Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct simulation of thermally and mechanically coupled particle-laden flow

This work describes a unique technique to simulate continuously and directly coupled fluid flow and moving particles including both mechanical and thermal interactions between the flow, particles, and flow paths. The particles/flow paths are discretized within a computational fluid dynamics flow domain so that the local flow and temperature field conditions surrounding each particle or other solid body are known along with the local temperature distribution within the particle and other solids. Contact conduction between solid bodies including contact resistance, conjugate heat transfer at the fluid–solid interfaces, and even radiation exchanges between solid surfaces and between solid surfaces and the fluid are incorporated in the thermal interactions and a soft collision model simulates the solid body mechanical contact. The ability to capture these local flow and thermal effects removes reliance on correlations for fluid forces and for heat transfer coefficients/exchange and removes restrictions on the flow regime and particle size and volume fraction considered. Larger particle sizes and higher particle concentration conditions can be studied with local effects captured. The method was tested for a range of particle thermal and mechanical properties, driving pressures, and for limited radiation parameters. The results reveal important information about the basic thermal and flow phenomena that cannot be obtained in standard modeling methods and demonstrate the utility of the modeling method. The technique can be applied to examine phenomena dependent on local thermal conditions such as chemical reactions, material property variation, agglomerate formation, and phase change. The methods can also be used as a basis for machine learning algorithm development for flows with large particle counts so that more detailed phenomena can be considered compared to those provided by standard techniques with reduced computational costs compared to those with fully resolved particles in the flow.
A Performance Analysis of Schottky Diode to support RF Energy Harvesting

Given the low density of the surrounding RF powers, the rectifying stage is the one on which the designers of the rectenna (rectifying antenna) carry the most attention. Rectifier circuits must be of high efficiency to have usable DC output powers. Since the conversion efficiency is directly related to the electrical parameters of the used rectifying diode, this paper proposes a comparative study of the RF/DC conversion efficiency of an RF rectifier for the commonly considered Schottky diodes. In addition to the electrical parameters of the used diode, the operating frequency, as well as the desired level of DC output voltage, are considered in the comparison. Five diodes manufactured by AVAGO (HSMS 2820, HSMS 2850, HSMS 2860) and two Skyworks diodes (SMS 7621 and SMS 7630) are treated in this paper. This study will then provide designers with useful information in the selection of the rectifier diode and associated design parameters.
Phytomolecules of Terminalia macroptera stem bark extracts Guill et Perr ( Combretaceae ) and antibacterial activity

Today, the phenomenon of multidrug resistance is growing and makes inefficient use of many antibiotics on the market. Faced with this therapeutic impasse, the Scientific World will invest the field of plants, in search of new molecules. It is in this context that the study on Terminalia maccroptera was initiated. The main objective of this study is to characterize the different phytomolecules of the 70% ethanolic extract of Terminalia macroptera by GC-MS and to identify the molecules susceptible to antibacterial activity. Tri phytochemistry was performed using the tube reagent method for determining chemical components and gas chromatography-mass spectrometry (GC-MS) to characterize the existing phytomolecules in the extract. The tri-phytochemical study showed the chemical groups: Poly-phenols, Flavonoids, saponins, tannins gallic, Cardiac glycosides, sterols and terpenes. The GC-MS has firstly confirmed the great chemical groups, and also identified 20 molecules accompanied by their mass spectra, their molecular weight and their chemical structures. Among these molecules Beta-sitosterol is the most important in ethanol extract70%. It belongs to the family of triterpenes (known for their antibacterial activity). This molecule is therefore responsible for the antibacterial activity of our extract.
A Deep Dive Into the Newest Avenues of Immunotherapy for Pediatric Osteosarcoma: A Systematic Review

Osteosarcoma (OS) is the most common primary bone cancer affecting children and young adults, most often occurring at the metaphysis of long bones. At present, treatment with combinations of surgery and chemotherapy for the localized OS has only brought minuscule improvements in prognosis. In comparison, the advanced, metastatic, or recurrent forms of OS are often non-responsive to chemotherapy, adding to the dire need to develop new and efficient therapies. The question of interest investigated in this systematic review is whether immunotherapy can play a meaningful role in improving the clinical outcomes of children with OS. This article aims to summarize the preclinical and clinical research conducted thus far on potential therapeutic avenues for pediatric OS using immunotherapy, including methods like checkpoint inhibition, adoptive cellular therapy with T-cells, chimeric antigen receptor T (CAR-T), and natural killer (NK) cells. It also highlights the influence of the innate and adaptive immune system on the tumor microenvironment, allowing for OS progression and metastasis. This systematic review contains 27 articles and analyses of multiple clinical trials employing immunotherapeutic drugs to 785 osteosarcoma participants and over 243 pediatric patients. The articles were obtained through PubMed, PubMed Central, and ClinicalTrials.gov and individually assessed for quality using the Assessment of Multiple Systematic Reviews (AMSTAR) checklist and the Cochrane risk-of-bias tool. The reviews reveal that immunotherapy's most significant impact on pediatric OS includes combining immune checkpoint blockers with traditional chemotherapy and surgery. However, due to the bimodal distribution of this aggressive malignancy, these studies cannot precisely estimate the overall effect and any potential life-threatening adverse events following therapy in children. Further research is required to fully assess the impact of these immunotherapies, including more extensive multinational clinical trials to focus on the pediatric population.
Explicit and Efficient Construction of (nearly) Optimal Rate Codes for Binary Deletion Channel and the Poisson Repeat Channel

Two of the most common models for channels with synchronisation errors are the Binary Deletion Channel with parameter p (BDC p ) – a channel where every bit of the codeword is deleted i.i. d with probability p , and the Poisson Repeat Channel with parameter λ (PRC λ ) – a channel where every bit of the codeword is repeated Poisson( λ ) times. Previous constructions based on synchronisation strings yielded codes with rates far lower than the capacities of these channels [6, 9], and the only efficient construction to achieve capacity on the BDC at the time of writing this paper is based on the far more advanced methods of polar codes [23]. In this work, we present a new method for concatenating synchronisation codes and use it to construct simple and efficient encoding and decoding algorithms for both channels with nearly optimal rates.
Computing Matching Statistics on Repetitive Texts

Computing the matching statistics of a string $P[1.m]$ with respect to a text $T[1.n]$ is a fundamental problem which has application to genome sequence comparison. In this paper, we study the problem of computing the matching statistics upon highly repetitive texts. We design three different data structures that are similar to LZ-compressed indexes. The space costs of all of them can be measured by $\gamma$, the size of the smallest string attractor [STOC'2018] and $\delta$, a better measure of repetitiveness [LATIN'2020].
Resonance Tuning Cost in Radiation Efficiency of Electrically Small Antennas

Numerical tools are used to predict the upper-bounds on radiation efficiency with and without a constraint on self-resonance. It is demonstrated that the assumption of lossless external tuning produces unrealistic results. The main result of this contribution is that, when realistic (e.g., finite conductivity) materials are used, the cost of resonance tuning in the radiation efficiency of small antenna systems is high and is manifested by a dissipation factor which scales as a fourth power of electrical size.
Interpretable contrastive word mover's embedding

This paper shows that a popular approach to the supervised embedding of documents for classification, namely, contrastive Word Mover's Embedding, can be significantly enhanced by adding interpretability. This interpretability is achieved by incorporating a clustering promoting mechanism into the contrastive loss. On several public datasets, we show that our method improves significantly upon existing baselines while providing interpretation to the clusters via identifying a set of keywords that are the most representative of a particular class. Our approach was motivated in part by the need to develop Natural Language Processing (NLP) methods for the \textit{novel problem of assessing student work for scientific writing and thinking} - a problem that is central to the area of (educational) Learning Sciences (LS). In this context, we show that our approach leads to a meaningful assessment of the student work related to lab reports from a biology class and can help LS researchers gain insights into student understanding and assess evidence of scientific thought processes.
Enhanced Language Representation with Label Knowledge for Span Extraction

Span extraction, aiming to extract text spans (such as words or phrases) from plain text, is a fundamental process in Information Extraction. Recent works introduce the label knowledge to enhance the text representation by formalizing the span extraction task into a question answering problem (QA Formalization), which achieves state-of-the-art performance. However, such a QA Formalization does not fully exploit the label knowledge and causes a dramatic decrease in efficiency of training/inference. To address those problems, we introduce a fresh paradigm to integrate label knowledge and further propose a novel model to explicitly and efficiently integrate label knowledge into text representations. Specifically, it encodes texts and label annotations independently and then integrates label knowledge into text representation with an elaborate-designed semantics fusion module. We conduct extensive experiments on three typical span extraction tasks: flat NER, nested NER, and event detection. The empirical results show that 1) our model achieves a new state-of-the-art performance on four benchmarks, and 2) reduces training time and inference time by 76% and 77% on average, respectively, compared with the QA Formalization paradigm.
SeisBench—A Toolbox for Machine Learning in Seismology

Machine-learning (ML) methods have seen widespread adoption in seismology in recent years. The ability of these techniques to efficiently infer the statistical properties of large datasets often provides significant improvements over traditional techniques when the number of data are large (millions of examples). With the entire spectrum of seismological tasks, for example, seismic picking and detection, magnitude and source property estimation, ground-motion prediction, hypocenter determination, among others, now incorporating ML approaches, numerous models are emerging as these techniques are further adopted within seismology. To evaluate these algorithms, quality-controlled benchmark datasets that contain representative class distributions are vital. In addition to this, models require implementation through a common framework to facilitate comparison. Accessing these various benchmark datasets for training and implementing the standardization of models is currently a time-consuming process, hindering further advancement of ML techniques within seismology. These development bottlenecks also affect “practitioners” seeking to deploy the latest models on seismic data, without having to necessarily learn entirely new ML frameworks to perform this task. We present SeisBench as a software package to tackle these issues. SeisBench is an open-source framework for deploying ML in seismology—available via GitHub. SeisBench standardizes access to both models and datasets, while also providing a range of common processing and data augmentation operations through the API. Through SeisBench, users can access several seismological ML models and benchmark datasets available in the literature via a single interface. SeisBench is built to be extensible, with community involvement encouraged to expand the package. Having such frameworks available for accessing leading ML models forms an essential tool for seismologists seeking to iterate and apply the next generation of ML techniques to seismic data.
Self-Verification in Image Denoising

We devise a new regularization, called self-verification, for image denoising. This regularization is formulated using a deep image prior learned by the network, rather than a traditional predefined prior. Specifically, we treat the output of the network as a ``prior'' that we denoise again after ``re-noising''. The comparison between the again denoised image and its prior can be interpreted as a self-verification of the network's denoising ability. We demonstrate that self-verification encourages the network to capture low-level image statistics needed to restore the image. Based on this self-verification regularization, we further show that the network can learn to denoise even if it has not seen any clean images. This learning strategy is self-supervised, and we refer to it as Self-Verification Image Denoising (SVID). SVID can be seen as a mixture of learning-based methods and traditional model-based denoising methods, in which regularization is adaptively formulated using the output of the network. We show the application of SVID to various denoising tasks using only observed corrupted data. It can achieve the denoising performance close to supervised CNNs.
TorchXRayVision: A library of chest X-ray datasets and models

TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models. It provides a common interface and common pre-processing chain for a wide set of publicly available chest X-ray datasets. In addition, a number of classification and representation learning models with different architectures, trained on different data combinations, are available through the library to serve as baselines or feature extractors.
Combining expert knowledge and neural networks to model environmental stresses in agriculture

In this work we combine representation learning capabilities of neural network with agricultural knowledge from experts to model environmental heat and drought stresses. We first design deterministic expert models which serve as a benchmark and inform the design of flexible neural-network architectures. Finally, a sensitivity analysis of the latter allows a clustering of hybrids into susceptible and resistant ones.
Online edge coloring via tree recurrences and correlation decay

We give an online algorithm that with high probability computes a (e/e−1 + o(1))Δ edge coloring on a graph G with maximum degree Δ = ω(logn) under online edge arrivals against oblivious adversaries, making first progress on the conjecture of Bar-Noy, Motwani, and Naor in this general setting. Our algorithm is based on reducing to a matching problem on locally treelike graphs, and then applying a tree recurrences based approach for arguing correlation decay.
Gravitational self-force and conservative effects: a testing ground for theories of gravity

Considering extreme-mass-ratio inspirals along with the conservative dynamics of gravitational self-force, we compare viable theories of gravity. In particular, by examining a Schwarzschild background we analyse the self-force-induced corrections to gauge-invariant benchmarks given by the orbital frequency at the ISCO and the spin-precession rate. Moreover, following an established indication of modifications to the equations of motion in extended theories of gravity, we exploit a phenomenological approach, relying on the variability of the gravitational constant G, to incorporate these modifications. We find that conservative effects shape up to be a test-bed for theories of gravity, allowing us to contrast General Relativity with competing theories. By examining strong-field constraints, we highlight a wide margin of investigation in the context of LISA Mission.
Geometry reconfiguration of a reflector with an auxetic material surface

Mechanically reconfigurable reflectors can modify their geometries to realize large angle beam scanning. This paper studies a method for reconfiguring a reflector geometry from a standard parabolic shape to offset parabolic shapes with varies offset angles. To fulfil the geometry control, the coordinate change of the nodes on the reflector surface are studied with the coordinate transformation method. To accommodate the large deformation of the reflector surface, a negative Poisson's ratio (NPR) material is applied to the reconfigurable reflector.
Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training

Conditional Generative Adversarial Networks (cGAN) generate realistic images by incorporating class information into GAN. While one of the most popular cGANs is an auxiliary classifier GAN with softmax cross-entropy loss (ACGAN), it is widely known that training ACGAN is challenging as the number of classes in the dataset increases. ACGAN also tends to generate easily classifiable samples with a lack of diversity. In this paper, we introduce two cures for ACGAN. First, we identify that gradient exploding in the classifier can cause an undesirable collapse in early training, and projecting input vectors onto a unit hypersphere can resolve the problem. Second, we propose the Data-to-Data Cross-Entropy loss (D2D-CE) to exploit relational information in the class-labeled dataset. On this foundation, we propose the Rebooted Auxiliary Classifier Generative Adversarial Network (ReACGAN). The experimental results show that ReACGAN achieves state-of-the-art generation results on CIFAR10, Tiny-ImageNet, CUB200, and ImageNet datasets. We also verify that ReACGAN benefits from differentiable augmentations and that D2D-CE harmonizes with StyleGAN2 architecture. Model weights and a software package that provides implementations of representative cGANs and all experiments in our paper are available at https://github.com/POSTECH-CVLab/PyTorch-StudioGAN.
Geodesic Models With Convexity Shape Prior

The minimal geodesic models established upon the eikonal equation framework are capable of finding suitable solutions in various image segmentation scenarios. Existing geodesic-based segmentation approaches usually exploit image features in conjunction with geometric regularization terms, such as euclidean curve length or curvature-penalized length, for computing geodesic curves. In this paper, we take into account a more complicated problem: finding curvature-penalized geodesic paths with a convexity shape prior. We establish new geodesic models relying on the strategy of orientation-lifting, by which a planar curve can be mapped to an high-dimensional orientation-dependent space. The convexity shape prior serves as a constraint for the construction of local geodesic metrics encoding a particular curvature constraint. Then the geodesic distances and the corresponding closed geodesic paths in the orientation-lifted space can be efficiently computed through state-of-the-art Hamiltonian fast marching method. In addition, we apply the proposed geodesic models to the active contours, leading to efficient interactive image segmentation algorithms that preserve the advantages of convexity shape prior and curvature penalization.
Multidimensional self-trapping in linear and nonlinear potentials

Solitons are typically stable objects in diverse one-dimensional (1D) models, but their straightforward extensions to 2D and 3D settings tend to be unstable. In particular, the ubiquitous nonlinear Schrödinger (NLS) equation with the cubic self-focusing, which is also widely known as the GrossPitaevskii (GP) equation in the theory of Bose-Einstein condensates (BECs), creates only unstable 2D and 3D solitons, because the same equation gives rise to destructive effects in the form of the critical and supercritical wave collapse in the 2D and 3D cases, respectively. This chapter offers, first, a review of physically relevant settings which, nevertheless, make it possible to create stable 2D and 3D solitons, including ones with embedded vorticity. The main stabilization schemes considered here are: (i) competing (e.g., cubic-quintic) and saturable nonlinearities; (2) linear and nonlinear trapping potentials; (3) the Lee-Huang-Yang correction to the mean-field BEC dynamics, leading to the formation of robust quantum droplets; (4) spin-orbit-coupling (SOC) effects in binary BEC; (5) emulation of SOC in nonlinear optical waveguides, including PT -symmetric ones. Further, the chapter presents a detailed summary of results which demonstrate the creation of stable 2D and 3D solitons by the schemes based on the usual linear trapping potentials or effective nonlinear ones, which may be induced by means of spatial modulation of the local nonlinearity strength. The latter setting is especially promising, making it possible to use self-defocusing media, with the local nonlinearity strength growing fast enough from the center to periphery, for the creation of a great variety of stable multidimensional modes. In addition to fundamental states and vortex rings, the respective 3D modes may be hopfions, i.e., twisted vortex rings which carry two indpednent topological charges. Many results for the multidimensional solitons have been obtained, in such settings, not only in a numerical form, but also by means of analytical methods, such as the variational and Thomas-Fermi approximation.
Mixture Proportion Estimation and PU Learning: A Modern Approach

Given only positive examples and unlabeled examples (from both positive and negative classes), we might hope nevertheless to estimate an accurate positive-versus-negative classifier. Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE) -- determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning -- given such an estimate, learning the desired positive-versus-negative classifier. Unfortunately, classical methods for both problems break down in high-dimensional settings. Meanwhile, recently proposed heuristics lack theoretical coherence and depend precariously on hyperparameter tuning. In this paper, we propose two simple techniques: Best Bin Estimation (BBE) (for MPE); and Conditional Value Ignoring Risk (CVIR), a simple objective for PU-learning. Both methods dominate previous approaches empirically, and for BBE, we establish formal guarantees that hold whenever we can train a model to cleanly separate out a small subset of positive examples. Our final algorithm (TED)$^n$, alternates between the two procedures, significantly improving both our mixture proportion estimator and classifier