Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain tumor MR image segmentation based on self-integrated U-Net network

In view of the limitations of traditional fusion methods in U-Net networks due to the requirement for a large amount of memory and resulting in low segmentation accuracy caused by insufficient labeled samples, an algorithm for brain tumor MR image segmentation based on a self-integrated segmentation network structure of U-Net is proposed. A self-integrated segmentation network module based on recursive fusion method is introduced into the U-Net network to address the issue of low segmentation accuracy caused by insufficient labeled samples and excessive memory consumption. The proposed algorithm is evaluated using brain tumor MR image data provided by BraTS (the brain tumor image segmentation challenge), and metrics such as Dice similarity coefficient are used for evaluation. The results show that the Dice values for complete tumor, tumor core, and enhancing tumor can reach 0.84, 0.85, and 0.78 respectively, which is an improvement compared to the U-Net model. Experimental results demonstrate that the proposed algorithm effectively enhances the accuracy of brain tumor segmentation with good segmentation performance.
GPT-4V with Emotion: A Zero-shot Benchmark for Multimodal Emotion Understanding

Recently, GPT-4 with Vision (GPT-4V) has shown remarkable performance across various multimodal tasks. However, its efﬁcacy in emotion recognition remains a question. This paper quantitatively evaluates GPT-4V’s capabilities in multimodal emotion understanding, encompassing tasks such as facial emotion recognition, visual sentiment analysis, micro-expression recognition, dynamic facial emotion recognition, and multimodal emotion recognition. Our experiments show that GPT-4V exhibits impressive multimodal and temporal understanding capabilities, even surpassing supervised systems in some tasks. Despite these achievements, GPT-4V is currently tailored for general domains. It performs poorly in micro-expression recognition that requires specialized expertise. The main purpose of this paper is to present quantitative results of GPT-4V on emotion understanding and establish a zero-shot benchmark for future research. Code and evaluation results are available at: https://github.com/zeroQiaoba/gpt4v-emotion.
Mortality and healthcare assessment among patients with chronic disease over 2 years of COVID-19: a population-based study in a large hard-hit Italian region

Objectives We aimed to provide a region-wide comprehensive account of the indirect effects of COVID-19 on patients with chronic disease, in terms of non-COVID-19 mortality, and access to both inpatient and outpatient health services over a 2-year pandemic period. Design Population-based retrospective study. Setting Adult patients, affected by at least 1 of 32 prevalent chronic conditions, residing in the Emilia-Romagna Region in Italy, during the years 2020 (N=1 791 189, 47.7% of the overall adult regional population) and 2021 (N=1 801 071, 47.8%). Results Overall, non-COVID-19 mortality among patients with chronic disease during the pandemic (2.7%) did not differ substantially from the expected mortality (2.5%), based on a 3 years prepandemic period (2017–2019) and adjusting for the demographic and clinical characteristics of the population under study. Indeed, while the first pandemic wave was characterised by a significant non-COVID-19 excess mortality (March: +35%), the subsequent phases did not show such disruptive variations in non-COVID-19 deaths, which remained around or even below the excess mortality threshold. End-of-life care of patients with chronic disease, especially for non-COVID-19 cases, significantly shifted from hospitalisations (−19%), to homecare (ADI: +7%; w/o ADI: +9%). Overall, healthcare of patients without COVID-19 chronic disease decreased, with similar negative trends in hospitalisations (−15.5%), major procedures (−19.6%) and ER accesses (−23.7%). Homecare was the least affected by the pandemic, with an overall reduction of −9.8%. COVID-19 outbreak also impacted on different types of outpatient care. Rehabilitation therapies, specialist visits, diagnostic and lab tests were considerably reduced during the first pandemic wave and consequent lockdown, with access rates of patients without COVID-19 chronic disease below −60%. Conclusions This work thoroughly describes how a large and well-defined population of patients without COVID-19 chronic disease has been affected by the changes and reorganisation in the healthcare system during 2 years of the pandemic, highlighting health priorities and challenges in chronic disease management under conditions of limited resources.
PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation

This paper proposes a cross-modal distillation framework, PartDistill, which transfers 2D knowledge from vision-language models (VLMs) to facilitate 3D shape part segmentation. PartDistill addresses three major challenges in this task: the lack of 3D segmentation in invisible or undetected regions in the 2D projections, inaccurate and inconsistent 2D predictions by VLMs, and the lack of knowledge accumulation across different 3D shapes. PartDistill consists of a teacher network that uses a VLM to make 2D predictions and a student network that learns from the 2D predictions while extracting geometrical features from multiple 3D shapes to carry out 3D part segmentation. A bi-directional distillation, including forward and backward distillations, is carried out within the framework, where the former forward distills the 2D predictions to the student network, and the latter improves the quality of the 2D predictions, which subsequently enhances the final 3D part segmentation. Moreover, PartDistill can exploit generative models that facilitate effortless 3D shape creation for generating knowledge sources to be distilled. Through extensive experiments, PartDistill boosts the existing methods with substantial margins on widely used ShapeNetPart and PartE datasets, by more than 15% and 12% higher mIoU scores, respectively.
Pearl: A Production-ready Reinforcement Learning Agent

Reinforcement Learning (RL) offers a versatile framework for achieving long-term goals. Its generality allows us to formalize a wide range of problems that real-world intelligent systems encounter, such as dealing with delayed rewards, handling partial observability, addressing the exploration and exploitation dilemma, utilizing offline data to improve online performance, and ensuring safety constraints are met. Despite considerable progress made by the RL research community in addressing these issues, existing open-source RL libraries tend to focus on a narrow portion of the RL solution pipeline, leaving other aspects largely unattended. This paper introduces Pearl, a Production-ready RL agent software package explicitly designed to embrace these challenges in a modular fashion. In addition to presenting preliminary benchmark results, this paper highlights Pearl's industry adoptions to demonstrate its readiness for production usage. Pearl is open sourced on Github at github.com/facebookresearch/pearl and its official website is located at pearlagent.github. io.
Polarimetric Light Transport Analysis for Specular Inter-reflection

Polarization is well known for its ability to decompose diffuse and specular reflections. However, the existing decomposition methods only focus on direct reflection and overlook multiple reflections, especially specular inter-reflection. In this paper, we propose a novel decomposition method for handling specular inter-reflection of metal objects by using a unique polarimetric feature: the rotation direction of linear polarization. This rotation direction serves as a discriminative factor between direct and inter-reflection on specular surfaces. To decompose the reflectance components, we actively rotate the linear polarization of incident light and analyze the rotation direction of the reflected light. We evaluate our method using both synthetic and real data, demonstrating its effectiveness in decomposing specular inter-reflections of metal objects. Furthermore, we demonstrate that our method can be combined with other decomposition methods for a detailed analysis of light transport. As a practical application, we show its effectiveness in improving the accuracy of 3D measurement against strong specular inter-reflection.
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification

Recently, graph neural networks (GNNs) have shown prominent performance in semi-supervised node classification by leveraging knowledge from the graph database. However, most existing GNNs follow the homophily assumption, where connected nodes are more likely to exhibit similar feature distributions and the same labels, and such an assumption has proven to be vulnerable in a growing number of practical applications. As a supplement, heterophily reflects dissimilarity in connected nodes, which has gained significant attention in graph learning. To this end, data engineers aim to develop a powerful GNN model that can ensure performance under both homophily and heterophily. Despite numerous attempts, most existing GNNs struggle to achieve optimal node representations due to the constraints of undirected graphs. The neglect of directed edges results in sub-optimal graph representations, thereby hindering the capacity of GNNs. To address this issue, we introduce AMUD, which quantifies the relationship between node profiles and topology from a statistical perspective, offering valuable insights for Adaptively Modeling the natural directed graphs as the Undirected or Directed graph to maximize the benefits from subsequent graph learning. Furthermore, we propose Adaptive Directed Pattern Aggregation (ADPA) as a new directed graph learning paradigm for AMUD. Empirical studies have demonstrated that AMUD guides efficient graph learning. Meanwhile, extensive experiments on 16 benchmark datasets substantiate the impressive performance of ADPA, outperforming baselines by significant margins of 3.96.
Population Monte Carlo With Normalizing Flow

Adaptive importance sampling (AIS) methods provide a useful alternative to Markov Chain Monte Carlo (MCMC) algorithms for performing inference of intractable distributions. Population Monte Carlo (PMC) algorithms constitute a family of AIS approaches which adapt the proposal distributions iteratively to improve the approximation of the target distribution. Recent work in this area primarily focuses on ameliorating the proposal adaptation procedure for high-dimensional applications. However, most of the AIS algorithms use simple proposal distributions for sampling, which might be inadequate in exploring target distributions with intricate geometries. In this work, we construct expressive proposal distributions in the AIS framework using normalizing flow, an appealing approach for modeling complex distributions. We use an iterative parameter update rule to enhance the approximation of the target distribution. Numerical experiments show that in high-dimensional settings, the proposed algorithm offers significantly improved performance compared to the existing techniques.
Dynamics of spin-momentum entanglement from superradiant phase transitions

Exploring operational regimes of many-body cavity QED with multi-level atoms remains an exciting research frontier for their enhanced storage capabilities of intra-level quantum correlations. In this work, we propose an extension of a prototypical many-body cavity QED experiment from a two to a four-level description by optically addressing a combination of momentum and spin states of the ultracold atoms in the cavity. The resulting model comprises a pair of Dicke Hamiltonians constructed from pseudo-spin operators, effectively capturing two intertwined superradiant phase transitions. The phase diagram reveals regions featuring weak and strong entangled states of spin and momentum atomic degrees of freedom. These states exhibit different dynamical responses, ranging from slow to fast relaxation, with the added option of persistent entanglement temporal oscillations. We discuss the role of cavity losses in steering the system dynamics into such entangled states and propose a readout scheme that leverages different light polarizations within the cavity. Our work paves the way to connect the rich variety of non-equilibrium phase transitions that occur in many-body cavity QED to the buildup of quantum correlations in systems with multi-level atom descriptions.
Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series

Multi-modal biomedical time series (MBTS) data offers a holistic view of the physiological state, holding significant importance in various bio-medical applications. Owing to inherent noise and distribution gaps across different modalities, MBTS can be complex to model. Various deep learning models have been developed to learn representations of MBTS but still fall short in robustness due to the ignorance of modal-to-modal variations. This paper presents a multi-scale and multi-modal biomedical time series representation learning (MBSL) network with contrastive learning to migrate these variations. Firstly, MBTS is grouped based on inter-modal distances, then each group with minimum intra-modal variations can be effectively modeled by individual encoders. Besides, to enhance the multi-scale feature extraction (encoder), various patch lengths and mask ratios are designed to generate tokens with semantic information at different scales and diverse contextual perspectives respectively. Finally, cross-modal contrastive learning is proposed to maximize consistency among inter-modal groups, maintaining useful information and eliminating noises. Experiments against four bio-medical applications show that MBSL outperforms state-of-the-art models by 33.9% mean average errors (MAE) in respiration rate, by 13.8% MAE in exercise heart rate, by 1.41% accuracy in human activity recognition, and by 1.14% F1-score in obstructive sleep apnea-hypopnea syndrome.
On Distributed Nonconvex Optimisation via Modified ADMM

This letter addresses the problem of nonconvex nonsmooth decentralised optimisation in multi-agent networks with undirected connected communication graphs. Our contribution lies in introducing an algorithmic framework designed for the distributed minimisation of the sum of a smooth (possibly nonconvex and non-separable) function and a convex (possibly nonsmooth and non-separable) regulariser. The proposed algorithm can be seen as a modified version of the ADMM algorithm where, at each step, an “inner loop” needs to be iterated for a number of iterations. The role of the inner loop is to aggregate and disseminate information across the network. We observe that a naive decentralised approach (one iteration of the inner loop) may not converge. We establish the asymptotic convergence of the proposed algorithm to the set of stationary points of the nonconvex problem where the number of iterations of the inner loop increases logarithmically with the step count of the ADMM algorithm. We present numerical results demonstrating the proposed method’s correctness and performance.
SoK: Unintended Interactions among Machine Learning Defenses and Risks

Machine learning (ML) models cannot neglect risks to security, privacy, and fairness. Several defenses have been proposed to mitigate such risks. When a defense is effective in mitigating one risk, it may correspond to increased or decreased susceptibility to other risks. Existing research lacks an effective framework to recognize and explain these unintended interactions. We present such a framework, based on the conjecture that overfitting and memorization underlie unintended interactions. We survey existing literature on unintended interactions, accommodating them within our framework. We use our framework to conjecture on two previously unexplored interactions, and empirically validate our conjectures.
Representational Dissimilarity of Faces and Places during a Working Memory Task is Associated with Subsequent Recognition Memory during Development

Abstract Nearly 50 years of research has focused on faces as a special visual category, especially during development. Yet it remains unclear how spatial patterns of neural similarity of faces and places relate to how information processing supports subsequent recognition of items from these categories. The current study uses representational similarity analysis and functional imaging data from 9- and 10-year-old youth during an emotional n-back task from the Adolescent Brain and Cognitive Development Study 3.0 data release to relate spatial patterns of neural similarity during working memory to subsequent out-of-scanner performance on a recognition memory task. Specifically, we examine how similarities in representations within face categories (neutral, happy, and fearful faces) and representations between visual categories (faces and places) relate to subsequent recognition memory of these visual categories. Although working memory performance was higher for faces than places, subsequent recognition memory was greater for places than faces. Representational similarity analysis revealed category-specific patterns in face-and place-sensitive brain regions (fusiform gyrus, parahippocampal gyrus) compared with a nonsensitive visual region (pericalcarine cortex). Similarity within face categories and dissimilarity between face and place categories in the parahippocampus was related to better recognition of places from the n-back task. Conversely, in the fusiform, similarity within face categories and their relative dissimilarity from places was associated with better recognition of new faces, but not old faces. These findings highlight how the representational distinctiveness of visual categories influence what information is subsequently prioritized in recognition memory during development.
UvA-DARE (Digital Academic Repository) Effects of Ocean Acidification on Calcification of the Sub-Antarctic Pteropod Limacina retroversa

is expected to impact the high latitude oceans ﬁrst, as CO 2 dissolves more easily in colder waters. At the current rate of anthropogenic CO 2 emissions, the sub-Antarctic Zone will start to experience undersaturated conditions with respect to aragonite within the next few decades, which will affect marine calcifying organisms. Shelled pteropods, a group of calcifying zooplankton, are considered to be especially sensitive to changes in carbonate chemistry because of their thin aragonite shells. Limacina retroversa is the most abundant pteropod in sub-Antarctic waters, and plays an important role in the carbonate pump. However, not much is known about its response to ocean acidiﬁcation. In this study, we investigated differences in calciﬁcation between L. retroversa individuals exposed to ocean carbonate chemistry conditions of the past (pH 8.19; mid-1880s), present (pH 8.06), and near-future (pH 7.93; predicted for 2050) in the sub-Antarctic. After 3 days of exposure, calciﬁcation responses were quantiﬁed by calcein staining, shell weighing, and Micro-CT scanning. In pteropods exposed to past conditions, calciﬁcation occurred over the entire shell and the leading edge of the last whorl, whilst individuals incubated under present and near-future conditions mostly invested in extending their shells, rather than calcifying over their entire shell. Moreover, individuals exposed to past conditions formed larger shell volumes compared to present and future conditions, suggesting that calciﬁcation is already decreased in today’s sub-Antarctic waters. Shells of individuals incubated under near-future conditions did not increase in shell weight during the incubation, and had a lower density compared to past and present conditions, suggesting that calciﬁcation will be further compromised in the future. This demonstrates the high sensitivity of L. retroversa to relatively small and short-term changes in carbonate chemistry. A reduction in calciﬁcation of L. retroversa in the rapidly acidifying waters of the sub-Antarctic will have a major impact on aragonite-CaCO 3 export from oceanic surface waters to the deep sea.
Competing 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mi>x</mml:mi><mml:mi>y</mml:mi></mml:mrow></mml:msub></mml:math>
 and 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>s</mml:mi><mml:mo>±</mml:mo></mml:msub></mm

With recent discoveries of superconductivity in infinite--layer nickelates, and in La$_{3}$Ni$_{2}$O$_{7}$ under high pressure, new opportunities appeared that yet another family of high--temperature superconductors based on Ni element may exist in Nature as was previously the case of cuprates and iron based materials. With their famous strong Coulomb correlations among 3d electrons and the proximity to antiferromagnetic instability these systems represent a challenge for their theoretical description, and most previous studies of superconductivity relied on the solutions of simplified few--orbital model Hamiltonians. Here, on the other hand, we use a recently developed combination of density functional theory with momentum and frequency resolved self--energies deduced from the so--called Fluctuational--Exchange (FLEX)--type Random Phase Approximation (RPA) to study spin fluctuation mediated pairing tendencies in La$_{3}$Ni$_{2}$O$_{7}$ under pressure. This methodology uses first--principle electronic structures of an actual material and is free of tight--binding parametrizations employed in model Hamiltonian approach. Based on our numerical diagonalization of the BCS Gap equation we show that competing d$_{xy}$ and s$_{\pm }$ pairing symmetries emerge in superconducting La$_{3}$Ni$_{2}$O$% _{7}$ with the corresponding coupling constants becoming large in the proximity of spin density wave instability. The results presented here are discussed in light of numerous other calculations and provide on--going experimental efforts with predictions that will allow further tests of our understanding of unconventional superconductors.
Learning High-Dimensional Differential Graphs From Multiattribute Data

We consider the problem of estimating differences in two Gaussian graphical models (GGMs) which are known to have similar structure. The GGM structure is encoded in its precision (inverse covariance) matrix. In many applications one is interested in estimating the difference in two precision matrices to characterize underlying changes in conditional dependencies of two sets of data. Existing methods for differential graph estimation are based on single-attribute (SA) models where one associates a scalar random variable with each node. In multi-attribute (MA) graphical models, each node represents a random vector. In this paper, we analyze a group lasso penalized D-trace loss function approach for differential graph learning from multi-attribute data. An alternating direction method of multipliers (ADMM) algorithm is presented to optimize the objective function. Theoretical analysis establishing consistency in support recovery and estimation in high-dimensional settings is provided. Numerical results based on synthetic as well as real data are presented.