Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Mixture-of-Experts Approach for Cytotoxic Edema Assessment in Infants and Children

This paper presents a deep learning framework for image classification aimed at increasing predictive performance for Cytotoxic Edema (CE) diagnosis in infants and children. The proposed framework includes two 3D network architectures optimized to learn from two types of clinical MRI data – a trace Diffusion Weighted Image (DWI) and the calculated Apparent Diffusion Coefficient map (ADC). This work proposes a robust and novel solution based on volumetric analysis of 3D images (using pixels from time slices) and 3D convolutional neural network (CNN) models. While simple in architecture, the proposed framework shows significant quantitative results on the domain problem. We use a dataset curated from a Children’s Hospital Colorado (CHCO) patient registry to report a predictive performance F1 score of 0.91 at distinguishing CE patients from children with severe neurologic injury without CE. In addition, we perform analysis of our system’s output to determine the association of CE with Abusive Head Trauma (AHT) – a type of traumatic brain injury (TBI) associated with abuse – and overall functional outcome and in-hospital mortality of infants and young children. We used two clinical variables, AHT diagnosis and Functional Status Scale (FSS) score, to arrive at the conclusion that CE is highly correlated with overall outcome and that further study is needed to determine whether CE is a biomarker of AHT. With that, this paper introduces a simple yet powerful deep learning-based solution for automated CE classification. This solution also enables an in-depth analysis of progression of CE and its correlation to AHT and overall neurologic outcome, which in turn has the potential to empower experts to diagnose and mitigate AHT during early stages of a child’s life.
A detailed study of interpretability of deep neural network based top taggers

Recent developments in the methods of explainable artificial intelligence (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input–output relationships and realizing how data connects with machine learning models. In this paper we explore interpretability of DNN models designed to identify jets coming from top quark decay in high energy proton–proton collisions at the Large Hadron Collider. We review a subset of existing top tagger models and explore different quantitative methods to identify which features play the most important roles in identifying the top jets. We also investigate how and why feature importance varies across different XAI metrics, how correlations among features impact their explainability, and how latent space representations encode information as well as correlate with physically meaningful quantities. Our studies uncover some major pitfalls of existing XAI methods and illustrate how they can be overcome to obtain consistent and meaningful interpretation of these models. We additionally illustrate the activity of hidden layers as neural activation pattern diagrams and demonstrate how they can be used to understand how DNNs relay information across the layers and how this understanding can help to make such models significantly simpler by allowing effective model reoptimization and hyperparameter tuning. These studies not only facilitate a methodological approach to interpreting models but also unveil new insights about what these models learn. Incorporating these observations into augmented model design, we propose the particle flow interaction network model and demonstrate how interpretability-inspired model augmentation can improve top tagging performance.
KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding

With the advent of pre-trained language models (LMs), increasing research efforts have been focusing on infusing commonsense and domain-specific knowledge to prepare LMs for downstream tasks. These works attempt to leverage knowledge graphs, the de facto standard of symbolic knowledge representation, along with pre-trained LMs. While existing approaches leverage external knowledge, it remains an open question how to jointly incorporate knowledge graphs represented in varying contexts — from local (e.g., sentence), document-level, to global knowledge, to enable knowledge-rich and interpretable exchange across contexts. In addition, incorporating varying contexts can especially benefit long document understanding tasks that leverage pre-trained LMs, typically bounded by the input sequence length. In light of these challenges, we propose KALM, a language model that jointly leverages knowledge in local, document-level, and global contexts for long document understanding. KALM firstly encodes long documents and knowledge graphs into the three knowledge-aware context representations. KALM then processes each context with context-specific layers. These context-specific layers are followed by a ContextFusion layer that facilitates knowledge exchange to derive an overarching document representation. Extensive experiments demonstrate that KALM achieves state-of-the-art performance on three long document understanding tasks across 6 datasets/settings. Further analyses reveal that the three knowledge-aware contexts are complementary and they all contribute to model performance, while the importance and information exchange patterns of different contexts vary on different tasks and datasets.
Are All Steps Equally Important? Benchmarking Essentiality Detection in Event Processes

Natural language expresses events with varying granularities, where coarse-grained events (goals) can be broken down into finer-grained event sequences (steps). A critical yet overlooked aspect of understanding event processes is recognizing that not all step events hold equal importance toward the completion of a goal. In this paper, we address this gap by examining the extent to which current models comprehend the essentiality of step events in relation to a goal event. Cognitive studies suggest that such capability enables machines to emulate human commonsense reasoning about preconditions and necessary efforts of everyday tasks. We contribute a high-quality corpus of (goal, step) pairs gathered from the community guideline website WikiHow, with steps manually annotated for their essentiality concerning the goal by experts. The high inter-annotator agreement demonstrates that humans possess a consistent understanding of event essentiality. However, after evaluating multiple statistical and largescale pre-trained language models, we find that existing approaches considerably underperform compared to humans. This observation highlights the need for further exploration into this critical and challenging task. The dataset and code are available at http://cogcomp.org/page/publication_view/1023.
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm

The Partial Area Under the ROC Curve (PAUC), typically including One-way Partial AUC (OPAUC) and Two-way Partial AUC (TPAUC), measures the average performance of a binary classifier within a specific false positive rate and/or true positive rate interval, which is a widely adopted measure when decision constraints must be considered. Consequently, PAUC optimization has naturally attracted increasing attention in the machine learning community within the last few years. Nonetheless, most of the existing methods could only optimize PAUC approximately, leading to inevitable biases that are not controllable. Fortunately, a recent work presents an unbiased formulation of the PAUC optimization problem via distributional robust optimization. However, it is based on the pair-wise formulation of AUC, which suffers from the limited scalability w.r. t. sample size and a slow convergence rate, especially for TPAUC. To address this issue, we present a simpler reformulation of the problem in an asymptotically unbiased and instance-wise manner. For both OPAUC and TPAUC, we come to a nonconvex strongly concave minimax regularized problem of instance-wise functions. On top of this, we employ an efficient solver enjoys a linear per-iteration computational complexity w.r. t. the sample size and a time-complexity of $O(\epsilon^{-1/3})$ to reach a $\epsilon$ stationary point. Furthermore, we find that the minimax reformulation also facilitates the theoretical analysis of generalization error as a byproduct. Compared with the existing results, we present new error bounds that are much easier to prove and could deal with hypotheses with real-valued outputs. Finally, extensive experiments on several benchmark datasets demonstrate the effectiveness of our method.
Leave-group-out cross-validation for latent Gaussian models

Evaluating the predictive performance of a statistical model is commonly done using cross-validation. Although the leave-one-out method is frequently employed, its application is justified primarily for independent and identically distributed observations. However, this method tends to mimic interpolation rather than prediction when dealing with dependent observations. This paper proposes a modified cross-validation for dependent observations. This is achieved by excluding an automatically determined set of observations from the training set to mimic a more reasonable prediction scenario. Also, within the framework of latent Gaussian models, we illustrate a method to adjust the joint posterior for this modified cross-validation to avoid model refitting. This new approach is accessible in the R-INLA package (www.r-inla.org).
Symmetry Subgroup Defense Against Adversarial Attacks

Adversarial attacks and defenses disregard the lack of invariance of convolutional neural networks (CNNs), that is, the inability of CNNs to classify samples and their symmetric transformations the same. The lack of invariance of CNNs with respect to symmetry transformations is detrimental when classifying transformed original samples but not necessarily detrimental when classifying transformed adversarial samples. For original images, the lack of invariance means that symmetrically transformed original samples are classiﬁed differently from their correct labels. However, for adversarial images, the lack of invariance means that symmetrically transformed adversarial images are classiﬁed differently from their incorrect adversarial labels. Might the CNN lack of invariance revert symmetrically transformed adversarial samples to the correct classiﬁcation? This paper answers this question afﬁrmatively for a threat model that ranges from zero-knowledge adversaries to perfect-knowledge adversaries. We base our defense against perfect-knowledge adversaries on devising a Klein four symmetry subgroup that incorporates an additional ar-tiﬁcial symmetry of pixel intensity inversion. The closure property of the subgroup not only provides a framework for the accuracy evaluation but also conﬁnes the transformations that an adaptive, perfect-knowledge adversary can apply. We ﬁnd that by using only symmetry defense, no adversarial samples, and by changing nothing in the model architecture and parameters, we can defend against white-box PGD adversarial attacks, surpassing the PGD adversarial training defense by up to ∼ 50% even against a perfect-knowledge adversary for ImageNet. The proposed defense also maintains and surpasses the classiﬁcation accuracy for non-adversarial samples.
Spanning trees of smallest maximum degree in subdivisions of graphs

Given a graph G and a positive integer k , we study the question whether G ⋆ has a spanning tree of maximum degree at most k where G ⋆ is the graph that is obtained from G by subdividing every edge once. Using matroid intersection, we obtain a polynomial algorithm for this problem and a characterization of its positive instances. We use this characterization to show that G ⋆ has a spanning tree of bounded maximum degree if G is contained in some particular graph class. We study the class of 3-connected graphs which are embeddable in a ﬁxed surface and the class of ( p − 1)-connected K p -minor-free graphs for a ﬁxed integer p . We also give tightness examples for most of these classes.
Coded Residual Transform for Generalizable Deep Metric Learning

A fundamental challenge in deep metric learning is the generalization capability of the feature embedding network model since the embedding network learned on training classes need to be evaluated on new test classes. To address this challenge, in this paper, we introduce a new method called coded residual transform (CRT) for deep metric learning to significantly improve its generalization capability. Specifically, we learn a set of diversified prototype features, project the feature map onto each prototype, and then encode its features using their projection residuals weighted by their correlation coefficients with each prototype. The proposed CRT method has the following two unique characteristics. First, it represents and encodes the feature map from a set of complimentary perspectives based on projections onto diversified prototypes. Second, unlike existing transformer-based feature representation approaches which encode the original values of features based on global correlation analysis, the proposed coded residual transform encodes the relative differences between the original features and their projected prototypes. Embedding space density and spectral decay analysis show that this multi-perspective projection onto diversified prototypes and coded residual representation are able to achieve significantly improved generalization capability in metric learning. Finally, to further enhance the generalization performance, we propose to enforce the consistency on their feature similarity matrices between coded residual transforms with different sizes of projection prototypes and embedding dimensions. Our extensive experimental results and ablation studies demonstrate that the proposed CRT method outperform the state-of-the-art deep metric learning methods by large margins and improving upon the current best method by up to 4.28% on the CUB dataset.
A revision of the generalized uncertainty principle

The generalized uncertainty principle arises from the Heisenberg uncertainty principle when gravity is taken into account, so the leading order correction to the standard formula is expected to be proportional to the gravitational constant GN = L2Pl. On the other hand, the emerging picture suggests a set of departures from the standard theory which demand a revision of all the arguments used to deduce heuristically the new rule. In particular, one can now argue that the leading order correction to the Heisenberg uncertainty principle is proportional to the first power of the Planck length LPl. If so, the departures from ordinary quantum mechanics would be much less suppressed than what is commonly thought.
Quantum state tomography via non-convex Riemannian gradient descent

The recovery of an unknown density matrix of large size requires huge computational resources. The recent Factored Gradient Descent (FGD) algorithm and its variants achieved state-of-the-art performance since they could mitigate the dimensionality barrier by utilizing some of the underlying structures of the density matrix. Despite their theoretical guarantee of a linear convergence rate, the convergence in practical scenarios is still slow because the contracting factor of the FGD algorithms depends on the condition number $\kappa$ of the ground truth state. Consequently, the total number of iterations can be as large as $O(\sqrt{\kappa}\ln(\frac{1}{\varepsilon}))$ to achieve the estimation error $\varepsilon$. In this work, we derive a quantum state tomography scheme that improves the dependence on $\kappa$ to the logarithmic scale; namely, our algorithm could achieve the approximation error $\varepsilon$ in $O(\ln(\frac{1}{\kappa\varepsilon}))$ steps. The improvement comes from the application of the non-convex Riemannian gradient descent (RGD). The contracting factor in our approach is thus a universal constant that is independent of the given state. Our theoretical results of extremely fast convergence and nearly optimal error bounds are corroborated by numerical results.
Optical calibration of large format adaptive mirrors

Adaptive (or deformable) mirrors are widely used as wavefront correctors in adaptive optics systems. The optical calibration of an adaptive mirror is a fundamental step during its life-cycle: the process is in facts required to compute a set of known commands to operate the adaptive optics system, to compensate alignment and non common-path aberrations, to run chopped or field-stabilized acquisitions. In this work we present the sequence of operations for the optical calibration of adaptive mirrors, with a specific focus on large aperture systems such as the adaptive secondaries. Such systems will be one of the core components of the extremely large telescopes. Beyond presenting the optical procedures, we discuss in detail the actors, their functional requirements and the mutual interactions. A specific emphasys is put on automation, through a clear identification of inputs, outputs and quality indicators for each step: due to a high degrees-of-freedom count (thousands of actuators), an automated approach is preferable to constraint the cost and schedule. In the end we present some algorithms for the evaluation of the measurement noise; this point is particularly important since the calibration setup is typically a large facility in an industrial environment, where the noise level may be a major show-stopper.
A State Feedback Controller for Mitigation of Continuous-Time Networked SIS Epidemics

The paper considers continuous-time networked susceptible-infected-susceptible (SIS) diseases spreading over a population. Each agent represents a sub-population and has its own healing rate and infection rate; the state of the agent at a time instant denotes what fraction of the said sub-population is infected with the disease at the said time instant. By taking account of the changes in behaviors of the agents in response to the infection rates in real-time, our goal is to devise a feedback strategy such that the infection level for each agent strictly stays below a pre-specified value. Furthermore, we are also interested in ensuring that the closed-loop system converges either to the disease-free equilibrium or, when it exists, to the endemic equilibrium. The upshot of devising such a strategy is that it allows health administration officials to ensure that there is sufficient capacity in the healthcare system to treat the most severe cases. We demonstrate the effectiveness of our controller via numerical examples.
Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering

Generative question answering (QA) models generate answers to questions either solely based on the parameters of the model (the closed-book setting) or additionally retrieving relevant evidence (the open-book setting). Generative QA models can answer some relatively complex questions, but the mechanism through which they do so is still poorly understood. We perform several studies aimed at better understanding the multi-hop reasoning capabilities of generative QA models. First, we decompose multi-hop questions into multiple corresponding single-hop questions, and find marked inconsistency in QA models’ answers on these pairs of ostensibly identical question chains. Second, we find that models lack zero-shot multi-hop reasoning ability: when trained only on single-hop questions, models generalize poorly to multi-hop questions. Finally, we demonstrate that it is possible to improve models’ zero-shot multi-hop reasoning capacity through two methods that approximate real multi-hop natural language (NL) questions by training on either concatenation of single-hop questions or logical forms (SPARQL). In sum, these results demonstrate that multi-hop reasoning does not emerge naturally in generative QA models, but can be encouraged by advances in training or modeling techniques. Code is available at https://github.com/jzbjyb/multihop.
Rotationally symmetric vs. oriented frequency channels for the Hotelling observer: a comparison with human observers

This study investigated the inclusion of oriented channels into the Hotelling observer. The performances of the channelized Hotelling observer with rotationally symmetric channels (CHO/sub RS/), the channelized Hotelling observer with rotationally oriented channels (CHO/sub RO/), and human observers were compared for a simple signal detection task. There were four Gaussian signals-circular, elliptical horizontal, elliptical diagonal and elliptical vertical-of equal maximum intensity and signal power. There were four types of lumpy backgrounds-circular, horizontal and vertical-created by filtering a zero-mean, white, Gaussian noise image. For the CHO/sub RO/, the CHO/sub RS/, and the human observers, detectability indices for each of the 16 combinations of signal and background were calculated. The CHO/sub RS/ and CHO/sub RO/ were equivalent for the images where the signal and background had the same orientation; however, the CHO/sub RO/ was more predictive of human performance overall. Neither the CHO/sub RS/ nor CHO/sub RO/ models were predictive of human performance for images of elliptical signals on circularly oriented backgrounds-a result that underscores the need for further refinement of the channel models.
Real McKay Correspondence: KR-Theory of Graded Kleinian Groups

This project considers the finite symmetry subgroups of the orthogonal group $\mathrm{O}(3) \subset \mathrm{GL}(3,\mathbb{R})$ and the index $2$ containments $G\lhd \widehat{G}$. The special orthogonal group $\mathrm{SO}(3) \subset \mathrm{SL}(3,\mathbb{R})$ admits a double cover from the spinor group $\mathrm{Spin}(3) \cong \mathrm{SU}(2) \subset \mathrm{SL}(2,\mathbb{C})$, and lifting our subgroups up preserves the network of containments. Those subgroups not contained in $\mathrm{SO}(3) \subset \mathrm{O}(3) $ are lifted to the pinor groups $\mathrm{Pin}_{\pm}(3)$ of which there are two choices. For the index $2$ containments $G\lhd \widehat{G}$, we calculate the Real and complex Frobenius-Schur indicators, and apply Dyson's classification of antilinear block structures to produce decorated McKay graphs for each case. We then explore $KR$-theory as introduced by Atiyah in 1966, which is a variant of topological $K$-theory for working with topological spaces equipped with an involution. The GIT quotient spaces $\mathbb{C}^2 // G$, can be equipped by an involution via the action of $\widehat{G} / G$. In 1983, Gonzalez-Sprinberg and Verdier showed how one can view the McKay correspondence as an isomorphism between the $G$-equivariant $K$-theory $K_G(\mathbb{C}^2)$ and the $K$-theory of the minimal resolution of the singularity $\widetilde{\mathbb{C}^2 // G}$. We use this to conjecture an analogous a form of the McKay correspondence for $C_2$-graded groups and $KR$-theory.