Miracle-dz/UniFAR-FTUs huggingface dataset:
Differences in the Cortical Structure of the Whole Fibula and Tibia Between Long-Distance Runners and Untrained Controls. Toward a Wider Conception of the Biomechanical Regulation of Cortical Bone Structure

Differences in the Cortical Structure of the Whole Fibula and Tibia Between Long-Distance Runners and Untrained Controls. Toward a Wider Conception of the Biomechanical Regulation of Cortical Bone Structure. The cortical structure of human ﬁbula varies widely throughout the bone suggesting a more selective adaptation to different mechanical environments with respect to the adjacent tibia. To test this hypothesis, serial-pQCT scans of the dominant ﬁbulae and tibiae of 15/15 men/women chronically trained in long-distance running were compared with those of 15/15 untrained controls. When compared to controls, the ﬁbulae of trained individuals had similar (distally) or lower (proximally) cortical area, similar moments of inertia (MI) for anterior-posterior bending (xMI) and lower for lateral bending (yMI) with a lower “shape-index” (yMI/xMI ratio) throughout, and higher resistance to buckling distally. These group differences were more evident in men and independent of group differences in bone mass. These results contrast with those observed in the tibia, where, as expected, structural indicators of bone strength were greater in trained than untrained individuals. Proximally, the larger lateral ﬂexibility of runners’ ﬁbulae could improve the ability to store energy, and thereby contribute to fast-running optimization. Distally, the greater lateral ﬁbular ﬂexibility could reduce bending strength. The latter appears to have been compensated by a higher buckling strength. Assuming that these differences could be ascribed to training effects, this suggests that usage-derived strains in some bones may modify their relative structural resistance to different kinds of deformation in different regions, not only regarding strength, but also concerning other physiological roles of the skeleton.
SCREENING FOR THE EXTENDED SPECTRUM BETA LACTAMASES AMONG SALMONELLA SPP. ISOLATED FROM BROILER CHICKEN IN EGYPT.

1. Bacteriology Department, Animal Health Research Institute, Mansoura branch, 12618, Egypt. 2. Food Hygiene Department, Animal Health Research Institute, Mansoura branch, 12618, Egypt. 3. Biochemistry Department, Animal Health Research Institute, Mansoura branch, 12618, Egypt. Manuscript Info Abstract . Manuscript History Received: 04 November 2018 Final Accepted: 06 December 2018 Published: January 2019
Harmonic Morphisms on Lie groups and Minimal Conformal Foliations of Codimension two

Let G be a Lie group equipped with a left-invariant semi-Riemannian metric. Let K be a semisimple subgroup of G generating a left-invariant conformal foliation F of codimension two on G. We then show that the foliation F is minimal. This means that locally the leaves of F are fibres of a complex-valued harmonic morphism. In the Riemannian case, we prove that if the metric restricted to K is biinvariant then F is totally geodesic.
Certifying C program correctness with respect to CH2O with VeriFast

VeriFast is a powerful tool for verification of various correctness properties of C programs using symbolic execution. However, VeriFast itself has not been verified. We present a proof-of-concept extension which generates a correctness certificate for each successful verification run individually. This certificate takes the form of a Coq script which, when successfully checked by Coq, removes the need for trusting in the correctness of VeriFast itself. The Coq script achieves this by applying a chain of soundness results, allowing us to prove correctness of the program with regards to the third-party CH2O small step semantics for C11 by proving correctness in terms of symbolic execution in Coq. This proof chain includes two intermediate auxiliary big step semantics, the most important of which describes VeriFast's interpretation of C. Finally, symbolic execution in Coq is implemented by transforming the exported AST of the program into a Coq proposition representing the symbolic execution performed by VeriFast itself.
Alleviating the H0 Tension in Scalar–Tensor and Bi-Scalar–Tensor Theories

Herein, we investigate scalar–tensor and bi-scalar–tensor modified theories of gravity that can alleviate the H0 tension. In the first class of theories, we show that by choosing particular models with a shift-symmetric friction term we are able to alleviate the tension by obtaining a smaller effective Newton’s constant at intermediate times, a feature that cannot be easily obtained in modified gravity. In the second class of theories, which involve two extra propagating degrees of freedom, we show that the H0 tension can be alleviated, and the mechanism behind this is the phantom behavior of the effective dark-energy equation-of-state parameter. Hence, scalar–tensor and bi-scalar–tensor theories have the ability to alleviate the H0 tension with both known sufficient late-time mechanisms.
The influence of secondary resection using NeuroSAFE-technique on sexual function in unilateral nerve-sparing robot-assisted laparoscopic prostatectomies.

OBJECTIVE To demonstrate the surgical influence of secondary resection on sexual function in finally unilateral nerve-sparing robot- assisted laparoscopic prostatectomies (RALPs) performed with the 'neurovascular structure-adjacent frozen-section examination' (NeuroSAFE) technique by prospectively collecting EPIC-26-questionnaires. MATERIAL & METHODS Sexual function status measured by the sexual-symptom-score (SexSS) in the EPIC-26-questionnaires was collected preoperatively and 12 months after RALP from 378 patients between 09/2019 and 04/2021. Cohorts of interest were defined as those patients undergoing unilateral nerve-sparing by secondary resection of the other neurovascular bundle (NVB), and as those patients undergoing primarily planned and successful unilateral nerve-sparing (unilateral nerve-sparing without secondary resection) in ≤cT2 prostate cancer. NeuroSAFE frozen section technique was performed in all nerve-sparing RALPs, and in case of cancer-positive surgical margins, the complete NVB was resected. RESULTS In 109 RALPs with unilateral nerve-sparing (48 primarily vs. 61 by secondary resection), analyses showed a significant difference in postoperative SexSS for 'unilateral nerve-sparing by secondary resection' compared with 'unilateral nerve-sparing without secondary resection' (43 [interquartile range (IQR): 14;50] vs. 26 [IQR: 22;62], P = 0.04). In multivariable analyses, the preoperative SexSS was predictive for postoperative erectile dysfunction (OR = 0.96, 95% confidence interval: 0.93-0.98, P < 0.001). Oncological safety was not compromised by secondary resection (prostate-specific antigen after 12 months 0.01 ng/mL vs. 0.01 ng/mL [P = 0.3] for unilateral nerve-sparing by secondary resection vs. unilateral nerve-sparing without secondary resection). CONCLUSION The results of this study suggest that nerve-sparing attempts applying the NeuroSAFEtechnique should be generously performed since a unilateral complete secondary resection leading to a unilateral nerve-sparing RALP did not seem to have a negative influence on sexual function and did not seem to compromise oncological safety compared with primarily performed and successful unilateral nerve-sparing RALP.
Ripples of the QCD Critical Point

We investigate the impact of a critical end point (CEP) on the experimentally accessible baryon number fluctuations of different orders. By now, its potential location has been constrained fairly accurately within first principles functional QCD, together with the location of the chiral crossover line and further thermodynamic observables. This information is incorporated in an advanced QCD-assisted low energy effective theory which is used for the computation of baryon number fluctuations at the chemical freeze-out. This computation also takes care of global baryon number conservation at larger density, where the system changes from grand-canonical to canonical statistics. We observe a prominent peak structure, whose amplitude depends on the location of the CEP, while its position is more sensitive to the location of the freeze-out curve. Our results provide guidance for future low energy heavy-ion experiments.
Understanding coupled mass-heat transport in fluids by approach-to-equilibrium molecular dynamics

We present a generalization of AEMD approach, routinely applied to estimate thermal conductivity, to the more general case in which Soret and Dufour effects determine a coupled heat-mass transfer. We show that, by starting from microscopical definitions of heat and mass currents, conservation laws dictates the form of the differential equations governing the time evolution. In particular, we focus to the well specific case in which a closed-form solution of the system is possible and derive the analytical form of time-evolution of temperature and concentration scalar fields in the case in which step-like initial profiles are imposed across a rectangular simulation cell. The validity of this new generalized expression is finally validated using as benchamrk system a two-component Lennard-Jones liquid system, for which generalized diffusivities are estimated in different reduced temperature and density region of phase diagram.
Spectrum of Laplacian matrices associated with large random elliptic matrices

A Laplacian matrix is a square matrix whose row sums are zero. We study the limiting eigenvalue distribution of a Laplacian matrix formed by taking a random elliptic matrix and subtracting the diagonal matrix containing its row sums. Under some mild assumptions, we show that the empirical spectral distribution of the Laplacian matrix converges to a deterministic probability distribution as the size of the matrix tends to infinity. The limiting measure can be interpreted as the Brown measure of the sum of an elliptic operator and a freely independent normal operator with a Gaussian distribution.
Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting

The task of radiology reporting comprises describing and interpreting the medical findings in radiographic images, including description of their location and appearance. Automated approaches to radiology reporting require the image to be encoded into a suitable token representation for input to the language model. Previous methods commonly use convolutional neural networks to encode an image into a series of image-level feature map representations. However, the generated reports often exhibit realistic style but imperfect accuracy. Inspired by recent works for image captioning in the general domain in which each visual token corresponds to an object detected in an image, we investigate whether using local tokens corresponding to anatomical structures can improve the quality of the generated reports. We introduce a novel adaptation of Faster R-CNN in which finding detection is performed for the candidate bounding boxes extracted during anatomical structure localisation. We use the resulting bounding box feature representations as our set of finding-aware anatomical tokens. This encourages the extracted anatomical tokens to be informative about the findings they contain (required for the final task of radiology reporting). Evaluating on the MIMIC-CXR dataset of chest X-Ray images, we show that task-aware anatomical tokens give state-of-the-art performance when integrated into an automated reporting pipeline, yielding generated reports with improved clinical accuracy.
Understanding step selection analysis through numerical integration

Step selection functions (SSFs) are flexible statistical models used to jointly describe animals' movement and habitat preferences. The popularity of SSFs has grown rapidly, and various extensions have been developed to increase their utility, including the ability to use multiple statistical distributions to describe movement constraints, interactions to allow movements to depend on local environmental features, and random effects and latent states to account for within‐ and among‐individual variability. Although the SSF is a relatively simple statistical model, its presentation has not been consistent in the literature, leading to confusion about model flexibility and interpretation. We believe that part of the confusion has arisen from the conflation of the SSF model with the methods used for statistical inference, and in particular, parameter estimation. Notably, conditional logistic regression (CLR) can be used to fit SSFs in exponential form, and this model fitting approach is often presented interchangeably with the actual model (the SSF itself). However, reliance on CLR reduces model flexibility, and suggests a misleading interpretation of step selection analysis as being equivalent to a case–control study. In this review, we explicitly distinguish between model formulation and inference technique, presenting a coherent framework to fit SSFs based on numerical integration and maximum likelihood estimation. We provide an overview of common numerical integration techniques (including Monte Carlo integration, importance sampling and quadrature), and explain how they relate to popular methods used in step selection analyses. This general framework unifies different model fitting techniques for SSFs, and opens the way for improved inferential methods. In this approach, it is straightforward to model movement with distributions outside the exponential family, and to apply different SSF model formulations to the same data set and compare them with AIC. By separating the model formulation from the inference technique, we hope to clarify many important concepts in step selection analysis.
System-size dependence of the hadronic rescattering effect at energies available at the CERN Large Hadron Collider

The first measurements of $\mathrm{K^{*}(892)^{0}}$ resonance production as a function of charged-particle multiplicity in Xe$-$Xe collisions at $\sqrt{s_{\mathrm{NN}}}=$ 5.44 TeV and pp collisions at $\sqrt{s}=$ 5.02 TeV using the ALICE detector are presented. The resonance is reconstructed at midrapidity ($|y|<0.5$) using the hadronic decay channel $\mathrm{K^{*0}} \rightarrow \mathrm{K^{\pm} \pi^{\mp}}$. Measurements of transverse-momentum integrated yield, mean transverse-momentum, nuclear modification factor of $\mathrm{K^{*0}}$, and yield ratios of resonance to stable hadron ($\mathrm{K^{*0}}$/K) are compared across different collision systems (pp, p$-$Pb, Xe$-$Xe, and Pb$-$Pb) at similar collision energies to investigate how the production of $\mathrm{K^{*0}}$ resonances depends on the size of the system formed in these collisions. The hadronic rescattering effect is found to be independent of the size of colliding systems and mainly driven by the produced charged-particle multiplicity, which is a proxy of the volume of produced matter at the chemical freeze-out. In addition, the production yields of $\mathrm{K^{*0}}$ in Xe$-$Xe collisions are utilized to constrain the dependence of the kinetic freeze-out temperature on the system size using the hadron resonance gas in partial chemical equilibrium (HRG-PCE) model.
Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization

Contrastive Language-Image Pre-training (CLIP) models have shown promising performance on zero-shot visual recognition tasks by learning visual representations under natural language supervision. Recent studies attempt the use of CLIP to tackle zero-shot anomaly detection by matching images with normal and abnormal state prompts. However, since CLIP focuses on building correspondence between paired text prompts and global image-level representations, the lack of fine-grained patch-level vision to text alignment limits its capability on precise visual anomaly localization. In this work, we propose AnoCLIP for zero-shot anomaly localization. In the visual encoder, we introduce a training-free value-wise attention mechanism to extract intrinsic local tokens of CLIP for patch-level local description. From the perspective of text supervision, we particularly design a unified domain-aware contrastive state prompting template for fine-grained vision-language matching. On top of the proposed AnoCLIP, we further introduce a test-time adaptation (TTA) mechanism to refine visual anomaly localization results, where we optimize a lightweight adapter in the visual encoder using AnoCLIP's pseudo-labels and noise-corrupted tokens. With both AnoCLIP and TTA, we significantly exploit the potential of CLIP for zero-shot anomaly localization and demonstrate the effectiveness of AnoCLIP on various datasets.
Cyclophobic Reinforcement Learning

In environments with sparse rewards, finding a good inductive bias for exploration is crucial to the agent's success. However, there are two competing goals: novelty search and systematic exploration. While existing approaches such as curiosity-driven exploration find novelty, they sometimes do not systematically explore the whole state space, akin to depth-first-search vs breadth-first-search. In this paper, we propose a new intrinsic reward that is cyclophobic, i.e., it does not reward novelty, but punishes redundancy by avoiding cycles. Augmenting the cyclophobic intrinsic reward with a sequence of hierarchical representations based on the agent's cropped observations we are able to achieve excellent results in the MiniGrid and MiniHack environments. Both are particularly hard, as they require complex interactions with different objects in order to be solved. Detailed comparisons with previous approaches and thorough ablation studies show that our newly proposed cyclophobic reinforcement learning is more sample efficient than other state of the art methods in a variety of tasks.
Fully Embedded Time-Series Generative Adversarial Networks

Generative Adversarial Networks (GANs) should produce synthetic data that fits the underlying distribution of the data being modeled. For real valued time-series data, this implies the need to simultaneously capture the static distribution of the data, but also the full temporal distribution of the data for any potential time horizon. This temporal element produces a more complex problem that can potentially leave current solutions under-constrained, unstable during training, or prone to varying degrees of mode collapse. In FETSGAN, entire sequences are translated directly to the generator's sampling space using a seq2seq style adversarial auto encoder (AAE), where adversarial training is used to match the training distribution in both the feature space and the lower dimensional sampling space. This additional constraint provides a loose assurance that the temporal distribution of the synthetic samples will not collapse. In addition, the First Above Threshold (FAT) operator is introduced to supplement the reconstruction of encoded sequences, which improves training stability and the overall quality of the synthetic data being generated. These novel contributions demonstrate a significant improvement to the current state of the art for adversarial learners in qualitative measures of temporal similarity and quantitative predictive ability of data generated through FETSGAN.
Quasioptimal alternating projections and their use in low-rank approximation of matrices and tensors

We study the convergence of specific inexact alternating projections for two non-convex sets in a Euclidean space. The $\sigma$-quasioptimal metric projection ($\sigma \geq 1$) of a point $x$ onto a set $A$ consists of points in $A$ the distance to which is at most $\sigma$ times larger than the minimal distance $\mathrm{dist}(x,A)$. We prove that quasioptimal alternating projections, when one or both projections are quasioptimal, converge locally and linearly for super-regular sets with transversal intersection. The theory is motivated by the successful application of alternating projections to low-rank matrix and tensor approximation. We focus on two problems -- nonnegative low-rank approximation and low-rank approximation in the maximum norm -- and develop fast alternating-projection algorithms for matrices and tensor trains based on cross approximation and acceleration techniques. The numerical experiments confirm that the proposed methods are efficient and suggest that they can be used to regularise various low-rank computational routines.
ACNPU: A 4.75TOPS/W 1080P@30FPS Super Resolution Accelerator With Decoupled Asymmetric Convolution

Deep learning-driven superresolution (SR) outperforms traditional techniques but also faces the challenge of high complexity and memory bandwidth. This challenge leads many accelerators to opt for simpler and shallow models like FSRCNN, compromising performance for real-time needs, especially for resource-limited edge devices. This paper proposes an energy-efficient SR accelerator, ACNPU, to tackle this challenge. The ACNPU enhances image quality by 0.34dB with a 27-layer model, but needs 36% less complexity than FSRCNN, while maintaining a similar model size, with the decoupled asymmetric convolution and split-bypass structure. The hardware-friendly 17K-parameter model enables holistic model fusion instead of localized layer fusion to remove external DRAM access of intermediate feature maps. The on-chip memory bandwidth is further reduced with the input stationary flow and parallel-layer execution to reduce power consumption. Hardware is regular and easy to control to support different layers by processing elements (PEs) clusters with reconfigurable input and uniform data flow. The implementation in the 40 nm CMOS process consumes 2333 K gate counts and 198Â KB SRAMs. The ACNPU achieves 31.7 FPS and 124.4 FPS for $\times 2$ and $\times 4$ scales Full-HD generation, respectively, which attains 4.75 TOPS/W energy efficiency.
SAM-Med2D

The Segment Anything Model (SAM) represents a state-of-the-art research advancement in natural image segmentation, achieving impressive results with input prompts such as points and bounding boxes. However, our evaluation and recent research indicate that directly applying the pretrained SAM to medical image segmentation does not yield satisfactory performance. This limitation primarily arises from significant domain gap between natural images and medical images. To bridge this gap, we introduce SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images. Specifically, we first collect and curate approximately 4.6M images and 19.7M masks from public and private datasets, constructing a large-scale medical image segmentation dataset encompassing various modalities and objects. Then, we comprehensively fine-tune SAM on this dataset and turn it into SAM-Med2D. Unlike previous methods that only adopt bounding box or point prompts as interactive segmentation approach, we adapt SAM to medical image segmentation through more comprehensive prompts involving bounding boxes, points, and masks. We additionally fine-tune the encoder and decoder of the original SAM to obtain a well-performed SAM-Med2D, leading to the most comprehensive fine-tuning strategies to date. Finally, we conducted a comprehensive evaluation and analysis to investigate the performance of SAM-Med2D in medical image segmentation across various modalities, anatomical structures, and organs. Concurrently, we validated the generalization capability of SAM-Med2D on 9 datasets from MICCAI 2023 challenge. Overall, our approach demonstrated significantly superior performance and generalization capability compared to SAM.
The role of plasmapheresis in severe acute disseminated encephalomyelitis with clinical findings of transverse myelitis

Acute disseminated encephalomyelitis is a rare acute demyelinating disease of the central nervous system (CNS). The pathogenesis remains unclear but is suspected to be autoimmune. High doses of methylprednisolone (HDMP) are currently considered standard of treatment. Plasmapheresis (PE) is typically given in steroid refractory cases. There is currently limited evidence supporting its use in ADEM.