Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the mitotic score of invasive breast carcinomas on digital slide: development and contribution of a mitosis detection algorithm

Introduction: Nottingham grading system is a major prognostic factor for invasive breast carcinoma (IBC). Its determination requires the evaluation of the mitotic score (MS) which is subject to low intra- and inter-observer reproducibility. The MS shall be performed in the most proliferative area of the tumor, which determination is hard but critical. Artificial intelligence based tools could help pathologists to detect mitosis on whole slide images (WSI). Objective: The aim of this study was to evaluate the contribution of a mitosis detection algorithms pecifically developed to assist the pathologist during the evaluation of the MS on WSI. Methods: Algorithmic mitosis detection is a two-step process: first the algorithm detects candidate objects resembling mitosis, then the selection is refined by a classifier. The densest mitoticregions are shown to the pathologist, then he can establish the MS with algorithm results. For this study, three expert pathologists have determined a consensual ground truth for MS on fifty WSI of IBC. Those slides were also submitted to two readers pathologists who evaluated the MS of each slide twice, with and without the assistance of the algorithm, with a four week wash-out period. Interobserver reproducibility was measured by evaluating the scores obtained with, and without assistance between two readers pathologists and was also measured between each reader pathologist and the expert ground truth to determine the accuracy of the established score. Results:Baseline linearly weighted Cohen's Kappa for interobserver agreement of MS between two readers pathologists was 0.482. Using the algorithm generated mitotic detection in WSI, the agreement score increased to 0.672. Baseline linearly weighted Cohen's Kappa for interobserver agreement of MS between each reader pathologist and expert consensus was 0.378 and 0.457 for pathologist 1 and 2 respectively. Using the algorithm generated mitoticdetection in WSI, the agreement score increased respectively to 0.629 and 0.726. Conclusion:The use of the developed algorithm constitutes a viable approach to assist the pathologist for the evaluation of the MS of IBC on WSI. Its use makes it possible to improve interobserver reproducibility between pathologists and the accuracy of the score established by expert consensus. The use of such a tool constitutes a new approach in the evaluation of the mitoticscore which could lead to an evolution of practices.
Climate Drift in a Multicentury Integration of the NCAR Climate System Model

Abstract The National Center for Atmospheric Research’s Climate System Model is a comprehensive model of the physical climate system. A 300-yr integration of the model has been carried out without flux correction. The solution shows very little drift in the surface temperature distribution, sea-ice extent, or atmospheric circulation. The lack of drift in the surface climate is attributed to relatively good agreement in the estimates of meridional heat transport in the uncoupled ocean model and that implied by the uncoupled atmospheric model. On the other hand, there is significant drift in the temperature and salinity distributions of the deep ocean. The ocean loses heat at an area-averaged rate of 0.35 W m−2, the upper ocean becomes fresher, and the deep ocean becomes colder and saltier than in the uncoupled ocean model equilibrium or in observations. The cause of this drift is an unreasonably large meridional transport of freshwater in the sea ice model, resulting in the production of excessively cold a.
A generalization of the achievable rate of a MISO system using Bode-Fano wideband matching theory

Impedance-matching networks affect power transfer from the radio frequency (RF) chains to the antennas. Their design impacts the signal to noise ratio (SNR) and the achievable rate. In this paper, we maximize the information-theoretic achievable rate of a multiple-input-single-output (MISO) system with wideband matching constraints. Using a multiport circuit theory approach with frequency-selective scattering parameters, we propose a general framework for optimizing the MISO achievable rate that incorporates Bode-Fano wideband matching theory. We express the solution to the achievable rate optimization problem in terms of the optimized transmission coefficient and the Lagrangian parameters corresponding to the Bode-Fano inequality constraints. We apply this framework to a single electric Chu's antenna and an array of two electric Chu's antennas. We compare the optimized achievable rate obtained numerically with other benchmarks like the ideal achievable rate computed by disregarding matching constraints and the achievable rate obtained by using sub-optimal matching strategies like conjugate matching and frequency-flat transmission. We also propose a practical methodology to approximate the achievable rate bound by using the optimal transmission coefficient to derive a physically realizable matching network through the ADS software.
Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models

We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models. Our method is derived from an existing technique that operates on the GAN latent space. Specifically, we employ a shift control module that works on h-space of pre-trained diffusion models to manipulate a sample into a shifted version of itself, followed by a reconstructor to reproduce both the type and the strength of the manipulation. By jointly optimizing them, the model will spontaneously discover disentangled and interpretable directions. To prevent the discovery of meaningless and destructive directions, we employ a discriminator to maintain the fidelity of shifted sample. Due to the iterative generative process of diffusion models, our training requires a substantial amount of GPU VRAM to store numerous intermediate tensors for back-propagating gradient. To address this issue, we propose a general VRAM-efficient training algorithm based on gradient checkpointing technique to back-propagate any gradient through the whole generative process, with acceptable occupancy of VRAM and sacrifice of training efficiency. Compared with existing related works on diffusion models, our method inherently identifies global and scalable directions, without necessitating any other complicated procedures. Extensive experiments on various datasets demonstrate the effectiveness of our method.
Socially reactive navigation models for mobile robots in dynamic environments

The objective of this work is to expand upon previous works, considering socially acceptable behaviours within robot navigation and interaction, and allow a robot to closely approach static and dynamic individuals or groups. The space models developed in this dissertation are adaptive, that is, capable of changing over time to accommodate the changing circumstances often existent within a social environment. The space model's parameters' adaptation occurs with the end goal of enabling a close interaction between humans and robots and is thus capable of taking into account not only the arrangement of the groups, but also the basic characteristics of the robot itself. This work also further develops a preexisting approach pose estimation algorithm in order to better guarantee the safety and comfort of the humans involved in the interaction, by taking into account basic human sensibilities. The algorithms are integrated into ROS's navigation system through the use of the $costmap2d$ and the $move\_base$ packages. The space model adaptation is tested via comparative evaluation against previous algorithms through the use of datasets. The entire navigation system is then evaluated through both simulations (static and dynamic) and real life situations (static). These experiments demonstrate that the developed space model and approach pose estimation algorithms are capable of enabling a robot to closely approach individual humans and groups, while maintaining considerations for their comfort and sensibilities.
APnP: A Less-constrained PnP Solver for Pose Estimation with Unknown Anisotropic Scaling or Focal Lengths

Perspective-$n$-Point (P$n$P) stands as a fundamental algorithm for pose estimation in various applications. In this paper, we present a new approach to the P$n$P problem with relaxed constraints, eliminating the need for precise 3D coordinates or complete calibration data. We refer to it as AP$n$P due to its ability to handle unknown anisotropic scaling factors of 3D coordinates or alternatively two distinct focal lengths in addition to the conventional rigid pose. Through algebraic manipulations and a novel parametrization, both cases are brought into similar forms that distinguish themselves primarily by the order of a rotation and an anisotropic scaling operation. AP$n$P furthermore brings down both cases to an identical polynomial problem, which is solved using the Gr\"obner basis approach. Experimental results on both simulated and real datasets demonstrate the effectiveness of AP$n$P, providing a more flexible and practical solution to several pose estimation tasks. Code: https://github.com/goldoak/APnP.
Mechanism of leached layer formation during chemical weathering of silicate minerals

The dissolution of most common multicomponent silicate minerals and glasses is typically incongruent, as shown by the nonstoichiometric release of the solid phase components. This results in the formation of so-called surface leached layers. Due to the important effects these leached layers may have on mineral dissolution rates and secondary mineral formation, they have attracted a great deal of research. However, the mechanism of leached layer formation is a matter of vigorous debate. Here we report on an in situ atomic force microscopy (AFM) study of the dissolution of wollastonite, CaSiO 3 , as an example of leached layer formation during dissolution. Our in situ AFM results provide, for the first time, clear direct experimental evidence that leached layers are formed in a tight interface-coupled two-step process: stoichiometric dissolution of the pristine mineral surfaces and subsequent precipitation of a secondary phase (most likely amorphous silica) from a supersaturated boundary layer of fluid in contact with the mineral surface. This occurs despite the fact that the bulk solution is undersaturated with respect to the secondary phase. Our results differ significantly from the concept of preferential leaching of cations, as postulated by most currently accepted incongruent dissolution models. This interface-coupled dissolution-precipitation model has important implications in understanding and evaluating dissolution kinetics of major rock-forming minerals.
Tunneling density of states of fractional quantum Hall edges: an unconventional bosonization approach

An unconventional bosonization approach that employs a modified Fermi-Bose correspondence is used to obtain the tunneling density of states (TDOS) of fractional quantum Hall (FQHE) edges in the vicinity of a point contact. The chiral Luttinger liquid model is generally used to describe FQHE edge excitations. We introduce a bosonization procedure to study edge state transport in Laughlin states at filling $\nu = 1/m$ with $m$ odd (single edge mode) in the presence of a point contact constriction that brings the top and bottom edges of the sample into close proximity. The unconventional bosonization involves modifying the Fermi-Bose correspondence to incorporate backscattering at the point contact, leaving the action of the theory purely quadratic even in presence of the inhomogeneity. We have shown convincingly in earlier works that this procedure correctly reproduces the most singular parts of the Green functions of the system even when mutual forward scattering between fermions are included. The most singular part of the density-density correlation function (DDCF) relevant to TDOS calculation is computed using a generating functional approach. The TDOS for both the electron tunneling as well as the Laughlin quasiparticle tunneling cases is obtained and is found to agree with previous results in the literature. For electron tunneling the well-known universal power laws for TDOS viz. $ \sim \mbox{ }\omega^{ m-1 }$ and for quasi-particle tunneling the power law $ \sim \mbox{ } \omega^{ \frac{1}{m}-1 } $ are both correctly recovered using our unconventional bosonization scheme. This demonstrates convincingly the utility of the present method which unlike conventional approaches, does not treat the point-contact as an afterthought and yet remains solvable so long as only the most singular parts of the correlation functions are desired.
A Multitor Formula for a Non-lci Intersection

In this paper we produce the first known formula for cohomologies of the derived tensor products of structure sheaves of subschemes in the case where the intersection of the subschemes is not a local complete intersection. The case covered here is where the intersection instead consists of two local complete intersection components, one of codimension 1 and the other of arbitrary codimension.
Progressive Evidence Refinement for Open-domain Multimodal Retrieval Question Answering

Pre-trained multimodal models have achieved significant success in retrieval-based question answering. However, current multimodal retrieval question-answering models face two main challenges. Firstly, utilizing compressed evidence features as input to the model results in the loss of fine-grained information within the evidence. Secondly, a gap exists between the feature extraction of evidence and the question, which hinders the model from effectively extracting critical features from the evidence based on the given question. We propose a two-stage framework for evidence retrieval and question-answering to alleviate these issues. First and foremost, we propose a progressive evidence refinement strategy for selecting crucial evidence. This strategy employs an iterative evidence retrieval approach to uncover the logical sequence among the evidence pieces. It incorporates two rounds of filtering to optimize the solution space, thus further ensuring temporal efficiency. Subsequently, we introduce a semi-supervised contrastive learning training strategy based on negative samples to expand the scope of the question domain, allowing for a more thorough exploration of latent knowledge within known samples. Finally, in order to mitigate the loss of fine-grained information, we devise a multi-turn retrieval and question-answering strategy to handle multimodal inputs. This strategy involves incorporating multimodal evidence directly into the model as part of the historical dialogue and question. Meanwhile, we leverage a cross-modal attention mechanism to capture the underlying connections between the evidence and the question, and the answer is generated through a decoding generation approach. We validate the model's effectiveness through extensive experiments, achieving outstanding performance on WebQA and MultimodelQA benchmark tests.
Existence and asymptotic behavior of entire large solutions for Hessian equations

In this paper, we give some existence and nonexistence results for nonradial entire large solutions of the Hessian equation $S_k\left(D^2 u\right)=b(x) u^\gamma$ in the sublinear case $0<\gamma<k$. The exact asymptotic behavior of large solutions at infinity is also studied when $b(x)$ is the oscillation of a radial function $|x|^{-l}$ at infinity for $l\leq k-1$.
ABACuS: All-Bank Activation Counters for Scalable and Low Overhead RowHammer Mitigation

We introduce ABACuS, a new low-cost hardware-counter-based RowHammer mitigation technique that performance-, energy-, and area-efficiently scales with worsening RowHammer vulnerability. We observe that both benign workloads and RowHammer attacks tend to access DRAM rows with the same row address in multiple DRAM banks at around the same time. Based on this observation, ABACuS's key idea is to use a single shared row activation counter to track activations to the rows with the same row address in all DRAM banks. Unlike state-of-the-art RowHammer mitigation mechanisms that implement a separate row activation counter for each DRAM bank, ABACuS implements fewer counters (e.g., only one) to track an equal number of aggressor rows. Our evaluations show that ABACuS securely prevents RowHammer bitflips at low performance/energy overhead and low area cost. We compare ABACuS to four state-of-the-art mitigation mechanisms. At a near-future RowHammer threshold of 1000, ABACuS incurs only 0.58% (0.77%) performance and 1.66% (2.12%) DRAM energy overheads, averaged across 62 single-core (8-core) workloads, requiring only 9.47 KiB of storage per DRAM rank. At the RowHammer threshold of 1000, the best prior low-area-cost mitigation mechanism incurs 1.80% higher average performance overhead than ABACuS, while ABACuS requires 2.50X smaller chip area to implement. At a future RowHammer threshold of 125, ABACuS performs very similarly to (within 0.38% of the performance of) the best prior performance- and energy-efficient RowHammer mitigation mechanism while requiring 22.72X smaller chip area. ABACuS is freely and openly available at https://github.com/CMU-SAFARI/ABACuS.
Benchmarking the Sim-to-Real Gap in Cloth Manipulation

Realistic physics engines play a crucial role for learning to manipulate deformable objects such as garments in simulation. By doing so, researchers can circumvent challenges such as sensing the deformation of the object in the real-world. In spite of the extensive use of simulations for this task, few works have evaluated the reality gap between deformable object simulators and real-world data. We present a benchmark dataset to evaluate the sim-to-real gap in cloth manipulation. The dataset is collected by performing a dynamic as well as a quasi-static cloth manipulation task involving contact with a rigid table. We use the dataset to evaluate the reality gap, computational time, and simulation stability of four popular deformable object simulators: MuJoCo, Bullet, Flex, and SOFA. Additionally, we discuss the benefits and drawbacks of each simulator. The benchmark dataset is open-source. Supplementary material, videos, and code, can be found at https://sites.google. com/view/cloth-sim2real-benchmark.
Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter

Social media platforms, particularly Twitter, have become pivotal arenas for influence campaigns, often orchestrated by state-sponsored information operations (IOs). This paper delves into the detection of key players driving IOs by employing similarity graphs constructed from behavioral pattern data. We unveil that well-known, yet underutilized network properties can help accurately identify coordinated IO drivers. Drawing from a comprehensive dataset of 49 million tweets from six countries, which includes multiple verified IOs, our study reveals that traditional network filtering techniques do not consistently pinpoint IO drivers across campaigns. We first propose a framework based on node pruning that emerges superior, particularly when combining multiple behavioral indicators across different networks. Then, we introduce a supervised machine learning model that harnesses a vector representation of the fused similarity network. This model, which boasts a precision exceeding 0.95, adeptly classifies IO drivers on a global scale and reliably forecasts their temporal engagements. Our findings are crucial in the fight against deceptive influence campaigns on social media, helping us better understand and detect them.
Unsupervised Domain Adaption for Neural Information Retrieval

Neural information retrieval requires costly annotated data for each target domain to be competitive. Synthetic annotation by query generation using Large Language Models or rule-based string manipulation has been proposed as an alternative, but their relative merits have not been analysed. In this paper, we compare both methods head-to-head using the same neural IR architecture. We focus on the BEIR benchmark, which includes test datasets from several domains with no training data, and explore two scenarios: zero-shot, where the supervised system is trained in a large out-of-domain dataset (MS-MARCO); and unsupervised domain adaptation, where, in addition to MS-MARCO, the system is fine-tuned in synthetic data from the target domain. Our results indicate that Large Language Models outperform rule-based methods in all scenarios by a large margin, and, more importantly, that unsupervised domain adaptation is effective compared to applying a supervised IR system in a zero-shot fashion. In addition we explore several sizes of open Large Language Models to generate synthetic data and find that a medium-sized model suffices. Code and models are publicly available for reproducibility.
Notes on Applicability of Explainable AI Methods to Machine Learning Models Using Features Extracted by Persistent Homology

Data analysis that uses the output of topological data analysis as input for machine learning algorithms has been the subject of extensive research. This approach offers a means of capturing the global structure of data. Persistent homology (PH), a common methodology within the field of TDA, has found wide-ranging applications in machine learning. One of the key reasons for the success of the PH-ML pipeline lies in the deterministic nature of feature extraction conducted through PH. The ability to achieve satisfactory levels of accuracy with relatively simple downstream machine learning models, when processing these extracted features, underlines the pipeline's superior interpretability. However, it must be noted that this interpretation has encountered issues. Specifically, it fails to accurately reflect the feasible parameter region in the data generation process, and the physical or chemical constraints that restrict this process. Against this backdrop, we explore the potential application of explainable AI methodologies to this PH-ML pipeline. We apply this approach to the specific problem of predicting gas adsorption in metal-organic frameworks and demonstrate that it can yield suggestive results. The codes to reproduce our results are available at https://github.com/naofumihama/xai_ph_ml
Machine learning in physics: A short guide

Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as causal inference, symbolic regression, and deep learning. We present some of the principal applications of machine learning in physics and discuss the associated challenges and perspectives.