Miracle-dz/UniFAR-FTUs huggingface dataset:
A 4-week high-AGE diet does not impair glucose metabolism and vascular function in obese individuals

BACKGROUND Accumulation of advanced glycation endproducts (AGEs) may contribute to the pathophysiology of type 2 diabetes and its vascular complications. AGEs are widely present in food, but whether restricting AGE intake improves risk factors for type 2 diabetes and vascular dysfunction is controversial. METHODS Abdominally obese but otherwise healthy individuals were randomly assigned to a specifically designed 4-week diet low or high in AGEs in a double-blind, parallel design. Insulin sensitivity, secretion, and clearance were assessed by a combined hyperinsulinemic-euglycemic and hyperglycemic clamp. Micro- and macrovascular function, inflammation, and lipid profiles were assessed by state-of-the-art in vivo measurements and biomarkers. Specific urinary and plasma AGEs Nε-(carboxymethyl)lysine (CML), Nε-(1-carboxyethyl)lysine (CEL), and Nδ-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine (MG-H1) were assessed by mass spectrometry. RESULTS In 73 individuals (22 males, mean ± SD age and BMI 52 ± 14 years, 30.6 ± 4.0 kg/m2), intake of CML, CEL, and MG-H1 differed 2.7-, 5.3-, and 3.7-fold between the low- and high-AGE diets, leading to corresponding changes of these AGEs in urine and plasma. Despite this, there was no difference in insulin sensitivity, secretion, or clearance; micro- and macrovascular function; overall inflammation; or lipid profile between the low and high dietary AGE groups (for all treatment effects, P > 0.05). CONCLUSION This comprehensive RCT demonstrates very limited biological consequences of a 4-week diet low or high in AGEs in abdominally obese individuals. TRIAL REGISTRATION Clinicaltrials.gov, NCT03866343; trialregister.nl, NTR7594. FUNDING Diabetesfonds and ZonMw.
Student Perceptions of Effective Simulation Instructor Teaching

Summary Statement Twenty-three focus groups were held with 183 first-year medical students to assess student perceptions of effective simulation instructors during preclinical training in a medical school. Qualitative descriptive analysis guided the interpretation of focus group data. Students identified 6 areas of knowledge (schedule, student learning goals, session scenario, tasks and checklists, technique, and session purpose); 5 effective instructor skill categories (setting up the learning environment, teaching at the appropriate level, teaching technique, providing deeper context, and giving effective feedback); and 8 positive instructor attributes (enthusiasm, engaged, prepared, knowledgeable, patient, relational, transparent, and calm) instructors should have. Each category of instructor attributes, skills, and knowledge was also described in detail providing illustrative examples of what effective instruction would look like in practice from the students' perspective. Recommendations for instructor faculty development methods and topics/goals are given.
Evolution of Amino Acid Propensities under Stability-Mediated Epistasis

Abstract Site-specific amino acid preferences are influenced by the genetic background of the protein. The preferences for resident amino acids are expected to, on average, increase over time because of replacements at other sites—a nonadaptive phenomenon referred to as the “evolutionary Stokes shift. ” Alternatively, decreases in resident amino acid propensity have recently been viewed as evidence of adaptations to external environmental changes. Using population genetics theory and thermodynamic stability constraints, we show that nonadaptive evolution can lead to both positive and negative shifts in propensities following the fixation of an amino acid, emphasizing that the detection of negative shifts is not conclusive evidence of adaptation. By examining propensity shifts from when an amino acid is first accepted at a site until it is subsequently replaced, we find that ≈50% of sites show a decrease in the propensity for the newly resident amino acid while the remaining sites show an increase. Furthermore, the distributions of the magnitudes of positive and negative shifts were comparable. Preferences were often conserved via a significant negative autocorrelation in propensity changes—increases in propensities often followed by decreases, and vice versa. Lastly, we explore the underlying mechanisms that lead propensities to fluctuate. We observe that stabilizing replacements increase the mutational tolerance at a site and in doing so decrease the propensity for the resident amino acid. In contrast, destabilizing substitutions result in more rugged fitness landscapes that tend to favor the resident amino acid. In summary, our results characterize propensity trajectories under nonadaptive stability-constrained evolution against which evidence of adaptations should be calibrated.
Developmental endothelial locus-1 protects from hypertension-induced cardiovascular remodeling via immunomodulation

The causative role of inflammation in hypertension-related cardiovascular diseases is evident and calls for development of specific immunomodulatory therapies. We tested the therapeutic efficacy and mechanisms of action of developmental endothelial locus-1 (DEL-1), an endogenous antiinflammatory factor, in angiotensin II– (ANGII–) and deoxycorticosterone acetate–salt–induced (DOCA-salt–induced) cardiovascular organ damage and hypertension. By using mice with endothelial overexpression of DEL-1 (EC-Del1 mice) and performing preventive and interventional studies by injecting recombinant DEL-1 in mice, we showed that DEL-1 improved endothelial function and abrogated aortic adventitial fibrosis, medial thickening, and loss of elastin. DEL-1 also protected the mice from cardiac concentric hypertrophy and interstitial and perivascular coronary fibrosis and improved left ventricular function and myocardial coronary perfusion. DEL-1 prevented aortic stiffness and abolished the progression of hypertension. Mechanistically, DEL-1 acted by inhibiting αvβ3 integrin–dependent activation of pro-MMP2 in mice and in human isolated aorta. Moreover, DEL-1 stabilized αvβ3 integrin–dependent CD25+FoxP3+ Treg numbers and IL-10 levels, which were associated with decreased recruitment of inflammatory cells and reduced production of proinflammatory cytokines in cardiovascular organs. The demonstrated effects and immune-modulating mechanisms of DEL-1 in abrogation of cardiovascular remodeling and progression of hypertension identify DEL-1 as a potential therapeutic factor.
Psychological symptoms of adolescent survivors of sexual abuse and characteristics of survivors displaying suicidal and/or self-harming behaviors

Abstract We examine the psychological symptoms and suicide attempts and/or self-injury behaviors of survivors of childhood sexual abuse (CSA) according to individual and familial characteristics. The participants of this study included 80 adolescents aged 14-17 years. We show that high psychological symptom scores may indicate that the perpetrator was a family member and that penetration occurred. In addition, most of the participating survivors have experienced numerous problems, largely related to psychological symptoms. Considering the results regarding survivors who had previously attempted suicide, we demonstrate that the perpetrators in these cases were mostly reliable/loved people, while these survivors generally hid the events and were exposed to penetration more often. We conclude that survivors exposed to CSA by a reliable/loved person, blaming themselves, having low social support, and displaying certain symptoms should be followed closely and necessary psychosocial interventions for suicide should be applied.
Signatures of immune senescence predict outcomes and define checkpoint blockade-unresponsive microenvironments in acute myeloid leukemia

The function of senescent-like T cells, transcriptomic features of immune effector senescence (IES) and their influence on therapeutic response were investigated in independent AML clinical cohorts comprising 1,864 patients treated with chemotherapy and/or immune checkpoint blockade (ICB). We show that senescent-like bone marrow CD8+ T cells are impaired in killing autologous AML blasts, and that their proportion negatively correlates with overall survival. We define new IES signatures using two gene expression platforms and report that IES scores correlate with adverse-risk molecular lesions, stemness, and poor outcomes as a potentially more powerful predictor of OS than 2017-ELN risk or LSC17 stemness score. IES expression signatures also identify an ICB-unresponsive tumor microenvironment and predict significantly worse OS in AML as well as in solid tumors. The newly described IES scores provide improved AML risk stratification and could facilitate the delivery of personalized immunotherapies to patients who are most likely to benefit.
Geometric properties of disintegration of measures

In this paper we study the connection between disintegration of measures and some geometric properties of probability spaces. We prove a disintegration theorem, and then address disintegration from the perspective of an optimal transport problem. We look at the disintegration of transport plans, which are used to define and study disintegration maps. Using these objects we study the regularity and absolute continuity of disintegration of measures. In particular, we discuss the weak continuity of the disintegration map, we study some conditions for which one can obtain a path of conditional measures in the space of probability measures and we show a rigidity condition for the disintegration of measures to be given into absolutely continuous measures.
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL

Solving goal-conditioned tasks with sparse rewards using self-supervised learning is promising because of its simplicity and stability over current reinforcement learning (RL) algorithms. A recent work, called Goal-Conditioned Supervised Learning (GCSL), provides a new learning framework by iteratively relabeling and imitating self-generated experiences. In this paper, we revisit the theoretical property of GCSL -- optimizing a lower bound of the goal reaching objective, and extend GCSL as a novel offline goal-conditioned RL algorithm. The proposed method is named Weighted GCSL (WGCSL), in which we introduce an advanced compound weight consisting of three parts (1) discounted weight for goal relabeling, (2) goal-conditioned exponential advantage weight, and (3) best-advantage weight. Theoretically, WGCSL is proved to optimize an equivalent lower bound of the goal-conditioned RL objective and generates monotonically improved policies via an iterated scheme. The monotonic property holds for any behavior policies, and therefore WGCSL can be applied to both online and offline settings. To evaluate algorithms in the offline goal-conditioned RL setting, we provide a benchmark including a range of point and simulated robot domains. Experiments in the introduced benchmark demonstrate that WGCSL can consistently outperform GCSL and existing state-of-the-art offline methods in the fully offline goal-conditioned setting.
Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods

We design accelerated algorithms with improved rates for several fundamental classes of optimization problems. Our algorithms all build upon techniques related to the analysis of primal-dual extragradient methods via relative Lipschitzness proposed recently by Cohen et al. (2021). (1) Separable minimax optimization. We study separable minimax optimization problems of the form minx maxy f(x) − g(y) + h(x, y), where f and g have smoothness and strong convexity parameters (L, μ), (L, μ), and h is convex-concave with a (Λ,Λ,Λ)-blockwise operator norm bounded Hessian. We provide an algorithm using Õ (√ Lx μx + √ Ly μy + Λ μx + Λ √ μxμy + Λ yy μy ) gradient queries. Notably, for convex-concave minimax problems with bilinear coupling (e.g. quadratics), where Λ = Λ = 0, our rate matches a lower bound of Zhang et al. (2019). (2) Finite sum optimization. We study finite sum optimization problems of the form minx 1 n ∑ i∈[n] fi(x), where each fi is Li-smooth and the overall problem is μ-strongly convex. We provide an algorithm using Õ ( n+ ∑ i∈[n] √ Li nμ ) gradient queries. Notably, when the smoothness bounds {Li}i∈[n] are non-uniform, our rate improves upon accelerated SVRG (Lin et al., 2015; Frostig et al., 2015) and Katyusha (Allen-Zhu, 2017) by up to a √ n factor. (3) Minimax finite sums. We generalize our algorithms for minimax and finite sum optimization to solve a natural family of minimax finite sum optimization problems at an accelerated rate, encapsulating both above results up to a logarithmic factor.
Supporting Developers in Vulnerability Detection during Code Review: Mental Attitude and Security Checklists

Reviewing source code from a security perspective has proven to be a difficult task. Indeed, previous research has shown that developers often miss even popular and easy-to-detect vulnerabilities during code review. Initial evidence suggests that a significant cause may lie in the reviewers’ mental attitude and common practices. In this study, we investigate whether and how explicitly asking developers to focus on security during a code review affects the detection of vulnerabilities. Furthermore, we evaluate the effect of providing a security checklist to guide the security review. To this aim, we conduct an online experiment with 150 participants, of which 71% report to have three or more years of professional development experience. Our results show that simply asking re-viewers to focus on security during the code review increases eight times the probability of vulnerability detection. The presence of a security checklist does not significantly improve the outcome further, even when the checklist is tailored to the change under review and the existing vulnerabilities in the change. These results provide evidence supporting the mental attitude hypothesis and call for further work on security checklists’ effectiveness and design.
L2B: Learning to Bootstrap Robust Models for Combating Label Noise

Deep neural networks have shown great success in representation learning. However, when learning with noisy labels (LNL), they can easily overfit and fail to generalize to new data. This paper introduces a simple and effective method, named Learning to Bootstrap (L2B), which enables models to bootstrap themselves using their own predictions without being adversely affected by erroneous pseudo-labels. It achieves this by dynamically adjusting the importance weight between real observed and generated labels, as well as between different samples through meta-learning. Unlike existing instance reweighting methods, the key to our method lies in a new, versatile objective that enables implicit relabeling concurrently, leading to significant improvements without incurring additional costs. L2B offers several benefits over the baseline methods. It yields more robust models that are less susceptible to the impact of noisy labels by guiding the bootstrapping procedure more effectively. It better exploits the valuable information contained in corrupted instances by adapting the weights of both instances and labels. Furthermore, L2B is compatible with existing LNL methods and delivers competitive results spanning natural and medical imaging tasks including classification and segmentation under both synthetic and real-world noise. Extensive experiments demonstrate that our method effectively mitigates the challenges of noisy labels, often necessitating few to no validation samples, and is well generalized to other tasks such as image segmentation. This not only positions it as a robust complement to existing LNL techniques but also underscores its practical applicability. The code and models are available at https://github.com/yuyinzhou/l2b.
Evaluating Causal Inference Methods

The fundamental challenge of drawing causal inference is that counterfactual outcomes are not fully observed for any unit. Furthermore, in observational studies, treatment assignment is likely to be confounded. Many statistical methods have emerged for causal inference under unconfoundedness conditions given pre-treatment covariates, including: propensity score-based methods, prognostic score-based methods, and doubly robust methods. Unfortunately for applied researchers, there is no ‘one-size-ﬁts-all’ causal method that can perform optimally universally.In practice, causal methods are primarily evaluated quantitatively on handcrafted simulated data. Such data-generative procedures can be of limited value because they are typically stylized models of reality. They are simpliﬁed for tractability and lack the complexities of real-world data. For applied researchers, it is critical to understand how well a method performs for data at hand. Our work introduces a deep generative model-based framework, Credence , to validate causal inference methods. The framework’s novelty stems from its ability to generate synthetic data anchored at the empirical distribution for the observed sample, and therefore virtually indistinguishable from the latter. The approach allows the user to specify ground truth for the form and magnitude of causal effects and confounding bias as functions of covariates. Thus simulated data sets are used to evaluate the potential performance of various causal estimation methods when applied to data similar to the observed sample. We demonstrate Credence’s ability to accurately assess the relative performance of causal estimation techniques in an extensive simulation study and two real-world data applications from Lalonde and Project STAR studies.
Growth of Sobolev norms in quasi integrable quantum systems

We prove an abstract result giving a 〈t〉ǫ upper bound on the growth of the Sobolev norms of a time dependent Schrödinger equation of the form iψ̇ = H0ψ + V (t)ψ. H0 is assumed to be the Hamiltonian of a steep quantum integrable system and to be a pseudodifferential operator of order d > 1; V (t) is a time dependent family of pseudodifferential operators, unbounded, but of order b < d. The abstract theorem is then applied to perturbations of the quantum anharmonic oscillators in dimension 2 and to perturbations of the Laplacian on a manifold with integrable geodesic flow, and in particular Zoll manifolds, rotation invariant surfaces and Lie groups. We also cover the case of several particles on a Zoll manifold or on a Lie group, possibly obeying some restrictions due to the Fermionic or Bosonic nature of the particles. The proof is based a on quantum version of the proof of the classical Nekhoroshev theorem.
Agree to Disagree: Diversity through Disagreement for Better Transferability

Gradient-based learning algorithms have an implicit simplicity bias which in effect can limit the diversity of predictors being sampled by the learning procedure. This behavior can hinder the transferability of trained models by (i) favoring the learning of simpler but spurious features -- present in the training data but absent from the test data -- and (ii) by only leveraging a small subset of predictive features. Such an effect is especially magnified when the test distribution does not exactly match the train distribution -- referred to as the Out of Distribution (OOD) generalization problem. However, given only the training data, it is not always possible to apriori assess if a given feature is spurious or transferable. Instead, we advocate for learning an ensemble of models which capture a diverse set of predictive features. Towards this, we propose a new algorithm D-BAT (Diversity-By-disAgreement Training), which enforces agreement among the models on the training data, but disagreement on the OOD data. We show how D-BAT naturally emerges from the notion of generalized discrepancy, as well as demonstrate in multiple experiments how the proposed method can mitigate shortcut-learning, enhance uncertainty and OOD detection, as well as improve transferability.
Optimal Hyperparameters and Structure Setting of Multi-Objective Robust CNN Systems via Generalized Taguchi Method and Objective Vector Norm

Recently, Machine Learning (ML), Artificial Intelligence (AI), and Convolutional Neural Network (CNN) have made huge progress with broad applications, where their systems have deep learning structures and a large number of hyperparameters that determine the quality and performance of the CNNs and AI systems. These systems may have multi-objective ML and AI performance needs. There is a key requirement to find the optimal hyperparameters and structures for multi-objective robust optimal CNN systems. This paper proposes a generalized Taguchi approach to effectively determine the optimal hyperparameters and structure for the multi-objective robust optimal CNN systems via their objective performance vector norm. The proposed approach and methods are applied to a CNN classification system with the original ResNet for CIFAR-10 dataset as a demonstration and validation, which shows the proposed methods are highly effective to achieve an optimal accuracy rate of the original ResNet on CIFAR-10.
Dimer model on the square lattice with interface

In this exposition, we consider the dimer problem on an infinite square lattice with partially nonperiodic edge weights, which we refer to as the square lattice with interface. In particular, we compute an exact integral form of the inverse Kasteleyn operator and study its asymptotics behavior in different regions of the lattice to gain a general understanding of the local statistics of the model.
Adapting to Mixing Time in Stochastic Optimization with Markovian Data

We consider stochastic optimization problems where data is drawn from a Markov chain. Existing methods for this setting crucially rely on knowing the mixing time of the chain, which in real-world applications is usually unknown. We propose the first optimization method that does not require the knowledge of the mixing time, yet obtains the optimal asymptotic convergence rate when applied to convex problems. We further show that our approach can be extended to: (i) finding stationary points in non-convex optimization with Markovian data, and (ii) obtaining better dependence on the mixing time in temporal difference (TD) learning; in both cases, our method is completely oblivious to the mixing time. Our method relies on a novel combination of multi-level Monte Carlo (MLMC) gradient estimation together with an adaptive learning method.
Soft Robots Learn to Crawl: Jointly Optimizing Design and Control with Sim-to-Real Transfer

This work provides a complete framework for the simulation, co-optimization, and sim-to-real transfer of the design and control of soft legged robots. The compliance of soft robots provides a form of"mechanical intelligence"-- the ability to passively exhibit behaviors that would otherwise be difficult to program. Exploiting this capacity requires careful consideration of the coupling between mechanical design and control. Co-optimization provides a promising means to generate sophisticated soft robots by reasoning over this coupling. However, the complex nature of soft robot dynamics makes it difficult to provide a simulation environment that is both sufficiently accurate to allow for sim-to-real transfer, while also being fast enough for contemporary co-optimization algorithms. In this work, we show that finite element simulation combined with recent model order reduction techniques provide both the efficiency and the accuracy required to successfully learn effective soft robot design-control pairs that transfer to reality. We propose a reinforcement learning-based framework for co-optimization and demonstrate successful optimization, construction, and zero-shot sim-to-real transfer of several soft crawling robots. Our learned robot outperforms an expert-designed crawling robot, showing that our approach can generate novel, high-performing designs even in well-understood domains.