Miracle-dz/UniFAR-FTUs huggingface dataset:
JrCUP: Joint RIS Calibration and User Positioning for 6G Wireless Systems

Reconfigurable intelligent surface (RIS)-assisted localization has attracted extensive attention as it can enable and enhance localization services in extreme scenarios. However, most existing works treat RISs as anchors with known positions and orientations, which is not realistic in applications with mobile or uncalibrated RISs. This work considers the joint RIS calibration and user positioning (JrCUP) problem with an active RIS. We propose a novel two-stage method to solve the considered JrCUP problem. The first stage comprises a tensor-estimation of signal parameters via rotational invariance techniques (tensorESPRIT), followed by a channel parameters refinement using least-squares. In the second stage, a two-dimensional search algorithm is proposed to estimate the three-dimensional user and RIS positions, one-dimensional RIS orientation, and clock bias from the estimated channel parameters. The Cramer-Rao lower bounds of the channel parameters and localization parameters are derived to verify the effectiveness of the proposed tensorESPRIT-based algorithms. In addition, simulation results reveal that the active RIS can significantly improve the localization performance compared to the passive case under the same system power supply in practical regions. Moreover, we observe the presence of blind areas with limited JrCUP localization performance, which can be mitigated by either leveraging more prior information or deploying extra base stations.
Correlated Information Reduces Accuracy of Pioneering Decision-Makers

Normative models are often used to describe how humans and animals make decisions. These models treat deliberation as the accumulation of uncertain evidence that terminates with a commitment to a choice. When extended to social groups, such models often assume that individuals make independent observations. However, individuals typically gather evidence from common sources, and their observations are rarely independent. Here we ask: For a group of ideal observers who do not exchange information, what is the impact of correlated evidence on decision accuracy? We show that even when agents are identical, correlated evidence causes decision accuracy to depend on temporal decision order. Surprisingly, the first decider is less accurate than a lone observer. Early deciders are less accurate than late deciders. These phenomena occur despite the fact that the rational observers use the same decision criterion, so they are equally confident in their decisions. We analyze discrete and continuum evidence-gathering models to explain why the first decider is less accurate than a lone observer when evidence is correlated. Pooling the decisions of early deciders using a majority rule does not rescue accuracy in the sense that such pooling results in only modest accuracy gain. Although we analyze an idealized model, we believe that our analysis offers insights that do not depend on exactly how groups integrate evidence and form decisions.
Characteristics of the variation of raindrop size distribution in Baiu season

Observational data of RSD (Raindrop Size Distribution) in the Baiu season were analyzed by using Principal Component Analysis in order to detect objectively the characteristics of variation of RSD. The results indicated that 88.4% of the variation of RSD was represented by the first and second principal components. The primary component of variation was to increase the number concentration for all sizes of drops; in other words, a parallel movement in a semi-logarithmic plot (linear diameter-logarithmic number concentration). It was found that this variation has a good correlation with that in the rainfall rate. This is different from the Marshall-Palmer-type RSD, which assumes only the slope parameter λ depending on the rainfall rate. The secondary component of variation was to increase (decrease) number concentration for larger (smaller) drops, in other words, the rotational movement in a semi-logarithmic plot. This component was considered to be connected with physical factors other than rainfall rate. With respect to small rainfall rate, comparison between RSDs in convective, and stratiform precipitations, showed that the slope of RSD was smaller in stratiform than in convective precipitation for equal rainfall rate. This means that the difference between convective and stratiform precipitation is one of the possible factors relating to the rotational variation.
Medical Pathologies Prediction : Systematic Review and Proposed Approach

The healthcare sector is an important pillar of every community, numerous research studies have been carried out in this context to optimize medical processes and improve care quality and facilitate patient management. In this article we have analyzed and examined different works concerning the exploitation of the most recent technologies such as big data, artificial intelligence, machine learning, and deep learning for the improvement of health care, which enabled us to propose our general approach concentrating on the collection, preprocessing and clustering of medical data to facilitate access, after analysis, to the patients and health professionals to predict the most frequent pathologies with better precision within a notable timeframe. keywords: Healthcare, big data, artificial intelligence, automatic language processing, data mining, predictive models.
RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement

Breast cancer is a significant public health concern and early detection is critical for triaging high risk patients. Sequential screening mammograms can provide important spatiotemporal information about changes in breast tissue over time. In this study, we propose a deep learning architecture called RADIFUSION that utilizes sequential mammograms and incorporates a linear image attention mechanism, radiomic features, a new gating mechanism to combine different mammographic views, and bilateral asymmetry-based finetuning for breast cancer risk assessment. We evaluate our model on a screening dataset called Cohort of Screen-Aged Women (CSAW) dataset. Based on results obtained on the independent testing set consisting of 1,749 women, our approach achieved superior performance compared to other state-of-the-art models with area under the receiver operating characteristic curves (AUCs) of 0.905, 0.872 and 0.866 in the three respective metrics of 1-year AUC, 2-year AUC and>2-year AUC. Our study highlights the importance of incorporating various deep learning mechanisms, such as image attention, radiomic features, gating mechanism, and bilateral asymmetry-based fine-tuning, to improve the accuracy of breast cancer risk assessment. We also demonstrate that our model's performance was enhanced by leveraging spatiotemporal information from sequential mammograms. Our findings suggest that RADIFUSION can provide clinicians with a powerful tool for breast cancer risk assessment.
Some numerical issues using element‐free Galerkin mesh‐less method for coupled hydro‐mechanical problems

A new formulation of the element‐free Galerkin (EFG) method is developed for solving coupled hydro‐mechanical problems. The numerical approach is based on solving the two governing partial differential equations of equilibrium and continuity of pore water simultaneously. Spatial variables in the weak form, i.e. displacement increment and pore water pressure increment, are discretized using the same EFG shape functions. An incremental constrained Galerkin weak form is used to create the discrete system equations and a fully implicit scheme is used for discretization in the time domain. Implementation of essential boundary conditions is based on a penalty method. Numerical stability of the developed formulation is examined in order to achieve appropriate accuracy of the EFG solution for coupled hydro‐mechanical problems. Examples are studied and compared with closed‐form or finite element method solutions to demonstrate the validity of the developed model and its capabilities. The results indicate that the EFG method is capable of handling coupled problems in saturated porous media and can predict well both the soil deformation and variation of pore water pressure over time. Some guidelines are proposed to guarantee the accuracy of the EFG solution for coupled hydro‐mechanical problems. Copyright © 2008 John Wiley & Sons, Ltd.
Paracrine relationship between incretin hormones and endogenous 5‐hydroxytryptamine in the small and large intestine

Enterochromaffin (EC) cell‐derived 5‐hydroxytryptamine (5‐HT) is a mediator of toxin‐induced reflexes, initiating emesis via vagal and central 5‐HT3 receptors. The amine is also involved in gastrointestinal (GI) reflexes that are prosecretory and promotile, and recently 5‐HT's roles in chemosensation in the distal bowel have been described. We set out to establish the efficacy of 5‐HT signaling, local 5‐HT levels and pharmacology in discrete regions of the mouse small and large intestine. We also investigated the inter‐relationships between incretin hormones, glucagon‐like peptide‐1 (GLP‐1) and gastric inhibitory polypeptide (GIP) and endogenous 5‐HT in mucosal and motility assays.
Clinical Practice Patterns in Abdominoplasty: 16-Year Analysis of Continuous Certification Data from the American Board of Plastic Surgery

Background: The authors evaluated trends in practice patterns for abdominoplasty based on a 16-year review of tracer data collected by the American Board of Plastic Surgery as part of the continuous certification process. Methods: To facilitate comparison of an equal number of patients over time, tracer data from 2005 to 2021 were split into an early cohort (EC) (from 2005 to 2014) and a recent cohort (RC) (from 2015 to 2021). Fisher exact tests and two-sample t tests were used to compare patient demographics, surgical techniques, and complication rates. Results: Data from 8990 abdominoplasties (EC, n = 4740; RC, n = 4250) were analyzed. RC abdominoplasties report a lower rate of complications (RC, 19%; EC, 22%; P < 0.001) and a lower rate of revision surgery (RC 8%; EC, 10%; P < 0.001). This has occurred despite the increased use of abdominal flap liposuction (RC, 25%; EC, 18%; P < 0.001). There has been a decline in the use of wide undermining (81% versus 75%; P < 0.001), vertical plication of the abdomen (89% versus 86%; P < 0.001), and surgical drains (93% versus 89%; P < 0.001). Abdominoplasty surgery is increasingly performed in an outpatient setting, with increased use of chemoprophylaxis for thrombosis prevention. Conclusions: Analysis of these American Board of Plastic Surgery tracer data highlights important trends in clinical practice over the past 16 years. Abdominoplasty continues to be a safe and effective procedure with similar complication and revision rates over the 16-year period.
Approximate polarization of plane waves in a medium having weak transverse isotropy

Many real rocks and sediments relevant to seismic exploration can be described by elastic, transversely isotropic media. The properties of plane waves propagating in a transversely isotropic medium can be given in an analytically exact form. Here the polarization is recast into a comprehensive form that includes Daley and Hron’s normalization and Helbig’s full range of elastic constants. But these formulas are rather lengthy and do not easily reveal the features caused by anisotropy. Hence Thomsen suggested an approximation scheme for weak transverse isotropy. His derivation of the approximate polarization, however, is based on a property that is not suitable to measure small differences between an isotropic and a weakly transversely isotropic medium. Therefore the approximation of the polarization is recast. The corrected approximation does show a dependence on weak transverse isotropy. This feature can be viewed as an additional rotation of the polarization with respect to the wavenormal. It depends on .
FMGNN: Fused Manifold Graph Neural Network

Graph representation learning has been widely studied and demonstrated effectiveness in various graph tasks. Most existing works embed graph data in the Euclidean space, while recent works extend the embedding models to hyperbolic or spherical spaces to achieve better performance on graphs with complex structures, such as hierarchical or ring structures. Fusing the embedding from different manifolds can further take advantage of the embedding capabilities over different graph structures. However, existing embedding fusion methods mostly focus on concatenating or summing up the output embeddings, without considering interacting and aligning the embeddings of the same vertices on different manifolds, which can lead to distortion and impression in the final fusion results. Besides, it is also challenging to fuse the embeddings of the same vertices from different coordinate systems. In face of these challenges, we propose the Fused Manifold Graph Neural Network (FMGNN), a novel GNN architecture that embeds graphs into different Riemannian manifolds with interaction and alignment among these manifolds during training and fuses the vertex embeddings through the distances on different manifolds between vertices and selected landmarks, geometric coresets. Our experiments demonstrate that FMGNN yields superior performance over strong baselines on the benchmarks of node classification and link prediction tasks.
Asymptotic bias reduction of maximum likelihood estimates via penalized likelihoods with differential geometry

A procedure for asymptotic bias reduction of maximum likelihood estimates of generic estimands is developed. The estimator is realized as a plug-in estimator, where the parameter maximizes the penalized likelihood with a penalty function that satisfies a quasi-linear partial differential equation of the first order. The integration of the partial differential equation with the aid of differential geometry is discussed. Applications to generalized linear models, linear mixed-effects models, and a location-scale family are presented.
Equivariantly formal 2-torus actions of complexity one

In this paper we study a specific class of actions of a $2$-torus $\mathbb{Z}_2^k$ on manifolds, namely, the actions of complexity one in general position. We describe the orbit space of equivariantly formal $2$-torus actions of complexity one in general position and restricted complexity one actions in the case of small covers. It is observed that the orbit spaces of such actions are topological manifolds. If the action is equivariantly formal, we prove that the orbit space is a $\mathbb{Z}_2$-homology sphere. We study a particular subclass of these $2$-torus actions: restrictions of small covers to a subgroup of index 2 in general position. The subgroup of this form exists if and only if the small cover is orientable, and in this case we prove that the orbit space of a restricted $2$-torus action is homeomorphic to a sphere.
Machine Learning with Confidential Computing: A Systematization of Knowledge

Privacy and security challenges in Machine Learning (ML) have become increasingly severe, along with ML's pervasive development and the recent demonstration of large attack surfaces. As a mature system-oriented approach, Confidential Computing has been utilized in both academia and industry to mitigate privacy and security issues in various ML scenarios. In this paper, the conjunction between ML and Confidential Computing is investigated. We systematize the prior work on Confidential Computing-assisted ML techniques that provide i) confidentiality guarantees and ii) integrity assurances, and discuss their advanced features and drawbacks. Key challenges are further identified, and we provide dedicated analyses of the limitations in existing Trusted Execution Environment (TEE) systems for ML use cases. Finally, prospective works are discussed, including grounded privacy definitions for closed-loop protection, partitioned executions of efficient ML, dedicated TEE-assisted designs for ML, TEE-aware ML, and ML full pipeline guarantees. By providing these potential solutions in our systematization of knowledge, we aim at building the bridge to help achieve a much strong TEE-enabled ML for privacy guarantees without introducing computation and system costs.
FACM: Intermediate Layer Still Retain Effective Features against Adversarial Examples

In strong adversarial attacks against deep neural networks (DNN), the generated adversarial example will mislead the DNN-implemented classifier by destroying the output features of the last layer. To enhance the robustness of the classifier, in our paper, a \textbf{F}eature \textbf{A}nalysis and \textbf{C}onditional \textbf{M}atching prediction distribution (FACM) model is proposed to utilize the features of intermediate layers to correct the classification. Specifically, we first prove that the intermediate layers of the classifier can still retain effective features for the original category, which is defined as the correction property in our paper. According to this, we propose the FACM model consisting of \textbf{F}eature \textbf{A}nalysis (FA) correction module, \textbf{C}onditional \textbf{M}atching \textbf{P}rediction \textbf{D}istribution (CMPD) correction module and decision module. The FA correction module is the fully connected layers constructed with the output of the intermediate layers as the input to correct the classification of the classifier. The CMPD correction module is a conditional auto-encoder, which can not only use the output of intermediate layers as the condition to accelerate convergence but also mitigate the negative effect of adversarial example training with the Kullback-Leibler loss to match prediction distribution. Through the empirically verified diversity property, the correction modules can be implemented synergistically to reduce the adversarial subspace. Hence, the decision module is proposed to integrate the correction modules to enhance the DNN classifier's robustness. Specially, our model can be achieved by fine-tuning and can be combined with other model-specific defenses.
Random perturbations of recursive sequences with an application to an epidemic model

We investigate the asymptotic sample path behaviour of a randomly perturbed discrete-time dynamical system. We consider the case where the trajectories of the non-perturbed dynamical system are attracted by a finite number of limit sets and characterize a case where this property remains valid for the perturbed dynamical system when the perturbation converges to zero. For this purpose, no further assumptions on the perturbation are needed and our main condition applies to the limit sets of the non-perturbed dynamical system. When the limit sets reduce to limit points we show that this main condition is more general than the usual assumption of the existence of a Lyapunov function for the non-perturbed dynamical system. An application to an epidemic model is given to illustrate our results.
Temporal properties of secondary drop breakup in the bag-stamen breakup regime

The situation of liquid drop bag-stamen breakup in a continuous air jet flow is investigated by a high speed camera. Test liquids include water, ethanol, and various glycerol mixtures. First, the morphology of bag-stamen breakup is observed and analyzed. The bag-stamen breakup range is found to be in good agreement with the model obtained by Rayleigh-Taylor instability. Then the disk and stamen deformation properties, the fragment average size, and size distribution of ring and stamen are researched in detail, respectively.
Diffusive Limit of the Boltzmann Equation in Bounded Domains

The rigorous justification of the hydrodynamic limits of kinetic equations in bounded domains has been actively investigated in recent years. In spite of the progress for the diffuse-reflection boundary case, the more challenging in-flow boundary case, in which the leading-order boundary layer effect is non-negligible, still remains open. In this work, we consider the stationary and evolutionary Boltzmann equation with the in-flow boundary in general (convex or non-convex) bounded domains, and demonstrate their incompressible Navier-Stokes-Fourier (INSF) limits in $L^2$. Our method relies on a novel and surprising gain of $\varepsilon^{\frac{1}{2}}$ in the kernel estimate, which is rooted from a key cancellation of delicately chosen test functions and conservation laws. We also introduce the boundary layer with grazing-set cutoff and investigate its BV regularity estimates to control the source terms of the remainder equation with the help of Hardy's inequality.
Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models

Studies on benign overfitting provide insights for the success of overparameterized deep learning models. In this work, we examine whether overfitting is truly benign in real-world classification tasks. We start with the observation that a ResNet model overfits benignly on Cifar10 but not benignly on ImageNet. To understand why benign overfitting fails in the ImageNet experiment, we theoretically analyze benign overfitting under a more restrictive setup where the number of parameters is not significantly larger than the number of data points. Under this mild overparameterization setup, our analysis identifies a phase change: unlike in the previous heavy overparameterization settings, benign overfitting can now fail in the presence of label noise. Our analysis explains our empirical observations, and is validated by a set of control experiments with ResNets. Our work highlights the importance of understanding implicit bias in underfitting regimes as a future direction.
Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation

Recently, source-free unsupervised domain adaptation (SFUDA) has emerged as a more practical and feasible approach compared to unsupervised domain adaptation (UDA) which assumes that labeled source data are always accessible. However, significant limitations associated with SFUDA approaches are often overlooked, which limits their practicality in real-world applications. These limitations include a lack of principled ways to determine optimal hyperparameters and performance degradation when the unlabeled target data fail to meet certain requirements such as a closed-set and identical label distribution to the source data. All these limitations stem from the fact that SFUDA entirely relies on unlabeled target data. We empirically demonstrate the limitations of existing SFUDA methods in real-world scenarios including out-of-distribution and label distribution shifts in target data, and verify that none of these methods can be safely applied to real-world settings. Based on our experimental results, we claim that fine-tuning a source pretrained model with a few labeled data (e.g., 1- or 3-shot) is a practical and reliable solution to circumvent the limitations of SFUDA. Contrary to common belief, we find that carefully fine-tuned models do not suffer from overfitting even when trained with only a few labeled data, and also show little change in performance due to sampling bias. Our experimental results on various domain adaptation benchmarks demonstrate that the few-shot fine-tuning approach performs comparatively under the standard SFUDA settings, and outperforms comparison methods under realistic scenarios. Our code is available at https://github.com/daintlab/fewshot-SFDA .