Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Relaxation of Signal Temporal Logic Specifications for Resilient Control Synthesis

We introduce a metric that can quantify the temporal relaxation of Signal Temporal Logic (STL) specifications and facilitate resilient control synthesis in the face of infeasibilities. The proposed metric quantifies a cumulative notion of relaxation among the subtasks, and minimizing it yields to structural changes in the original STL specification by i) modifying time-intervals, ii) removing subtasks entirely if needed. To this end, we formulate an optimal control problem that extracts state and input sequences by minimally violating the temporal requirements while achieving the desired predicates. We encode this problem in the form of a computationally efficient mixed-integer program. We show some theoretical results on the properties of the new metric. Finally, we present a case study of a robot that minimally violates the time constraints of desired tasks in the face of an infeasibility.
Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data

In this paper, we address the problem of conducting statistical inference in settings involving large-scale data that may be high-dimensional and contaminated by outliers. The high volume and dimensionality of the data require distributed processing and storage solutions. We propose a two-stage distributed and robust statistical inference procedures coping with high-dimensional models by promoting sparsity. In the first stage, known as model selection, relevant predictors are locally selected by applying robust Lasso estimators to the distinct subsets of data. The variable selections from each computation node are then fused by a voting scheme to find the sparse basis for the complete data set. It identifies the relevant variables in a robust manner. In the second stage, the developed statistically robust and computationally efficient bootstrap methods are employed. The actual inference constructs confidence intervals, finds parameter estimates and quantifies standard deviation. Similar to stage 1, the results of local inference are communicated to the fusion center and combined there. By using analytical methods, we establish the favorable statistical properties of the robust and computationally efficient bootstrap methods including consistency for a fixed number of predictors, and robustness. The proposed two-stage robust and distributed inference procedures demonstrate reliable performance and robustness in variable selection, finding confidence intervals and bootstrap approximations of standard deviations even when data is high-dimensional and contaminated by outliers.
SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation

This paper introduces a novel Self-supervised Fine-grained Dialogue Evaluation framework (SelF-Eval). The core idea is to model the correlation between turn quality and the entire dialogue quality. We first propose a novel automatic data construction method that can automatically assign fine-grained scores for arbitrarily dialogue data. Then we train SelF-Eval with a multi-level contrastive learning schema which helps to distinguish different score levels. Experimental results on multiple benchmarks show that SelF-Eval is highly consistent with human evaluations and better than the state-of-the-art models. We give a detailed analysis of the experiments in this paper. Our code is available on GitHub.
Perceptive Locomotion Through Nonlinear Model-Predictive Control

Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance, and planning of the underactuated dynamics of the system. Reliably optimizing for such motions and interactions in the presence of imperfect and often incomplete perceptive information is challenging. We present a complete perception, planning, and control pipeline, which can optimize motions for all degrees of freedom of the robot in real time. To mitigate the numerical challenges posed by the terrain, a sequence of convex inequality constraints is extracted as local approximations of foothold feasibility and embedded into an online model-predictive controller. Steppability classification, plane segmentation, and a signed distance field are precomputed per elevation map to minimize the computational effort during the optimization. A combination of multiple-shooting, real-time iteration, and a filter-based line search is used to solve the formulated problem reliably and at high rate. We validate the proposed method in scenarios with gaps, slopes, and stepping stones in simulation and experimentally on the ANYmal quadruped platform, resulting in state-of-the-art dynamic climbing.
Cyclic theory and the pericyclic category

We give a historical perspective on the role of the cyclic category in the development of cyclic theory. This involves a continuous interplay between the extension in characteristic one and in S-algebras, of the traditional development of cyclic theory, and the geometry of the toposes associated with several small categories involved. We clarify the link between various existing presentations of the cyclic and the epicyclic categories and we exemplify the role of the absolute coefficients by presenting the ring of the integers as polynomials in powers of 3, with coefficients in the spherical group ring S[C_2] of the cyclic group of order two. Finally, we introduce the pericyclic category which unifies and refines two conflicting notions of epicyclic space existing in the literature.
HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create Customized Content with Models

Controlling the text generated by language models and customizing the content has been a long-standing challenge. Existing prompting techniques proposed in pursuit of providing control are task-specific and lack generality; this provides overwhelming choices for non-expert users to find a suitable method for their task. The effort associated with those techniques, such as in writing examples, explanations, instructions, etc. further limits their adoption among non-expert users. In this paper, we propose a simple prompting strategy HELP ME THINK where we encourage GPT3 to help non-expert users by asking a set of relevant questions and leveraging user answers to execute the task. We demonstrate the efficacy of our technique HELP ME THINK on a variety of tasks. Specifically, we focus on tasks that are hard for average humans and require significant thinking to perform. We hope our work will encourage the development of unconventional ways to harness the power of large language models.
Deep Learning-Based Discrete Calibrated Survival Prediction

Deep neural networks for survival prediction outperform classical approaches in discrimination, which is the ordering of patients according to their time-of-event. Conversely, classical approaches like the Cox Proportional Hazards model display much better calibration, the correct temporal prediction of events of the underlying distribution. Especially in the medical domain, where it is critical to predict the survival of a single patient, both discrimination and calibration are important performance metrics. Here we present Discrete Calibrated Survival (DCS), a novel deep neural network for discriminated and calibrated survival prediction that outperforms competing survival models in discrimination on three medical datasets, while achieving best calibration among all discrete time models. The enhanced performance of DCS can be attributed to two novel features, the variable temporal output node spacing and the novel loss term that optimizes the use of uncensored and censored patient data. We believe that DCS is an important step towards clinical application of deep-learning-based survival prediction with state-of-the-art discrimination and good calibration.
Stability of Sampled-Data Systems With Packet Losses: A Nonuniform Sampling Interval Approach

In this article, inspired by the Halanay inequality, we study stability of sampled-data systems with packet losses by proposing a nonuniform sampling interval approach. First, a sampled-data controller with an exponential gain is put forward to reduce conservatism. We obtain the sufficient condition for linear sampled-data systems to be exponentially stable by extending the famous Halanay inequality to sampled-data systems. The obtained sufficient conditions indicate that the maximal-allowable bound of sampling intervals is determined by the constant terms in the Halanay inequality, and the decay rate is presented in the form of a Lambert function. Compared with some existing results on the stability of sampled-data systems by using the Gronwall–Bellman Lemma, the conservatism induced by the exponential term via the Gronwall–Bellman Lemma can be reduced to some extent. Considering the phenomenon of packet losses, a new lemma is further proposed to generalize the proposed Halanay-like inequality. The results derived by the new lemma permit that there exist some sampling intervals with the upper bound violating the desired condition of the Halanay-like inequality. This permits us to establish exponential stability in significant cases that do not satisfy the Halanay-like inequality needed in the previous results. Finally, the sampled-data local exponential stability is investigated for nonlinear systems with strong nonlinearity.
Mechanisms for discrete optimization with rational agents

We study “discrete optimization with a blindfold. ” We are given an optimization problem such as finding the shortest path in a graph or balancing load on machines, but some of the relevant data, such as edge costs or machine speeds, is missing. There are various selfish economic agents who each know part of the data, and who are affected by the decisions we make. We can ask them to report the data, but they might lie if it is in their self-interest to do so. We aim to design truthful mechanisms: algorithms for solving our optimization problems that, with the help of monetary incentives, induce the agents to report the true data. We identify and address four major problems with the Vickrey-Clarke-Groves (VCG) mechanism, the most famous general technique for designing truthful mechanisms. First, the VCG mechanism can be used only if we want to maximize the overall social welfare, e.g., minimize the cost to the agents. We develop other techniques for optimizing other types of objective functions. Second, even if our goal is to maximize the overall social welfare, the VCG mechanism may be computationally intractable because the underlying optimization problem is NP-hard. We investigate how to develop approximation algorithms that preserve the truth-inducing properties of the VCG mechanism. Third, even though the VCG mechanism can be used to minimize the total cost incurred by the agents, the amount the mechanism must pay to the agents can be significantly higher. We prove that in some natural cases, this is an inherent difficulty with truthful mechanisms, and is not peculiar to the VCG mechanism. Fourth, even though it is never in an agent's individual selfish interest to lie to the VCG mechanism, coalitions of agents can sometimes benefit by lying in a coordinated way. We study what types of coordination must occur for this type of cheating to be successful.
Towards Occlusion-Aware Pose Estimation of Surgical Suturing Threads

Objective:The technique of robust suture detection is vital in many applications including the skill evaluation for trainee, and suture augmentation in robotic-assisted surgery. Due to the complicated environment in surgery, the pose estimation of suture threads is challenged by the foreground and the background occlusion. Methods: To address this problem, we proposed an Occlusion-Aware Spatial Propagation model in this work. The challenging cases caused by self-intersection of threads are resolved by modeling the region connectivity. By taking the advantage of context-information, the background-occlusion is handled with the guided spatial propagation mechanism. Results: Experiments on phantom and ex-vivo datasets demonstrate the proposed method achieves superior accuracy on pose estimation compared to baseline methods, indicating the effectiveness of the occlusion-aware connectivity and spatial propagation. Conclusion: Our proposed method provides a general framework for fully end-to-end pose estimation of suturing thread that achieves promising quality without the external simulation. Significance: Our fully automated algorithm addresses the occlusion problem including foreground and background occlusion which are common in surgery, and we anticipate that it will substantially provide the prior for future autonomy of robotic surgery.
The prognostic role of galectin-3 and endothelial function in patients with heart failure

Background Heart failure (HF) is nowadays classified as HF with reduced ejection fraction (HFrEF), HF with mildly reduced EF (HFmrEF), and HF with preserved EF (HFpEF). Endothelial dysfunction (assessed by flow-mediated dilatation [FMD]), increased arterial stiffness (assessed by carotid-femoral pulse-wave velocity [PWV]), and galectin-3, a biomarker of myocardial fibrosis, have been linked to major adverse cardiovascular events (MACE) in patients with ischemic HF. Methods In this study we prospectively enrolled 340 patients with stable ischemic HF. We assessed the brachial artery FMD, carotid-femoral PWV, and galectin-3 levels, and patients were followed up for MACE according to HF group. Results Interestingly, the FMD values exhibited a stepwise improvement according to left ventricular ejection fraction (LVEF) (HFrEF: 4.74 ± 2.35% vs. HFmrEF: 4.97 ± 2.81% vs. HFpEF: 5.94 ± 3.46%, p = 0.01), which remained significant after the evaluation of possible confounders including age, sex, cardiovascular risk factors, and number of significantly stenosed epicardial coronary arteries (b coefficient: 0.990, 95% confidence interval: 0.166–1.814, p = 0.019). Single-vessel coronary artery disease was more frequent in the group of HFpEF (HFrEF: 56% vs. HFmrEF: 64% vs. HFpEF: 73%, p = 0.049). PWV did not display any association with LVEF. Patients who presented MACE exhibited worse FMD values (4.51 ± 2.35% vs. 5.32 ± 2.67%, p = 0.02), and the highest tertile of galectin-3 was linked to more MACEs (36% vs. 5.9%, p = 0.01). Conclusions Flow-mediated dilatation displayed a linear improvement with LVEF in patients with ischemic HF. Deteriorated values are associated with MACE. Higher levels of galectin-3 might be used for risk stratification of patients with ischemic HF.
Evaluating vision-specific quality of life and functional vision in a Brazilian population with Stargardt disease

Purpose To compare the vision-specific quality of life (QoL) of individuals with Stargardt disease (STGD) with that of healthy individuals and to investigate the association between vision-specific QoL and functional vision. Methods This cross-sectional study included 41 patients with STGD and 46 healthy volunteers matched by age and gender. Best corrected visual acuity (BCVA) was used to calculate the Functional Acuity Score (FAS) and Goldmann perimetry the Functional Field Score (FFS). These scores were combined to obtain the Functional Vision Score (FVS). Vision-specific QoL was assessed using the 25-item version of the National Eye Institute Visual Function Questionnaire (NEI VFQ-25). Results The groups had similar sociodemographic and clinical characteristics. There were significant differences in BCVA, vision-specific QoL assessed by the NEI VFQ-25, and FVS (p < 0.001) between individuals with STGD and controls. The final total score and all the subscales of the NEI VQF-25 questionnaire were significantly lower in the STGD group (p < 0.001), except for the subscales general health, color vision, and ocular pain. NEI VFQ-25 results in the STGD group were positively correlated with family income, FVS, and FFS. FVS was the score best correlated with the NEI VFQ-25 total score. Conclusions Individuals with STGD had significant impairment of vision-specific QoL and functional vision compared with controls and the FVS was the objective evaluation method most correlated with vision-specific QoL in this population.
Ultrarare Coding Variants and Cognitive Function in Schizophrenia

Key Points Question Are ultrarare constrained variants (URCVs) associated with reduced cognitive function in individuals diagnosed with schizophrenia? Findings In this within-case genetic association study of 802 individuals with schizophrenia who had undergone exome sequencing and cognitive testing, significantly reduced cognitive function was found in individuals carrying URCVs. Meaning This study found that URCVs were associated with reduced general cognitive function in schizophrenia; with better annotation of pathogenic variants, genomic data may contribute to identifying those with schizophrenia at particularly high risk of cognitive impairment in whom early remedial or preventative measures can be implemented.
Single housing but not changes in group composition causes stress-related immunomodulations in horses

Domestic horses are currently often subject to management practices that can entail social stressors, which in turn can negatively influence immunocompetence and disease susceptibility. The present study therefore aimed to characterize the number of various blood leukocyte subsets in horses, focusing on two potentially stressful housing environments: changes in group composition and relocation to individual stabling. Immune measurements were conducted before as well as one and eight days after changes were made. They were complemented by an assessment of plasma cortisol concentrations as well as behavioral observations. One and eight days after relocation to single housing, the mean numbers of eosinophils, T helper cells and cytotoxic T cells decreased by up to 31%, 20% and 22% respectively, whereas the mean numbers of neutrophils increased by 25%. In contrast, one and eight days after changes in group composition not only the mean number of neutrophils, but also of monocytes, T helper cells and cytotoxic T cells increased by up to 24%, 17%, 9%, and 15% respectively. In consequence, an increase in the neutrophil-to-lymphocyte ratio indicating stress-induced immune modulation was found after relocation to single housing, but not after changes in group composition. The changes in leukocyte numbers after relocation to single housing were accompanied by a transient increase in cortisol concentrations after one day and the occurrence of disturbed behavior patterns one week after change in housing condition. In contrast, changes in group composition did not result in an increase of cortisol concentrations or in an increase of aggressive interactions. The results strongly indicate that individual stabling is an intense stressor leading to acute and lasting alterations in blood counts of various leukocyte types. The study highlights a probable negative impact of single housing on welfare and health of horses and an advantage of group housing systems in view of immunocompetence.
Real-world utilization and outcomes of systemic therapy among patients with advanced or recurrent endometrial cancer in the United States

Abstract Objective Evaluate systemic therapy utilization patterns and outcomes by line of therapy among patients with advanced/recurrent endometrial cancer (EC) treated in the United States. Methods This retrospective observational study used the Optum Clinformatics Extended Data Mart Date of Death database (1 January 2004–31 December 2019) and included de-identified data from adult patients with advanced/recurrent EC who were treated with first-line (1L) platinum-based chemotherapy and initiated second-line (2L) anti-neoplastic therapy. The index date was the date of 1L therapy initiation. The number and sequence of treatments received and the proportion of patients who received each type of treatment for each line of therapy were evaluated. To account for new drug approvals, patients first treated in 2018 or 2019 were also assessed separately. Results Among the 1317 patients who met all eligibility criteria, 520 (39.5%) and 235 (17.8%) patients received 3 or 4+ lines of treatment, respectively, during a median total follow-up time of 25.2 months (range, 2.5–173.3 months) following the index date. Chemotherapy, including platinum- and non-platinum-based regimens, was the most common treatment across all lines of therapy: 2L, 80.0%; 3L, 66.2%; 4L+, 80.4%. Overall, 2.5%, 2.3%, and 8.9% of 2L, 3L, and 4L + patients, respectively, received anti-program death 1 (anti-PD-1) immunotherapies. In patients first treated in 2018 and 2019 (n = 163), 9.8% of patients received anti-PD-1 immunotherapy in the 2L. In the overall population, median time to next treatment (TTNT) was 19.3, 10.5, and 8.1 months for patients undergoing 2L, 3L, and 4L treatment, respectively. Conclusions Among patients with advanced/recurrent EC treated with 1L platinum-based therapy in clinical practice, chemotherapy was the most common treatment choice across all lines of therapy. Immunotherapy use was low overall but increased in patients who started treatment in 2018 or 2019. Overall, median TTNT decreased as lines of therapy increased.
Expansion of the Conceptual Model for the Accumulation of Selenium in Lentic Food Chains to Include Redox‐Controlled Generation and Diffusion of Selenite and Dissolved Organo‐Selenium Compounds

The controls governing the availability of reduced selenium (Se) species, namely selenite (Se[IV]) and dissolved organo‐Se (DOSe), to primary producers at the sediment–water interface in depositional environments (i.e., lentic systems) were assessed through consideration of theoretical principles and field data. Selenite is generated in suboxic sediment porewater via the microbially mediated reduction of selenate (Se[IV]) and/or reductive dissolution of Se‐bearing iron oxides. Field data for lentic environments demonstrate that the production of DOSe in sediment porewaters can also be redox‐ and depth‐dependent. In this manner, the remobilization depths of Se(IV) and DOSe in depositional environments are dependent on the vertical redox gradient (dEh/dz), where deeper depths of remobilization are observed in less reducing sedimentary environments (lower dEh/dz). In turn, remobilization depth has a direct bearing on the concentration of dissolved Se(IV) and DOSe that may be realized at the sediment–water interface because the depth of reaction governs the diffusive path length, concentration gradient, and rate of diffusional transport toward the sediment–water interface. The principles that link sediment redox gradients, depth of remobilization, diffusive transport processes, and concentration of reduced Se species at the sediment–water interface have a direct bearing on the potential for Se uptake by primary producers in lentic food chains (e.g., phytoplankton, biofilms, bacteria). Overall, these processes complement the current conceptual “benthic detrital food chain” model that describes the accumulation of Se in lentic systems. Environ Toxicol Chem 2022;41:2859–2869. © 2022 SETAC