage I 55.6%; II 58.1%; III 57.2%; and IV 70.6%. 77.5% did not tell their doctors about the use of HM. Commonest reasons to use HM were belief in cure, hope of symptom relief, lack of cancer services, and tradition. CONCLUSION The majority of patients used HM before consulting a doctor, which contributes to stage IV presentation. Use of HM alongside with chemotherapy is mostly not known to the treating physician and may lead to interactions. These findings must alert health care workers and health policymakers to further foster health education about cancer and its treatment. Further research is needed to understand the wide use of HM among Tanzanian patients with cancer and the role that traditional and spiritual healers play in the field of cancer care.
An integrated practical placement programme for students with disability: A pilot study

Supported work internship programmes for young adults with disability are an evidence-based model, leading to greater employment outcomes. This mixed methods pilot study evaluated the experiences of students, supervisors and a project coordinator, who participated in an Integrated Practical Placement (IPP) programme for students with disability in Australia. Intervention students (n = 10) completed 3, 9-week rotations, and accessed personal placement support and employment coaches. Comparison students (n = 38) completed 3, 2-week placements without additional support. Intervention students perceived significantly greater initial changes in work skills (p < 0.01) and work readiness (p < 0.05). Intervention students reported development of communication and self-organisational skills and stressed the value of staff support. Post programme 70% of intervention students gained employment, compared with 15.4% of comparison students. The findings suggest an evidence-based supported employment programme emphasising personalised assessment and training, could provide individuals with disability the required skills to enable successful employment.
One-dimensional symmetric phases protected by frieze symmetries

We make a systematic study of symmetry-protected topological gapped phases of quantum spin chains in the presence of the frieze space groups in one dimension using matrix product states. Here, the spatial symmetries of the one-dimensional lattice are considered together with an additional 'vertical reflection', which we take to be an on-site $\mathbb{Z}_2$ symmetry. We identify seventeen distinct non-trivial phases, define canonical forms, and compare the topological indices obtained from the MPS analysis with the group cohomological predictions. We furthermore construct explicit renormalization group fixed-point wave functions for symmetry-protected topological phases with global on-site symmetries, possibly combined with time-reversal and parity symmetry. En route, we demonstrate how group cohomology can be computed using the Smith normal form.
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels

State-of-the-art federated learning methods can perform far worse than their centralized counterparts when clients have dissimilar data distributions. For neural networks, even when centralized SGD easily finds a solution that is simultaneously performant for all clients, current federated optimization methods fail to converge to a comparable solution. We show that this performance disparity can largely be attributed to optimization challenges presented by nonconvexity. Specifically, we find that the early layers of the network do learn useful features, but the final layers fail to make use of them. That is, federated optimization applied to this non-convex problem distorts the learning of the final layers. Leveraging this observation, we propose a Train-Convexify-Train (TCT) procedure to sidestep this issue: first, learn features using off-the-shelf methods (e.g., FedAvg); then, optimize a convexified problem obtained from the network's empirical neural tangent kernel approximation. Our technique yields accuracy improvements of up to +36% on FMNIST and +37% on CIFAR10 when clients have dissimilar data.
Snowmass White Paper: Belle II physics reach and plans for the next decade and beyond

Belle II is an experiment operating at the intensity frontier. Over the next decades, it will record the decay of billions of bottom mesons, charm hadrons, and tau leptons produced in 10 GeV electron-positron collisions at the SuperKEKB high-luminosity collider at KEK. These data, collected in low-background and kinematically known conditions, will allow us to measure hundreds of parameters that test the standard model (SM) and probe for the existence of new particles, at mass scales orders of magnitudes higher than those studied at the energy frontier. We project our sensitivities for measurements that are of primary relevance and where Belle II will be unique or world leading for data corresponding to 1 to 50 ab-1. Belle II will uniquely probe non-SM contributions in sensitive b ->qqbar s decays and charmless b ->qqbar d(u) decays, semileptonic b ->s nu nubar and s tau+ \tau- decays, fully leptonic b ->ell nu decays, and select c ->u processes. Belle II will lead exploration of non-SM physics in b ->c tau nu and b ->s gamma decays and will most precisely determine the quark-mixing parameters |Vub| and |Vcb|. Belle II will measure many parameters in tau physics to precisions that will be world leading for the foreseeable future, including the electric and magnetic dipole moments, branching fractions for charged-lepton-flavor-violating decays, and quantities that test lepton-flavor universality. Belle II will perform unique searches for dark-sector particles with masses in the MeV--GeV range. We will also pursue a broad spectroscopy program for conventional and multiquark c cbar and b bbar states and provide essential inputs to sharpen the interpretation of muon magnetic-anomaly results. Our exploration of uncharted regions of non-SM parameter space with high precision will reveal non-SM particles or set stringent constraints on their existence, guiding future endeavors.
Hybrid Projective Synchronization via PI Controller Based on DNA Strand Displacement

Classical three-variable chaotic system coupling synchronization has been implemented in previous work based on DNA strand displacement (DSD). Herein, by using DSD reactions as the foundation, a proportional integral (PI) controller for chaotic system is introduced to realize the hybrid projective synchronization for different four-variable chaotic systems. DSD-based chaotic systems are composed of catalysis modules, annihilation modules and degradation modules for realizing the construction of chaotic attractors. PI controllers are consist of catalysis, annihilation and adjust DSD modules that are easy to modify and can be added to chaotic system for achieving hybrid projective synchronization. Our work can be acted as the reference for the investigation of chaos synchronization.
A new hope for network model generalization

Generalizing machine learning (ML) models for network traffic dynamics tends to be considered a lost cause. Hence for every new task, we design new models and train them on model-specific datasets closely mimicking the deployment environments. Yet, an ML architecture called Transformer has enabled previously unimaginable generalization in other domains. Nowadays, one can download a model pre-trained on massive datasets and only fine-tune it for a specific task and context with comparatively little time and data. These fine-tuned models are now state-of-the-art for many benchmarks. We believe this progress could translate to networking and propose a Network Traffic Transformer (NTT), a transformer adapted to learn network dynamics from packet traces. Our initial results are promising: NTT seems able to generalize to new prediction tasks and environments. This study suggests there is still hope for generalization through future research.
Monotonicity in Undirected Networks

Is it always beneficial to create a new relationship (have a new follower/friend) in a social network? This question can be formally stated as a property of the centrality measure that defines the importance of the actors of the network. Score monotonicity means that adding an arc increases the centrality score of the target of the arc; rank monotonicity means that adding an arc improves the importance of the target of the arc relatively to the remaining nodes. It is known that most centralities are both score and rank monotone on directed, strongly connected graphs. In this paper, we study the problem of score and rank monotonicity for classical centrality measures in the case of undirected networks: in this case, we require that score, or relative importance, improves at both endpoints of the new edge. We show that, surprisingly, the situation in the undirected case is very different, and in particular that closeness, harmonic centrality, betweenness, eigenvector centrality, Seeley’s index, Katz’s index, and PageRank are not rank monotone; betweenness and PageRank are not even score monotone. In other words, while it is always a good thing to get a new follower, it is not always beneficial to get a new friend.
Brick Tic-Tac-Toe: Exploring the Generalizability of AlphaZero to Novel Test Environments

Traditional reinforcement learning (RL) environments typically are the same for both the training and testing phases. Hence, current RL methods are largely not generalizable to a test environment which is conceptually similar but different from what the method has been trained on, which we term the novel test environment. As an effort to push RL research towards algorithms which can generalize to novel test environments, we introduce the Brick Tic-Tac-Toe (BTTT) test bed, where the brick position in the test environment is different from that in the training environment. Using a round-robin tournament on the BTTT environment, we show that traditional RL state-search approaches such as Monte Carlo Tree Search (MCTS) and Minimax are more generalizable to novel test environments than AlphaZero is. This is surprising because AlphaZero has been shown to achieve superhuman performance in environments such as Go, Chess and Shogi, which may lead one to think that it performs well in novel test environments. Our results show that BTTT, though simple, is rich enough to explore the generalizability of AlphaZero. We find that merely increasing MCTS lookahead iterations was insufficient for AlphaZero to generalize to some novel test environments. Rather, increasing the variety of training environments helps to progressively improve generalizability across all possible starting brick configurations.
Sequential Recommendation Model for Next Purchase Prediction

Timeliness and contextual accuracy of recommendations are increasingly important when delivering contemporary digital marketing experiences. Conventional recommender systems (RS) suggest relevant but time-invariant items to users by accounting for their past purchases. These recommendations only map to customers’ general preferences rather than a customer’s specific needs immediately preceding a purchase. In contrast, RSs that consider the order of transactions, purchases, or experiences to measure evolving preferences can offer more salient and effective recommendations to customers: Sequential RSs not only benefit from a better behavioral understanding of a user’s current needs but also provide better predictive power. In this paper, we demonstrate and rank the effectiveness of a sequential recommendation system by utilizing a production dataset of over 2.7 million credit card transactions for 46K cardholders. The methodfirst employs an autoencoder on raw transaction data and submits observed transaction encodings to a GRU-based sequential model. The sequential model produces a MAP@1 metric of 47% on the out-of-sample test set, in line with existing research. We also discuss implications for embedding real-timepredictions using the sequential RS into Nexus, a scalable, lowlatency, event-based digital experience architecture.
Interpretable Fusion Analytics Framework for fMRI Connectivity: Self-Attention Mechanism and Latent Space Item-Response Model

There have been several attempts to use deep learning based on brain fMRI signals to classify cognitive impairment diseases. However, deep learning is a hidden black box model that makes it difficult to interpret the process of classification. To address this issue, we propose a novel analytical framework that interprets the classification result from deep learning processes. We first derive the region of interest (ROI) functional connectivity network (FCN) by embedding functions based on their similar signal patterns. Then, using the self-attention equipped deep learning model, we classify diseases based on their FCN. Finally, in order to interpret the classification results, we employ a latent space item-response interaction network model to identify the significant functions that exhibit distinct connectivity patterns when compared to other diseases. The application of this proposed framework to the four types of cognitive impairment shows that our approach is valid for determining the significant ROI functions.
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting

Conventionally, Earth system (e.g., weather and climate) forecasting relies on numerical simulation with complex physical models and are hence both expensive in computation and demanding on domain expertise. With the explosive growth of the spatiotemporal Earth observation data in the past decade, data-driven models that apply Deep Learning (DL) are demonstrating impressive potential for various Earth system forecasting tasks. The Transformer as an emerging DL architecture, despite its broad success in other domains, has limited adoption in this area. In this paper, we propose Earthformer, a space-time Transformer for Earth system forecasting. Earthformer is based on a generic, flexible and efficient space-time attention block, named Cuboid Attention. The idea is to decompose the data into cuboids and apply cuboid-level self-attention in parallel. These cuboids are further connected with a collection of global vectors. We conduct experiments on the MovingMNIST dataset and a newly proposed chaotic N-body MNIST dataset to verify the effectiveness of cuboid attention and figure out the best design of Earthformer. Experiments on two real-world benchmarks about precipitation nowcasting and El Nino/Southern Oscillation (ENSO) forecasting show Earthformer achieves state-of-the-art performance. Code is available: https://github.com/amazon-science/earth-forecasting-transformer .
Cogenesis of Baryon asymmetry and gravitational dark matter from primordial black holes

We propose a scenario where dark matter (DM) with a wide mass range from a few keV to PeV can be produced solely from evaporating primordial black holes (PBH), while being consistent with the required free streaming length for structure formation. If DM does not have any other interactions apart from gravity and the universe has a PBH dominated phase at early epoch, then PBH evaporation typically leads to overproduction of DM in this mass range. By incorporating this gravitational DM within a Type-I seesaw scenario with three right handed neutrinos (RHN), we bring the abundance of PBH generated DM within observed limits by late entropy injection due to decay of one of the RHNs, acting as the diluter. The diluter, due to its feeble coupling with the bath particles, gets produced primarily from the PBH evaporation thereby leading to the second stage of early matter domination after the end of PBH dominated era. The other two RHNs contribute to the origin of light neutrino mass and also lead to the observed baryon asymmetry via leptogenesis with contributions from both thermally and PBH generated RHNs. The criteria of DM relic and baryon asymmetry can be satisfied simultaneously if DM mass gets restricted to a ballpark in the MeV-GeV regime with the requirement of resonant leptogenesis for heavier DM mass in order to survive the large entropy dilution at late epochs.