Miracle-dz/UniFAR-FTUs huggingface dataset:
To Explore or Not to Explore: Regret-Based LTL Planning in Partially-Known Environments

In this paper, we investigate the optimal robot path planning problem for high-level specifications described by co-safe linear temporal logic (LTL) formulae. We consider the scenario where the map geometry of the workspace is partially-known. Specifically, we assume that there are some unknown regions, for which the robot does not know their successor regions a priori unless it reaches these regions physically. In contrast to the standard game-based approach that optimizes the worst-case cost, in the paper, we propose to use regret as a new metric for planning in such a partially-known environment. The regret of a plan under a fixed but unknown environment is the difference between the actual cost incurred and the best-response cost the robot could have achieved if it realizes the actual environment with hindsight. We provide an effective algorithm for finding an optimal plan that satisfies the LTL specification while minimizing its regret. A case study on firefighting robots is provided to illustrate the proposed framework. We argue that the new metric is more suitable for the scenario of partially-known environment since it captures the trade-off between the actual cost spent and the potential benefit one may obtain for exploring an unknown region.
Transformers for 1D signals in Parkinson’s disease detection from gait

This paper focuses on the detection of Parkinson’s disease based on the analysis of a patient’s gait. The growing popularity and success of Transformer networks in natural language processing and image recognition motivated us to develop a novel method for this problem based on an automatic features extraction via Transformers. The use of Transformers in 1D signal is not really widespread yet, but we show in this paper that they are effective in extracting relevant features from 1D signals. As Transformers require a lot of memory, we decoupled temporal and spatial information to make the model smaller. Our architecture used temporal Transformers, dimension reduction layers to reduce the dimension of the data, a spatial Transformer, two fully connected layers and an output layer for the final prediction. Our model outperforms the current state-of-the-art algorithm with 95.2% accuracy in distinguishing a Parkinsonian patient from a healthy one on the Physionet dataset. A key learning from this work is that Transformers allow for greater stability in results. The source code and pre-trained models are released in https://github.com/DucMinhDimitriNguyen1.
CTAB-GAN+: enhancing tabular data synthesis

The usage of synthetic data is gaining momentum in part due to the unavailability of original data due to privacy and legal considerations and in part due to its utility as an augmentation to the authentic data. Generative adversarial networks (GANs), a paragon of generative models, initially for images and subsequently for tabular data, has contributed many of the state-of-the-art synthesizers. As GANs improve, the synthesized data increasingly resemble the real data risking to leak privacy. Differential privacy (DP) provides theoretical guarantees on privacy loss but degrades data utility. Striking the best trade-off remains yet a challenging research question. In this study, we propose CTAB-GAN+ a novel conditional tabular GAN. CTAB-GAN+ improves upon state-of-the-art by (i) adding downstream losses to conditional GAN for higher utility synthetic data in both classification and regression domains; (ii) using Wasserstein loss with gradient penalty for better training convergence; (iii) introducing novel encoders targeting mixed continuous-categorical variables and variables with unbalanced or skewed data; and (iv) training with DP stochastic gradient descent to impose strict privacy guarantees. We extensively evaluate CTAB-GAN+ on statistical similarity and machine learning utility against state-of-the-art tabular GANs. The results show that CTAB-GAN+ synthesizes privacy-preserving data with at least 21.9% higher machine learning utility (i.e., F1-Score) across multiple datasets and learning tasks under given privacy budget.
Designing for Emotion Regulation Interventions: An Agenda for HCI Theory and Research

There is a growing interest in human-computer interaction (HCI) to envision, design, and evaluate technology-enabled interventions that support users’ emotion regulation. This interest stems in part from increased recognition that the ability to regulate emotions is critical to mental health, and that a lack of effective emotion regulation is a transdiagnostic factor for mental illness. However, the potential to combine innovative HCI designs with the theoretical grounding and state-of-the-art interventions from psychology has yet to be fully realised. In this article, we synthesise HCI work on emotion regulation interventions and propose a three-part framework to guide technology designers in making: (i) theory-informed decisions about intervention targets; (ii) strategic decisions regarding the technology-enabled intervention mechanisms to be included in the system; and (iii) practical decisions around previous implementations of the selected intervention components. We show how this framework can both systematise HCI work to date and suggest a research agenda for future work.
Convergence Rate Bounds for the Mirror Descent Method: IQCs and the Bregman Divergence

This paper is concerned with convergence analysis for the mirror descent (MD) method, a well-known algorithm in convex optimization. An analysis framework via integral quadratic constraints (IQCs) is constructed to analyze the convergence rate of the MD method with strongly convex objective functions in both continuous-time and discrete-time. We formulate the problem of finding convergence rates of the MD algorithms into feasibility problems of linear matrix inequalities (LMIs) in both schemes. In particular, in continuoustime, we show that the Bregman divergence function, which is commonly used as a Lyapunov function for this algorithm, is a special case of the class of Lyapunov functions associated with the Popov criterion, when the latter is applied to an appropriate reformulation of the problem. Thus, applying the Popov criterion and its combination with other IQCs, can lead to convergence rate bounds with reduced conservatism. We also illustrate via examples that the convergence rate bounds derived can be tight.
A 3D-SCHRÖDINGER OPERATOR UNDER MAGNETIC STEPS

We deﬁne a Schrödinger operator on the half-space with a discontinuous magnetic ﬁeld having a piecewise-constant strength and a uniform direction. Motivated by applications in the theory of superconductivity, we study the inﬁmum of the spectrum of the operator. We further give suﬃcient conditions on the strength and the direction of the magnetic ﬁeld such that the aforementioned inﬁmum is an eigenvalue of a reduced model operator on the half-plane.
Aggregated Gaussian Processes with Multiresolution Earth Observation Covariates

For many survey-based spatial modelling problems, responses are observed as spatially aggregated over survey regions due to limited resources. Covariates, from weather models and satellite imageries, can be observed at many different spatial resolutions, making the pre-processing of covariates a key challenge for any spatial modelling task. We propose a Gaussian process regression model to ﬂexibly handle multiresolution covariates by employing an additive kernel that can efﬁciently aggregate features across resolutions. Compared to existing approaches that rely on resolution matching, our approach better maintains distributional information across resolutions, leading to better performance and interpretability. Our model yields stronger predictive performance and interpretability on both simulated and crop yield datasets.
Determinants of Longitudinal Measure Pulse Rate of Chronic Heart Failure Patients at Felege Hiwot Referral Hospital, Ethiopia: A Longitudinal Modeling Data Analysis

Background Heart rate is a significant modifiable factor in lowering mortality in heart failure patients. The heart rate, often known as the pulse rate, is the number of times the heart beats in a given amount of time. Irregular pulse rate or heart block are all measured using heart rate data. A higher heart rate has been associated with worse clinical outcomes. The main purpose of this study was to identify factors associated with the longitudinal pulse rate of congestive heart failure patients. Methods Hospital-based retrospective studies were conducted among 249 congestive heart failure patients who were 18 years old or older and who were on treatment follow-up from first February 2016 to thirty-one December 2017 in Felege Hiwot Referral Hospital, Bahir Dar, Ethiopia. Results Data were explored using descriptive statistics, individual and mean profile plots, and analyzed using linear mixed models. Out of the total 249 patients, 139 (55.8%) were females. The majority 179 (71.9%) of the CHF patients were rural residents. 65 (26.1%) patients had Hypertension, 30 (12.5%) patients had Diabetes Mellitus, 58 (23.3%) patients had Pneumonia. Thus age, LVEF, follow-up time in a month had a negative significant effect whereas NYHA class, male gender, pneumonia had a positive significant effect on the average evaluation of pulse rate of patients. The time interaction effect of NYHA Class (Time* NYHA Class), and LVEF (Time*LVEF) had a significant effect on average pulse rate evolution. Conclusion The variables age, LVEF, and follow-up time were negatively associated whereas, male gender, NYHA class, and presence of pneumonia were positively associated with the progression change of pulse rate of patients rate of progression of pulse rate decreases over time. Furthermore, health professionals give more attention to patients who are NYHA class IV, had low LVEF, pneumonic patients to reduce pulse rate during the follow-up time.
Transmission of SARS‐CoV‐2 in standardised first few X cases and household transmission investigations: A systematic review and meta‐analysis

We aimed to estimate the household secondary infection attack rate (hSAR) of SARS‐CoV‐2 in investigations aligned with the WHO Unity Studies Household Transmission Investigations (HHTI) protocol. We conducted a systematic review and meta‐analysis according to PRISMA 2020 guidelines. We searched Medline, Embase, Web of Science, Scopus and medRxiv/bioRxiv for “Unity‐aligned” First Few X cases (FFX) and HHTIs published 1 December 2019 to 26 July 2021. Standardised early results were shared by WHO Unity Studies collaborators (to 1 October 2021). We used a bespoke tool to assess investigation methodological quality. Values for hSAR and 95% confidence intervals (CIs) were extracted or calculated from crude data. Heterogeneity was assessed by visually inspecting overlap of CIs on forest plots and quantified in meta‐analyses. Of 9988 records retrieved, 80 articles (64 from databases; 16 provided by Unity Studies collaborators) were retained in the systematic review; 62 were included in the primary meta‐analysis. hSAR point estimates ranged from 2% to 90% (95% prediction interval: 3%–71%; I2 = 99.7%); I2 values remained >99% in subgroup analyses, indicating high, unexplained heterogeneity and leading to a decision not to report pooled hSAR estimates. FFX and HHTI remain critical epidemiological tools for early and ongoing characterisation of novel infectious pathogens. The large, unexplained variance in hSAR estimates emphasises the need to further support standardisation in planning, conduct and analysis, and for clear and comprehensive reporting of FFX and HHTIs in time and place, to guide evidence‐based pandemic preparedness and response efforts for SARS‐CoV‐2, influenza and future novel respiratory viruses.
Low carbon availability in paleosols nonlinearly attenuates temperature sensitivity of soil organic matter decomposition

Temperature sensitivity (Q10) of soil organic matter (SOM) decomposition is an important parameter in models of the global carbon (C) cycle. Previous studies have suggested that substrate quality controls the intrinsic Q10, whereas environmental factors can impose large constraints. For example, physical protection of SOM and its association with minerals attenuate the apparent Q10 through reducing substrate availability and accessibility ([S]). The magnitude of this dampening effect, however, has never been quantified. We simulated theoretical Q10 changes across a wide range of [S] and found that the relationship between Q10 and the log10‐transformed [S] followed a logistic rather than a linear function. Based on the unique Holocene paleosol chronosequence (7 soils from ca. 500 to 6900 years old), we demonstrated that the Q10 decreased nonlinearly with soil age up to 1150 years, beyond which Q10 remained stable. Hierarchical partitioning analysis indicated that an integrated C availability index, derived from principal component analysis of DOC content and parameters reflecting physical protection and mineral association, was the main explanatory variable for the nonlinear decrease of Q10 with soil age. Microbial inoculation and 13C‐labelled glucose addition showed that low C availability induced by physical protection and minerals association attenuated Q10 along the chronosequence. A separate soil incubation experiment indicated that Q10 increased exponentially with activation energy (Ea) in the modern soil, suggesting that SOM chemical complexity regulates Q10 only when C availability is high. In conclusion, organic matter availability strongly decreased with soil age, whereas Michelis–Menten kinetics defines the Q10 response depending on C availability, but Arrhenius equation describes the effects of increasing substrate complexity.
Association between sleep parameters and longitudinal shortening of telomere length

Background: The relationship between sleep parameters and longitudinal shortening of telomere length is unclear. This study aimed to investigate the relationship between sleep parameters and the shortening of leukocyte telomere length (LTL) over a year. Methods: Among the participants in the validation cohort of the Korea Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease, participants who measured both baseline and follow-up (two years later) of LTL were analyzed. They were dichotomized according to the degree of LTL attrition over two years. Clinical characteristics were compared between the faster and slower LTL shortening groups (cut-off points: −0.710 kbp, n = 119 each). Multivariable logistic regression analyses were performed to determine independent relationships between faster shortening of LTL length and sleep parameters. Results: A total of 238 participants, aged 55–88 years, were included. Participants with faster LTL shortening had a shorter duration of sleep (P = 0.013) and longer sleep latency (P = 0.007). Among the components of the PSQI, subjective measures of sleep quality, sleep latency, sleep duration, and sleep efficiency were significantly worse in participants with faster LTL shortening. Multivariate logistic regression analysis showed that sleep duration (per hour, OR = 0.831, 95% CI = 0.698–0.989), sleep latency (per minute, OR = 1.013, 95% CI = 1.002–1.024), global PSQI score (OR = 1.134, 95% CI = 1.040–1.236), shortest sleep duration (OR = 5.173, 95% CI = 1.563–17.126), and lowest sleep efficiency (OR = 7.351, 95% CI = 1.943–27.946) were independently associated with faster LTL shortening. Conclusions: Poor sleep quality, specifically short sleep duration, long sleep latency, and low sleep efficiency were associated with faster longitudinal shortening of LTL.
Comprehensive nutrient consumption estimation and metabolic profiling during ketogenic diet and relationship with myocardial glucose uptake on FDG-PET.

AIMS The ketogenic diet (KD) is standard-of-care to achieve myocardial glucose suppression (MGS) for assessing inflammation using fluorine-18 fluorodeoxyglucose-positron emission tomography (FDG-PET). As KD protocols remain highly variable between centres (including estimation of nutrient intake by dietary logs for adequacy of dietary preparation), we aimed to assess the predictive utility of nutrient intake in achieving MGS. METHODS AND RESULTS Nineteen healthy participants underwent short-term KD, with FDG-PET performed after 1 and 3 days of KD (goal carbohydrate intake <20 g/day). Nutrient consumption was estimated from dietary logs using nutrition research software. The area under receiver operating characteristics (AUROC) of macronutrients (carbohydrate, fat, and protein intake) for predicting MGS was analysed. The association between 133 nutrients and 4 biomarkers [beta-hydroxybutyrate (BHB), non-esterified fatty acids, insulin, and glucagon] with myocardial glucose uptake was assessed using mixed effects regression with false discovery rate (FDR) correction. Median (25th-75th percentile) age was 29 (25-34) years, 47% were women, and 42% were non-white. Median (25th-75th percentile) carbohydrate intake (g) was 18.7 (13.1-30.7), 16.9 (10.4-28.7), and 21.1 (16.6-29.0) on Days 1-3. No macronutrient intake (carbohydrate, fat, or protein) predicted MGS (c-statistic 0.45, 0.53, 0.47, respectively). Of 133 nutrients and 4 biomarkers, only BHB was associated with myocardial glucose uptake after FDR correction (corrected P-value 0.003). CONCLUSIONS During highly supervised, short-term KD, approximately half of patients meet strict carbohydrate goals. Yet, in healthy volunteers, dietary review does not provide reassurance for adequacy of myocardial preparation since no clear thresholds for carbohydrate or fat intake reliably predict MGS.
A framework for quantifying the effects of transcranial magnetic stimulation on motor recovery from hemiparesis: corticomuscular network

Objective. Transcranial magnetic stimulation (TMS) is an experimental therapy for promoting motor recovery from hemiparesis. At present, hemiparesis patients’ responses to TMS are variable. To maximize its therapeutic potential, we need an approach that relates the electrophysiology of motor recovery and TMS. To this end, we propose corticomuscular network (CMN) representing the holistic motor system, including the cortico-cortical pathway, corticospinal tract, and muscle co-activation. Approach. CMN is made up of coherence between pairs of electrode signals and spatial locations of the electrodes. We associated coherence and graph features of CMN with Fugl-Meyer Assessment (FMA) for the upper extremity. Besides, we compared CMN between 8 patients with hemiparesis and 6 healthy controls and contrasted CMN of patients before and after a 1 Hz TMS. Main results. Corticomuscular coherence (CMC) correlated positively with FMA. The regression model between FMA and CMC between five pairs of channels had 0.99 adjusted and a p-value less than 0.01. Compared to healthy controls, CMN of patients tended to be a small-world network and was more interconnected with higher CMC. CMC between cortex and triceps brachii long head was higher in patients. 15 min 1 Hz TMS protocol induced coherence changes beyond the stimulation side and had a limited impact on CMN parameters that are related to motor recovery. Significance. CMN is a potential clinical approach to quantify rehabilitating progress. It also sheds light on the desirable electrophysiological effects of TMS based on which rehabilitating strategies can be optimized.
Evaluation of the Effectiveness of an Exemplar Equestrian Air Jacket against Crush Injuries

Horse rider fatalities have occurred during the cross‐country phase of three‐day eventing when horses have fallen and landed on their riders. There are safety jackets available for horse riders, which are designed to inflate and offer protection when the rider falls from a horse, but the effectiveness of these ‘air jackets’ against crush injury from a falling horse is not well understood. TRL and British Eventing tested an exemplar air jacket to estimate its protective capacity and provide baseline data on the forces applied by a falling horse. Four tests were performed with an equine cadaver. In two tests, one with an air jacket and one without, the horse was dropped onto an instrumented Hybrid III ATD. In two further tests, the horse was dropped onto an array of 123 load cells with a measurement area of approximately 1 m x 2 m. The results indicated that the air jacket could have a beneficial effect in reducing the likelihood of a severe injury being sustained in an accident where a horse falls on a rider. However, with or without the air jacket, there was a high probability of a severe injury being sustained by a rider in the loading condition created for these tests.
ODYSSE: A routing Protocol for Wireless Sensor Networks

In this article, we propose, design, model and experiment an energy efficient protocol for Wireless Sensor Networks (WSNs), ODYSSE (“Opportunistic Duty-cYcle based routing protocol for wirelesS Sensor networks”). It combines three main elements: the first one is duty-cycling, where nodes alternate between active and sleep states, and is a classical but effective method to save energy. The second one is opportunistic routing where relaying (routing) is not rigidly fixed: at each hop, any node closer to the destination might become the relay. This requires less node synchronization, allows for path diversity and load balancing. The third one, is source coding (with LDPC, Low-Density Parity-Check codes). With uncoordinated duty-cycling as a starting point, the three techniques fit perfectly, yielding a robust low complexity protocol for highly constrained nodes. Modeling the average waiting delay of forwarders, we also show that simple relay selection strategies are effective. We focused on two heterogeneous scenarios: the most challenging scenario of bulk-transmission (of still images), and one of the most classical WSNs applications, i.e infrequent events reporting. Using a testbed of 45 Arduino nodes communicating with IEEE 802.15. 4 (XBee) within the large scale platform FIT IoT-LAB, we implemented and extensively studied the behavior and performance of the protocol.