Miracle-dz/UniFAR-FTUs huggingface dataset:
Nucleus Segmentation and Analysis in Breast Cancer with the MIScnn Framework

The NuCLS dataset contains over 220.000 annotations of cell nuclei in breast cancers. We show how to use these data to create a multi-rater model with the MIScnn Framework to automate the analysis of cell nuclei. For the model creation, we use the widespread U-Net approach embedded in a pipeline. This pipeline provides besides the high performance convolution neural network, several preprocessor techniques and a extended data exploration. The ﬁnal model is tested in the evaluation phase using a wide variety of metrics with a subsequent visualization. Finally, the results are compared and interpreted with the results of the NuCLS study. As an outlook, indications are given which are important for the future development of models in the context of cell nuclei.
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations

Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare by assisting decision-makers to answer ''what-iF'' questions. Existing causal inference approaches typically consider regular, discrete-time intervals between observations and treatment decisions and hence are unable to naturally model irregularly sampled data, which is the common setting in practice. To handle arbitrary observation patterns, we interpret the data as samples from an underlying continuous-time process and propose to model its latent trajectory explicitly using the mathematics of controlled differential equations. This leads to a new approach, the Treatment Effect Neural Controlled Differential Equation (TE-CDE), that allows the potential outcomes to be evaluated at any time point. In addition, adversarial training is used to adjust for time-dependent confounding which is critical in longitudinal settings and is an added challenge not encountered in conventional time-series. To assess solutions to this problem, we propose a controllable simulation environment based on a model of tumor growth for a range of scenarios with irregular sampling reflective of a variety of clinical scenarios. TE-CDE consistently outperforms existing approaches in all simulated scenarios with irregular sampling.
How to talk so your robot will learn: Instructions, descriptions, and pragmatics

From the earliest years of our lives, humans use language to express our beliefs and desires. Being able to talk to artiﬁcial agents about our preferences would thus fulﬁll a central goal of value alignment. Yet today, we lack computational models explaining such ﬂexible and abstract language use. To address this challenge, we consider social learning in a linear bandit setting and ask how a human might communicate preferences over behaviors (i.e. the reward function). We study two distinct types of language: instructions , which provide information about the desired policy, and descriptions , which provide information about the reward function. To explain how humans use these forms of language, we suggest they reason about both known present and unknown future states: instructions optimize for the present, while descriptions generalize to the future. We formalize this choice by extending reward design to consider a distribution over states. We then deﬁne a pragmatic listener agent that infers the speaker’s reward function by reasoning about how the speaker expresses themselves. We validate our models with a behavioral experiment, demonstrating that (1) our speaker model predicts spontaneous human behavior, and (2) our pragmatic listener is able to recover their reward functions. Finally, we show that in traditional reinforcement learning settings, pragmatic social learning can integrate with and accelerate individual learning. Our ﬁndings suggest that social learning from a wider range of language— in particular, expanding the ﬁeld’s present focus on instructions to include learning from descriptions—is a promising approach for value alignment and reinforcement learning more broadly.
DreamNet: A Deep Riemannian Network based on SPD Manifold Learning for Visual Classification

Image set-based visual classification methods have achieved remarkable performance, via characterising the image set in terms of a non-singular covariance matrix on a symmetric positive definite (SPD) manifold. To adapt to complicated visual scenarios better, several Riemannian networks (RiemNets) for SPD matrix nonlinear processing have recently been studied. However, it is pertinent to ask, whether greater accuracy gains can be achieved by simply increasing the depth of RiemNets. The answer appears to be negative, as deeper RiemNets tend to lose generalization ability. To explore a possible solution to this issue, we propose a new architecture for SPD matrix learning. Specifically, to enrich the deep representations, we adopt SPDNet [1] as the backbone, with a stacked Riemannian autoencoder (SRAE) built on the tail. The associated reconstruction error term can make the embedding functions of both SRAE and of each RAE an approximate identity mapping, which helps to prevent the degradation of statistical information. We then insert several residual-like blocks with shortcut connections to augment the representational capacity of SRAE, and to simplify the training of a deeper network. The experimental evidence demonstrates that our DreamNet can achieve improved accuracy with increased depth of the network.
Distributed Online Learning Algorithm With Differential Privacy Strategy for Convex Nondecomposable Global Objectives

—In the machine learning domain, datasets share several new features, including distributed storage, high velocity, and privacy concerns, which naturally requires the development of distributed privacy-preserving algorithms. Moreover, nodes (e.g., learners, sensors, GPUs, mobiles, etc.) in the real networks usually process tasks in real time, which inevitably requires nodes to have online learning capabilities. Therefore, we deal with a general distributed constrained online learning problem with privacy over time-varying networks, where a class of nondecomposable objective functions are considered. Under this setting, each node only controls a part of the global decision variable, and the goal of all nodes is to collaboratively minimize the global objective over a time horizon T while guarantees the security of the transmitted information. For such problems, we ﬁrst design a novel generic algorithm framework, named as DPSDA, of differentially private distributed online learning using the Laplace mechanism and the stochastic variants of dual averaging method. Note that in the dual updates, all nodes of DPSDA employ the noise-corrupted gradients for more generality. Then, we propose two algorithms, named as DPSDA-C and DPSDA-PS, under this framework. In DPSDA-C, the nodes implement a circulation-based communication in the primal updates so as to alleviate the disagreements over time-varying undirected networks. In addition, for the extension to time- varying directed ones, the nodes implement the broadcast-based push-sum dynamics in DPSDA-PS, which can achieve average consensus over arbitrary directed networks. Theoretical results show that both algorithms attain an expected regret upper bound in O ( √ T ) when the objective function is convex, which matches the best utility achievable by cutting-edge algorithms. Finally, numerical experiment results on both real-world and randomly generated datasets verify the effectiveness of our algorithms. objectives, distributed online learning, time-varying networks.
DCASE 2022: Comparative Analysis Of CNNs For Acoustic Scene Classification Under Low-Complexity Considerations

Acoustic scene classification is an automatic listening problem that aims to assign an audio recording to a pre-defined scene based on its audio data. Over the years (and in past editions of the DCASE) this problem has often been solved with techniques known as ensembles (use of several machine learning models to combine their predictions in the inference phase). While these solutions can show performance in terms of accuracy, they can be very expensive in terms of computational capacity, making it impossible to deploy them in IoT devices. Due to the drift in this field of study, this task has two limitations in terms of model complexity. It should be noted that there is also the added complexity of mismatching devices (the audios provided are recorded by different sources of information). This technical report makes a comparative study of two different network architectures: conventional CNN and Conv-mixer. Although both networks exceed the baseline required by the competition, the conventional CNN shows a higher performance, exceeding the baseline by 8 percentage points. Solutions based on Conv-mixer architectures show worse performance although they are much lighter solutions.
An Optimal Product-State Approximation for 2-Local Quantum Hamiltonians with Positive Terms

We resolve the approximability of the maximum energy of the Quantum Max Cut (QMC) problem using product states. A classical 0.498-approximation, using a basic semideﬁnite programming relaxation, is known for QMC, paralleling the celebrated 0.878-approximation for classical Max Cut. For Max Cut, improving the 0.878-approximation is Unique-Games-hard (UG-hard), and one might expect that improving the 0.498-approximation is UG-hard for QMC. In contrast, we give a classical 1/2-approximation for QMC that is unconditionally optimal, since simple examples exhibit a gap of 1/2 between the energies of an optimal product state and general quantum state. Our result relies on a new nonlinear monogamy of entanglement inequality on a triangle that is derived from the second level of the quantum Lasserre hierarchy. This inequality also applies to the quantum Heisenberg model, and our results generalize to instances of Max 2-Local Hamiltonian where each term is positive and has no 1-local parts. Finally, we give further evidence that product states are essential for approximations of 2-Local Hamiltonian.
HaGRID - HAnd Gesture Recognition Image Dataset

This paper introduces an enormous dataset, HaGRID (HAnd Gesture Recognition Image Dataset), to build a hand gesture recognition (HGR) system concentrating on interaction with devices to manage them. That is why all 18 chosen gestures are endowed with the semiotic function and can be interpreted as a specific action. Although the gestures are static, they were picked up, especially for the ability to design several dynamic gestures. It allows the trained model to recognize not only static gestures such as"like"and"stop"but also"swipes"and"drag and drop"dynamic gestures. The HaGRID contains 554,800 images and bounding box annotations with gesture labels to solve hand detection and gesture classification tasks. The low variability in context and subjects of other datasets was the reason for creating the dataset without such limitations. Utilizing crowdsourcing platforms allowed us to collect samples recorded by 37,583 subjects in at least as many scenes with subject-to-camera distances from 0.5 to 4 meters in various natural light conditions. The influence of the diversity characteristics was assessed in ablation study experiments. Also, we demonstrate the HaGRID ability to be used for pretraining models in HGR tasks. The HaGRID and pretrained models are publicly available.
CuPBoP: CUDA for Parallelized and Broad-range Processors

—CUDA is one of the most popular choices for GPU programming, but it can only be executed on NVIDIA GPUs. Executing CUDA on non-NVIDIA devices not only beneﬁts the hardware community, but also allows data-parallel computation in heterogeneous systems. To make CUDA programs portable, some researchers have proposed using source-to-source translators to translate CUDA to portable programming languages that can be executed on non-NVIDIA devices. However, most CUDA translators require additional manual modiﬁcations on the translated code, which imposes a heavy workload on developers. In this paper, CuPBoP is proposed to execute CUDA on non- NVIDIA devices without relying on any portable programming languages. Compared with existing work that executes CUDA on non-NVIDIA devices, CuPBoP does not require manual modiﬁ-cation of the CUDA source code, but it still achieves the highest coverage (69.6%), much higher than existing frameworks (56.6%) on the Rodinia benchmark. In particular, for CPU backends, CuPBoP supports several ISAs (e.g., X86, RISC-V, AArch64) and has close or even higher performance compared with other projects. We also compare and analyze the performance among CuPBoP, manually optimized OpenMP/MPI programs, and CUDA programs on the latest Ampere architecture GPU, and show future directions for supporting CUDA programs on non-NVIDIA devices with high performance.
Unifying Framework for Optimizations in non-boolean Formalisms

Search-optimization problems are plentiful in scientific and engineering domains. Artificial intelligence (AI) has long contributed to the development of search algorithms and declarative programming languages geared toward solving and modeling search-optimization problems. Automated reasoning and knowledge representation are the subfields of AI that are particularly vested in these developments. Many popular automated reasoning paradigms provide users with languages supporting optimization statements. Recall integer linear programming, MaxSAT, optimization satisfiability modulo theory, (constraint) answer set programming. These paradigms vary significantly in their languages in ways they express quality conditions on computed solutions. Here we propose a unifying framework of so-called extended weight systems that eliminates syntactic distinctions between paradigms. They allow us to see essential similarities and differences between optimization statements provided by distinct automated reasoning languages. We also study formal properties of the proposed systems that immediately translate into formal properties of paradigms that can be captured within our framework.
Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition

Intro to the ARPA-E Grid Optimization Competition In “Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition,” we review the state of the art in practical algorithms for scheduling power-systems operations in the short term and the results of the recent competition organized by the U.S. Advanced Research Projects Agency–Energy. We explain the mixed-integer nonlinear formulation used in the competition for nonspecialists in electrical engineering, the context and organization of the competition, and the performance of competitors. We find that the collective approaches and results of competitors provide support for efforts to move nonlinear optimization techniques into industrial applications, as they have proven to be a robust and efficient alternative to current linear approximation techniques.
Image Captioning based on Feature Refinement and Reflective Decoding

Image captioning is the process of automatically generating a description of an image in natural language. Image captioning is one of the signiﬁcant challenges in image understanding since it requires not only recognizing salient objects in the image but also their attributes and the way they interact. The system must then generate a syntactically and semantically correct caption that describes the image content in natural language. With the signiﬁcant progress in deep learning models and their ability to e ﬀ ectively encode large sets of images and generate correct sentences, several neural-based captioning approaches have been proposed recently, each trying to achieve better accuracy and caption quality. This paper introduces an encoder-decoder-based image captioning system in which the encoder extracts spatial features from the image using ResNet-101. This stage is followed by a reﬁning model, which uses an attention-on-attention mechanism to extract the visual features of the target image objects, then determine their interactions. The decoder consists of an attention-based recurrent module and a reﬂective attention module, which collaboratively apply attention to the visual and textual features to enhance the decoder’s ability to model long-term sequential dependencies. Extensive experiments performed on Flickr30K, show the e ﬀ ectiveness of the proposed approach and the high quality of the generated captions.
Diversity-induced anti-resonance in a globally coupled excitable neural network

We analyze the eﬀect of small-amplitude noise and heterogeneity in a network of coupled excitable neurons with strong time scale separation. Using mean-ﬁeld analysis, we uncover the mechanism of a new nontrivial eﬀect — diversity-induced anti-resonance (DIAR) — in which heterogeneity modulates the mechanism of self-induced stochastic resonance to inhibit spike train coherence. We argue that DIAR may oﬀer one possible mechanism via which, in excitable neural systems, generic heterogeneity and background noise can synergistically prevent unwanted resonances that may be related to hyperkinetic movement disorders.
Time Interval-Enhanced Graph Neural Network for Shared-Account Cross-Domain Sequential Recommendation

Shared-account cross-domain sequential recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains. It is gaining immense research attention as more and more users tend to sign up on different platforms and share accounts with others to access domain-specific services. Existing works on SCSR mainly rely on mining sequential patterns via recurrent neural network (RNN)-based models, which suffer from the following limitations: 1) RNN-based methods overwhelmingly target discovering sequential dependencies in single-user behaviors and they are not expressive enough to capture the relationships among multiple entities in SCSR; 2) all existing methods bridge two domains via knowledge transfer in the latent space and ignore the explicit cross-domain graph structure; and 3) none existing studies consider the time interval information among items, which is essential in the sequential recommendation for characterizing different items and learning discriminative representations for them. In this work, we propose a new graph-based solution, namely, time interval-enhanced domain-aware graph convolutional network (TiDA-GCN), to address the above challenges. Specifically, we first link users and items in each domain as a graph. Then, we devise a domain-aware graph convolution network to learn user-specific node representations. To fully account for users’ domain-specific preferences on items, two effective attention mechanisms are further developed to selectively guide the message-passing process. Moreover, to further enhance item- and account-level representation learning, we incorporate the time interval into the message passing and design an account-aware self-attention module for learning items’ interactive characteristics. Experiments demonstrate the superiority of our proposed method from various aspects.
Multi-View Imputation and Cross-Attention Network Based on Incomplete Longitudinal and Multi-Modal Data for Alzheimer's Disease Prediction

Longitudinal variations and complementary information inherent in longitudinal and multi-modal data play an important role in Alzheimer’s disease (AD) prediction, par-ticularly in identifying subjects with mild cognitive impairment who are about to have AD. However, longitudinal and multi-modal data may have missing data, which hin-ders the effective application of these data. Additionally, previous longitudinal studies require existing longitudinal data to achieve prediction, but AD prediction is expected to be conducted at patients’ baseline visit (BL) in clinical practice. Thus, we proposed a multi-view imputation and cross-attention network (MCNet) to integrate data imputation and AD prediction in a uniﬁed framework and achieve accurate AD prediction. First, a multi-view imputation method combined with adversarial learning, which can handle a wide range of missing data situations and reduce imputation errors, was presented. Second, two cross-attention blocks were introduced to exploit the potential associations in longitudinal and multi-modal data. Finally, a multi-task learning model was built for data imputation, longitudinal classiﬁcation, and AD prediction tasks. When the model was properly trained, the disease progression information learned from longitudinal data can be leveraged by BL data to improve AD prediction. The proposed method was tested on two independent testing sets and single-model data at BL to verify its effectiveness and ﬂexibility on AD prediction. Results showed that MCNet outperformed several state-of-the-art methods. Moreover, the interpretability of MCNet was presented. Thus, our MCNet is a tool with a great application potential in longitudinal and multi-modal data analysis for AD prediction. Codes are available at https://github.com/Meiyan88/MCNET .
Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm

Quantum computing is anticipated to offer immense computational capabilities which could provide efficient solutions to many data science problems. However, the current generation of quantum devices are small and noisy, which makes it difficult to process large data sets relevant for practical problems. Coreset selection aims to circumvent this problem by reducing the size of input data without compromising the accuracy. Recent work has shown that coreset selection can help to implement quantum K-Means clustering problem. However, the impact of coreset selection on the performance of quantum K-Means clustering has not been explored. In this work, we compare the relative performance of two coreset techniques (BFL16 and ONESHOT), and the size of coreset construction in each case, with respect to a variety of data sets and layout the advantages and limitations of coreset selection in implementing quantum algorithms. We also investigated the effect of depolarisation quantum noise and bit-flip error, and implemented the Quantum AutoEncoder technique for supressing the noise effect. Our work provides useful insights for future implementation of data science algorithms on near-term quantum devices where problem size has been reduced by coreset selection. selection implementation
Invariance principle for the maximal position process of branching Brownian motion in random environment

In this paper we study the maximal position process of branching Brownian motion in random spatial environment. The random environment is given by a process $\xi = \left(\xi(x)\right)_{x\in\mathbb{R}}$ satisfying certain conditions. We show that the maximum position $M_t$ of particles alive at time $t$ satisfies a quenched strong law of large numbers and an annealed invariance principle.