Miracle-dz/UniFAR-FTUs huggingface dataset:
Desmoid Fibromatosis With TP53 Mutation and Striking Nuclear Pleomorphism

Desmoid fibromatosis is a myofibroblastic neoplasm of intermediate biologic potential, which has a strong predilection for local recurrence but does not metastasize. Arranged in long, sweeping fascicles with infiltrative borders, desmoid fibromatosis typically consists of uniform, bland myofibroblastic spindle cells that harbor mutation of CTNNB1 (or less often APC). In this report, we present a remarkable case of desmoid fibromatosis associated with striking nuclear pleomorphism. We hypothesize that this striking pleomorphism is due to a germline TP53 mutation, a finding first suspected due to strong and diffuse p53 staining by immunohistochemistry and subsequently confirmed by molecular testing. The combination of the pleomorphism and TP53 mutation in desmoid fibromatosis represents a novel finding unreported in the literature.
EM ALGORITHM FOR ESTIMATING THE NOISE DEVIATION OF THE IMAGE IN THE WAVELET DOMAIN

A new method was proposed to estimate noisy image’s quality. This approach uses Gaussian mixture model to simulate the histogram of the highest subband’s coefficients in the wavelet domain. The parameters of this model can be calculated by EM algorithm. The smallest deviation of the Gaussian mixture model is selected as the noise deviation. The authors' proposed method can provide better result than the other traditional ones.
Whole genome analysis of Neisseria meningitidis isolates from invasive meningococcal disease collected in the Czech Republic over 28 years (1993–2020)

Invasive meningococcal disease belongs among the most dangerous infectious diseases in the world. Several polysaccharide conjugate vaccines against serogroups A, C, W and Y are available and two recombinant peptide vaccines against serogroup B (MenB vaccines) have been developed: MenB-4C (Bexsero) and MenB-fHbp (Trumenba). The aim of this study was to define the clonal composition of the Neisseria meningitidis population in the Czech Republic, to determine changes in this population over time and to estimate the theoretical coverage of isolates by MenB vaccines. This study presents the analysis of whole genome sequencing data of 369 Czech N. meningitidis isolates from invasive meningococcal disease covering 28 years. Serogroup B isolates (MenB) showed high heterogeneity and the most common clonal complexes were cc18, cc32, cc35, cc41/44, and cc269. Isolates of clonal complex cc11 were predominately serogroup C (MenC). The highest number of serogroup W isolates (MenW) belonged to clonal complex cc865, which we described as exclusive to the Czech Republic. Our study supports the theory that this cc865 subpopulation originated in the Czech Republic from MenB isolates by a capsule switching mechanism. A dominant clonal complex of serogroup Y isolates (MenY) was cc23, which formed two genetically quite distant subpopulations and which showed constant representation throughout the observed period. The theoretical coverage of isolates by two MenB vaccines was determined using the Meningococcal Deduced Vaccine Antigen Reactivity Index (MenDeVAR). Estimated Bexsero vaccine coverage was 70.6% (for MenB) and 62.2% (for MenC, W, Y). For Trumenba vaccine, estimated coverage was 74.6% (for MenB) and 65.7% (for MenC, W, Y). Our results demonstrated sufficient coverage of Czech heterogeneous population of N. meningitidis with MenB vaccines and, together with surveillance data on invasive meningococcal disease in the Czech Republic, were the basis for updating recommendations for vaccination against invasive meningococcal disease.
Self-planning Code Generation with Large Language Model

Although large language models have demonstrated impressive ability in code generation, they are still struggling to address the complicated intent provided by humans. It is widely acknowledged that humans typically employ planning to decompose complex problems and schedule the solution steps prior to implementation. Thus we introduce planning into code generation to help the model understand complex intent and reduce the difficulty of problem solving. This paper proposes a self-planning code generation method with large language model, which consists of two phases, namely planning phase and implementation phase. Specifically, in the planning phase, the language model plans out the solution steps from the intent combined with in-context learning. Then it enters the implementation phase, where the model generates code step by step, guided by the solution steps. The effectiveness of self-planning code generation has been rigorously evaluated on multiple code generation datasets and the results have demonstrated a marked superiority over naive direct generation approaches with language model. The improvement in performance is substantial, highlighting the significance of self-planning in code generation tasks.
The System Description of dun_oscar team for The ICPR MSR Challenge

This paper introduces the system submitted by dun_oscar team for the ICPR MSR Challenge. Three subsystems for task1-task3 are descripted respectively. In task1, we develop a visual system which includes a OCR model, a text tracker, and a NLP classifier for distinguishing subtitles and non-subtitles. In task2, we employ an ASR system which includes an AM with 18 layers and a 4-gram LM. Semi-supervised learning on unlabeled data is also vital. In task3, we employ the ASR system to improve the visual system, some false subtitles can be corrected by a fusion module.
Mobile Mapping Mesh Change Detection and Update

Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The resulting point cloud sampling of the scene geometry can be meshed in order to create a continuous representation for different applications: visualization, simulation, navigation, etc. Because of the highly dynamic nature of these urban scenes, long term mapping should rely on frequent map updates. A trivial solution is to simply replace old data with newer data each time a new acquisition is made. However it has two drawbacks: 1) the old data may be of higher quality (resolution, precision) than the new and 2) the coverage of the scene might be different in various acquisitions, including varying occlusions. In this paper, we propose a fully automatic pipeline to address these two issues by formulating the problem of merging meshes with different quality, coverage and acquisition time. Our method is based on a combined distance and visibility based change detection, a time series analysis to assess the sustainability of changes, a mesh mosaicking based on a global boolean optimization and finally a stitching of the resulting mesh pieces boundaries with triangle strips. Finally, our method is demonstrated on Robotcar and Stereopolis datasets.
Evaluating Visual Number Discrimination in Deep Neural Networks

The ability to discriminate between large and small quantities is a core aspect of basic numerical competence in both humans and animals. In this work, we examine the extent to which the state-of-the-art neural networks designed for vision exhibit this basic ability. Motivated by studies in animal and infant numerical cognition, we use the numerical bisection procedure to test number discrimination in different families of neural architectures. Our results suggest that vision-specific inductive biases are helpful in numerosity discrimination, as models with such biases have lowest test errors on the task, and often have psychometric curves that qualitatively resemble those of humans and animals performing the task. However, even the strongest models, as measured on standard metrics of performance, fail to discriminate quantities in transfer experiments with differing training and testing conditions, indicating that such inductive biases might not be sufficient.
Energy flux and high-order statistics of hydrodynamic turbulence

Abstract We use the Dyson–Wyld diagrammatic technique to analyse the infinite series for the correlation functions of the velocity in hydrodynamic turbulence. We demonstrate the fundamental role played by the triple correlator of the velocity in determining the entire statistics of the hydrodynamic turbulence. All higher-order correlation functions are expressed through the triple correlator. This is shown through the suggested triangular re-summation of the infinite diagrammatic series for multi-point correlation functions. The triangular re-summation is the next logical step after the Dyson–Wyld line re-summation for the Green's function and the double correlator. In particular, it allows us to explain why the inverse cascade of the two-dimensional hydrodynamic turbulence is close to Gaussian. Since the triple correlator dictates the flux of energy $\varepsilon$ through the scales, we support the Kolmogorov-1941 idea that $\varepsilon$ is one of the main characteristics of hydrodynamic turbulence.
Another Generic Setting for Entity Resolution: Basic Theory

Benjelloun et al. \cite{BGSWW} considered the Entity Resolution (ER) problem as the generic process of matching and merging entity records judged to represent the same real world object. They treated the functions for matching and merging entity records as black-boxes and introduced four important properties that enable efficient generic ER algorithms. In this paper, we shall study the properties which match and merge functions share, model matching and merging black-boxes for ER in a partial groupoid, based on the properties that match and merge functions satisfy, and show that a partial groupoid provides another generic setting for ER. The natural partial order on a partial groupoid is defined when the partial groupoid satisfies Idempotence and Catenary associativity. Given a partial order on a partial groupoid, the least upper bound and compatibility ($LU_{pg}$ and $CP_{pg}$) properties are equivalent to Idempotence, Commutativity, Associativity, and Representativity and the partial order must be the natural one we defined when the domain of the partial operation is reflexive. The partiality of a partial groupoid can be reduced using connected components and clique covers of its domain graph, and a noncommutative partial groupoid can be mapped to a commutative one homomorphically if it has the partial idempotent semigroup like structures. In a finitely generated partial groupoid $(P,D,\circ)$ without any conditions required, the ER we concern is the full elements in $P$. If $(P,D,\circ)$ satisfies Idempotence and Catenary associativity, then the ER is the maximal elements in $P$, which are full elements and form the ER defined in \cite{BGSWW}. Furthermore, in the case, since there is a transitive binary order, we consider ER as ``sorting, selecting, and querying the elements in a finitely generated partial groupoid. "
Improving the Robustness of Deep Convolutional Neural Networks Through Feature Learning

Deep convolutional neural network (DCNN for short) models are vulnerable to examples with small perturbations. Adversarial training (AT for short) is a widely used approach to enhance the robustness of DCNN models by data augmentation. In AT, the DCNN models are trained with clean examples and adversarial examples (AE for short) which are generated using a specific attack method, aiming to gain ability to defend themselves when facing the unseen AEs. However, in practice, the trained DCNN models are often fooled by the AEs generated by the novel attack methods. This naturally raises a question: can a DCNN model learn certain features which are insensitive to small perturbations, and further defend itself no matter what attack methods are presented. To answer this question, this paper makes a beginning effort by proposing a shallow binary feature module (SBFM for short), which can be integrated into any popular backbone. The SBFM includes two types of layers, i.e., Sobel layer and threshold layer. In Sobel layer, there are four parallel feature maps which represent horizontal, vertical, and diagonal edge features, respectively. And in threshold layer, it turns the edge features learnt by Sobel layer to the binary features, which then are feeded into the fully connected layers for classification with the features learnt by the backbone. We integrate SBFM into VGG16 and ResNet34, respectively, and conduct experiments on multiple datasets. Experimental results demonstrate, under FGSM attack with $\epsilon=8/255$, the SBFM integrated models can achieve averagely 35\% higher accuracy than the original ones, and in CIFAR-10 and TinyImageNet datasets, the SBFM integrated models can achieve averagely 75\% classification accuracy. The work in this paper shows it is promising to enhance the robustness of DCNN models through feature learning.
Rate-Distortion Analysis for Semantic-Aware Multi-Terminal Source Coding Problem

A novel distributed source coding model which named semantic-aware multi-terminal source coding problem is proposed and studied in the paper. This is motivated by the new communication paradigm being aware of semantic information, in which invisible semantic features are observed by multiple agents, and both semantic and observation reconstructions are imposed distortion constraints. The theoretical analysis of this model is provided in this work, in which we present a generalized Berger- Tung based sum rate region considering the semantic source, and further obtain upper and lower bounds when sources are joint Gaussian distributed. Under this case, the tradeoff between two distortions and optimal rate allocation scheme are discussed. Moreover, since the model couples the conventional multiterminal coding and CEO problems, the degeneration of generalized bounds to existing works are shown. Finally, we also present the sum rate bounds in special cases when sources are Bernoulli and distortion measure adopts logarithmic loss.
Low frequency, 100–600 MHz, searches with axion cavity haloscopes

We investigate reentrant and dielectric loaded cavities for the purpose of extending the range of axion cavity haloscopes to lower masses, below the range where the Axion Dark Matter eXperiment (ADMX) has already searched. Reentrant and dielectric loaded cavities were simulated numerically to calculate and optimize their form factors and quality factors. A prototype reentrant cavity was built and its measured properties were compared with the simulations. We estimate the sensitivity of axion dark matter searches using reentrant and dielectric loaded cavities inserted in the existing ADMX magnet at the University of Washington and a large magnet being installed at Fermilab.
Bayesian Inference of Gene Expression Dynamics in Alzheimer Brains

Alzheimer's disease (AD) is a serious neurodegenerative disease consisting of four stages where the illness gets progressively worse. It is of great significance to detect the gene regulatory mechanism as AD progresses and, thus, to help us better understand the causes of AD and find ways to treat or control AD. There are numerous researches to conduct this kind of study. However, the majority of methods are processing region by region of brain, stage by stage of AD, and then compare the results to detect changes. It is unclear how to combine these three dimensions, i.e., gene, region and stage, simultaneously to study gene expression dynamics of AD. This is the motivation of our research. In our study, we propose a statistical model of increments to clarify the relationship between gene expression in adjacent stages, so that we could better estimate the missing data we want and obtain a complete reasonable dynamic regulatory network model. Simulations are conducted to validate the statistical power of our algorithm. Moreover, a real data analysis shows that our method can capture the dynamic gene regulatory relationships among this complex brain data.
Topological phase transitions generated by the order from quantum disorder

The order from quantum disorder (OFQD) phenomenon was first discovered in quantum spin systems in geometric frustrated lattice. Similar phenomenon was also discovered in interacting bosonic systems or quantum spin systems with spin-orbit coupling in a bipartite lattice. Here we show that the OFQD also leads to a topological phase transition. We demonstrate this new connection in the experimentally realized weakly interacting Quantum Anomalous Hall system of spinor bosons in an optical lattice. There are two classes of topological phenomena: the first class is a perturbative one smoothly connected to the non-interacting limit. The second one is a non-perturbative one which has no analog in the non-interacting limit. Their experimental detections are also discussed.
Amélioration de la qualité d'images avec un algorithme d'optimisation inspirée par la nature

Reproducible images preprocessing is important in the field of computer vision, for efficient algorithms comparison or for new images corpus preparation. In this paper, we propose a method to obtain an explicit and ordered sequence of transformations that improves a given image: the computation is performed via a nature-inspired optimization algorithm based on quality assessment techniques. Preliminary tests show the impact of the approach on different state-of-the-art data sets. -- L'application de pr\'etraitements explicites et reproductibles est fondamentale dans le domaine de la vision par ordinateur, pour pouvoir comparer efficacement des algorithmes ou pour pr\'eparer un nouveau corpus d'images. Dans cet article, nous proposons une m\'ethode pour obtenir une s\'equence reproductible de transformations qui am\'eliore une image donn\'ee: le calcul est r\'ealis\'e via un algorithme d'optimisation inspir\'ee par la nature et bas\'e sur des techniques d'\'evaluation de la qualit\'e. Des tests montrent l'impact de l'approche sur diff\'erents ensembles d'images de l'\'etat de l'art.
An Automated Pipeline to Create an Atlas of in Situ Hybridization Gene Expression Data in the Adult Marmoset Brain

We present the first automated pipeline to create an atlas of in situ hybridization gene expression in the adult marmoset brain in the same stereotaxic space. The pipeline consists of segmentation of gene expression from microscopy images and registration of images to a standard space. Automation of this pipeline is necessary to analyze the large volume of data in the genome-wide whole-brain dataset, and to process images that have varying intensity profiles and expression patterns with minimal human bias. To reduce the number of labelled images required for training, we develop a semi-supervised segmentation model. We further develop an iterative algorithm to register images to a standard space, enabling comparative analysis between genes and concurrent visualization with other datasets, thereby facilitating a more holistic understanding of primate brain structure and function.
Spherical harmonics representation of the gravitational phase shift

We investigate the general relativistic phase of an electromagnetic wave as it propagates in the gravitational field of the Earth, which is modeled as an isolated, weakly aspherical gravitating body. We introduce coordinate systems to describe light propagation in the Earth's vicinity along with the relevant coordinate transformations, and discuss the transformations between proper and coordinate times. We represent the Earth's gravitational field using Cartesian symmetric trace-free (STF) mass multipole moments. The light propagation equation is solvable along the trajectory of a light ray to all STF orders $\ell$. Although we focus primarily on the quadrupole ($\ell=2$), octupole ($\ell=3$), and hexadecapole ($\ell=4$) cases, our approach is valid to all orders. We express the STF moments via spherical harmonic coefficients of various degree and order, $C_{\ell k}, S_{\ell k}$. The result is the gravitational phase shift expressed in terms of the spherical harmonics. These results are new. We also consider contributions due to tides and the Earth's rotation. We estimate the characteristic magnitudes of each term of the resulting overall gravitational phase shift. The terms assessed are either large enough to impact current-generation clocks or will become significant as future-generation clocks offer greater sensitivity. Our formulation is useful for many practical and scientific applications, including space-based time and frequency transfers, relativistic geodesy and navigation, as well as quantum communication links and space-based tests of fundamental physics.
One-form symmetries in $\mathcal{N} = 3$ $S$-folds

We classify the global one-form symmetries for non-Lagrangian $\mathcal{N}=3$ SCFTs that arise by the action of $S$-fold projections on D3-branes. Such a classification is dictated, on a generic point of the Coulomb branch, by probing the charge spectrum of $(p, q)$-strings in the brane setup. The charge lattice of lines is then obtained by finding the ones that are genuine modulo screening by dynamical particles. The one-form symmetries are then extracted from the maximal sub-lattices of mutually local lines. We further comment on the existence of non-invertible symmetries for some of these $\mathcal{N}=3$ SCFTs.
Anisotropic thermal conductivity of high bandwidth memory

Thermal management of integrated circuits (ICs) is important to prevent thermal hotspots which are the leading cause of IC failure. Thermal management is even more critical in 3D integrated circuits (3D ICs) as the prevalence of thermal hotspots is expected to increase due to the presence of polymers and solder materials that are of low thermal conductivity. Understanding how thermal conductivity is affected by the presence of these materials is required for developing thermally aware IC design. The 3{\omega} method can measure thermal conductivities spanning several orders of magnitude and is appropriate for measuring the thermal properties of layered structures such as 3D ICs. In this work, we use the 3{\omega} method with planar and cylindrical heat flow geometries to determine thermal conductivities of the memory layers and layers with polymer and solder bumps in High Bandwidth Memory (HBM) Random Access Memory (RAM). We determine the in-plane thermal conductivity of the memory layers in HBM as 140 W/m-K, while the through-plane conductivity of the polymer/solder bump layer is 2 W/m-K. Combining the results of x-ray tomography and the 3-omega measurements, we estimate that the effective in-plane thermal conductivity of the overall HBM device is 100 W/m-K while the effective through-plane thermal conductivity is 7 W/m-K. Our results show that the presence of polymers and solder metals results in a significantly decreased through-plane thermal conductivity of a 3D IC compared to a single IC die. Improvement in the thermal performance of 3D ICs will require improvement in the thermal conductivity or the increased contact area of the solder metals used in 3D ICs.
An Analysis of the Impact of a Split-Front Rainband on Appalachian Cold-Air Damming

Abstract Appalachian cold-air damming (CAD) is characterized by the development of a cool, stable air mass that is advected southwestward along the eastern slopes of the Appalachian Mountains by low-level ageostrophic flow. Operational forecasters have identified the demise of CAD as a major forecasting challenge, in part because numerical weather prediction models have a tendency to erode the cold air too quickly. Previous studies have considered the role of clouds and precipitation in the initiation and maintenance of CAD; generally, precipitation is thought to reinforce CAD due to the cooling and stabilization resulting from evaporation. Here, the impact of precipitation on CAD during a situation where the lower-tropospheric air mass was near saturation prior to the arrival of precipitation is considered. Previous studies have indicated that the passage of a cold front can bring about CAD demise, as the synoptic-scale flow becomes northwesterly behind the front and low-level stable air is scoured. Addi.