Miracle-dz/UniFAR-FTUs huggingface dataset:
CCFC: Bridging Federated Clustering and Contrastive Learning

Federated clustering, an essential extension of centralized clustering for federated scenarios, enables multiple data-holding clients to collaboratively group data while keeping their data locally. In centralized scenarios, clustering driven by representation learning has made significant advancements in handling high-dimensional complex data. However, the combination of federated clustering and representation learning remains underexplored. To bridge this, we first tailor a cluster-contrastive model for learning clustering-friendly representations. Then, we harness this model as the foundation for proposing a new federated clustering method, named cluster-contrastive federated clustering (CCFC). Benefiting from representation learning, the clustering performance of CCFC even double those of the best baseline methods in some cases. Compared to the most related baseline, the benefit results in substantial NMI score improvements of up to 0.4155 on the most conspicuous case. Moreover, CCFC also shows superior performance in handling device failures from a practical viewpoint.
Identifying Policy Gradient Subspaces

Policy gradient methods hold great potential for solving complex continuous control tasks. Still, their training efficiency can be improved by exploiting structure within the optimization problem. Recent work indicates that supervised learning can be accelerated by leveraging the fact that gradients lie in a low-dimensional and slowly-changing subspace. In this paper, we conduct a thorough evaluation of this phenomenon for two popular deep policy gradient methods on various simulated benchmark tasks. Our results demonstrate the existence of such gradient subspaces despite the continuously changing data distribution inherent to reinforcement learning. These findings reveal promising directions for future work on more efficient reinforcement learning, e.g., through improving parameter-space exploration or enabling second-order optimization.
Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables

Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene regulatory effects, that is, loci where the same genetic variants are associated to multiple nearby genes, MVMR can potentially be used to predict candidate causal genes. However, consensus in the field dictates that the genetic instruments in MVMR must be independent (not in linkage disequilibrium), which is usually not possible when considering a group of candidate genes from the same locus. Here we used causal inference theory to show that MVMR with correlated instruments satisfies the instrumental set condition. This is a classical result by Brito and Pearl (2002) for structural equation models that guarantees the identifiability of individual causal effects in situations where multiple exposures collectively, but not individually, separate a set of instrumental variables from an outcome variable. Extensive simulations confirmed the validity and usefulness of these theoretical results even at modest sample sizes (n≳500 –1000). Importantly, the causal effect estimates remain unbiased and their variance small when instruments are highly correlated. We applied MVMR with correlated instrumental variable sets at genome-wide significant loci for coronary artery disease (CAD) risk using expression Quantitative Trait Loci (eQTL) data from seven vascular and metabolic tissues in the STARNET study. Our method predicts causal genes at twelve loci, each associated with multiple colocated genes in multiple tissues. We confirm causal roles for PHACTR1 and ADAMTS7 in arterial tissues, among others. However, the extensive degree of regulatory pleiotropy across tissues and the limited number of causal variants in each locus still require that MVMR is run on a tissue-by-tissue basis, and testing all gene-tissue pairs with cis-eQTL associations at a given locus in a single model to predict causal gene-tissue combinations remains infeasible. Our results show that within tissues, MVMR with dependent, as opposed to independent, sets of instrumental variables significantly expands the scope for predicting causal genes in disease risk loci with pleiotropic regulatory effects. However, considering risk loci with regulatory pleiotropy that also spans across tissues remains an unsolved problem.
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding

Vision-language foundation models, represented by Contrastive language-image pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual tasks. However, existing approaches primarily focus on training models to match global image representations with textual descriptions, thereby overlooking the critical alignment between local regions and corresponding text tokens. This paper extends CLIP with multi-granularity alignment. Notably, we deliberately construct a new dataset comprising pseudo annotations at various levels of granularities, encompassing image-level, region-level, and pixel-level captions/tags. Accordingly, we develop a unified multi-granularity learning framework, named UMG-CLIP, that simultaneously empowers the model with versatile perception abilities across different levels of detail. Equipped with parameter efficient tuning, UMG-CLIP surpasses current widely used CLIP models and achieves state-of-the-art performance on diverse image understanding benchmarks, including open-world recognition, retrieval, semantic segmentation, and panoptic segmentation tasks. We hope UMG-CLIP can serve as a valuable option for advancing vision-language foundation models.
Deep Learning model predicts the c-Kit-11 mutational status of canine cutaneous mast cell tumors by HE stained histological slides

Numerous prognostic factors are currently assessed histopathologically in biopsies of canine mast cell tumors to evaluate clinical behavior. In addition, PCR analysis of the c-Kit exon 11 mutational status is often performed to evaluate the potential success of a tyrosine kinase inhibitor therapy. This project aimed at training deep learning models (DLMs) to identify the c-Kit-11 mutational status of MCTs solely based on morphology without additional molecular analysis. HE slides of 195 mutated and 173 non-mutated tumors were stained consecutively in two different laboratories and scanned with three different slide scanners. This resulted in six different datasets (stain-scanner variations) of whole slide images. DLMs were trained with single and mixed datasets and their performances was assessed under scanner and staining domain shifts. The DLMs correctly classified HE slides according to their c-Kit 11 mutation status in, on average, 87% of cases for the best-suited stain-scanner variant. A relevant performance drop could be observed when the stain-scanner combination of the training and test dataset differed. Multi-variant datasets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant. In summary, DLM-assisted morphological examination of MCTs can predict c-Kit-exon 11 mutational status of MCTs with high accuracy. However, the recognition performance is impeded by a change of scanner or staining protocol. Larger data sets with higher numbers of scans originating from different laboratories and scanners may lead to more robust DLMs to identify c-Kit mutations in HE slides.
Are We Still Missing an Item?

The missing item problem, as introduced by Stoeckl in his work at SODA 23, focuses on continually identifying a missing element $e$ in a stream of elements ${e_1, . , e_{\ell}}$ from the set $\{1,2,. ,n\}$, such that $e \neq e_i$ for any $i \in \{1,. ,\ell\}$. Stoeckl's investigation primarily delves into scenarios with $\ell<n$, providing bounds for the (i) deterministic case, (ii) the static case -- where the algorithm might be randomized but the stream is fixed in advanced and (iii) the adversarially robust case -- where the algorithm is randomized and each stream element can be chosen depending on earlier algorithm outputs. Building upon this foundation, our paper addresses previously unexplored aspects of the missing item problem. In the first segment, we examine the static setting with a long stream, where the length of the steam $\ell$ is close to or even exceeds the size of the universe $n$. We present an algorithm demonstrating that even when $\ell$ is very close to $n$ (say $\ell=n-1$), polylog($n$) bits of memory suffice to identify the missing item. When the stream's length $\ell$ exceeds the size of the universe $n$ i.e. $\ell = n +k$, we show a tight bound of roughly $\Theta(k)$. The second segment focuses on the adversarially robust setting. We show a lower bound for a pseudo-deterministic error-zero (where the algorithm reports its errors) algorithm of approximating $\Omega(\ell)$, up to polylog factors. Based on Stoeckl's work and the previous result, we establish a tight bound for a random-start (only use randomness at initialization) error-zero streaming algorithm of roughly $\Theta(\sqrt{\ell})$.
Bicycle Stabilization using mechanism optimization and Digital LQR

This study introduces lateral pendulum as an innovative balancer design for bicycle stabilization. This pendulum, operating in the bicycle's vertical plane, enables the bicycle to remain stationary. The paper develops a dynamic model for a bicycle equipped with this lateral pendulum, using Lagrange's method, where the equations are validated with ADAMS software. The stabilization is demonstrated with traditional vertical and novel lateral pendulums, managed through a genetic-pole placement control algorithm. This approach showcases the superiority of the lateral pendulum over traditional methods, including vertical pendulums and steering the handlebar. Additionally, a Digital Linear Quadratic Regulator controller is implemented for practical application, further enhancing system stability.
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine learning.
UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution

In recent years, there has been a growing interest in utilizing reinforcement learning (RL) to optimize long-term rewards in recommender systems. Since industrial recommender systems are typically designed as multi-stage systems, RL methods with a single agent face challenges when optimizing multiple stages simultaneously. The reason is that different stages have different observation spaces, and thus cannot be modeled by a single agent. To address this issue, we propose a novel UNidirectional-EXecution-based multi-agent Reinforcement Learning (UNEX-RL) framework to reinforce the long-term rewards in multi-stage recommender systems. We show that the unidirectional execution is a key feature of multi-stage recommender systems, bringing new challenges to the applications of multi-agent reinforcement learning (MARL), namely the observation dependency and the cascading effect. To tackle these challenges, we provide a cascading information chain (CIC) method to separate the independent observations from action-dependent observations and use CIC to train UNEX-RL effectively. We also discuss practical variance reduction techniques for UNEX-RL. Finally, we show the effectiveness of UNEX-RL on both public datasets and an online recommender system with over 100 million users. Specifically, UNEX-RL reveals a 0.558% increase in users' usage time compared with single-agent RL algorithms in online A/B experiments, highlighting the effectiveness of UNEX-RL in industrial recommender systems.
Updated analysis of $D\to PP, V\!P$ and $VV$ decays: Implications for $K_S^0-K_L^0$ asymmetries and $D^0$-$\overline {D}^0$ mixing

An updated analysis of the two-body $D\to PP, V\! P$ and $VV$ decays within the framework of the topological diagram approach is performed. A global fit to the Cabibbo-favored (CF) modes in the $V\! P$ sector gives many solutions with similarly small local minima in $\chi^2$. The solution degeneracy is lifted once we use them to predict for the singly Cabibbo-suppressed (SCS) modes. Topological amplitudes are extracted for the $\eta-\eta'$ mixing angles $\phi=40.4^\circ$ and $43.5^\circ$. The $K_S^0-K_L^0$ asymmetries in $D\to K_{S,L}^0M$ decays denoted by $R(D,M)$ are studied. While the predicted $R(D^0,P)$ for $P=\pi^0, \eta$ and $\eta'$ agree with experiment, the calculated $R(D^+,\pi^+)$, $R(D_s^+, K^+)$, $R(D^0,\omega)$ and $R(D^0,\phi)$ deviate from the data. We conjecture that the relative phase between the topological amplitudes $(C+A)$ and $(T+C)$ should be slightly smaller than $90^\circ$ in order to explain the first two discrepancies and that additional singlet contributions due to the SU(3)-singlet nature of $\omega$ and $\phi$ are needed to account for the last two. For doubly Cabibbo-suppressed (DCS) $D\to V\! P$ decays, their topological amplitudes (double-primed) cannot be all the same as the corresponding ones in the CF modes. The assumption of $E_{V,P}''=E_{V,P}$ for the $W$-exchange amplitude leads to some inconsistencies with the experiment. Through the measured relative phases between CF and DCS channels, the relations of $E_{V,P}''$ with $E_{V,P}$ are determined. Long-distance contributions to the $D^0$-$\overline {D}^0$ mixing parameter $y$ are evaluated in the exclusive approach. In particular, we focus on $D\to PP$ and $V\! P$ decays where $y$ can be reliably estimated. We conclude that $y_{_{P\! P}}\sim (0.110\pm 0.011)\%$ and the lower bound on $y_{_{V\! P}}$ is $(0.220\pm 0.071)\%$.
Manipulating multiple optical parametric processes in photonic topological insulators

Topological quantum optics, an emerging area of study, holds the potential to bring about substantial enhancements for integrated quantum devices. Here we propose integrated topological quantum devices performing various functions including optical parametric amplification, frequency division, and frequency entangled biphoton generation. We show two distinct edge modes corresponding to different frequency ranges in both sandwich kagome and honeycomb topological designs that emulate the quantum valley Hall effect. These two topological edge modes enable two types of optical parametric processes through four-wave mixing, specifically inter-band and intra-band cases. The devices emulating photonic valley-Hall insulators allow the frequency division of two transverse modes, and furthermore, enable the separation of two quantum functionalities - optical parametric amplification and frequency entangled biphoton state generation. More importantly, the parametric processes are inborn topological protected, showing robustness against sharp bends and disorders. Our proposal significantly widens the possibilities for robust, multifunctional topological quantum devices on-chip, which may find applications in quantum information processing.
A Study on Self-Supervised Pretraining for Vision Problems in Gastrointestinal Endoscopy

Solutions to vision tasks in gastrointestinal endoscopy (GIE) conventionally use image encoders pretrained in a supervised manner with ImageNet-1k as backbones. However, the use of modern self-supervised pretraining algorithms and a recent dataset of 100k unlabelled GIE images (Hyperkvasir-unlabelled) may allow for improvements. In this work, we study the fine-tuned performance of models with ResNet50 and ViT-B backbones pretrained in self-supervised and supervised manners with ImageNet-1k and Hyperkvasir-unlabelled (self-supervised only) in a range of GIE vision tasks. In addition to identifying the most suitable pretraining pipeline and backbone architecture for each task, out of those considered, our results suggest: that self-supervised pretraining generally produces more suitable backbones for GIE vision tasks than supervised pretraining; that self-supervised pretraining with ImageNet-1k is typically more suitable than pretraining with Hyperkvasir-unlabelled, with the notable exception of monocular depth estimation in colonoscopy; and that ViT-Bs are more suitable in polyp segmentation and monocular depth estimation in colonoscopy, ResNet50s are more suitable in polyp detection, and both architectures perform similarly in anatomical landmark recognition and pathological finding characterisation. We hope this work draws attention to the complexity of pretraining for GIE vision tasks, informs this development of more suitable approaches than the convention, and inspires further research on this topic to help advance this development. Code available: \underline{github.com/ESandML/SSL4GIE}
An ontology alignment method with user intervention using compact differential evolution with adaptive parameter control

User interaction is one of the most effective ways to improve the ontology alignment quality. However, this approach faces the challenge of how users can participate effectively in the matching process. To solve this challenge. In this paper, an interactive ontology alignment approach using compact differential evolution algorithm with adaptive parameter control (IOACDE) is proposed. In this method, the ontology alignment process is modeled as an interactive optimization problem and users are allowed to intervene in matching in two ways. One is that the mapping suggestions generated by IOACDE as a complete candidate alignment is evaluated by user during optimization process. The other is that the user ameliorates the alignment results by evaluating single mapping after the automatic matching process. To demonstrate the effectiveness of the proposed algorithm, the neural embedding model and K nearest neighbor (KNN) is employed to simulate user for the ontologies of the real world. The experimental results show that the proposed interactive approach can improve the alignment quality compared to the non-interactive. Compared with the state-of-the-art methods from OAEI, the results show that the proposed algorithm has a better performance under the same error rate.
Energy Patterns for Web: An Exploratory Study

As the energy footprint generated by software is increasing at an alarming rate, understanding how to develop energy-efficient applications has become a necessity. Previous work has introduced catalogs of coding practices, also known as energy patterns. These patterns are yet limited to Mobile or third-party libraries. In this study, we focus on the Web domain--a main source of energy consumption. First, we investigated whether and how Mobile energy patterns could be ported to this domain and found that 20 patterns could be ported. Then, we interviewed six expert web developers from different companies to challenge the ported patterns. Most developers expressed concerns for antipatterns, specifically with functional antipatterns, and were able to formulate guidelines to locate these patterns in the source code. Finally, to quantify the effect of Web energy patterns on energy consumption, we set up an automated pipeline to evaluate two ported patterns: 'Dynamic Retry Delay' (DRD) and 'Open Only When Necessary' (OOWN). With this, we found no evidence that the DRD pattern consumes less energy than its antipattern, while the opposite is true for OOWN. Data and Material: https://doi.org/10.5281/zenodo.8404487
Asymptotically de-Sitter black holes have non-zero tidal Love numbers

Love numbers of compact objects quantify their tidal deformability against external perturbations. It is expected that Love numbers of asymptotically flat black holes (BHs) in General Relativity are identically zero. We show that quite contrary to common expectations, the tidal Love numbers of asymptotically de Sitter black holes are non-zero.
Earths Are Not Super-Earths, Saturns Are Not Jupiters: Imprints of Pressure-bump Planet Formation on Planetary Architectures

In protoplanetary disks, sufficiently massive planets excite pressure bumps, which can then be preferred locations for forming new planet cores. We discuss how this loop may affect the architecture of multiplanet systems and compare our predictions with observations. Our main prediction is that low-mass planets and giant planets can each be divided into two subpopulations with different levels of mass uniformity. Low-mass planets that can and cannot reach the pebble isolation mass (the minimum mass required to produce a pressure bump) develop into intra-system similarity “super-Earths” and more diverse “Earths,” respectively. Gas giants that do and do not accrete envelopes quickly develop into similar “Jupiters” and more diverse “Saturns,” respectively. Super-Earths prefer to form long chains via repeated pressure-bump planet formation, while Jupiter formation is usually terminated at pairs or triplets due to dynamical instability. These predictions are broadly consistent with observations. In particular, we discover a previously overlooked mass uniformity dichotomy among the observed populations of both low-mass planets (Earths versus super-Earths) and gas giants (Saturns versus Jupiters). For low-mass planets, planets well below the pebble isolation mass (≲3 M ⊕ or ≲1.5 R ⊕ for Sun-like stars) show significantly higher intra-system pairwise mass differences than planets around the pebble isolation mass. For gas giants, the period ratios of intra-system pairs show a bimodal distribution, which can be interpreted as two subpopulations with different levels of mass uniformity. These findings suggest that pressure-bump planet formation could be an important ingredient in shaping planetary architectures.
Heat loss and internal dynamics of Venus from global surface heat flow estimates

The absence of plate tectonics and the young surface age (0.3-1 billion years) of Venus have led to diverse geodynamic models for Venus. The energetics of the Venusian interior drive these models; however, the lack of direct constraints on surface heat flow hampers their quantitative assessment. Here we present the first global heat flow map for Venus, obtained from an inversion of geophysical data, including crustal thickness, effective elastic thickness, and radioactive heat production. Heat flow on Venus is lower and less geographically structured than on Earth but with highs reaching values typical of magmatically active terrestrial areas. The obtained total heat loss is 11-14.5 TW, similar to estimates of the total radioactive heat production. Therefore, at present, Venus proportionally dissipates much less heat than Earth. Furthermore, the calculated crustal temperatures imply that crustal melting or eclogitization are not dominant in the Venusian crust.