Miracle-dz/UniFAR-FTUs huggingface dataset:
Blood pressure control measures and cardiovascular outcomes: a prospective hypertensive cohort

Abstract Purpose We investigated whether blood pressure (BP) control measures, visit-to-visit BP variability, and time in therapeutic range (TTR) are associated with future cardiovascular outcomes in hypertensive patients. Materials and Methods Among 1,408 hypertensive patients without cardiovascular disease, we prospectively evaluated the incident major cardiovascular events over 6 years. In newly diagnosed patients, antihypertensive drug treatment was initiated. We estimated two markers of on-treatment BP control, (1) visit-to-visit BPV as the coefficient of variation of office systolic BP (BP-CV), and (2) TTR calculated as the percentage of office systolic BP measurements within 120–140mmHg across visits. Results The hypertensive cohort (672 males, mean age 60 years, 31% newly diagnosed) had a mean systolic/diastolic BP of 142/87 mmHg. The mean number of visits was 4.9 ± 2.6, while the mean attained systolic/diastolic BP during follow-up was 137/79 mmHg using 2.7 ± 1.1 antihypertensive drugs. The BP-CV and TTR were 9.1 ± 4.1% and 45 ± 29%, respectively, and the incidence of the composite outcome was 8.3% (n = 117). After adjustment for relevant confounders and standardization to z-scores, BP-CV and TTR were associated with a 43% (95% CI, 27–62%) increase and a 33% (95% CI, 15–47%) reduction in the outcome. However, the joint evaluation of TTR and BP-CV in a common multivariable model indicated that a standardized change of TTR was associated with the outcome to a greater extent than BP-CV (mean hazard ratios of 30% vs. 24%, respectively). When combined with the higher BP standardized-CV quartile, the lower TTR quartile predicted the outcome by 2.3 times (95% CI, 1.1–5.4) compared to the inverse TTR and BP-CV quartile pattern. Conclusion High BP-CV or low TTR was associated with future cardiovascular events in a cohort of treated hypertensive patients. As a determinant, the extent of TTR value appears greater than BP-CV when these measures are considered in the same multivariable model.
Physiological responses and adaptations to exercise training in people with or without chronic obstructive pulmonary disease: protocol for a systematic review and meta-analysis

Introduction Exercise training is a cornerstone in managing chronic obstructive pulmonary disease (COPD), leading to several physiological adaptations including, but not limited to, structural and muscular alterations, increased exercise capacity and improved cognitive function. Still, it is not uncommon that the acute physiological response to an exercise session and the extent of chronic adaptations to exercise training are altered compared with people without COPD. To date, potential differences in acute physiological responses and chronic adaptations in people with or without COPD are not fully understood, and results from individual studies are contradictory. Therefore, the current study aims to synthesise and compare the acute physiological responses and chronic adaptations to exercise training in people with COPD compared with people without COPD. Methods and analyses A systematic review of randomised controlled trials (RCTs), non-randomised studies of interventions (NRSIs) and cross-sectional studies (CSSs) will be conducted. A comprehensive search strategy will identify relevant studies from MEDLINE, Scopus, CINAHL, SPORTDiscus, CENTRAL and Cochrane Airways Trials Register databases. Studies including adults with and without COPD will be considered. Outcomes will include cardiorespiratory, muscular and cognitive function, intramuscular adaptations, lung volumes and cardiometabolic responses. The protocol is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols and the Cochrane Methodological Expectations of Cochrane Intervention Reviews. Risk of bias assessment will be conducted using Cochrane Risk-of-Bias 2 Tool (for RCTs), Risk-of-Bias in Non-Randomised Studies Tool (for NRSIs) and Downs and Black checklist (for CSS). Meta-analyses will be conducted when appropriate, supplemented with a systematic synthesis without meta-analysis. Ethics and dissemination As this study is a systematic review, ethical approval is not required. The final review results will be submitted for publication in a peer-reviewed journal and presented at international conferences. PROSPERO registration number CRD42022307577
Coupling of microtubule bundles isolates them from local disruptions to set the structural stability of the anaphase spindle

Significance Many cytoskeletal structures maintain stability as they respond to physiologic forces that drive processes ranging from cell division to migration. For instance, the spindle midzone, which is comprised of microtubule bundles between segregating chromosomes, remains mechanically stable through anaphase. The midzone is also compliant enough to accommodate disruptions, such as those resulting from lagging chromosomes. It is unclear how this mechanical behavior is achieved since this time-evolving structure has been difficult to probe. To address this, we combine quantitative microscopy with laser microsurgery of midzone bundles. Our data suggest a model wherein multiple connections mechanically isolate microtubule bundles from local perturbations and stabilize the midzone. We expect this design principle sets the integrity of other essential cytoskeletal scaffolds.
Restoration of Manifold-Valued Images by Half-Quadratic Minimization

The paper addresses the generalization of the half-quadratic minimization method for the restoration of images having values in a complete Riemannian manifold. We recall the half-quadratic minimization method using the notation of the c-transform and adapt the algorithm to our special variational setting. We prove the convergence of the method for Hadamard spaces. Extensive numerical examples for images with values on spheres, in the rotation group SO(3) and in the manifold of positive definite matrices demonstrate the excellent performance of the algorithm. In particular, the method with SO(3)-valued data shows promising results for the restoration of images obtained from Electron Backscattered Diffraction which are of interest in material science.
Sparse Vicious Attacks on Graph Neural Networks

Graph Neural Networks (GNNs) have proven to be successful in several predictive modeling tasks for graph-structured data. Amongst those tasks, link prediction is one of the fundamental problems for many real-world applications, such as recommender systems. However, GNNs are not immune to adversarial attacks, i.e., carefully crafted malicious examples that are designed to fool the predictive model. In this work, we focus on a specific, white-box attack to GNN-based link prediction models, where a malicious node aims to appear in the list of recommended nodes for a given target victim. To achieve this goal, the attacker node may also count on the cooperation of other existing peers that it directly controls, namely on the ability to inject a number of ``vicious'' nodes in the network. Specifically, all these malicious nodes can add new edges or remove existing ones, thereby perturbing the original graph. Thus, we propose SAVAGE, a novel framework and a method to mount this type of link prediction attacks. SAVAGE formulates the adversary's goal as an optimization task, striking the balance between the effectiveness of the attack and the sparsity of malicious resources required. Extensive experiments conducted on real-world and synthetic datasets demonstrate that adversarial attacks implemented through SAVAGE indeed achieve high attack success rate yet using a small amount of vicious nodes. Finally, despite those attacks require full knowledge of the target model, we show that they are successfully transferable to other black-box methods for link prediction.
A Spiking Neural Network Learning Markov Chain

In this paper, the question how spiking neural network (SNN) learns and fixes in its internal structures a model of external world dynamics is explored. This question is important for SNN implementation of the model-based reinforcement learning (RL), finding anomalies in time series and other problems. In the present work, we formalize world dynamics as a Markov chain with a priori unknown state transition probabilities, which should be learnt by the network. To make this problem formulation more realistic, we solve it in the continuous time, so that the duration of every state in the Markov chain may be different and is unknown. It is demonstrated how this task can be accomplished by an SNN with a specially designed structure and local synaptic plasticity rules. As an example, we show how this network motif works in the simple but non-trivial world where a ball moves inside a square box and bounces from its walls with a random new direction and velocity.
Detection of Malicious Websites Using Machine Learning Techniques

In detecting malicious websites, a common approach is the use of blacklists which are not exhaustive in themselves and are unable to generalize to new malicious sites. Detecting newly encountered malicious websites automatically will help reduce the vulnerability to this form of attack. In this study, we explored the use of ten machine learning models to classify malicious websites based on lexical features and understand how they generalize across datasets. Specifically, we trained, validated, and tested these models on different sets of datasets and then carried out a cross-datasets analysis. From our analysis, we found that K-Nearest Neighbor is the only model that performs consistently high across datasets. Other models such as Random Forest, Decision Trees, Logistic Regression, and Support Vector Machines also consistently outperform a baseline model of predicting every link as malicious across all metrics and datasets. Also, we found no evidence that any subset of lexical features generalizes across models or datasets. This research should be relevant to cybersecurity professionals and academic researchers as it could form the basis for real-life detection systems or further research work.
Regularisation by turbulence for transport equations in Hölder space

We consider a transport equation whose the coefficient b can be in a negative Besov space, we provide controls in γ-Hölder spaces, γ ∈ (0, 1), of some kind of vanishing viscosity solutions based on a parabolic approximation. The regularity in γ-Hölder space of the solution exactly matches the one of the source functions, but the regularisation occurs on the coefficients irregularity. Indeed, we define some kind of solutions of the transport equation which do not require b to be Lipschitz continuous.
Activity report analysis with automatic single or multispan answer extraction

In the era of loT (Internet of Things) we are surrounded by a plethora of Al enabled devices that can transcribe images, video, audio, and sensors signals into text descriptions. When such transcriptions are captured in activity reports for monitoring, life logging and anomaly detection applications, a user would typically request a summary or ask targeted questions about certain sections of the report they are interested in. Depending on the context and the type of question asked, a question answering (QA) system would need to automatically determine whether the answer covers single-span or multi-span text components. Currently available QA datasets primarily focus on single span responses only (such as SQuAD[4]) or contain a low proportion of examples with multiple span answers (such as DROP[3]). To investigate automatic selection of single/multi-span answers in the use case described, we created a new smart home environment dataset comprised of questions paired with single-span or multi-span answers depending on the question and context queried. In addition, we propose a RoBERTa[6]-based multiple span extraction question answering (MSEQA) model returning the appropriate answer span for a given question. Our experiments show that the proposed model outperforms state-of-the-art QA models on our dataset while providing compara-ble performance on published individual single/multi-span task datasets.
Robust Beamforming and Rate-Splitting Design for Next Generation Ultra-Reliable and Low-Latency Communications

—The next generation ultra-reliable and low-latency communications (xURLLC) need novel design to provide sat- isfactory services to the emerging mission-critical applications. To improve the spectrum efﬁciency and enhance the robustness of xURLLC, this paper proposes a robust beamforming and rate-splitting design in the ﬁnite blocklength (FBL) regime for downlink multi-user multi-antenna xURLLC systems. In the design, adaptive rate-splitting is introduced to ﬂexibly handle the complex inter-user interference and thus improve the spectrum efﬁciency. Taking the imperfection of the channel state infor- mation at the transmitter (CSIT) into consideration, a max-min user rate problem is formulated to optimize the common and private beamforming vectors and the rate-splitting vector under the premise of ensuring the requirements of transmission latency and reliability of all the users. The optimization problem is intractable due to the non-convexity of the constraint set and the inﬁnite constraints caused by CSIT uncertainties. To solve it, we convert the inﬁnite constraints into ﬁnite ones by the S-Procedure method and transform the original problem into a difference of convex (DC) programming. A constrained concave convex pro- cedure (CCCP) and the Gaussian randomization based iterative algorithm is proposed to obtain a local minimum. Simulation results conﬁrm the convergence, robustness and effectiveness of the proposed robust beamforming and rate-splitting design in the FBL regime. It is also shown that the proposed robust design achieves considerable performance gain in the worst user rate compared with existing transmission schemes under various blocklength and block error rate requirements.
Real-Time Unified Trajectory Planning and Optimal Control for Urban Autonomous Driving Under Static and Dynamic Obstacle Constraints

Trajectory planning and control have historically been separated into two modules in automated driving stacks. Trajectory planning focuses on higher-level tasks like avoiding obstacles and staying on the road surface, whereas the controller tries its best to follow an ever changing reference trajectory. We argue that this separation is (1) flawed due to the mismatch between planned trajectories and what the controller can feasibly execute, and (2) unnecessary due to the flexibility of the model predictive control (MPC) paradigm. Instead, in this paper, we present a unified MPC-based trajectory planning and control scheme that guarantees feasibility with respect to road boundaries, the static and dynamic environment, and enforces passenger comfort constraints. The scheme is evaluated rigorously in a variety of scenarios focused on proving the effectiveness of the optimal control problem (OCP) design and real-time solution methods. The prototype code will be released at github.com/WATonomous/control.
SparCL: Sparse Continual Learning on the Edge

Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, i.e., model performance deterioration on past tasks when learning a new task. However, the training efficiency of a CL system is under-investigated, which limits the real-world application of CL systems under resource-limited scenarios. In this work, we propose a novel framework called Sparse Continual Learning(SparCL), which is the first study that leverages sparsity to enable cost-effective continual learning on edge devices. SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity. Specifically, we propose task-aware dynamic masking (TDM) to learn a sparse network throughout the entire CL process, dynamic data removal (DDR) to remove less informative training data, and dynamic gradient masking (DGM) to sparsify the gradient updates. Each of them not only improves efficiency, but also further mitigates catastrophic forgetting. SparCL consistently improves the training efficiency of existing state-of-the-art (SOTA) CL methods by at most 23X less training FLOPs, and, surprisingly, further improves the SOTA accuracy by at most 1.7%. SparCL also outperforms competitive baselines obtained from adapting SOTA sparse training methods to the CL setting in both efficiency and accuracy. We also evaluate the effectiveness of SparCL on a real mobile phone, further indicating the practical potential of our method.
Understanding reinforcement learned crowds

Simulating trajectories of virtual crowds is a commonly encountered task in Computer Graphics. Several recent works have applied Reinforcement Learning methods to animate virtual agents, however they often make different design choices when it comes to the fundamental simulation setup. Each of these choices comes with a reasonable justification for its use, so it is not obvious what is their real impact, and how they affect the results. In this work, we analyze some of these arbitrary choices in terms of their impact on the learning performance, as well as the quality of the resulting simulation measured in terms of the energy efficiency. We perform a theoretical analysis of the properties of the reward function design, and empirically evaluate the impact of using certain observation and action spaces on a variety of scenarios, with the reward function and energy usage as metrics. We show that directly using the neighboring agents' information as observation generally outperforms the more widely used raycasting. Similarly, using nonholonomic controls with egocentric observations tends to produce more efficient behaviors than holonomic controls with absolute observations. Each of these choices has a significant, and potentially nontrivial impact on the results, and so researchers should be mindful about choosing and reporting them in their work.
Evidence for nonunitary triplet-pairing superconductivity in noncentrosymmetric TaRuSi and comparison with isostructural TaReSi

We have studied the superconducting properties of the isostructural ternary noncentrosymmetric superconductors TaXSi (X = Re, Ru) with the help of muon spin rotation/relaxation ($\mu$SR) and density functional theory calculations. Our transverse-field $\mu$SR measurements indicate isotropic s-wave superconductivity in TaReSi and multi-gap superconductivity in TaRuSi. Zero-field $\mu$SR measurements, highly sensitive to very small magnetic fields, and no evidence for spontaneous fields in the superconducting state of TaReSi, whereas we observe small spontaneous fields that onset with superconductivity indicating broken time-reversal symmetry (TRS) superconductivity in TaRuSi. Using density functional theory calculations, we find that spin-orbit coupling is relatively weak in TaRuSi and strong in TaReSi. Using symmetry analysis, we attribute the broken time-reversal symmetry (TRS) in TaRuSi to a non-unitary triplet pairing state. Such a state is not allowed in the presence of strong spin-orbit coupling: our finding of no evidence for broken TRS in TaReSi is consistent with this expectation.
EffEval: A Comprehensive Evaluation of Efficiency for MT Evaluation Metrics

Efficiency is a key property to foster inclusiveness and reduce environmental costs, especially in an era of LLMs. In this work, we provide a comprehensive evaluation of efficiency for MT evaluation metrics. Our approach involves replacing computation-intensive transformers with lighter alternatives and employing linear and quadratic approximations for alignment algorithms on top of LLM representations. We evaluate six (reference-free and reference-based) metrics across three MT datasets and examine 16 lightweight transformers. In addition, we look into the training efficiency of metrics like COMET by utilizing adapters. Our results indicate that (a) TinyBERT provides the optimal balance between quality and efficiency, (b) CPU speed-ups are more substantial than those on GPU; (c) WMD approximations yield no efficiency gains while reducing quality and (d) adapters enhance training efficiency (regarding backward pass speed and memory requirements) as well as, in some cases, metric quality. These findings can help to strike a balance between evaluation speed and quality, which is essential for effective NLG systems. Furthermore, our research contributes to the ongoing efforts to optimize NLG evaluation metrics with minimal impact on performance. To our knowledge, ours is the most comprehensive analysis of different aspects of efficiency for MT metrics conducted so far.
Progress on the unfair 0-1-polynomials conjecture using linear recurrences and numerical analysis

If the product of two monic polynomials with real nonnegative coefficients has all coefficients equal to 0 or 1, does it follow that all the coefficients of the two factors are also equal to 0 or 1? Here is an equivalent formulation of this intriguing problem: is it possible to weigh unfairly a pair of dice so that the probabilities of every possible outcome (roll them and take the sum) were the same? If the two dice have six faces numbered 1 to 6, it is easy to show that the answer is no. But for general dice with finitely many faces, this is an open problem with no significant advancement since 1937. In this paper we examine, in some sense, the first infinite family of cases that cannot be treated with classical methods: the first die has three faces numbered with 0,2 and 5, while the second die is arbitrary. In other words, we examine factorizations of 0-1-polynomials with one factor equal to $x^5+a x^2 +1$ for some nonnegative $a$. We discover that this case may be solved (that is, necessarily $a=0$ or $a=1$) using the theory of linear recurrence sequences, computation of resultants, a fair amount of analytic and numerical approximations. and a little bit of luck.
Synthesis of Cost-Optimal Multi-Agent Systems for Resource Allocation

Multi-agent systems for resource allocation (MRAs) have been introduced as a concept for modelling competitive resource allocation problems in distributed computing. An MRA is composed of a set of agents and a set of resources. Each agent has goals in terms of allocating certain resources. For MRAs it is typically of importance that they are designed in a way such that there exists a strategy that guarantees that all agents will achieve their goals. The corresponding model checking problem is to determine whether such a winning strategy exists or not, and the synthesis problem is to actually build the strategy. While winning strategies ensure that all goals will be achieved, following such strategies does not necessarily involve an optimal use of resources. In this paper, we present a technique that allows to synthesise cost-optimal solutions to distributed resource allocation problems. We consider a scenario where system components such as agents and resources involve costs. A multi-agent system shall be designed that is cost-minimal but still capable of accomplishing a given set of goals. Our approach synthesises a winning strategy that minimises the cumulative costs of the components that are required for achieving the goals. The technique is based on a propositional logic encoding and a reduction of the synthesis problem to the maximum satisfiability problem (Max-SAT). Hence, a Max-SAT solver can be used to perform the synthesis. From a truth assignment that maximises the number of satisfied clauses of the encoding a cost-optimal winning strategy as well as a cost-optimal system can be immediately derived.