Miracle-dz/UniFAR-FTUs huggingface dataset:
Raw Eggs To Support Postexercise Recovery in Healthy Young Men: Did Rocky Get It Right or Wrong?

ABSTRACT Background Egg protein is ingested during recovery from exercise to facilitate the postexercise increase in muscle protein synthesis rates and, as such, to support the skeletal muscle adaptive response to exercise training. The impact of cooking egg protein on postexercise muscle protein synthesis is unknown. Objectives We sought to compare the impact of ingesting unboiled (raw) compared with boiled eggs during postexercise recovery on postprandial myofibrillar protein synthesis rates. Methods In a parallel design, 45 healthy, resistance-trained young men (age: 24 y; 95% CI: 23, 25 y) were randomly assigned to ingest 5 raw eggs (∼30 g protein), 5 boiled eggs (∼30 g protein), or a control breakfast (∼5 g protein) during recovery from a single session of whole-body resistance-type exercise. Primed continuous l-[ring-13C6]-phenylalanine infusions were applied, with frequent blood sampling. Muscle biopsies were collected immediately after cessation of resistance exercise and at 2 and 5 h into the postexercise recovery period. Primary (myofibrillar protein synthesis rates) and secondary (plasma amino acid concentrations) outcomes were analyzed using repeated-measures (time × group) ANOVA. Results Ingestion of eggs significantly increased plasma essential amino acid (EAA) concentrations, with 20% higher peak concentrations following ingestion of boiled compared with raw eggs (time × group: P < 0.001). Myofibrillar protein synthesis rates were significantly increased during the postexercise period when compared with basal, postabsorptive values in all groups (2–4-fold increase: P < 0.001). Postprandial myofibrillar protein synthesis rates were 20% higher after ingesting raw eggs [0.067%/h; 95% CI: 0.056, 0.077%/h; effect size (Cohen d): 0.63], and 18% higher after ingesting boiled eggs (0.065%/h; 95% CI: 0.058, 0.073%/h; effect size: 0.69) when compared with the control breakfast (0.056%/h; 95% CI: 0.048, 0.063%/h), with no significant differences between groups (time × group: P = 0.077). Conclusions The ingestion of raw, as opposed to boiled, eggs attenuates the postprandial rise in circulating EAA concentrations. However, postexercise muscle protein synthesis rates do not differ after ingestion of 5 raw compared with 5 boiled eggs in healthy young men. This trial was registered at the Nederlands Trial Register as NL6506 (www.trialregister.nl).
Enumerative combinatorics of posets

To David and Fern, both of whom at points almost derailed this project, but ultimately became the reason that it got done. iii ACKNOWLEDGEMENTS It takes a village to write a thesis. Although I cannot begin to list all of the people who have touched my life during this journey, there are several I want to make sure to thank by name: Prasad: For his attention to detail and for so many other things. For mentoring which has touched this document in many ways, both large and small. For teaching me how to stay sane during this process. In this thesis we present several problems in enumerative and asymptotic combinatorics. Though each component problem is distinct, the work explores several common themes. The fundamental structures for which we pose questions are primarily partially ordered sets. For a more in-depth discussion of the connections between the chapters, please refer to the Conclusion (Chapter 7). Before describing the main contributions of this thesis, in Section 1.1 we will recall three classical results concerning posets and graphs, which provide the basis and motivation for most of our work. These are (i) Sperner's theorem concerning the size of the largest antichain in the Boolean lattice, (ii) Kleitman's theorem addressing the logarithm of the number of antichains in the Boolean lattice, and (iii) Brégman's theorem concerning the maximal number of perfect matchings in bipar-tite graphs. In order to state the classical theorems, as well as the results of this thesis, in the introduction, we assume knowledge of standard definitions concerning graphs and partially
Dielectric response of microcomposite ferroelectrics

Abstract The dielectric response in two-phase composites of ferroelectric and dielectric materials was studied. Different effective medium theories (isolated particles, Maxwell-Garnet theory, the effective medium approximation and the Bergman representation of the effective dielectric function) were used together with the theory of absorption in finite ferroelectric crystals in the quasistatic approximation to describe the response of the composite. One of the main results concerns the existence of modes due to geometric resonances which among other effects can lead to new low-frequency absorption peaks near the percolation threshold of the ferroelectric material. These peaks appear always higher than the transverse polar mode frequencies of the bulk components but below their corresponding longitudinal mode frequencies. The soft mode with renormalized mode strength but unchanged frequency exists only in the composite with percolated ferroelectric clusters. When the ferroelectric clusters are only finite .
Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation

Collaborative filtering (CF) is widely searched in recommendation with various types of solutions. Recent success of Graph Convolution Networks (GCN) in CF demonstrates the effectiveness of modeling high-order relationships through graphs, while repetitive graph convolution and iterative batch optimization limit their efficiency. Instead, item similarity models attempt to construct direct relationships through efficient interaction encoding. Despite their great performance, the growing item numbers result in quadratic growth in similarity modeling process, posing critical scalability problems. In this paper, we investigate the graph sampling strategy adopted in latest GCN model for efficiency improving, and identify the potential item group structure in the sampled graph. Based on this, we propose a novel item similarity model which introduces graph partitioning to restrict the item similarity modeling within each partition. Specifically, we show that the spectral information of the original graph is well in preserving global-level information. Then, it is added to fine-tune local item similarities with a new data augmentation strategy acted as partition-aware prior knowledge, jointly to cope with the information loss brought by partitioning. Experiments carried out on 4 datasets show that the proposed model outperforms state-of-the-art GCN models with 10x speed-up and item similarity models with 95% parameter storage savings.
Infinite-volume states with irreducible localization sets for gradient models on trees

We consider general classes of gradient models on regular trees with values in a countable Abelian group $S$ such as $\mathbb{Z}$ or $\mathbb{Z}_q$, in regimes of strong coupling (or low temperature). This includes unbounded spin models like the p-SOS model and finite-alphabet clock models. We prove the existence of families of distinct homogeneous tree-indexed Markov chain Gibbs states $\mu_A$ whose single-site marginals concentrate on a given finite subset $A \subset S$ of spin values, under a strong coupling condition for the interaction, depending only on the cardinality $\vert A \vert$ of $A$. The existence of such states is a new and robust phenomenon which is of particular relevance for infinite spin models. These states are not convex combinations of each other, and in particular the states with $\vert A \vert \geq 2$ can not be decomposed into homogeneous Markov-chain Gibbs states with a single-valued concentration center. As a further application of the method we obtain moreover the existence of new types of $\mathbb{Z}$-valued gradient Gibbs states, whose single-site marginals do not localize, but whose correlation structure depends on the finite set $A$.
The newest twists in protostellar outflows: triggered star formation

Abstract Winds and outflows powered by the release of gravitational potential energy of infalling matter have been invoked as the triggering agent for further star-formation on scales from dwarf galaxies to individual, low-mass stars. This brief review will touch upon the circumstances under which outflows can serve as star-formation triggers, from specific observations of present-day, nearby protostellar flows.
Effect of Chemical Cleaning on Steam Generator Tube Performance

Boiling experiments were performed on new, chemically cleaned, and fouled steam generator tubes to determine the heat transfer performance of each. It was found that the heat transfer performance of the fouled tube was the best, followed by the chemically cleaned tube. The performance of the new tube was the worst. Scanning electron microscope (SEM) photographs of the boiling surfaces were taken to identify differences in surface characteristics. Results revealed the presence of significant amounts of porous deposits on the surface of the fouled tube that provided ample nucleation sites for boiling. Chemical cleaning removed most of the deposits such that the boiling performance of the cleaned surface was degraded. The new tube was very smooth and there were relatively fewer nucleation sites as evidenced in the SEM photographs. Available correlations were used to predict the heat flux for a given wall superheat and were compared with the experimental data.
Tackling Bias in the Dice Similarity Coefficient: Introducing NDSC for White Matter Lesion Segmentation

The development of automatic segmentation techniques for medical imaging tasks requires assessment metrics to fairly judge and rank such approaches on benchmarks. The Dice Similarity Coefficient (DSC) is a popular choice for comparing the agreement between the predicted segmentation against a ground-truth mask. However, the DSC metric has been shown to be biased to the occurrence rate of the positive class in the ground-truth, and hence should be considered in combination with other metrics. This work describes a detailed analysis of the recently proposed normalised Dice Similarity Coefficient (nDSC) for binary segmentation tasks as an adaptation of DSC which scales the precision at a fixed recall rate to tackle this bias. White matter lesion segmentation on magnetic resonance images of multiple sclerosis patients is selected as a case study task to empirically assess the suitability of nDSC. We validate the normalised DSC using two different models across 59 subject scans with a wide range of lesion loads. It is found that the nDSC is less biased than DSC with lesion load on standard white matter lesion segmentation benchmarks measured using standard rank correlation coefficients. An implementation of nDSC is made available at: https://github.com/NataliiaMolch/nDSC.
Crystallization and topology-induced dynamical heterogeneities in soft granular clusters

Soft-granular media such as dense emulsions, foams or tissues tend to exhibit either fluid- or solid-like properties depending on the applied stresses. However, the internal dynamics of soft granular systems bound by closed interfaces is poorly understood, while it remains of significant interest in diverse fields ranging from material science (porous materials) and tissue engineering (granular bioinks) to developmental biology (embryos, organoids) and medicine (circulating tumor cell clusters). Here, we report the spontaneous occurrence of crystalline-like hexagonal structures within soft granular clusters which self-organize under external flow. We use a densely packed double emulsion as a model soft granular material to produce clusters of sizes of 20-40 grains (droplets). We find that, optimally, the crystallites emerge under weak flows, when the internal shear stresses slightly agitate the system allowing internal rearrangements. Excessive flows destroy the delicate crystalline structure and lead to constant internal recirculations, i.e., effective fluidization of the granular medium. Upon subjecting the clusters to cycles of constriction and relaxation/expansion, we also find differences between the behaviours of two groups of droplets: those within the inner part of the cluster and those at its rim, with the latter subjected to larger deformations and less frequent rearrangements, effectively acting as an elastic solid-like membrane around the inner fluid-like core. This structural-dynamical heterogeneity appears to be of purely topological origin and as such we expect it to remain universal in various types of soft granular clusters or jets including also cell aggregates, organoids or bioprinted tissues.
Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features

Transfer learning with a small amount of target data is an effective and common approach to adapting a pre-trained model to distribution shifts. In some situations, target data labels may be expensive to obtain, so we may only have access to a limited number of target data points. To make the most of a very small target dataset, we propose a lightweight, sample-efficient approach that learns a diverse set of features and adapts to a target distribution by interpolating these features. Our approach, Project and Probe (Pro$^2$), first learns a linear projection that maps a pre-trained embedding onto orthogonal directions while being predictive of labels in the source dataset. The goal of this step is to learn a variety of predictive features, so that at least some of them remain useful after distribution shift. Pro$^2$ then learns a linear classifier on top of these projected features using a small target dataset. Theoretically, we find that Pro$^2$ results in more sample-efficient generalization by inducing a favorable bias-variance tradeoff. Our experiments on four datasets, with multiple distribution shift settings for each, show that Pro$^2$ improves performance by 5-15% when given limited target data compared to prior methods such as standard linear probing.
Correlations of flow maldistribution parameters in an air cooled heat exchanger

The present paper provides correlations of flow maldistribution parameters in air‐cooled heat exchangers. The flow field in the inlet header was obtained through the numerical solution of the governing partial differential equations including the conservation equations of mass and momentum in addition to the equations of the turbulence model. The results were obtained for different number of nozzles of 2–4, different inlet flow velocities of 1–2.5m/s and different nozzle geometries in addition to incorporation of a second header. The results are presented in terms of mass flow rate distributions in the tubes of the heat exchanger and their standard deviations. The results indicate that the inlet flow velocity has insignificant influence on maldistribution while the nozzle geometry shape has a slight effect. Also, the results indicate that reducing the nozzle diameter results in an increase in the flow maldistribution. A 25% increase is obtained in the standard deviation as a result of decreasing the diameter by 25%. Increasing the number of nozzles has a significant influence on the maldistribution. A reduction of 62.5% in the standard deviation of the mass flow rate inside the tubes is achieved by increasing the number of nozzles from 2 to 4. The results indicate that incorporating a second header results in a significant reduction in the flow maldistribution. A 50% decrease in the standard deviation is achieved as a result of incorporation of a second header of seven holes. It is also found that the hole‐diameter distribution at the exit of the second header has a slight influence on the flow maldistribution. Correlations of the flow maldistribution in terms of the investigated parameters are presented. Copyright © 2007 John Wiley & Sons, Ltd.
Orthogonal cycle systems with cycle length less than 10

An H $H$ ‐decomposition of a graph G $G$ is a partition of the edge set of G $G$ into subsets, where each subset induces a copy of the graph H $H$ . A k $k$ ‐orthogonal H $H$ ‐decomposition of G $G$ is a set of k $k$ H $H$ ‐decompositions of G $G$ such that any two copies of H $H$ in distinct H $H$ ‐decompositions intersect in at most one edge. When G=Kv $G={K}_{v}$ , we call the H $H$ ‐decomposition an H $H$ ‐system of order v $v$ . In this paper, we consider the case H $H$ is an ℓ $\ell $ ‐cycle and construct a pair of orthogonal ℓ $\ell $ ‐cycle systems for all admissible orders when ℓ∈{5,6,7,8,9} $\ell \in \{5,6,7,8,9\}$ , except when ℓ=v $\ell =v$ .
Language-Aware Multilingual Machine Translation with Self-Supervised Learning

Multilingual machine translation (MMT) benefits from cross-lingual transfer but is a challenging multitask optimization problem. This is partly because there is no clear framework to systematically learn language-specific parameters. Self-supervised learning (SSL) approaches that leverage large quantities of monolingual data (where parallel data is unavailable) have shown promise by improving translation performance as complementary tasks to the MMT task. However, jointly optimizing SSL and MMT tasks is even more challenging. In this work, we first investigate how to utilize **intra-distillation** to learn more *language-specific* parameters and then show the importance of these language-specific parameters. Next, we propose a novel but simple SSL task, **concurrent denoising**, that co-trains with the MMT task by concurrently denoising monolingual data on both the encoder and decoder. Finally, we apply **intra-distillation** to this co-training approach. Combining these two approaches significantly improves MMT performance, outperforming three state-of-the-art SSL methods by a large margin, e.g., 11.3{% and 3.7{% improvement on an 8-language and a 15-language benchmark compared with MASS, respectively.
Electrostatic decay of Langmuir/z‐mode waves in type III solar radio bursts

About 40% of the waveforms observed by STEREO during type III solar radio bursts exhibits Langmuir beating and have split spectral peaks, suggestive of decay into product Langmuir and ion‐acoustic waves. For lower electron beam speeds vb/c≲0.1, the spectra of Langmuir events with split spectral peaks are shown to be consistent with electrostatic (ES) decay into Langmuir‐like waves with frequencies above the electron plasma frequency. For faster beam speeds vb/c≳0.1 , the spectra are consistent with one or more successive generations of ES decay and an end state of low wave number Langmuir/z‐mode waves with strong electric fields perpendicular to the magnetic field. For many of the split spectral‐peak events, an intense low‐frequency response occurs that is consistent with ion‐acoustic waves produced by ES decay, providing further evidence that these events are ES decay. An observed event is shown to be consistent with three successive backscatter decays, but such events are very rare in type III bursts. About 90% of the split spectral‐peak events occur when Ti/Te≲0.6. Similarly, over 80% of the split peak events have energy densities above the theoretical nonlinear threshold for ES decay (for reasonable Langmuir damping rates ≲10−3ωp). All events have beam speeds and energy densities below the maxima appropriate for ES decay of beam‐driven Langmuir waves.
CGA-PoseNet: Camera Pose Regression via a 1D-Up Approach to Conformal Geometric Algebra

We introduce CGA-PoseNet, which uses the 1D-Up approach to Conformal Geometric Algebra (CGA) to represent rotations and translations with a single mathematical object, the motor, for camera pose regression. We do so starting from PoseNet, which successfully predicts camera poses from small datasets of RGB frames. State-of-the-art methods, however, require expensive tuning to balance the orientational and translational components of the camera pose.This is usually done through complex, ad-hoc loss function to be minimized, and in some cases also requires 3D points as well as images. Our approach has the advantage of unifying the camera position and orientation through the motor. Consequently, the network searches for a single object which lives in a well-behaved 4D space with a Euclidean signature. This means that we can address the case of image-only datasets and work efficiently with a simple loss function, namely the mean squared error (MSE) between the predicted and ground truth motors. We show that it is possible to achieve high accuracy camera pose regression with a significantly simpler problem formulation. This 1D-Up approach to CGA can be employed to overcome the dichotomy between translational and orientational components in camera pose regression in a compact and elegant way.
Trauma-Informed Social Media: Towards Solutions for Reducing and Healing Online Harm

Social media platforms exacerbate trauma, and many users experience various forms of trauma unique to them (e.g., doxxing and swatting). Trauma is the psychological and physical response to experiencing a deeply disturbing event. Platforms’ failures to address trauma threaten users’ well-being globally, especially amongst minoritized groups. Platform policies also expose moderators and designers to trauma through content they must engage with as part of their jobs (e.g., child sexual abuse). We consider how a trauma-informed approach might help address or decrease the likelihood of (re)experiencing trauma online. A trauma-informed approach to social media recognizes that everyone likely has a trauma history and that trauma is experienced at the individual, secondary, collective, and cultural levels. This paper proceeds by detailing trauma and its impacts. We then describe how the six trauma-informed principles can be applied to social media design, content moderation, and companies. We conclude by offering recommendations that balance platform responsibility and accountability with well-being and healing for all.
Exact dynamics of the critical Kauffman model with connectivity one

The critical Kauffman model with connectivity one is the simplest class of critical Boolean networks. Nevertheless, it exhibits intricate behavior at the boundary of order and chaos. We introduce a formalism for expressing the dynamics of multiple loops as a product of the dynamics of individual loops. Using it, we prove that the number of attractors scales as $2^m$, where $m$ is the number of nodes in loops - as fast as possible, and much faster than previously believed.
Resonance between planar self-affine measures

We show that if $\lbrace \phi_i\rbrace_{i\in \Gamma}$ and $\lbrace \psi_j\rbrace_{j\in\Lambda}$ are self-affine iterated function systems on the plane that satisfy strong separation, domination and irreducibility, then for any associated self-affine measures $\mu$ and $\nu$, the inequality $$\dim_{\rm H}(\mu*\nu)<\min \lbrace 2, \dim_{\rm H} \mu + \dim_{\rm H} \nu \rbrace$$ implies that there is algebraic resonance between the eigenvalues of the linear parts of $\phi_i$ and $\psi_j$. This extends to planar non-conformal setting the existing analogous results for self-conformal measures on the line.