Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble CNNs for Breast Tumor Classification

To improve the recognition ability of computer-aided breast mass classification among mammographic images, in this work we explore the state-of-the-art classification networks to develop an ensemble mechanism. First, the regions of interest (ROIs) are obtained from the original dataset, and then three models, i.e., XceptionNet, DenseNet, and EfficientNet, are trained individually. After training, we ensemble the mechanism by summing the probabilities outputted from each network which enhances the performance up to 5%. The scheme has been validated on a public dataset and we achieved accuracy, precision, and recall 88%, 85%, and 76% respectively.
MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results

Depth completion from RGB images and sparse Time-of-Flight (ToF) measurements is an important problem in computer vision and robotics. While traditional methods for depth completion have relied on stereo vision or structured light techniques, recent advances in deep learning have enabled more accurate and efficient completion of depth maps from RGB images and sparse ToF measurements. To evaluate the performance of different depth completion methods, we organized an RGB+sparse ToF depth completion competition. The competition aimed to encourage research in this area by providing a standardized dataset and evaluation metrics to compare the accuracy of different approaches. In this report, we present the results of the competition and analyze the strengths and weaknesses of the top-performing methods. We also discuss the implications of our findings for future research in RGB+sparse ToF depth completion. We hope that this competition and report will help to advance the state-of-the-art in this important area of research. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2023/.
Almost periodic and Asymptotically Almost Periodic Navier-Stokes flows on non-compact Riemannian manifolds

In this paper, we study the existence, uniqueness and asymptotic behaviour of almost periodic (AP-) and asymptotically almost periodic (AAP-) mild solutions to the incompressible Navier-Stokes equations on non-compact manifolds with negative Ricci curvature tensors. We use certain exponential estimates of the Stokes semigroup to prove the Massera-type principle which guarantees the wellposedness of AP and AAP- mild solutions for the inhomogeneous Stokes equations. Then, by using fixed point arguments and Gronwall's inequality we establish the wellposedness and exponential decay for global-in-time of such solutions of Navier-Stokes equations.
Large upper critical fields and dimensionality crossover of superconductivity in the infinite-layer nickelate 
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The recently emerging superconductivity in infinite-layer nickelates, with isostructure and isoelectron of cuprates, provides a new platform to explore the pairing mechanism of high-temperature superconductors. In this work, we studied the upper critical field ($H_{\rm{c2}}$) of a high-quality La$_{0.8}$Sr$_{0.2}$NiO$_{2}$ thin film with superconducting transition temperature, $T_{\rm{c}}$ = 18.8 K, using high magnetic field up to 56 T. A very large $H_{\rm{c2}}$, $\sim$ 40 T for $H$ $\Arrowvert$ $c$ and $\sim$ 52 T for $H$ $\Arrowvert$ $ab$, was confirmed, which suggests that infinite-layer nickelates also have great application potential. The anisotropy of $H_{\rm{c2}}$ monotonically decreases from $\sim$ 10 near $T_{\rm{c}}$ to $\sim$ 1.5 at 2 K. Angle dependence of $H_{\rm{c2}}$ confirms the crossover of superconductivity from two-dimensional (2D) to three-dimensional (3D) as the temperature decreases. We discussed that the interstitial orbital effect causes the weakening of anisotropy. The observed abnormal upturning of $H_{\rm{c2}}$ at low temperatures is found to be a universal behavior independent of film quality and rare earth elements. Therefore, it should not be the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state due to the fact that it is in the dirty limit and insensitive to disorder.
Wavelet resolution and Sobolev regularity of Calder\'on-Zygmund operators on domains

Given a uniform domain $\Omega \subset {\mathbb R}^d$, we resolve each element of a suitably defined class of Calder\`on-Zygmund (CZ) singular integrals on $\Omega$ as the linear combination of Triebel wavelet operators and paraproduct terms. Our resolution formula entails a testing type characterization, loosely in the vein of the David-Journ\'e theorem, of weighted Sobolev space bounds in terms of Triebel-Lizorkin and tree Carleson measure norms of the paraproduct symbols, which is new already in the case $\Omega={\mathbb R}^d$ with Lebesgue measure. Our characterization covers the case of compressions to $\Omega$ of global CZ operators, extending and sharpening past results of Prats and Tolsa for the convolution case. The weighted estimates we obtain, particularized to the Beurling operator on a Lipschitz domain with normal to the boundary in the corresponding sharp Besov class, may be used to deduce quantitative estimates for quasiregular mappings with dilatation in the Sobolev space $W^{1,p}(\Omega)$, $p>2$.
Latent Fingerprint Recognition: Fusion of Local and Global Embeddings

One of the most challenging problems in fingerprint recognition continues to be establishing the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene (i.e., latent prints or fingermarks). Despite the success of fixed-length embeddings for rolled and slap fingerprint recognition, the features learned for latent fingerprint matching have mostly been limited to local minutiae-based embeddings and have not directly leveraged global representations for matching. In this paper, we combine global embeddings with local embeddings for state-of-the-art latent to rolled matching accuracy with high throughput. The combination of both local and global representations leads to improved recognition accuracy across NIST SD 27, NIST SD 302, MSP, MOLF DB1/DB4, and MOLF DB2/DB4 latent fingerprint datasets for both closed-set (84.11%, 54.36%, 84.35%, 70.43%, 62.86% rank-1 retrieval rate, respectively) and open-set (0.50, 0.74, 0.44, 0.60, 0.68 FNIR at FPIR=0.02, respectively) identification scenarios on a gallery of 100K rolled fingerprints. Not only do we fuse the complimentary representations, we also use the local features to guide the global representations to focus on discriminatory regions in two fingerprint images to be compared. This leads to a multi-stage matching paradigm in which subsets of the retrieved candidate lists for each probe image are passed to subsequent stages for further processing, resulting in a considerable reduction in latency (requiring just 0.068 ms per latent to rolled comparison on an AMD EPYC 7543 32-Core Processor, roughly 15K comparisons per second). Finally, we show the generalizability of the fused representations for improving authentication accuracy across several rolled, plain, and contactless fingerprint datasets.
XAI-based Comparison of Input Representations for Audio Event Classification

Deep neural networks are a promising tool for Audio Event Classification. In contrast to other data like natural images, there are many sensible and non-obvious representations for audio data, which could serve as input to these models. Due to their black-box nature, the effect of different input representations has so far mostly been investigated by measuring classification performance. In this work, we leverage eXplainable AI (XAI), to understand the underlying classification strategies of models trained on different input representations. Specifically, we compare two model architectures with regard to relevant input features used for Audio Event Detection: one directly processes the signal as the raw waveform, and the other takes in its time-frequency spectrogram representation. We show how relevance heatmaps obtained via"Siren"{Layer-wise Relevance Propagation} uncover representation-dependent decision strategies. With these insights, we can make a well-informed decision about the best input representation in terms of robustness and representativity and confirm that the model's classification strategies align with human requirements.
Solving Data-Driven Newsvendor Pricing Problems with Decision-Dependent Effect

This paper investigates the data-driven pricing newsvendor problem, which focuses on maximizing expected profit by deciding on inventory and pricing levels based on historical demand and feature data. We first build an approximate model by assigning weights to historical samples. However, due to decision-dependent effects, the resulting approximate model is complicated and unable to solve directly. To address this issue, we introduce the concept of approximate gradients and design an Approximate Gradient Descent (AGD) algorithm. We analyze the convergence of the proposed algorithm in both convex and non-convex settings, which correspond to the newsvendor pricing model and its variants respectively. Finally, we perform numerical experiment on both simulated and real-world dataset to demonstrate the efficiency and effectiveness of the AGD algorithm. We find that the AGD algorithm can converge to the local maximum provided that the approximation is effective. We also illustrate the significance of two characteristics: distribution-free and decision-dependent of our model. Consideration of the decision-dependent effect is necessary for approximation, and the distribution-free model is preferred when there is little information on the demand distribution and how demand reacts to the pricing decision. Moreover, the proposed model and algorithm are not limited to the newsvendor problem, but can also be used for a wide range of decision-dependent problems.
Zero-shot Unsupervised Transfer Instance Segmentation

Segmentation is a core computer vision competency, with applications spanning a broad range of scientifically and economically valuable domains. To date, however, the prohibitive cost of annotation has limited the deployment of flexible segmentation models. In this work, we propose Zero-shot Unsupervised Transfer Instance Segmentation (ZUTIS), a framework that aims to meet this challenge. The key strengths of ZUTIS are: (i) no requirement for instance-level or pixel-level annotations; (ii) an ability of zero-shot transfer, i.e., no assumption on access to a target data distribution; (iii) a unified framework for semantic and instance segmentations with solid performance on both tasks compared to state-or-the art unsupervised methods. While comparing to previous work, we show ZUTIS achieves a gain of 2.2 mask AP on COCO-20K and 14.5 mIoU on ImageNet-S with 919 categories for instance and semantic segmentations, respectively. Code will be made publicly available.1
Improving the Quality of Commit Messages in Students’ Projects

Commit messages play a crucial role in collaborative software development. They provide a clear and concise description of the changes made to the source code. However, many commit messages among students’ projects lack useful information. This is a concern, as low-quality commit messages can negatively impact communication of software development and future maintenance. To address this issue, this research aims to help students write high-quality commit messages by “nudging” them in the right direction. We modified the GitHub Desktop application by incorporating specific requirements for commit messages, specifically “what” and “why” parts. To test whether this affects the quality of commit messages, we divided students from an Information Retrieval class into two groups, with one group using the modified application and the other using other interfaces. The results show that the quality of commit messages is improved in terms of informativeness, clearness, and length.
Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis

COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among domain experts, mathematical modelers, and scientific computing specialists. Computationally, however, it also revealed critical gaps in the ability of researchers to exploit advanced computing systems. These challenging areas include gaining access to scalable computing systems, porting models and workflows to new systems, sharing data of varying sizes, and producing results that can be reproduced and validated by others. Informed by our team's work in supporting public health decision makers during the COVID-19 pandemic and by the identified capability gaps in applying high-performance computing (HPC) to the modeling of complex social systems, we present the goals, requirements, and initial implementation of OSPREY, an open science platform for robust epidemic analysis. The prototype implementation demonstrates an integrated, algorithm-driven HPC workflow architecture, coordinating tasks across federated HPC resources, with robust, secure and automated access to each of the resources. We demonstrate scalable and fault-tolerant task execution, an asynchronous API to support fast time-to-solution algorithms, an inclusive, multi-language approach, and efficient wide-area data management. The example OSPREY code is made available on a public repository.
ClusterNet: A Perception-Based Clustering Model for Scattered Data

Visualizations for scattered data are used to make users understand certain attributes of their data by solving different tasks, e.g. correlation estimation, outlier detection, cluster separation. In this paper, we focus on the later task, and develop a technique that is aligned to human perception, that can be used to understand how human subjects perceive clusterings in scattered data and possibly optimize for better understanding. Cluster separation in scatterplots is a task that is typically tackled by widely used clustering techniques, such as for instance k-means or DBSCAN. However, as these algorithms are based on non-perceptual metrics, we can show in our experiments, that their output do not reflect human cluster perception. We propose a learning strategy which directly operates on scattered data. To learn perceptual cluster separation on this data, we crowdsourced a large scale dataset, consisting of 7,320 point-wise cluster affiliations for bivariate data, which has been labeled by 384 human crowd workers. Based on this data, we were able to train ClusterNet, a point-based deep learning model, trained to reflect human perception of cluster separability. In order to train ClusterNet on human annotated data, we use a PointNet++ architecture enabling inference on point clouds directly. In this work, we provide details on how we collected our dataset, report statistics of the resulting annotations, and investigate perceptual agreement of cluster separation for real-world data. We further report the training and evaluation protocol of ClusterNet and introduce a novel metric, that measures the accuracy between a clustering technique and a group of human annotators. Finally, we compare our approach against existing state-of-the-art clustering techniques and can show, that ClusterNet is able to generalize to unseen and out of scope data.
The out-of-sample prediction error of the square-root-LASSO and related estimators

We study the classical problem of predicting an outcome variable, $Y$, using a linear combination of a $d$-dimensional covariate vector, $\mathbf{X}$. We are interested in linear predictors whose coefficients solve: % \begin{align*} \inf_{\boldsymbol{\beta} \in \mathbb{R}^d} \left( \mathbb{E}_{\mathbb{P}_n} \left[ \left(Y-\mathbf{X}^{\top}\beta \right)^r \right] \right)^{1/r} +\delta \, \rho\left(\boldsymbol{\beta}\right), \end{align*} where $\delta>0$ is a regularization parameter, $\rho:\mathbb{R}^d\to \mathbb{R}_+$ is a convex penalty function, $\mathbb{P}_n$ is the empirical distribution of the data, and $r\geq 1$. We present three sets of new results. First, we provide conditions under which linear predictors based on these estimators % solve a \emph{distributionally robust optimization} problem: they minimize the worst-case prediction error over distributions that are close to each other in a type of \emph{max-sliced Wasserstein metric}. Second, we provide a detailed finite-sample and asymptotic analysis of the statistical properties of the balls of distributions over which the worst-case prediction error is analyzed. Third, we use the distributionally robust optimality and our statistical analysis to present i) an oracle recommendation for the choice of regularization parameter, $\delta$, that guarantees good out-of-sample prediction error; and ii) a test-statistic to rank the out-of-sample performance of two different linear estimators. None of our results rely on sparsity assumptions about the true data generating process; thus, they broaden the scope of use of the square-root lasso and related estimators in prediction problems.
Mechanics of Hydraulic Fractures

Hydraulic fractures represent a particular class of tensile fractures that propagate in solid media under pre-existing compressive stresses as a result of internal pressurization by an injected viscous fluid. The main application of engineered hydraulic fractures is the stimulation of oil and gas wells to increase production. Several physical processes affect the propagation of these fractures, including the flow of viscous fluid, creation of solid surfaces, and leak-off of fracturing fluid. The interplay and the competition between these processes lead to multiple length scales and timescales in the system, which reveal the shifting influence of the far-field stress, viscous dissipation, fracture energy, and leak-off as the fracture propagates.
Logistic‐growth models measuring density feedback are sensitive to population declines, but not fluctuating carrying capacity

Abstract Analysis of long‐term trends in abundance of animal populations provides insights into population dynamics. Population growth rates are the emergent interplay of inter alia fertility, survival, and dispersal. However, the density feedbacks operating on some vital rates (“component feedback”) can be decoupled from density feedbacks on population growth rates estimated using abundance time series (“ensemble feedback”). Many of the mechanisms responsible for this decoupling are poorly understood, thereby questioning the validity of using logistic‐growth models versus vital rates to infer long‐term population trends. To examine which conditions lead to decoupling, we simulated age‐structured populations of long‐lived vertebrates experiencing component density feedbacks on survival. We then quantified how imposed stochasticity in survival rates, density‐independent mortality (catastrophes, harvest‐like removal of individuals) and variation in carrying capacity modified the ensemble feedback in abundance time series simulated from age‐structured populations. The statistical detection of ensemble density feedback from census data was largely unaffected by density‐independent processes. Long‐term population decline caused from density‐independent mortality was the main mechanism decoupling the strength of component versus ensemble density feedbacks. Our study supports the use of simple logistic‐growth models to capture long‐term population trends, mediated by changes in population abundance, when survival rates are stochastic, carrying capacity fluctuates, and populations experience moderate catastrophic mortality over time.
Charge transfer engineering to achieve extraordinary power generation in GeTe-based thermoelectric materials

By the fine manipulation of the exceptional long-range germanium-telluride (Ge─Te) bonding through charge transfer engineering, we have achieved exceptional thermoelectric (TE) and mechanical properties in lead-free GeTe. This chemical bonding mechanism along with a semiordered zigzag nanostructure generates a notable increase of the average zT to a record value of ~1.73 in the temperature range of 323 to 773 K with ultrahigh maximum zT ~ 2.7. In addition, we significantly enhanced the Vickers microhardness numbers (Hv) to an extraordinarily high value of 247 Hv and effectively eliminated the thermal expansion fluctuation at the phase transition, which was problematic for application, by the present charge transfer engineering process and concomitant formation of microstructures. We further fabricated a single-leg TE generator and obtained a conversion efficiency of ~13.4% at the temperature difference of 463 K on a commercial instrument, which is located at the pinnacle of TE conversion.
Cardiac computed tomography: from anatomy to function

Abstract Ischaemic heart disease (IHD) is one of the world’s leading causes of morbidity and mortality. Likewise, the diagnosis and risk stratification of patients with coronary artery disease (CAD) have always been based on the detection of the presence and extent of ischaemia by physical or pharmacological stress tests with or without the aid of imaging methods (e.g. exercise stress, test, stress echocardiography, single-photon emission computed tomography, or stress cardiac magnetic resonance). These methods show high performance to assess obstructive CAD, whilst they do not show accurate power to detect non-obstructive CAD. The introduction into clinical practice of coronary computed tomography angiography, the only non-invasive method capable of analyzing the coronary anatomy, allowed to add a crucial piece in the puzzle of the assessment of patients with suspected or chronic IHD. The current review evaluates the technical aspects and clinical experience of coronary computed tomography in the evaluation of atherosclerotic burden with a special focus about the new emerging application such as functional relevance of CAD with fractional flow reserve computed tomography (CT)-derived (FFRct), stress CT perfusion, and imaging inflammatory makers discussing the strength and weakness of each approach.
Atrial fibrillation fundamentals: from physiopathology to transcatheter ablation

Abstract Atrial fibrillation (AF) is a common and harmful arrhythmia. Its complex pathogenesis can be outlined using Coumel’s Triangle, that considers at the base of AF three different factors: substrate, trigger, and catalyst factor. The triangle can serve as a guide to understand the mechanism of action of the different possible treatments. Anti-arrhythmic drug therapies have a modest efficacy and no proven benefit on prognosis. Interventional therapy is more effective, especially if employed in the first stages of the disease, and can reduce mortality in selected populations. Ablative schemes must be different depending on the type of AF (paroxysmal, persistent) and the presence or absence of atrial dilation.
Beneath Our Feet: Strategies for Locomotion in Granular Media

“If you find yourself in a hole, stop digging. ” Although Denis Healey's famous adage (Metcalfe 2007) may offer sound advice for politicians, it is less relevant to worms, clams, and other higher organisms that rely on their digging ability for survival. In this article, we review recent work on the development of simple models that elucidate the fundamental principles underlying digging and burrowing strategies employed by biological systems. Four digging regimes are identified based on dimensionless digger size and the dimensionless inertial number. We select biological organisms to represent three of the four regimes: razor clams, sandfish, and nematodes. Models for all three diggers are derived and discussed, and analogies are drawn to low–Reynolds number swimmers.