Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Image Regions Not Well Encoded by an INN

This paper proposes a method to clarify image regions that are not well encoded by an invert-ible neural network (INN), i.e. image regions that significantly decrease the likelihood of the input image. The proposed method can diagnose the limitation of the representation capacity of an INN. Given an input image, our method extracts image regions, which are not well encoded, by maximizing the likelihood of the image. We explicitly model the distribution of not-well-encoded regions. A metric is proposed to evaluate the extraction of the not-well-encoded regions. Finally, we use the proposed method to analyze several state-of-the-art INNs trained on various benchmark datasets.
Adaptive A/B Test on Networks with Cluster Structures

Units in online A/B tests are often involved in social networks. Thus, their outcomes may depend on the treatment of their neighbors. Many of such networks exhibit certain cluster structures allowing the use of these features in the design to reduce the bias from network interference. When the average treatment effect (ATE) is considered from the individual perspective, conditions for the valid estimation restrict the use of these features in the design. We show that such restrictions can be alleviated if the ATE from the cluster perspective is considered. Using an illustrative example, we further show that the weights employed by the HorvitzThompson estimator may not appropriately accommodate the network structure, and purely relying on graph-cluster randomization may generate very unbalanced cluster-treated structures across the treatment arms. The measures of such structures for one cluster may depend on the treatment of other clusters and pose a great challenge for the design of A/B tests. To address these issues, we propose a rerandomized-adaptive randomization to balance the clusters and a cluster-adjusted estimator to alleviate the problem of the weights. Numerical studies are conducted to demonstrate the usage of the proposed procedure.
Modeling Partially Observable Systems using Graph-Based Memory and Topological Priors

Solving partially observable Markov decision processes (POMDPs) is critical when applying re-inforcement learning to real-world problems, where agents have an incomplete view of the world. Recurrent neural networks (RNNs) are the defacto approach for solving POMDPs in reinforcement learning (RL). Although they perform well in supervised learning, noisy gradients reduce their capabilities in RL. This leads researchers to hand-design task-specific memory models to stabilize learning, based on their prior knowledge of the task at hand. In this paper, we present graph convolutional memory (GCM) 1 , the first RL memory framework with swappable task-specific priors, enabling users to inject expertise into their models. GCM uses human-defined topological priors to form graph neighborhoods, combining them into a larger network topology. We query the graph using graph convolution, coalescing relevant memories into a context-dependent summary of the past. Results demonstrate that GCM outperforms state of the art methods on control, memorization, and navigation tasks while using fewer parameters.
Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks

We consider the problem of learning the underlying structure of a general discrete pair-wise Markov network. Existing approaches that rely on empirical risk minimization may perform poorly in settings with noisy or scarce data. To overcome these limitations, we propose a computationally efficient and robust learning method for this problem with near-optimal sample complexities. Our approach builds upon distributionally robust optimization (DRO) and maximum conditional log-likelihood. The proposed DRO estimator minimizes the worst-case risk over an ambiguity set of adversarial distributions within bounded transport cost or f-divergence of the empirical data distribution. We show that the primal minimax learning problem can be efficiently solved by leveraging sufficient statistics and greedy maximization in the ostensibly intractable dual formulation. Based on DRO’s approximation to Lipschitz and variance regularization, we derive near-optimal sample complexities matching existing results. Extensive empirical evidence with different corruption models corroborates the effectiveness of the proposed methods.
Chaining for accurate alignment of erroneous long reads to acyclic variation graphs

Aligning reads to a variation graph is a standard task in pangenomics, with downstream applications such as improving variant calling. While the vg toolkit (Garrison et al., Nature Biotechnology, 2018) is a popular aligner of short reads, GraphAligner (Rautiainen and Marschall, Genome Biology, 2020) is the state-of-the-art aligner of erroneous long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds. We present a new algorithm to co-linearly chain a set of seeds in a string labeled acyclic graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of erroneous long reads to acyclic variation graphs, GraphChainer. Compared to GraphAligner, GraphChainer aligns 12% to 17% more reads, and 21% to 28% more total read length, on real PacBio reads from human chromosomes 1, 22 and the whole human pangenome. On both simulated and real data, GraphChainer aligns between 95% and 99% of all reads, and of total read length. We also show that minigraph (Li et al., Genome Biology, 2020) and minichain (Chandra and Jain, RECOMB, 2023) obtain an accuracy of less than 60% on this setting. GraphChainer is freely available at https://github.com/algbio/GraphChainer. The datasets and evaluation pipeline can be reached from the previous address.
Phage resistance profiling identifies new genes required for biogenesis and modification of the corynebacterial cell envelope

Bacteria of the order Corynebacteriales including pathogens such as Mycobacterium tuberculosis and Corynebacterium diphtheriae are characterized by their complex, multi-layered envelope. In addition to a peptidoglycan layer, these organisms possess an additional polysaccharide layer made of arabinogalactan and an outer membrane layer composed predominantly of long-chain fatty acids called mycolic acids. This so-called mycolata envelope structure is both a potent barrier against antibiotic entry into cells and a target of several antibacterial therapeutics. A better understanding of the mechanisms underlying mycolata envelope assembly therefore promises to reveal new ways of disrupting this unique structure for the development of antibiotics and antibiotic potentiators. Because they engage with receptors on the cell surface during infection, bacteriophages have long been used as tools to uncover important aspects of host envelope assembly. However, surprisingly little is known about the interactions between Corynebacteriales phages and their hosts. We therefore made use of the phages Cog and CL31 that infect Corynebacterium glutamicum (Cglu), a model member of the Corynebacteriales, to discover host factors important for phage infection. A high-density transposon library of Cglu was challenged with these phages followed by transposon sequencing to identify resistance loci. The analysis identified an important role for mycomembrane porins in phage infection as well as components of the arabinogalactan and mycolic acid synthesis pathways. Importantly, the approach also implicated a new gene (cgp_0396) in the process of arabinogalactan modification and identified a conserved new factor (AhfA, Cpg_0475) required for mycolic acid synthesis in Cglu.
High-fidelity optical diffraction tomography of live organisms using non-toxic tunable refractive index media

Optical diffraction tomography (ODT) enables the three-dimensional (3D) refractive index (RI) reconstruction. However, when the RI difference between a sample and a medium increases, effects of light scattering become significant, preventing the acquisition of high-quality and accurate RI reconstructions. Herein, we present a method for high-fidelity ODT by introducing non-toxic RI matching media. Optimally reducing the RI contrast enhances the fidelity and accuracy of 3D RI reconstruction, enabling visualization of the morphology and intra-organization of live biological samples without producing toxic effects. We validate our method using various biological organisms, including C. albicans and C. elegans.
Understanding the Potential for Robot Assistance for Older Adults in the Home Environment

This research was supported in part by a grant from the National Institutes of Health (National Institute on Aging) Grant P01 AG17211 under the auspices of the Center for Research and Education on Aging and Technology Enhancement (CREATE; www.create-center.org). The report was inspired by our collaboration with Willow Garage (www.willowgarage.com) who selected the Georgia Institute of Technology as a beta PR2 site for research (www.willowgarage.com/blog/2010/06/07/spotlight-georgia-tech). This project is a collaborative research effort on human-robot interaction between the Human Factors and Aging Laboratory (Co-Directors Wendy A. Rogers and Arthur D. Fisk; www.hfaging.org) and the Healthcare Robotics Laboratory (Director: Charles C. Kemp; www.healthcare-robotics.com). Many thanks to the researchers in both laboratories for their contributions.
Distinct nano-structures support a multifunctional role of actin at presynapses

Synapses are the nexus of signal transmission in the nervous system. Despite decades of work, the architecture of the actin cytoskeleton that concentrates at presynapses remain poorly known, hindering our comprehensive understanding of its roles in presynaptic physiology. In this work, we take advantage of a validated model of bead-induced presynapses to measure and visualize isolated presynaptic actin by diffraction-limited and super-resolution microscopy. We first identify a major population of actin-enriched presynapses that concentrates more presynaptic components, and shows higher synaptic vesicle cycling than their non-enriched counterparts. Using pharmacological perturbations, we determine that an optimal amount of actin is necessary for this effect of actin enrichment. Modulation of this effect by actin nucleation inhibitors indicates its dependance on distinct presynaptic actin assemblies. Using Single Molecule Localization Microscopy (SMLM), we directly visualize these nano-structures in isolated presynapses, defining an actin mesh at the active zone, actin rails between the active zone and deeper reserve pools, and actin corrals around the whole presynaptic compartment. We finally show that these three types of presynaptic actin nano-structures are differentially affected by actin nucleation inhibitors, consistent with their effect on presynaptic component concentration and on synaptic vesicle cycling.
MaLAdapt Reveals Novel Targets of Adaptive Introgression From Neanderthals and Denisovans in Worldwide Human Populations

Adaptive introgression (AI) facilitates local adaptation in a wide range of species. Many state-of-the-art methods detect AI with ad-hoc approaches that identify summary statistic outliers or intersect scans for positive selection with scans for introgressed genomic regions. Although widely used, these outlier-based approaches are vulnerable to a high false-negative rate as the power of different methods vary, especially for complex introgression events. Moreover, population genetic processes unrelated to AI, such as background selection or heterosis, may create similar genomic signals as AI, compromising the reliability of methods that rely on neutral null distributions. In recent years, machine learning (ML) methods have been increasingly applied to population genetic questions. Here, we present an ML-based method called MaLAdapt for identifying AI loci from genome-wide sequencing data. Using an Extra-Trees Classifier algorithm, our method combines information from a large number of biologically meaningful summary statistics to capture a powerful composite signature of AI across the genome. In contrast to existing methods, MaLAdapt is especially well-powered to detect AI with mild beneficial effects, including selection on standing archaic variation, and is robust to non-AI selection sweeps, heterosis, and demographic misspecifications. Further, MaLAdapt outperforms existing methods for detecting AI based on the analysis of simulated data and on a validation of empirical signals through visual impaction of haplotype patterns. We apply MaLAdapt to the 1000 Genomes Project human genomic data, and discover novel AI candidate regions in non-African populations, including genes that are enriched in functionally important biological pathways regulating metabolism and immune responses.
All Staphylococcus aureus bacteraemia strains have the potential to cause infective endocarditis: results of GWAS and experimental animal studies

Aims Infective endocarditis (IE) complicates 10-20% of Staphylococcus aureus bacteraemia (SAB). We aimed to determine whether IE strains of S. aureus are genotypically different or behave differently in experimental endocarditis models as compared to non-IE SAB strains. Methods and Results We conducted a genome wide association study (GWAS) of 924 S. aureus genomes from IE (274) and non-IE (650) SAB patients, and tested a subset of strains in two experimental animal models of IE, one studying the early step of bacterial adhesion to inflamed mice valves, the second evaluating the local and systemic developmental process of IE on mechanically damaged rabbit valves. The genetic profile of S. aureus IE and non-IE SAB strains did not differ when considering single nucleotide polymorphisms, coding sequences and k-mers analyses in GWASs. In the inflammation-induced IE model in mice no difference was observed between IE and non-IE SAB strains both in adhesion to the cardiac valves and in the propensity to cause IE; in the mechanical IE-induced rabbit model, there was no difference between IE and non-IE SAB strains regarding vegetation size and CFU. Conclusion S. aureus isolates from SAB patients with and without IE were indistinguishable, by GWAS and by two in vivo models of IE. Thus, S. aureus strain variation is not the primary determinant of IE. Pending the possible identification of host factors predisposing to IE, all strains of S. aureus must be considered in patients as capable of causing this common, lethal infection once they have accessed the bloodstream. Translational Perspective Staphylococcus aureus endocarditis (IE) is a deadly complication of S. aureus bacteraemia (SAB). Beyond well-identified host related IE risk factors, whether bacterial features may influence the occurrence of IE in the course of bacteraemia remain elusive. We analysed the genomes of 924 S. aureus strains from IE and non-IE SAB and compared some in two in vivo IE models. We demonstrated that the propensity of S. aureus to cause IE in the course of bacteraemia does not depend on the intrinsic genetic or virulence factors of S. aureus. These findings are of importance for the management of S. aureus bacteraemia.
On the Interplay between Information Loss and Operation Loss in Representations for Classification

Information-theoretic measures have been widely adopted in the design of features for learning and decision problems. Inspired by this, we look at the relationship between i) a weak form of information loss in the Shannon sense and ii) operational loss in the minimum probability of error (MPE) sense when considering a family of lossy continuous representations of an observation. Our ﬁrst result oﬀers a lower bound on a weak form of information loss as a function of its respective operation loss when adopting a discrete lossy representation (quantization) instead of the original raw observation. From this, our main result shows that a speciﬁc form of vanishing information loss (a weak notion of asymptotic informational suﬃciency) implies a vanishing MPE loss (or asymptotic operational suﬃ-ciency) when considering a family of lossy continuous representations. Our theoretical ﬁndings support the observation that the selection of feature representations that attempt to capture informational suﬃciency is appropriate for learning, but this design principle is a rather conservative if the intended goal is achieving MPE in classiﬁcation. On this last point, we discuss about studying weak forms of informational suﬃciencies to achieve operational suﬃciency in learning settings.
A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants

Background Quantifying an individual’s risk for common diseases is an important goal of precision health. The polygenic risk score (PRS), which aggregates multiple risk alleles of candidate diseases, has emerged as a standard approach for identifying high-risk individuals. A variety of tools have been developed to implement PRS. However, benchmarks for comparatively evaluating the performance of these different methods and for assessing their potential to guide future clinical applications are lacking. Results We systematically validated and compared thirteen statistical methods, five machine learning models and two ensemble models using simulated data, twenty-two common diseases with internal training sets and four diseases with external summary statistics from the UK Biobank resource. The effects of disease heritability, single nucleotide polymorphism (SNP) effect size and sample size are evaluated using simulated data. We also investigated the correlations between methods and their standard deviations of different diseases. Conclusions In general, statistical methods outperform machine learning models, and ensemble models, such as Super Learner, generally perform the best for most situations. We observed the correlations were relatively high if the methods were from the same category and the external summary statistics from large cohort GWAS could decrease the standard deviation of method correlations. By varying three factors in the simulated data, we also identified that disease heritability had a strong effect on the predictive performance of individual methods. Both the number and effect sizes of risk SNPs are important; and while sample size strongly influences the performance of machine learning models, but not statistical methods.
Choice of 3D morphometric method leads to diverging interpretations of form–function relationships in the carnivoran calcaneus

Three dimensional morphometric methods are a powerful tool for comparative analysis of shape. However, morphological shape is often represented using landmarks selected by the user to describe features of perceived importance, and this may lead to over confident prediction of form-function relationships in subsequent analyses. We used Generalized Procrustes Analysis (GPA) of 13 homologous 3D landmarks and spherical harmonics (SPHARM) analysis, a homology-free method that describes the entire shape of a closed surface, to quantify the shape of the calcaneus, a landmark poor structure that is important in hind-limb mechanics, for 111 carnivoran species spanning 12 of 13 terrestrial families. Both approaches document qualitatively similar patterns of shape variation, including a dominant continuum from short/stout to long/narrow calcanea. However, while phylogenetic generalized linear models indicate that locomotor mode best explains shape from the GPA, the same analyses find that shape described by SPHARM is best predicted by foot posture and body mass without a role for locomotor mode, though effect sizes for all are small. User choices regarding morphometric methods can dramatically impact macroevolutionary interpretations of shape change in a single structure, an outcome that is likely exacerbated when readily landmarkable features are few.
Mechanisms of speed encoding in the human middle temporal cortex measured by 7T fMRI

Perception of dynamic scenes in our environment results from the evaluation of visual features such as the fundamental spatial and temporal frequency components of a moving object. The ratio between these two components represents the object’s speed of motion. The human middle temporal cortex hMT+ has a crucial biological role in the direct encoding of object speed. However, the link between hMT+ speed encoding and the spatiotemporal frequency components of a moving object is still under explored. Here, we recorded high resolution 7T blood oxygen level-dependent BOLD responses to different visual motion stimuli as a function of their fundamental spatial and temporal frequency components. We fitted each hMT+ BOLD response with a 2D Gaussian model allowing for two different speed encoding mechanisms: 1) distinct and independent selectivity for the spatial and temporal frequencies of the visual motion stimuli 2) pure tuning for the speed of motion. We show that both mechanisms occur but in different neuronal groups within hMT+, with the largest subregion of the complex showing separable tuning for the spatial and temporal frequency of the visual stimuli. Both mechanisms were highly reproducible within participants, reconciling single cell recordings from MT in animals that have showed both encoding mechanisms. Our findings confirm that a more complex process is involved in the perception of speed than initially thought and suggest that hMT+ plays a primary role in the evaluation of the spatial features of the moving visual input.
Variational Continual Proxy-Anchor for Deep Metric Learning

The recent proxy-anchor method achieved out-standing performance in deep metric learning, which can be acknowledged to its data efficient loss based on hard example mining, as well as far lower sampling complexity than pair-based approaches. In this paper we extend the proxy-anchor method by posing it within the continual learning framework, motivated from its batch-expected loss form (instead of instance-expected, typical in deep learning), which can potentially incur the catastrophic forgetting of historic batches. By regarding each batch as a task in continual learning, we adopt the Bayesian variational continual learning approach to derive a novel loss function. Interestingly the resulting loss has two key modifications to the original proxy-anchor loss: i) we inject noise to the proxies when optimizing the proxy-anchor loss, and ii) we encourage momentum update to avoid abrupt model changes. As a result, the learned model achieves higher test accuracy than proxy-anchor due to the robustness to noise in data (through model perturbation during training), and the reduced batch forgetting effect. We demonstrate the improved results on several benchmark datasets.
ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics

Many classiﬁcation algorithms require the use of surrogate losses when the intended loss function is combinatorial or non-decomposable. This paper introduces a fast and exact stage-wise optimization algorithm, dubbed Exact-Boost, that boosts instead to the actual loss function. By developing a novel extension of margin theory to the non-decomposable setting, it is possible to provably bound the generalization error of ExactBoost for many important metrics with diﬀerent levels of non-decomposability. Through extensive examples, it is shown that such theoretical guarantees translate to competitive empirical performance. In particular, when used as an ensembler, ExactBoost is able to signiﬁcantly outperform other surrogate-based and exact algorithms available.