Miracle-dz/UniFAR-FTUs huggingface dataset:
Constellation: Learning relational abstractions over objects for compositional imagination

Learning structured representations of visual scenes is currently a major bottleneck to bridging perception with reasoning. While there has been exciting progress with slot-based models, which learn to segment scenes into sets of objects, learning configurational properties of entire groups of objects is still under-explored. To address this problem, we introduce Constellation, a network that learns relational abstractions of static visual scenes, and generalises these abstractions over sensory particularities, thus offering a potential basis for abstract relational reasoning. We further show that this basis, along with language association, provides a means to imagine sensory content in new ways. This work is a first step in the explicit representation of visual relationships and using them for complex cognitive procedures.
Generating $N$-point spherical configurations with low mesh ratios using spherical area coordinates

This short contribution presents a method for generating $N$-point spherical configurations with low mesh ratios. The method extends Caspar-Klug icosahedral point-grids to non-icosahedral nets through the use of planar barycentric coordinates, which are subsequently interpreted as spherical area coordinates for spherical point sets. The proposed procedure may be applied iteratively and is parameterised by a sequence of integer pairs. For well-chosen input parameters, the proposed method is able to generate point sets with mesh ratios that are lower than previously reported for $N<10^6$.
FNetAR: Mixing Tokens with Autoregressive Fourier Transforms

In this note we examine the autoregressive generalization of the FNet algorithm, in which self-attention layers from the standard Transformer architecture are substituted with a trivial sparse-uniformsampling procedure based on Fourier transforms. Using the Wikitext-103 benchmark, we demonstratethat FNetAR retains state-of-the-art performance (25.8 ppl) on the task of causal language modelingcompared to a Transformer-XL baseline (24.2 ppl) with only half the number self-attention layers,thus providing further evidence for the superfluity of deep neural networks with heavily compoundedattention mechanisms. The autoregressive Fourier transform could likely be used for parameterreduction on most Transformer-based time-series prediction models.
AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling Training

We consider unsupervised cell nuclei segmentation in this paper. Exploiting the recently-proposed unpaired image-to-image translation between cell nuclei images and randomly synthetic masks, existing approaches, e.g., CycleGAN, have achieved encouraging results. However, these methods usually take a two-stage pipeline and fail to learn end-to-end in cell nuclei images. More seriously, they could lead to the lossy transformation problem, i.e., the content inconsistency between the original images and the corresponding segmentation output. To address these limitations, we propose a novel end-to-end unsupervised framework called Aligned Disentangling Generative Adversarial Network (AD-GAN). Distinctively, AD-GAN introduces representation disentanglement to separate content representation (the underling spatial structure) from style representation (the rendering of the structure). With this framework, spatial structure can be preserved explicitly, enabling a significant reduction of macro-level lossy transformation. We also propose a novel training algorithm able to align the disentangled content in the latent space to reduce micro-level lossy transformation. Evaluations on real-world 2D and 3D datasets show that AD-GAN substantially outperforms the other comparison methods and the professional software both quantitatively and qualitatively. Specifically, the proposed AD-GAN leads to significant improvement over the current best unsupervised methods by an average 17.8% relatively (w.r.t. the metric DICE) on four cell nuclei datasets. As an unsupervised method, AD-GAN even performs competitive with the best supervised models, taking a further leap towards end-to-end unsupervised nuclei segmentation.
Structack: Structure-based Adversarial Attacks on Graph Neural Networks

Recent work has shown that graph neural networks (GNNs) are vulnerable to adversarial attacks on graph data. Common attack approaches are typically informed, i.e. they have access to information about node attributes such as labels and feature vectors. In this work, we study adversarial attacks that are uninformed, where an attacker only has access to the graph structure, but no information about node attributes. Here the attacker aims to exploit structural knowledge and assumptions, which GNN models make about graph data. In particular, literature has shown that structural node centrality and similarity have a strong influence on learning with GNNs. Therefore, we study the impact of centrality and similarity on adversarial attacks on GNNs. We demonstrate that attackers can exploit this information to decrease the performance of GNNs by focusing on injecting links between nodes of low similarity and, surprisingly, low centrality. We show that structure-based uninformed attacks can approach the performance of informed attacks, while being computationally more efficient. With our paper, we present a new attack strategy on GNNs that we refer to as Structack. Structack can successfully manipulate the performance of GNNs with very limited information while operating under tight computational constraints. Our work contributes towards building more robust machine learning approaches on graphs.
Adaptively Weighted Top-N Recommendation for Organ Matching

Reducing the shortage of organ donations to meet the demands of patients on the waiting list has being a major challenge in organ transplantation. Because of the shortage, organ matching decision is the most critical decision to assign the limited viable organs to the most “suitable” patients. Currently, organ matching decisions are only made by matching scores calculated via scoring models, which are built by the first principles. However, these models may disagree with the actual post-transplantation matching performance (e.g., patient's post-transplant quality of life (QoL) or graft failure measurements). In this paper, we formulate the organ matching decision-making as a top-N recommendation problem and propose an Adaptively Weighted Top-N Recommendation (AWTR) method. AWTR improves performance of the current scoring models by using limited actual matching performance in historical datasets as well as the collected covariates from organ donors and patients. AWTR sacrifices the overall recommendation accuracy by emphasizing the recommendation and ranking accuracy for top-N matched patients. The proposed method is validated in a simulation study, where KAS [60] is used to simulate the organ-patient recommendation response. The results show that our proposed method outperforms seven state-of-the-art top-N recommendation benchmark methods.
Highly Available Queue-oriented Speculative Transaction Processing

Deterministic database systems have received increasing attention from the database research community in recent years. Despite their current limitations, recent proposals of distributed deterministic transaction processing systems demonstrated significant improvements over systems using traditional transaction processing techniques (e.g., two-phase-locking or optimistic concurrency control with two-phase-commit). However, the problem of ensuring high availability in deterministic distributed transaction processing systems has received less attention from the research community, and this aspect has not been analyzed and evaluated well. This paper proposes a generic framework to model the replication process in deterministic transaction processing systems and use it to study three cases. We design and implement QR-Store, a queue-oriented replicated transaction processing system, and extensively evaluate it with various workloads based on a transactional version of YCSB. Our prototype implementation QR-Store can achieve a throughput of 1.9 million replicated transactions per second in under 200 milliseconds and a replication overhead of 8%-25% compared to non-replicated configurations.
Optimum Risk Portfolio and Eigen Portfolio: A Comparative Analysis Using Selected Stocks from the Indian Stock Market

Designing an optimum portfolio that allocates weights to its constituent stocks in a way that achieves the best trade-off between the return and the risk is a challenging research problem. The classical mean-variance theory of portfolio proposed by Markowitz is found to perform sub-optimally on the real-world stock market data since the error in estimation for the expected returns adversely affect the performance of the portfolio. This paper presents three approaches to portfolio design, viz, the minimum risk portfolio, the optimum risk portfolio, and the eigen portfolio, for seven important sectors of the Indian stock market. The daily historical prices of the stocks are scraped from Yahoo Finance website from January 1, 2016, to December 31, 2020. Three portfolios are built for each of the seven sectors chosen for this study, and the portfolios are analyzed on the training data based on several metrics such as annualized return and risk, weights assigned to the constituent stocks, the correlation heatmaps, and the principal components of the eigen portfolios. Finally, the optimum risk portfolios and the eigen portfolios for all sectors are tested on their return over a period of six-month period. The performances of the portfolios are compared and the portfolio yielding the higher return for each sector is identified.
Improving Early Sepsis Prediction with Multi Modal Learning

Sepsis is a life-threatening disease with high morbidity, mortality and healthcare costs. The early prediction and administration of antibiotics and intravenous fluids is considered crucial for the treatment of sepsis and can save potentially millions of lives and billions in health care costs. Professional clinical care practitioners have proposed clinical criterion which aid in early detection of sepsis; however, performance of these criterion is often limited. Clinical text provides essential information to estimate the severity of the sepsis in addition to structured clinical data. In this study, we explore how clinical text can complement structured data towards early sepsis prediction task. In this paper, we propose multi modal model which incorporates both structured data in the form of patient measurements as well as textual notes on the patient. We employ state-of-the-art NLP models such as BERT and a highly specialized NLP model in Amazon Comprehend Medical to represent the text. On the MIMIC-III dataset containing records of ICU admissions, we show that by using these notes, one achieves an improvement of 6.07 points in a standard utility score for Sepsis prediction and 2.89% in AUROC score. Our methods significantly outperforms a clinical criteria suggested by experts, qSOFA, as well as the winning model of the PhysioNet Computing in Cardiology Challenge for predicting Sepsis.
Label Distribution Amendment with Emotional Semantic Correlations for Facial Expression Recognition

By utilizing label distribution learning, a probability distribution is assigned for a facial image to express a compound emotion, which effectively improves the problem of label uncertainties and noises occurred in one-hot labels. In practice, it is observed that correlations among emotions are inherently different, such as surprised and happy emotions are more possibly synchronized than surprised and neutral. It indicates the correlation may be crucial for obtaining a reliable label distribution. Based on this, we propose a new method that amends the label distribution of each facial image by leveraging correlations among expressions in the semantic space. Inspired by inherently diverse correlations among word2vecs, the topological information among facial expressions is firstly explored in the semantic space, and each image is embedded into the semantic space. Specially, a class-relation graph is constructed to transfer the semantic correlation among expressions into the task space. By comparing semantic and task class-relation graphs of each image, the confidence of its label distribution is evaluated. Based on the confidence, the label distribution is amended by enhancing samples with higher confidence and weakening samples with lower confidence. Experimental results demonstrate the proposed method is more effective than compared state-of-the-art methods.
Multi-Channel Automatic Music Transcription Using Tensor Algebra

Music is an art, perceived in unique ways by every listener, coming from acoustic signals. In the meantime, standards as musical scores exist to describe it. Even if humans can make this transcription, it is costly in terms of time and efforts, even more with the explosion of information consecutively to the rise of the Internet. In that sense, researches are driven in the direction of Automatic Music Transcription. While this task is considered solved in the case of single notes, it is still open when notes superpose themselves, forming chords. This report aims at developing some of the existing techniques towards Music Transcription, particularly matrix factorization, and introducing the concept of multi-channel automatic music transcription. This concept will be explored with mathematical objects called tensors.
Robust Adaptive Submodular Maximization

The goal of a sequential decision-making problem is to design an interactive policy that adaptively selects a group of items, each selection is based on the feedback from the past, to maximize the expected utility of selected items. It has been shown that the utility functions of many real-world applications are adaptive submodular. However, most of existing studies on adaptive submodular optimization focus on the average-case, that is, their objective is to find a policy that maximizes the expected utility over a known distribution of realizations. Unfortunately, a policy that has a good average-case performance may have very poor performance under the worst-case realization. In this study, we propose to study two variants of adaptive submodular optimization problems, namely, worst-case adaptive submodular maximization and robust submodular maximization. The first problem aims to find a policy that maximizes the worst-case utility and the latter one aims to find a policy, if any, that achieves both near optimal average-case utility and worst-case utility simultaneously. We introduce a new class of stochastic functions, called worst-case submodular function. For the worst-case adaptive submodular maximization problem subject to a p-system constraint, we develop an adaptive worst-case greedy policy that achieves a [Formula: see text] approximation ratio against the optimal worst-case utility if the utility function is worst-case submodular. For the robust adaptive submodular maximization problem subject to cardinality constraints (respectively, partition matroid constraints), if the utility function is both worst-case submodular and adaptive submodular, we develop a hybrid adaptive policy that achieves an approximation close to [Formula: see text] (respectively, 1/3) under both worst- and average-case settings simultaneously. We also describe several applications of our theoretical results, including pool-base active learning, stochastic submodular set cover, and adaptive viral marketing.
SNAC: An Unbiased Metric Evaluating Topology Recognize Ability of Network Alignment

Network alignment is a problem of finding the node mapping between similar networks. It links the data from separate sources and is widely studied in bioinformation and social network fields. The critical difference between network alignment and exact graph matching is that the network alignment considers node mapping in non-isomorphic graphs with error tolerance. Researchers usually utilize AC (accuracy) to measure the performance of network alignments which comparing each output element with the benchmark directly. However, this metric neglects that some nodes are naturally indistinguishable even in single graphs (e.g., nodes have the same neighbors) and no need to distinguish across graphs. Such neglect leads to the underestimation of models. We propose an unbiased metric for network alignment that takes indistinguishable nodes into consideration to address this problem. Our detailed experiments with different scales on both synthetic and real-world datasets demonstrate that the proposed metric correctly reflects the deviation of result mapping from benchmark mapping as standard metric AC does. Comparing with the AC, the proposed metric effectively blocks the effect of indistinguishable nodes and retains stability under increasing indistinguishable nodes.
Modeling Fast Radio Burst Dispersion and Scattering Properties in the First CHIME/FRB Catalog

We present a Monte Carlo–based population synthesis study of fast radio burst (FRB) dispersion and scattering focusing on the first catalog of sources detected with the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) project. We simulate intrinsic properties and propagation effects for a variety of FRB population models and compare the simulated distributions of dispersion measures and scattering timescales with the corresponding distributions from the CHIME/FRB catalog. Our simulations confirm the results of previous population studies, which suggested that the interstellar medium of the host galaxy alone (simulated based on the NE2001 model) cannot explain the observed scattering timescales of FRBs. We therefore consider additional sources of scattering, namely, the circumgalactic medium (CGM) of intervening galaxies and the circumburst medium whose properties are modeled based on typical Galactic plane environments. We find that a population of FRBs with scattering contributed by these media is marginally consistent with the CHIME/FRB catalog. In this scenario, our simulations favor a population of FRBs offset from their galaxy centers over a population that is distributed along the spiral arms. However, if the models proposing the CGM as a source of intense scattering are incorrect, then we conclude that FRBs may inhabit environments with more extreme properties than those inferred for pulsars in the Milky Way.
Coexistence of topological and nontopological Fermi-superfluid phases

The two-dimensional spin-imbalanced Fermi gas subject to s -wave pairing and spin-orbit coupling is considered a promising platform for realizing a topological chiral- p -wave superﬂuid. In the BCS limit of s -wave pairing, i.e., when Cooper pairs are only weakly bound, the system enters the topological phase via a second-order transition driven by increasing the Zeeman spin-splitting energy. Stronger attractive two-particle interactions cause the system to undergo the BCS-BEC crossover, in the course of which the topological transition becomes ﬁrst order. As a result, topological and nontopological superﬂuids coexist in spatially separated domains in an extended region of phase space spanned by the strength of s -wave interactions and the Zeeman energy. Here we investigate this phase-coexistence region theoretically using a zero-temperature mean-ﬁeld approach. Exact numerical results are presented to illustrate basic physical characteristics of the coexisting phases and to validate an approximate analytical description derived for weak spin-orbit coupling. Besides extending our current understanding of spin-imbalanced superﬂuid Fermi systems, the present approach also provides a platform for future studies of unconventional Majorana excitations that, according to topology, should be present at the internal interface between coexisting topological and nontopological superﬂuid parts of the system.
Rethinking the Ranks of Visual Channels

Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants could report the ratio between two depicted values. There is an assumption that this ranking should hold for different tasks and for different numbers of marks. However, there is surprisingly little existing work that tests this assumption, especially given that visually computing ratios is relatively unimportant in real-world visualizations, compared to seeing, remembering, and comparing trends and motifs, across displays that almost universally depict more than two values. To simulate the information extracted from a glance at a visualization, we instead asked participants to immediately reproduce a set of values from memory after they were shown the visualization. These values could be shown in a bar graph (position (bar)), line graph (position (line)), heat map (luminance), bubble chart (area), misaligned bar graph (length), or ‘wind map’ (angle). With a Bayesian multilevel modeling approach, we show how the rank positions of visual channels shift across different numbers of marks (2, 4 or 8) and for bias, precision, and error measures. The ranking did not hold, even for reproductions of only 2 marks, and the new probabilistic ranking was highly inconsistent for reproductions of different numbers of marks. Other factors besides channel choice had an order of magnitude more influence on performance, such as the number of values in the series (e.g., more marks led to larger errors), or the value of each mark (e.g., small values were systematically overestimated). Every visual channel was worse for displays with 8 marks than 4, consistent with established limits on visual memory. These results point to the need for a body of empirical studies that move beyond two-value ratio judgments as a baseline for reliably ranking the quality of a visual channel, including testing new tasks (detection of trends or motifs), timescales (immediate computation, or later comparison), and the number of values (from a handful, to thousands).
Diagnostics of entanglement dynamics in noisy and disordered spin chains via the measurement-induced steady-state entanglement transition

We utilize the concept of a measurement-induced entanglement transition to analyze the interplay and competition of processes that generate and destroy entanglement in a one-dimensional quantum spin chain evolving under a locally noisy and disordered Hamiltonian. We employ continuous measurements of variable strength to induce a transition from volume to area-law scaling of the steady-state entanglement entropy. While static background disorder systematically reduces the critical measurement strength, this critical value depends non-monotonically on the strength of non-static noise. According to the extracted fine-size scaling exponents, the universality class of the transition is independent of the noise and disorder strength. We interpret the results in terms of the effect of static and non-static disorder on the intricate dynamics of the entanglement generation rate due to the Hamiltonian in the absence of measurement, which is fully reflected in the behavior of the critical measurement strength. Our results establish a firm connection between this entanglement growth and the steady-state behavior of the measurement-controlled systems, which therefore can serve as a tool to quantify and investigate features of transient entanglement dynamics in complex many-body systems via a steady-state phase transition.