Miracle-dz/UniFAR-FTUs huggingface dataset:
Probiotic powder ameliorates colorectal cancer by regulating Bifidobacterium animalis, Clostridium cocleatum, and immune cell composition

Based on the relationship between the gut microbiota and colorectal cancer, we developed a new probiotic powder for treatment of colorectal cancer. Initially, we evaluated the effect of the probiotic powder on CRC using hematoxylin and eosin staining, and evaluated mouse survival rate and tumor size. We then investigated the effects of the probiotic powder on the gut microbiota, immune cells, and apoptotic proteins using 16S rDNA sequencing, flow cytometry, and western blot, respectively. The results showed that the probiotic powder improved the intestinal barrier integrity, survival rate, and reduced tumor size in CRC mice. This effect was associated with changes in the gut microbiota. Specifically, the probiotic powder increased the abundance of Bifidobacterium animalis and reduced the abundance of Clostridium cocleatum. In addition, the probiotic powder resulted in decreased numbers of CD4+ Foxp3+ Treg cells, increased numbers of IFN-γ+ CD8+ T cells and CD4+ IL-4+ Th2 cells, decreased expression of the TIGIT in CD4+ IL-4+ Th2 cells, and increased numbers of CD19+ GL-7+ B cells. Furthermore, the expression of the pro-apoptotic protein BAX was significantly increased in tumor tissues in response to the probiotic powder. In summary, the probiotic powder ameliorated CRC by regulating the gut microbiota, reducing Treg cell abundance, promoting the number of IFN-γ+ CD8+ T cells, increasing Th2 cell abundance, inhibiting the expression of TIGIT in Th2 cells, and increasing B cell abundance in the immune microenvironment of CRC, thereby increasing the expression of BAX in CRC.
Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation

Objective We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. Materials and Methods We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. Results The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%–91.27%] vs. [standardized, 93.16%–96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%–91.37% vs. standardized, 1.99%–4.41%). In all protocols, CCCs improved after image conversion (original, -0.006–0.964 vs. standardized, 0.990–0.998). Conclusion Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.
Leaving no one behind in health: Financial hardship to access health care in Ethiopia

Background Financial hardship (of health care) is a global and a national priority area. All people should be protected from financial hardship to ensure inclusive better health outcome. However, financial hardship of healthcare has not been well studied in Ethiopia in general and in Debre Tabor town in particular. Therefore, this study aimed to assess the incidence of financial hardship of healthcare and associated factors among households in Debre Tabor town. Methods Community based cross sectional study was conducted, from May 24/2022 to June 17/2022, on 423 (selected through simple random sampling) households. Financial hardship was measured through catastrophic (using 10% threshold level) and impoverishing (using $1.90 poverty line) health expenditures. Patient perspective bottom up and prevalence based costing approach were used. Indirect cost was estimated through human capital approach. Bi-variable and multiple logistic regressions were used. Results The response rate was 95%. The mean household annual healthcare expenditure was Ethiopian birr 12050.64 ($227.37). About 37.1% (95%CI: 32, 42%) of the households spend catastrophic health expenditure with a 10% threshold level and 10.4% of households were impoverished with $1.90 per day poverty line. Being old, with age above 60, (AOR: 4.21, CI: 1.23, 14.45), being non-insured (AOR: 2.19, CI: 1.04, 4.62), chronically ill (AOR: 7.20, CI: 3.64, 14.26), seeking traditional healthcare (AOR: 2.63, CI: 1.37. 5.05) and being socially unsupported (AOR: 2.77, CI: 1.25, 6.17) were statistically significant factors for catastrophic health expenditure. Conclusion The study showed that significant number of households was not yet protected from financial hardship of healthcare. The financial hardship of health care is stronger among the less privileged populations: non-insured, the chronically diseased, the elder and socially unsupported. Therefore, financial risk protection strategies should be strengthened by the concerned bodies.
Distance classifier correlation filters for distortion tolerance, discrimination, and clutter rejection

A new approach to correlation filters based on quadratic distance calculations is described. The problem of distortion tolerance is addressed in terms of similarity measures. Discrimination is simultaneously addressed by optimizing the filters to maximally separate the classes. Mathematically, filter synthesis requires the inversion of diagonal matrices in the frequency domain and is a generalization of the MACE idea. The approach is shift-invariant, does not require feature extraction or image registration, and is significantly different from traditional pattern recognition techniques such as the Fisher LDF. The proposed approach is also suitable for the rejection of unknown clutter. Since recognition is based on similarity, clutter and false images which exhibit `large' distances from true target classes, are easily rejected. With improved recognition and discrimination performance, and low false alarm rates, the proposed distance classifier is a promising method for multiclass target recognition in cluttered environments.
Fine-grained Visual Classification with High-temperature Refinement and Background Suppression

Fine-grained visual classification is a challenging task due to the high similarity between categories and distinct differences among data within one single category. To address the challenges, previous strategies have focused on localizing subtle discrepancies between categories and enhencing the discriminative features in them. However, the background also provides important information that can tell the model which features are unnecessary or even harmful for classification, and models that rely too heavily on subtle features may overlook global features and contextual information. In this paper, we propose a novel network called ``High-temperaturE Refinement and Background Suppression'' (HERBS), which consists of two modules, namely, the high-temperature refinement module and the background suppression module, for extracting discriminative features and suppressing background noise, respectively. The high-temperature refinement module allows the model to learn the appropriate feature scales by refining the features map at different scales and improving the learning of diverse features. And, the background suppression module first splits the features map into foreground and background using classification confidence scores and suppresses feature values in low-confidence areas while enhancing discriminative features. The experimental results show that the proposed HERBS effectively fuses features of varying scales, suppresses background noise, discriminative features at appropriate scales for fine-grained visual classification.The proposed method achieves state-of-the-art performance on the CUB-200-2011 and NABirds benchmarks, surpassing 93% accuracy on both datasets. Thus, HERBS presents a promising solution for improving the performance of fine-grained visual classification tasks. code: https://github.com/chou141253/FGVC-HERBS
The lattice dynamics of HCN

Dispersive curves for crystalline HCN have been calculated on the basis of a theoretical intermolecular potential which had previously been used to study the lattice statics of this crystal. Good agreement is obtained with measured Raman frequencies, Debye-Waller factors and specific heat. The phase change from the orthorhombic to the tetragonal form can be understood in terms of the 'softening' of a transverse acoustic mode whose wavevector is close to the (110) direction.
Improving physics-informed neural networks with meta-learned optimization

We show that the error achievable using physics-informed neural networks for solving systems of differential equations can be substantially reduced when these networks are trained using meta-learned optimization methods rather than to using fixed, hand-crafted optimizers as traditionally done. We choose a learnable optimization method based on a shallow multi-layer perceptron that is meta-trained for specific classes of differential equations. We illustrate meta-trained optimizers for several equations of practical relevance in mathematical physics, including the linear advection equation, Poisson's equation, the Korteweg--de Vries equation and Burgers' equation. We also illustrate that meta-learned optimizers exhibit transfer learning abilities, in that a meta-trained optimizer on one differential equation can also be successfully deployed on another differential equation.
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning

In recent decades, wildfires have caused tremendous property losses, fatalities, and extensive damage to forest ecosystems. Inspired by the abundance of publicly available remote sensing projects and the burgeoning development of deep learning in computer vision, our research focuses on assessing fire risk using remote sensing imagery.In this work, we propose a novel remote sensing dataset, FireRisk, consisting of 7 fire risk classes with a total of 91872 labelled images for fire risk assessment. This dataset is labelled with the fire risk classes supplied by the Wildfire Hazard Potential (WHP) raster dataset [9], and remote sensing images are collected using the National Agriculture Imagery Program (NAIP) [27], a high-resolution remote sensing imagery program. On FireRisk, we present benchmark performance for supervised and self-supervised representations, with Masked Autoencoders (MAE) [16] pre-trained on ImageNet1k [8] achieving the highest classification accuracy, 65.29%. This remote sensing dataset, FireRisk, provides a new direction for fire risk assessment, and we make it publicly available on https://github.com/CharmonyShen/FireRisk.
A Multiblock Approach to Solving the Three-Dimensional Unsteady Euler Equations about a Wing-Pylon-Store Configuration f

The objective of this study is the develop- ment of a numerical method which can accurately and economically solve the unsteady Euler equa- tions for three-dimensional flowfields around complex configurations, particularly a generic aircraft with a store in the captive and vertical launch positions. A finite volume, multiblock i.mplementation of a high accuracy, total varia- tion diminishing (TVD) formulation (based on Roe averaging) is used to solve the unsteady Euler equations. A multiblock grid approach is used for calculating the flowfield around a complex aircraft configuration. The multiblock t echnique utilizes the concept of decomposing the flowfield between the surfaces of the configuration and some outer farfield boundary into a set of blocks. The union of these blocks fills the en- tire flowf ield without either holes or overlaps. Calculated results compared with experimental data indicate that the present Euler solver can calculate transonic f lowfields efficiently and accurately over complex geometries. Furthermore, the results demonstrate how computational fluid dynamics (CFD) can be used to accurately s imulate steady and unsteady fluid flow over a complete lwi.ng-pylon-store configuration with the store in Lhe captive and vertical launch positions.
E2CoPre: Energy Efficient and Cooperative Collision Avoidance for UAV Swarms with Trajectory Prediction

This paper presents a novel solution to address the challenges in achieving energy efficiency and cooperation for collision avoidance in UAV swarms. The proposed method combines Artificial Potential Field (APF) and Particle Swarm Optimization (PSO) techniques. APF provides environmental awareness and implicit coordination to UAVs, while PSO searches for collision-free and energy-efficient trajectories for each UAV in a decentralized manner under the implicit coordination. This decentralized approach is achieved by minimizing a novel cost function that leverages the advantages of the active contour model from image processing. Additionally, future trajectories are predicted by approximating the minima of the novel cost function using calculus of variation, which enables proactive actions and defines the initial conditions for PSO. We propose a two-branch trajectory planning framework that ensures UAVs only change altitudes when necessary for energy considerations. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method in various situations.
Sawtooth behaviour in highly elongated TCV plasmas

The effect of plasma elongation κ on the sawtooth behaviour is studied in ohmically heated TCV plasmas up to values of κ = 2.8. Above a certain value of κ, which varies from 2.2 to 2.6 and depends on the current profile, the sawtooth oscillations disappear completely. The sawteeth are then replaced by continuous oscillations with frequencies in the range from 5 to 20 kHz, which are resonant at the q = 1 surface and seem to prevent a peaking of the temperature and pressure profiles in the plasma core. It is suggested that the transition is a consequence of an ideal internal kink mode becoming unstable, even with a flat pressure profile inside the q = 1 surface, if κ is sufficiently high.
Provably Convergent Plug-and-Play Quasi-Newton Methods

Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine data fidelity terms and deep denoisers using classical optimization algorithms, such as ISTA or ADMM, with applications in inverse problems and imaging. Provable PnP methods are a subclass of PnP methods with convergence guarantees, such as fixed point convergence or convergence to critical points of some energy function. Many existing provable PnP methods impose heavy restrictions on the denoiser or fidelity function, such as non-expansiveness or strict convexity, respectively. In this work, we propose a novel algorithmic approach incorporating quasi-Newton steps into a provable PnP framework based on proximal denoisers, resulting in greatly accelerated convergence while retaining light assumptions on the denoiser. By characterizing the denoiser as the proximal operator of a weakly convex function, we show that the fixed points of the proposed quasi-Newton PnP algorithm are critical points of a weakly convex function. Numerical experiments on image deblurring and super-resolution demonstrate 2--8x faster convergence as compared to other provable PnP methods with similar reconstruction quality.
Who's Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbook

Deployed artificial intelligence (AI) often impacts humans, and there is no one-size-fits-all metric to evaluate these tools. Human-centered evaluation of AI-based systems combines quantitative and qualitative analysis and human input. It has been explored to some depth in the explainable AI (XAI) and human-computer interaction (HCI) communities. Gaps remain, but the basic understanding that humans interact with AI and accompanying explanations, and that humans' needs -- complete with their cognitive biases and quirks -- should be held front and center, is accepted by the community. In this paper, we draw parallels between the relatively mature field of XAI and the rapidly evolving research boom around large language models (LLMs). Accepted evaluative metrics for LLMs are not human-centered. We argue that many of the same paths tread by the XAI community over the past decade will be retread when discussing LLMs. Specifically, we argue that humans' tendencies -- again, complete with their cognitive biases and quirks -- should rest front and center when evaluating deployed LLMs. We outline three developed focus areas of human-centered evaluation of XAI: mental models, use case utility, and cognitive engagement, and we highlight the importance of exploring each of these concepts for LLMs. Our goal is to jumpstart human-centered LLM evaluation.
The inverse scattering problem for a partially coated penetrable cavity with interior measurements

In this paper we consider the inverse scattering problem for a cavity that is bounded by a partially coated penetrable inhomogeneous medium of compact support and recover the shape of the cavity and the surface conductivity from a knowledge of measured scattered waves due to point sources located on a curve or surface inside the cavity. First, we prove that both the shape of the cavity and the surface conductivity on the coated part can be uniquely determined from a knowledge of the measured data. Next, we establish a linear sampling method for determining both the shape of the cavity and the surface conductivity. A central role in our justification is played by an eigenvalue problem which we call the exterior transmission eigenvalue problem. Finally, we present some numerical examples to illustrate the validity of our method.
Probing the Nature of the First Galaxies with JWST and ALMA

By implementing a model of primordial dust emission, we predict dust-continuum fluxes for massive galaxy sources similar to those recently detected by James Webb Space Telescope (JWST) at z ≳ 7. Current upper flux limits, obtained with Atacama Large Millimeter/submillimeter Array (ALMA) for some of these sources, can constrain the gas metallicity and dust fraction of the first galaxies. Encouragingly, if assuming expected properties for typical first galaxies (i.e., dust-to-metal mass ratio: D/M = 5 × 10−3, gas metallicity: Z g = 5 × 10−3 Z ☉, star formation efficiency: η = 0.01), model far-infrared (FIR) fluxes are consistent with current upper flux limits inferred from ALMA bands 6 and 7 (≲104 nJy). Such low D/M values and metallicities are in agreement with some scenarios proposed in the literature to explain the nondetection of the FIR dust continuum for high-z JWST galaxy candidates. On the other hand, higher values of model parameters D/M (≳0.06) and Z g (≳5 × 10−2 Z ☉) are ruled out by observational data, unless a higher η is assumed. According to our findings, ALMA multiband observations could constrain the dust chemistry and dust grain size distribution in the early universe. In this context, future observational challenges would involve not only reaching higher FIR sensitivities, but also increasing the wavelength coverage by exploring distinct ALMA bands.
A quantum spectral method for simulating stochastic processes, with applications to Monte Carlo

Stochastic processes play a fundamental role in physics, mathematics, engineering and finance. One potential application of quantum computation is to better approximate properties of stochastic processes. For example, quantum algorithms for Monte Carlo estimation combine a quantum simulation of a stochastic process with amplitude estimation to improve mean estimation. In this work we study quantum algorithms for simulating stochastic processes which are compatible with Monte Carlo methods. We introduce a new ``analog'' quantum representation of stochastic processes, in which the value of the process at time t is stored in the amplitude of the quantum state, enabling an exponentially efficient encoding of process trajectories. We show that this representation allows for highly efficient quantum algorithms for simulating certain stochastic processes, using spectral properties of these processes combined with the quantum Fourier transform. In particular, we show that we can simulate $T$ timesteps of fractional Brownian motion using a quantum circuit with gate complexity $\text{polylog}(T)$, which coherently prepares the superposition over Brownian paths. We then show this can be combined with quantum mean estimation to create end to end algorithms for estimating certain time averages over processes in time $O(\text{polylog}(T)\epsilon^{-c})$ where $3/2<c<2$ for certain variants of fractional Brownian motion, whereas classical Monte Carlo runs in time $O(T\epsilon^{-2})$ and quantum mean estimation in time $O(T\epsilon^{-1})$. Along the way we give an efficient algorithm to coherently load a quantum state with Gaussian amplitudes of differing variances, which may be of independent interest.
A Newton-like Method based on Model Reduction Techniques for Implicit Numerical Methods

In this paper, we present a Newton-like method based on model reduction techniques, which can be used in implicit numerical methods for approximating the solution to ordinary differential equations. In each iteration, the Newton-like method solves a reduced order linear system in order to compute the Newton step. This reduced system is derived using a projection matrix, obtained using proper orthogonal decomposition, which is updated in each time step of the numerical method. We demonstrate that the method can be used together with Euler’s implicit method to simulate CO2 injection into an oil reservoir, and we compare with using Newton’s method. The Newton-like method achieves a speedup of between 39% and 84% for systems with between 4,800 and 52,800 state variables.
Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science

Scientists and statisticians often want to learn about the complex relationships that connect two variables that vary over time. Recent work on sparse functional historical linear models confirms that they are promising for this purpose, but several notable limitations exist. Most importantly, previous works have imposed sparsity on the coefficient function, but have not allowed the sparsity, hence lag, to vary with time. We simplify the framework of sparse functional historical linear models by using a rectangular coefficient structure along with Whittaker smoothing, then relax the previous frameworks by estimating the dynamic time lag from a hierarchical coefficient structure. We motivate our study by aiming to extract the physical rainfall-runoff processes hidden within hydrological data. We show the promise and accuracy of our method using four simulation studies, justified by two real sets of hydrological data.