Miracle-dz/UniFAR-FTUs huggingface dataset:
The acute effects of varied resistance training methods on blood lactate and loading characteristics in recreationally trained men

Several resistance training methods (RTM) have been developed in order to manipulate physiological stimuli and obtain better results with training. The purpose of this study is to compare the metabolic and mechanical responses among seven different RTM reported in the literature. The RTM were compared with regard to blood lactate, time under tension (TUT) and total loading (TUT x load) in recreationally trained young men. The RTM tested were 10RM (TEN), super-slow (SL), functional isometrics (FI), adapted vascular occlusion (VO), 6RM (SIX), forced repetitions (FR) and breakdowns (BD). All RTM produced significant increases in blood lactate, with no difference among them. The BD method elicited higher TUT and total loading compared to the other RTM tested.
Control of breast cancer cell Interactions and mechanisms growth by steroids and growth factors :

Over the past two decades, the simple model for control of breast cancer growth involving one or two factors acting directly or indirectly via endocrine pathways has turned into a complex model implicating numerous interacting factors and the diverse cell populations constituting breast tumors. Current approaches to breast cancer therapy now require integration of these multiple parameters and enhanced understanding of the different levels of their intricate interactions.
Probability of Coverage and Interval Length for Two-Group Techniques Assessing the Median and Trimmed Mean

The purpose of the present study was to assess the probability of coverage and interval length of selected statistical techniques that have a higher finite sample breakdown point than the mean and appropriate levels of probability of coverage when using Bradley’s (1978) criterion. The techniques were examined using real education and psychology datasets (Sawilowsky & Fahoome, 2003, Sawilowsky & Blair, 1992). Welch’s test exhibited appropriate coverage for the smooth symmetric, mass at zero, digit preference, and extreme bimodal distributions. Yuen’s technique performed well under an extreme bimodal distribution. Results concerning the Maritz-Jarrett and the McKean-Schrader techniques are also presented.
Some existence theorems on differential equations of generalized order.

Theorem 1. // /(#, y) is a real-valued, continuous and Lipschitzian function, i. e. (#, y) € (C, Lip), on a domain D of the (xy)-plane and sup /(#, y) \ = M < σο, then there exists a unique and continuous solution y=y(x) which satisfies the differential equation y" = /(s,y), o < α <; i ), together with the condition y~~(a) = η in the region R c: D: a < χ <Ξ a + A, \(x — a)~~*y(x) — b \ ̂ k, , , η , ΛίΑ ftere b = r , : >-ττ/—τ~τγ· Proof. Define the sequence of functions {^Γ}*=0 s follows:
Epidemiological Features of Klebsiella pneumoniae Infection in the Hepatobiliary System of Patients in Yantai, China, Based on Clinical and Genetic Analyses

Purpose To investigate the epidemiological features of Klebsiella pneumoniae infection of the hepatobiliary system of patients in Yantai, China. Methods This retrospective study was conducted from January to December 2019 in Yantai Yuhuangding Hospital. Patients for whom K. pneumoniae was isolated from the hepatobiliary system were considered for inclusion. The clinical features and genetic analyses were conducted to explore the epidemiological characteristics. Results A total of 88 cases were enrolled, including 69 cases of hypervirulent K. pneumoniae (hvKP) and 19 cases of classical K. pneumoniae (cKP). Community-acquired infections, fever, liver abscess, and C-reactive protein (CRP) and procalcitonin (PCT) levels were significantly higher, while biliary tract disease was lower in the hvKP group compared with the cKP group. Among the 69 hvKP infections, 61 developed a liver abscess. Community-acquired infections, fever, and CRP and PCT levels were higher, whereas biliary tract disease and malignancy were lower in the liver abscess group compared with the non-liver abscess group. All strains were susceptible to the majority of antibiotics tested. All hvKP strains possessed the blaSHV, oqxA, oqxB and fosA resistance genes. K1 and K2 accounted for 78% of hvKP strains. K1 strains belonged to sequence types ST23 and ST700, whereas K2 strains belonged to ST65, ST86 and ST5212. K1 isolates possessed the most virulence determinants, followed by K2 and non-K1/K2 isolates. K2 isolates lacked the allS gene, which was rare in non K1/K2 isolates, but present in most K1 isolates. The mceG gene was only detected in K1 isolates. AllS and virulence determinants were significantly more prevalent in the liver abscess group than in the non-liver abscess group. Conclusion The prevalence of hvKP among K. pneumoniae infections of the hepatobiliary system is high in Yantai, China. Greater vigilance of hvKP infection is required in clinical and microbiological laboratories.
Scaling Principles of White Matter Connectivity in the Human and Nonhuman Primate Brain

Abstract Brains come in many shapes and sizes. Nature has endowed big-brained primate species like humans with a proportionally large cerebral cortex. Comparative studies have suggested, however, that the total volume allocated to white matter connectivity—the brain’s infrastructure for long-range interregional communication—does not keep pace with the cortex. We investigated the consequences of this allometric scaling on brain connectivity and network organization. We collated structural and diffusion magnetic resonance imaging data across 14 primate species, describing a comprehensive 350-fold range in brain size across species. We show volumetric scaling relationships that indeed point toward a restriction of macroscale connectivity in bigger brains. We report cortical surface area to outpace white matter volume, with larger brains showing lower levels of overall connectedness particularly through sparser long-range connectivity. We show that these constraints on white matter connectivity are associated with longer communication paths, higher local network clustering, and higher levels of asymmetry in connectivity patterns between homologous areas across the left and right hemispheres. Our findings reveal conserved scaling relationships of major brain components and show consequences for macroscale brain circuitry, providing insights into the connectome architecture that could be expected in larger brains such as the human brain.
Self-starting microring laser solitons from the periodic cubic complex Ginzburg-Landau equation

Dissipative Kerr solitons are optical pulses propagating in a nonlinear dielectric waveguide without dispersing. These attractive properties have spurred much research into integrated soliton generation in microring resonators at telecom wavelenghts. However, these solitons are generated via external pumping of a nonlinear medium, which limits system compactness and the wavelength coverage. Recently it was shown that dissipative Kerr solitons can be generated directly in the gain medium of mid-infrared semiconductor ring lasers, where the energy is supplied via electrical pumping. These technological advances enable a new route towards the monolithic generation of diverse light states in a wide frequency range, requiring insight into the conditions necessary for achieving control over the system. In this work, we study in depth the phase diagram of such systems, exhibiting fast gain dynamics and no external pumping, and which are described by the complex cubic Ginzburg-Landau equation with periodic boundary conditions. While previous studies have focused on large system sizes, we here focus on ﬁnite ring sizes leading to a modiﬁcation of the phase space and enlargement of the region for stable soliton formation. In addition to localized, dispersion-less soliton pulses on a ﬁnite background, breather solitons are predicted to occur under feasible experimental conditions. This enables monolithic generation of solitions in electrically pumped media such as quantum cascade lasers, extending the availability of soliton sources to mid-infrared and terahertz frequencies which are very attractive for molecular spectroscopy, free-space communication, imaging, and coating thickness measurements.
Axial and radial axonal diffusivities from single encoding strongly diffusion-weighted MRI

axondiﬀusionradiusdiameterMRIHumanConnectomepowderaveragingsphericalmeansphericalvariancevariableprojectionzonalharmonicssphericalharmonics Abstract We enable the estimation of the per-axon axial diﬀusivity from single encoding, strongly diﬀusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diﬀusivity compared to estimates based on spherical averaging. The use of strong diﬀusion weightings in magnetic resonance imaging (MRI) allows to approximate the signal in white matter as the sum of the contributions from axons. At the same time, spherical averaging leads to a major simpliﬁcation of the modeling by removing the need to explicitly account for the unknown orientation distribution of axons. However, the spherically averaged signalacquiredatstrongdiﬀusionweightingsisnotsensitivetotheaxialdiﬀusivity,whichcannotthereforebeestimated.Afterrevisingexistingtheory,weintroduceanewgeneralmethodfortheestimationofbothaxonaldiﬀusivitiesatstrongdiﬀusionweightingsbasedonzonalharmonicsmodeling. Weadditionallyshowhowthiscouldleadtoestimatesthatarefreefrompartialvolumebiaswith,forinstance,graymatter. WetestthemethodonpubliclyavailabledatafromtheMGHAdultDiﬀusionHumanConnectomeprojectdataset. Wereportreferencevaluesofaxonaldiﬀusivitiesbasedon34subjects,andderiveestimatesofaxonalradii.Weaddresstheestimationproblemalsofromtheangleoftherequireddatapreprocessing,thepresenceofbiasesrelatedtomodelingassumptions,currentlimitations,andfuturepossibilities.
Distributed domination on sparse graph classes

We show that the dominating set problem admits a constant factor approximation in a constant number of rounds in the LOCAL model of distributed computing on graph classes with bounded expansion. This generalizes a result of Czygrinow et al. for graphs with excluded topological minors to very general classes of uniformly sparse graphs. We demonstrate how our general algorithm can be modified and fine-tuned to compute an ($11+\epsilon$)-approximation (for any $\epsilon>0)$ of a minimum dominating set on planar graphs. This improves on the previously best known approximation factor of 52 on planar graphs, which was achieved by an elegant and simple algorithm of Lenzen et al.
SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval

In this paper, we propose SimLM (Similarity matching with Language Model pre-training), a simple yet effective pre-training method for dense passage retrieval. It employs a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training. We use a replaced language modeling objective, which is inspired by ELECTRA (Clark et al., 2020), to improve the sample efficiency and reduce the mismatch of the input distribution between pre-training and fine-tuning. SimLM only requires access to an unlabeled corpus and is more broadly applicable when there are no labeled data or queries. We conduct experiments on several large-scale passage retrieval datasets and show substantial improvements over strong baselines under various settings. Remarkably, SimLM even outperforms multi-vector approaches such as ColBERTv2 (Santhanam et al., 2021) which incurs significantly more storage cost. Our code and model checkpoints are available at https://github.com/microsoft/unilm/tree/master/simlm .
The asymptotics of the optimal holomorphic extensions of holomorphic jets along submanifolds

We study the asymptotics of the L 2 -optimal holomorphic extensions of holomorphic jets associated with high tensor powers of a positive line bundle along submanifolds. More precisely, for a ﬁxed complex submanifold in a complex manifold, we consider the operator which for a given holomorphic jet along the submanifold of a positive line bundle associates the L 2 -optimal holomorphic extension of it to the ambient manifold. When the tensor power of the line bundle tends to inﬁnity, we give an explicit asymptotic formula for this extension operator. This is done by a careful study of the Schwartz kernels of the extension operator and related Bergman projectors. It extends our previous results, done for holomorphic sections instead of jets. As an application, we prove the asymptotic isometry between two natural norms on the space of holomorphic jets: one induced from the ambient manifold and another from the submanifold.
Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods

This paper introduces \textit{online bilevel optimization} in which a sequence of time-varying bilevel problems is revealed one after the other. We extend the known regret bounds for single-level online algorithms to the bilevel setting. Specifically, we provide new notions of \textit{bilevel regret}, develop an online alternating time-averaged gradient method that is capable of leveraging smoothness, and give regret bounds in terms of the path-length of the inner and outer minimizer sequences.
Zero-point lattice expansion and band gap renormalization: Grüneisen approach versus free energy minimization

The zero-point lattice expansion (ZPLE) is a small variation of the lattice parameters induced by the presence of phonons in a material compared to the static lattice picture. It contributes significantly to the zero-point renormalization (ZPR) of the band gap energy, but its consequences have not been investigated as thoroughly as those stemming from electron-phonon interactions. In the usual first-principles approach, one evaluates the ZPLE by minimizing the $T=0$ K Helmholtz free energy. In this work, we show that the formalism based on the Gr\"uneisen parameters, which commonly neglects zero-point effects, can be efficiently used to compute ZPLE for both isotropic and anisotropic materials at much lower computational cost. We systematically test this formalism on 22 cubic and wurtzite materials and obtain excellent agreement with free energy minimization results for both the ZPLE and the resulting band gap ZPR. We use our results to validate an empirical expression estimating the ZPLE-induced ZPR and unveil its sensitivity to the temperature range involved in estimating the ZPLE from experimental data. Our findings finally reveal that the ZPLE contribution to the band gap ZPR can reach 20% to more than 80% of the electron-phonon interaction contribution for heavier or more ionic materials, including materials containing light atoms. Considering both contributions on an equal footing is thus essential should one attempt to compare theoretical ZPR results with experimental data.
Exploring gluon tomography with polarization dependent diffractive J/$\psi$ production

We study azimuthal asymmetries in diﬀractive J/ ψ production in ultraperipheral heavy-ion collisions at RHIC and LHC energies using the color glass condensate eﬀective theory. Our calculation successfully describes azimuthal averaged J/ψ production cross section measured by STAR and ALICE. We further predict very large cos 2 φ and cos 4 φ azimuthal asymmetries for diﬀractive J/ψ production both in UPCs at RHIC and LHC energies and in eA collisions at EIC energy. These novel polarization dependent observables may provide complementary information for constraining gluon transverse spatial distribution inside large nuclei. As compared to all previous analysis of diﬀractive J/ψ production, the essential new elements integrated in our theoretical calculations are: the double-slit interference eﬀect, the linear polarization of coherent photons, and the ﬁnal state soft photon radiation eﬀect.
Robust Counterfactual Explanations for Tree-Based Ensembles

Counterfactual explanations inform ways to achieve a desired outcome from a machine learning model. However, such explanations are not robust to certain real-world changes in the underlying model (e.g., retraining the model, changing hyperparameters, etc.), questioning their reliability in several applications, e.g., credit lending. In this work, we propose a novel strategy -- that we call RobX -- to generate robust counterfactuals for tree-based ensembles, e.g., XGBoost. Tree-based ensembles pose additional challenges in robust counterfactual generation, e.g., they have a non-smooth and non-differentiable objective function, and they can change a lot in the parameter space under retraining on very similar data. We first introduce a novel metric -- that we call Counterfactual Stability -- that attempts to quantify how robust a counterfactual is going to be to model changes under retraining, and comes with desirable theoretical properties. Our proposed strategy RobX works with any counterfactual generation method (base method) and searches for robust counterfactuals by iteratively refining the counterfactual generated by the base method using our metric Counterfactual Stability. We compare the performance of RobX with popular counterfactual generation methods (for tree-based ensembles) across benchmark datasets. The results demonstrate that our strategy generates counterfactuals that are significantly more robust (nearly 100% validity after actual model changes) and also realistic (in terms of local outlier factor) over existing state-of-the-art methods.
Instance-optimal PAC Algorithms for Contextual Bandits

In the stochastic contextual bandit setting, regret-minimizing algorithms have been extensively researched, but their instance-minimizing best-arm identification counterparts remain seldom studied. In this work, we focus on the stochastic bandit problem in the $(\epsilon,\delta)$-$\textit{PAC}$ setting: given a policy class $\Pi$ the goal of the learner is to return a policy $\pi\in \Pi$ whose expected reward is within $\epsilon$ of the optimal policy with probability greater than $1-\delta$. We characterize the first $\textit{instance-dependent}$ PAC sample complexity of contextual bandits through a quantity $\rho_{\Pi}$, and provide matching upper and lower bounds in terms of $\rho_{\Pi}$ for the agnostic and linear contextual best-arm identification settings. We show that no algorithm can be simultaneously minimax-optimal for regret minimization and instance-dependent PAC for best-arm identification. Our main result is a new instance-optimal and computationally efficient algorithm that relies on a polynomial number of calls to an argmax oracle.
A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

The accelerating technological landscape and drive towards net-zero emission made the power system grow in scale and complexity. Serial computational approaches for grid planning and operation struggle to execute necessary calculations within reasonable times. Resorting to high-performance and parallel computing approaches has become paramount. Moreover, the ambitious plans for the future grid and IoT integration make a shift towards utilizing Cloud computing inevitable. This article recounts the dawn of parallel computation and its appearance in power system studies, reviewing the most recent literature and research on exploiting the available computational resources and technologies today. The article starts with a brief introduction to the field. The relevant hardware and paradigms are then explained thoroughly in this article providing a base for the reader to understand the literature. Later, parallel power system studies are reviewed, reciting the study development from older papers up to the 21st century, emphasizing the most impactful work of the last decade. The studies included system stability studies, state estimation and power system operation, and market optimization. The reviews are split into \ac{CPU} based,\ac{GPU} based, and Cloud-based studies. Finally, the state-of-the-art is discussed, highlighting the issue of standardization and the future of computation in power system studies.
Local rigidity for hyperbolic toral automorphisms

We consider a hyperbolic toral automorphism L and its C 1 -small perturbation f . It is well-known that f is Anosov and topologically conjugate to L , but a conjugacy H is only H¨older continuous in general. We discuss conditions for smoothness of H , such as conjugacy of the periodic data of f and L , coincidence of their Lyapunov exponents, and weaker regularity of H , and we summarize questions, results, and techniques in this area. Then we introduce our new results: if H is weakly diﬀerentiable then it is C 1+H¨older and, if L is also weakly irreducible, then H is C ∞ . hyperbolic systems is structural stability : any diﬀeomorphism f of M suﬃciently C 1 close to an Anosov diﬀeomorphism g is also Anosov and is topologically conjugate to g [A67]. The latter means that there exists a homeomorphism H of M , called a conjugacy , such that g ◦ H = H ◦ f . Moreover, H is unique in a C 0 neighborhood of the identity. A conjugacy H is always bi-H¨older, but it is usually not even C 1 , as there are various obstructions to smoothness. The problem of establishing smoothness of the conjugacy H from some weaker assumptions has been extensively stud-ied. It is often referred to as local rigidity , in the sense that weak equivalence of f and g implies strong equivalence.
A Furstenberg-type problem for circles, and a Kaufman-type restricted projection theorem in $\mathbb{R}^3$

We resolve a conjecture of F\"assler and Orponen on the dimension of exceptional projections to one-dimensional subspaces indexed by a space curve in $\mathbb{R}^3$. We do this by obtaining sharp $L^p$ bounds for a variant of the Wolff circular maximal function over fractal sets for a class of $C^2$ curves related to Sogge's cinematic curvature condition. A key new tool is the use of lens cutting techniques from discrete geometry.
Deep Learning-Based Partial Volume Correction in Standard and Low-Dose PET-CT Imaging

A standard dose of radioactive tracer must be delivered into the patients body to obtain high-quality Positron Emission Tomography (PET) images for diagnostic purposes, which raises the risk of radiation harm. A reduced tracer dose, on the other hand, results in poor image quality and a noise-induced quantitative bias in PET imaging. The partial volume effect (PVE), which is the result of PET intrinsic limited spatial resolution, is another source of quality and quantity degradation in PET imaging. The utilization of anatomical information for PVE correction (PVC) is not straightforward due to the internal organ motions, patient involuntary motions, and discrepancies in the appearance and size of the structures in anatomical and functional images. Furthermore, an additional MR imaging session is necessary for anatomical information, which may not be available. We set out to build a deep learning-based framework for predicting partial volume corrected full-dose (FD-PVC) pictures from either standard or low-dose (LD) PET images without requiring any anatomical data in order to provide a joint solution for PVC and denoise low-dose PET images.