cssi/SciSciGPT-SciSciNet huggingface dataset:
NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs

Graph Neural Networks (GNNs) have achieved promising results for semi-supervised learning tasks on graphs such as node classification. Despite the great success of GNNs, many real-world graphs are often sparsely and noisily labeled, which could significantly degrade the performance of GNNs, as the noisy information could propagate to unlabeled nodes via graph structure. Thus, it is important to develop a label noise-resistant GNN for semi-supervised node classification. Though extensive studies have been conducted to learn neural networks with noisy labels, they mostly focus on independent and identically distributed data and assume a large number of noisy labels are available, which are not directly applicable for GNNs. Thus, we investigate a novel problem of learning a robust GNN with noisy and limited labels. To alleviate the negative effects of label noise, we propose to link the unlabeled nodes with labeled nodes of high feature similarity to bring more clean label information. Furthermore, accurate pseudo labels could be obtained by this strategy to provide more supervision and further reduce the effects of label noise. Our theoretical and empirical analysis verify the effectiveness of these two strategies under mild conditions. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method in learning a robust GNN with noisy and limited labels.
A new bandwidth selection criterion for using SVDD to analyze hyperspectral data

This paper presents a method for hyperspectral image classification that uses support vector data description (SVDD) with the Gaussian kernel function. SVDD has been a popular machine learning technique for single-class classification, but selecting the proper Gaussian kernel bandwidth to achieve the best classification performance is always a challenging problem. This paper proposes a new automatic, unsupervised Gaussian kernel bandwidth selection approach which is used with a multiclass SVDD classification scheme. The performance of the multiclass SVDD classification scheme is evaluated on three frequently used hyperspectral data sets, and preliminary results show that the proposed method can achieve better performance than published results on these data sets.
Knot Floer homology and strongly homotopy-ribbon concordances

We prove that the map on knot Floer homology induced by a strongly homotopy-ribbon concordance is injective.
COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List

Classical information retrieval systems such as BM25 rely on exact lexical match and can carry out search efficiently with inverted list index. Recent neural IR models shifts towards soft matching all query document terms, but they lose the computation efficiency of exact match systems. This paper presents COIL, a contextualized exact match retrieval architecture, where scoring is based on overlapping query document tokens’ contextualized representations. The new architecture stores contextualized token representations in inverted lists, bringing together the efficiency of exact match and the representation power of deep language models. Our experimental results show COIL outperforms classical lexical retrievers and state-of-the-art deep LM retrievers with similar or smaller latency.
Universality, Vulnerability, and Collective Responsibility

Vulnerability theory as developed in the Vulnerability and Human Condition Initiative is an alternative to a rights-based or social contract paradigm for thinking about foundation concepts of state responsibility. One fundamental premise of the theory is that the individuals and groups currently described as “vulnerable populations” should not be labelled vulnerable, nor should they be sequestered in discreet categories for the purposes of law and policy. This plea for their inclusion in a larger whole is not to deny that discrimination, harm, and relative disadvantage arising from all sorts of circumstances and situations exist. Nor is it to suggest that particular instances of harm should not be addressed by appropriate state action. Rather, it is an argument that “vulnerability” is the wrong concept to use to define and isolate these groups, or any other specific group, from the whole of humanity. Human vulnerability is universal and constant, inherent in the human condition. Recognizing the theoretical mandates of accepting the universal, vulnerability theory presents a “vulnerable subject” as the only appropriate object of law and policy. This inclusive, universal legal subject incorporates the realities of the ontological body and its life-long dependence on social institutions and relationships, building a theory of essential (not voluntarily or consensual) social cohesion and reciprocity in which the state (or governing system) has the responsibility to see that these vital social institutions and relationships operate justly.
Multi-structural games and number of quantifiers

We study multi-structural games, played on two sets ${\mathcal{A}}$ and ${\mathcal{B}}$ of structures. These games generalize Ehrenfeucht-Fraisse games. Whereas Ehrenfeucht-Fraisse games capture the quantifier rank of a first-order sentence, multi-structural games capture the number of quantifiers, in the sense that Spoiler wins the r-round game if and only if there is a first-order sentence ϕ with at most r quantifiers, where every structure in ${\mathcal{A}}$ satisfies ϕ and no structure in ${\mathcal{B}}$ satisfies ϕ. We use these games to give a complete characterization of the number of quantifiers required to distinguish linear orders of different sizes, and develop machinery for analyzing structures beyond linear orders.
Quantitative live-cell PALM reveals nanoscopic Faa4 redistributions and dynamics on lipid droplets during metabolic transitions of yeast.

We employ quantitative photo-activated localization microscopy to study the nanoscopic redistribution of the fatty acid-activating enzyme Faa4 on lipid droplets (LDs). Our results show a dramatic i...
GEAR: On optimal decision making with auxiliary data

Personalized optimal decision making, finding the optimal decision rule (ODR) based on individual characteristics, has attracted increasing attention recently in many fields, such as education, economics, and medicine. Current ODR methods usually require the primary outcome of interest in samples for assessing treatment effects, namely the experimental sample. However, in many studies, treatments may have a long-term effect, and as such the primary outcome of interest cannot be observed in the experimental sample due to the limited duration of experiments, which makes the estimation of ODR impossible. This paper is inspired to address this challenge by making use of an auxiliary sample to facilitate the estimation of ODR in the experimental sample. We propose an auGmented inverse propensity weighted Experimental and Auxiliary sample-based decision Rule (GEAR) by maximizing the augmented inverse propensity weighted value estimator over a class of decision rules using the experimental sample, with the primary outcome being imputed based on the auxiliary sample. The asymptotic properties of the proposed GEAR estimators and their associated value estimators are established. Simulation studies are conducted to demonstrate its empirical validity with a real AIDS application.
Total Nonnegativity and Induced Sign Characters of the Hecke Algebra

Let $${\mathfrak {S}}_{[i,j]}$$ be the subgroup of the symmetric group $${\mathfrak {S}}_n$$ generated by adjacent transpositions $$(i,i+1), \dotsc , (j-1,j)$$ , assuming $$1 \le i < j \le n$$ . We give a combinatorial rule for evaluating induced sign characters of the type A Hecke algebra $$H_n(q)$$ at all elements of the form $$\sum _{w \in {\mathfrak {S}}_{[i,j]}} T_w$$ and at all products of such elements. This includes evaluation at some elements $$C'_w(q)$$ of the Kazhdan–Lusztig basis.
Time Resolved Stereo Particle Image Velocimetry Measurements of the Instabilities Downstream of a Backward-Facing Step in a Swept-Wing Boundary Layer

Time-resolved particle image velocimetry (TRPIV) measurements are performed down-stream of a swept backward-facing step, with a height of 49% of the boundary-layer thickness. The results agree well qualitatively with previously reported hotwire measurements, though the amplitudes of the fluctuating components measured using TRPIV are higher. Nonetheless, the low-amplitude instabilities in the flow are fairly well resolved using TR- PIV. Proper orthogonal decomposition is used to study the development of the traveling cross flow and Tollmien-Schlichting (TS) instabilities downstream of the step and to study how they interact to form the large velocity spikes that ultimately lead to transition. A secondary mode within the traveling cross flow frequency band develops with a wavelength close to that of the stationary cross flow instability, so that at a certain point in the phase, it causes an increase in the spanwise modulation initially caused by the stationary cross flow mode. This increased modulation leads to an increase in the amplitude of the TS mode, which, itself, is highly modulated through interactions with the stationary cross flow. When the traveling cross flow and TS modes align in time and space, the large velocity spikes occur. Thus, these three instabilities, which are individually of low amplitude when the spikes start to occur (U'rms/Ue <0.03), interact and combine to cause a large flow disturbance that eventually leads to transition.
Framework for A Personalized Intelligent Assistant to Elderly People for Activities of Daily Living.

The increasing population of elderly people is associated with the need to meet their increasing requirements and to provide solutions that can improve their quality of life in a smart home. In addition to fear and anxiety towards interfacing with systems; cognitive disabilities, weakened memory, disorganized behavior and even physical limitations are some of the problems that elderly people tend to face with increasing age. The essence of providing technology-based solutions to address these needs of elderly people and to create smart and assisted living spaces for the elderly; lies in developing systems that can adapt by addressing their diversity and can augment their performances in the context of their day to day goals. Therefore, this work proposes a framework for development of a Personalized Intelligent Assistant to help elderly people perform Activities of Daily Living (ADLs) in a smart and connected Internet of Things (IoT) based environment. This Personalized Intelligent Assistant can analyze different tasks performed by the user and recommend activities by considering their daily routine, current affective state and the underlining user experience. To uphold the efficacy of this proposed framework, it has been tested on a couple of datasets for modelling an average user and a specific user respectively. The results presented show that the model achieves a performance accuracy of 73.12% when modelling a specific user, which is considerably higher than its performance while modelling an average user, this upholds the relevance for development and implementation of this proposed framework.
Mapping the phase diagram of the quantum anomalous Hall and topological Hall effects in a dual-gated magnetic topological insulator heterostructure

The authors use a dual-gated device to map out the phase diagram created by the interplay between Berry curvature in real space and Berry curvature in momentum space in a magnetic topological insulator heterostructure
HodgeNet: learning spectral geometry on triangle meshes

Constrained by the limitations of learning toolkits engineered for other applications, such as those in image processing, many mesh-based learning algorithms employ data flows that would be atypical from the perspective of conventional geometry processing. As an alternative, we present a technique for learning from meshes built from standard geometry processing modules and operations. We show that low-order eigenvalue/eigenvector computation from operators parameterized using discrete exterior calculus is amenable to efficient approximate backpropagation, yielding spectral per-element or per-mesh features with similar formulas to classical descriptors like the heat/wave kernel signatures. Our model uses few parameters, generalizes to high-resolution meshes, and exhibits performance and time complexity on par with past work.
Efficient, Decentralized, and Collaborative Multi-Robot Exploration using Optimal Transport Theory

In this paper, an Optimal Transport (OT)-based decentralized collaborative multi-robot exploration strategy is proposed. This method is to achieve an efficient exploration with a predefined priority in the given domain. In this context, the efficiency indicates how a team of robots (agents) covers the domain reflecting the corresponding priority map (or degrees of importance) in the domain. The decentralized exploration implies that each agent carries out their exploration task independently in the absence of any supervisory agent/computer. When an agent encounters another agent within a communication range, each agent receives the information about which areas are already covered by other agents, yielding a collaborative exploration. The OT theory is employed to quantify the difference between the distribution formed by the robot trajectories and the given reference spatial distribution indicating the priority. A computationally feasible way is developed to measure the performance of the proposed exploration scheme. Further, the formal algorithm is provided for an efficient, decentralized, and collaborative exploration plan. Simulation results are presented to validate the proposed methods.
Diabetic ketoacidosis and mortality in COVID-19 infection.

Abstract Aim - Patients with diabetes have increased morbidity and mortality from COVID-19. Case reports describe patients with simultaneous COVID-19 and diabetic acidosis (DKA), however there is limited data on the prevalence, predictors and outcomes of DKA in these patients. Methods - Patients with COVID-19 were identified from the electronic medical record. DKA was defined by standardized criteria. Proportional hazard regression models were used to determine risk factors for, and mortality from DKA in COVID-19. Results - Of 2366 patients admitted for COVID-19, 157 (6.6%) patients developed DKA, 94% of whom had antecedent type 2 diabetes, 0.6% had antecedent type 1 diabetes, and 5.7% patients had no prior diagnosis of diabetes. Patients with DKA had increased hospital length of stay and in-patient mortality. Higher HbA1c predicted increased risk of incident DKA (HR 1.47 per 1% increase, 95% CI 1.40–1.54). Risk factors for mortality included older age (HR 1.07 per 5 years, 95% CI 1.06–1.08) and need for pressors (HR 2.33, 95% CI 1.82–2.98). Glucocorticoid use was protective in patients with and without DKA. Conclusion - The combination of DKA and COVID-19 is associated with greater mortality, driven by older age and COVID-19 severity.
Infrared effects in the late stages of black hole evaporation

As a black hole evaporates, each outgoing Hawking quantum carries away some of the black holes asymptotic charges associated with the extended Bondi-Metzner-Sachs group. These include the Poincare charges of energy, linear momentum, intrinsic angular momentum, and orbital angular momentum or center-of-mass charge, as well as extensions of these quantities associated with supertranslations and super-Lorentz transformations, namely supermomentum, superspin and super center-of-mass charges (also known as soft hair). Since each emitted quantum has fluctuations that are of order unity, fluctuations in the black hole’s charges grow over the course of the evaporation. We estimate the scale of these fluctuations using a simple model. The results are, in Planck units: (i) The black hole position has a uncertainty of $$ \sim {M}_i^2 $$ at late times, where Mi is the initial mass (previously found by Page). (ii) The black hole mass M has an uncertainty of order the mass M itself at the epoch when M ∼ $$ {M}_i^{2/3} $$ , well before the Planck scale is reached. Correspondingly, the time at which the evaporation ends has an uncertainty of order $$ \sim {M}_i^2 $$ . (iii) The supermomentum and superspin charges are not independent but are determined from the Poincare charges and the super center-of-mass charges. (iv) The supertranslation that characterizes the super center-of-mass charges has fluctuations at multipole orders l of order unity that are of order unity in Planck units. At large l, there is a power law spectrum of fluctuations that extends up to l ∼ $$ {M}_i^2/M $$ , beyond which the fluctuations fall off exponentially, with corresponding total rms shear tensor fluctuations ∼ MiM−3/2.
Distributed Cooperative Control of Battery Energy Storage Systems in DC Microgrids

The control of battery energy storage systems (BESSs) plays an important role in the management of microgrids. In this paper, the problem of balancing the state-of-charge (SoC) of the networked battery units in a BESS while meeting the total charging/discharging power demand is formulated and solved as a distributed control problem. Conditions on the communication topology among the battery units and the accessibility of the knowledge of the desired power are established under which a control law is designed for each battery unit based on distributed average power estimators and distributed average unit state estimators. We show that, under the proposed control laws, SoC balancing of all battery units is achieved and the total charging/discharging power of the BESS tracks the desired power. A simulation example is presented to verify the theoretical results.
Personalized Adaptive Cruise Control and Impacts on Mixed Traffic

This paper presents a personalized adaptive cruise control (PACC) design that can learn human driver behavior and adaptively control the semi-autonomous vehicle (SAV) in the car-following scenario, and investigates its impacts on mixed traffic. In mixed traffic where the SAV and human-driven vehicles share the road, the SAV's driver can choose a PACC tuning that better fits the driver's preferred driving strategies. The individual driver's preferences are learned through the inverse reinforcement learning (IRL) approach by recovering a unique cost function from the driver's demonstrated driving data that best explains the observed driving style. The proposed PACC design plans the motion of the SAV by minimizing the learned unique cost function considering the short preview information of the preceding human-driven vehicle. The results reveal that the learned driver model can identify and replicate the personalized driving behaviors accurately and consistently when following the preceding vehicle in a variety of traffic conditions. Furthermore, we investigated the impacts of the PACC with different drivers on mixed traffic by considering time headway, gap distance, and fuel economy assessments. A statistical investigation shows that the impacts of the PACC on mixed traffic vary among tested drivers due to their intrinsic driving preferences.
Generic scarring for minimal hypersurfaces along stable hypersurfaces

Let $$M^{n+1}$$ be a closed manifold of dimension $$3\le n+1\le 7$$ . We show that for a $$C^\infty $$ -generic metric g on M, to any connected, closed, embedded, 2-sided, stable, minimal hypersurface $$S\subset (M,g)$$ corresponds a sequence of closed, embedded, minimal hypersurfaces $$\{\Sigma _k\}$$ scarring along S, in the sense that the area and Morse index of $$\Sigma _k$$ both diverge to infinity and, when properly renormalized, $$\Sigma _k$$ converges to S as varifolds. We also show that scarring of immersed minimal surfaces along stable surfaces occurs in most closed Riemannian 3-manifods.
Optimal Fine-Grained Hardness of Approximation of Linear Equations.

The problem of solving linear systems is one of the most fundamental problems in computer science, where given a satisfiable linear system (A,b), for A ∈ ℝ^{n×n} and b ∈ ℝⁿ, we wish to find a vector x ∈ ℝⁿ such that Ax = b. The current best algorithms for solving dense linear systems reduce the problem to matrix multiplication, and run in time O(n^ω). We consider the problem of finding e-approximate solutions to linear systems with respect to the L₂-norm, that is, given a satisfiable linear system (A ∈ ℝ^{n×n}, b ∈ ℝⁿ), find an x ∈ ℝⁿ such that ||Ax - b||₂ ≤ e||b||₂. Our main result is a fine-grained reduction from computing the rank of a matrix to finding e-approximate solutions to linear systems. In particular, if the best known O(n^ω) time algorithm for computing the rank of n × O(n) matrices is optimal (which we conjecture is true), then finding an e-approximate solution to a dense linear system also requires Ω(n^ω) time, even for e as large as (1 - 1/poly(n)). We also prove (under some modified conjectures for the rank-finding problem) optimal hardness of approximation for sparse linear systems, linear systems over positive semidefinite matrices and well-conditioned linear systems. At the heart of our results is a novel reduction from the rank problem to a decision version of the approximate linear systems problem. This reduction preserves properties such as matrix sparsity and bit complexity.
An improved uncertainty principle for functions with symmetry

Abstract Chebotarev proved that every minor of a discrete Fourier matrix of prime order is nonzero. We prove a generalization of this result that includes analogues for discrete cosine and discrete sine matrices as special cases. We establish these results via a generalization of the Biro–Meshulam–Tao uncertainty principle to functions with symmetries that arise from certain group actions, with some of the simplest examples being even and odd functions. We show that our result is best possible and in some cases is stronger than that of Biro–Meshulam–Tao. Some of these results hold in certain circumstances for non-prime fields; Gauss sums play a central role in such investigations.
Learning Orientations: a Discrete Geometry Model

In the mammalian brain, many neuronal ensembles are involved in representing spatial structure of the environment. In particular, there exist cells that encode the animal9s location and cells that encode head direction. A number of studies have addressed properties of the spatial maps produced by these two populations of neurons, mainly by establishing correlations between their spiking parameters and geometric characteristics of the animal9s environments. The question remains however, how the brain may intrinsically combine the direction and the location information into a unified spatial framework that enables animals9 orientation. Below we propose a model of such a framework, using ideas and constructs from algebraic topology and synthetic affine geometry.
Representing Navy tactical computer system knowledge for reengineering and integration

This article describes an ongoing project in representing knowledge about Navy tactical computer systems. The primary concern is for their continuing evolution and/or integration with other tactical systems. Following an introduction to this class of application and a review of how the Naval Surface Warfare Center is addressing the issue of extending the investment in these systems, the software process is examined and two alternative, incompatible paradigms are presented. The latter, which relies on the use of software knowledge, is described briefly, and an environment that implements this paradigm is presented as a proof of concept. Because the transfer of this paradigm to the desired Navy applications will not be completed in the near future, the article concludes with the presentation of a general framework for classifying Navy reengineering projects that should improve assessment and also aid in the building of a knowledge base to support the improved process model.
Robust and Efficient Multilevel-ILU Preconditioning of Hybrid Newton-GMRES for Incompressible Navier-Stokes Equations

We introduce a robust and efficient preconditioner for a hybrid Newton-GMRES method for solving the nonlinear systems arising from incompressible Navier-Stokes equations. When the Reynolds number is relatively high, these systems often involve millions of degrees of freedom (DOFs), and the nonlinear systems are difficult to converge, partially due to the strong asymmetry of the system and the saddle-point structure. In this work, we propose to alleviate these issues by leveraging a multilevel ILU preconditioner called HILUCSI, which is particularly effective for saddle-point problems and can enable robust and rapid convergence of the inner iterations in Newton-GMRES. We further use Picard iterations with the Oseen systems to hot-start Newton-GMRES to achieve global convergence, also preconditioned using HILUCSI. To further improve efficiency and robustness, we use the Oseen operators as physics-based sparsifiers when building preconditioners for Newton iterations and introduce adaptive refactorization and iterative refinement in HILUCSI. We refer to the resulting preconditioned hybrid Newton-GMRES as HILUNG. We demonstrate the effectiveness of HILUNG by solving the standard 2D driven-cavity problem with Re 5000 and a 3D flow-over-cylinder problem with low viscosity. We compare HILUNG with some state-of-the-art customized preconditioners for INS, including two variants of augmented Lagrangian preconditioners and two physics-based preconditioners, as well as some general-purpose approximate-factorization techniques. Our comparison shows that HILUNG is much more robust for solving high-Re problems and it is also more efficient in both memory and runtime for moderate-Re problems.
Competitive Advertising on Brand Search: Traffic Stealing and Click Quality

Competitive advertising on brand keywords of rivals in sponsored search is effective in generating traffic, if rivals do not defend their traffic.
Comparative Genomic Analysis of the DUF34 Protein Family Suggests Role as a Metal Ion Chaperone or Insertase

Members of the DUF34 (domain of unknown function 34) family, also known as the NIF3 protein superfamily, are ubiquitous across superkingdoms. Proteins of this family have been widely annotated as “GTP cyclohydrolase I type 2” through electronic propagation based on one study. Here, the annotation status of this protein family was examined through a comprehensive literature review and integrative bioinformatic analyses that revealed varied pleiotropic associations and phenotypes. This analysis combined with functional complementation studies strongly challenges the current annotation and suggests that DUF34 family members may serve as metal ion insertases, chaperones, or metallocofactor maturases. This general molecular function could explain how DUF34 subgroups participate in highly diversified pathways such as cell differentiation, metal ion homeostasis, pathogen virulence, redox, and universal stress responses.
Toward Automatic Interpretation of 3D Plots.

This paper explores the challenge of teaching a machine how to reverse-engineer the grid-marked surfaces used to represent data in 3D surface plots of two-variable functions. These are common in scientific and economic publications; and humans can often interpret them with ease, quickly gleaning general shape and curvature information from the simple collection of curves. While machines have no such visual intuition, they do have the potential to accurately extract the more detailed quantitative data that guided the surface’s construction. We approach this problem by synthesizing a new dataset of 3D grid-marked surfaces (SurfaceGrid) and training a deep neural net to estimate their shape. Our algorithm successfully recovers shape information from synthetic 3D surface plots that have had axes and shading information removed, been rendered with a variety of grid types, and viewed from a range of viewpoints.
Multiple Moist Climate Equilibrium States on Arid Rocky M-dwarf Planets: A Last-saturation Tracer Analysis

Terrestrial-type exoplanets orbiting nearby red dwarf stars (M dwarfs) are the first potentially habitable exoplanets suitable for atmospheric characterization in the near future. Understanding the stability of water in cold-trap regions on such planets is critical because it directly impacts transmission spectroscopy observations, the global energy budget, and long-term surface water evolution. Here we diagnose the humidity distribution in idealized general circulation model (GCM) simulations of terrestrial-type exoplanets. We use the `tracer of last saturation' technique to study the saturation statistics of air parcels. We find that on synchronously rotating planets, the water vapor abundance in the nightside upper troposphere depends weakly on planetary rotation, while more water vapor builds up in the nightside lower troposphere on fast-rotating planets. We then discuss how last-saturation statistics can elucidate the multiple moist climate equilibrium states on synchronously and asynchronously rotating arid planets. We show that the multiple moist climate states arise from the cold-trapping competition between the substellar upper atmosphere and cold surface regions. We find that fast synchronously rotating planets tend to trap surface water on the nightside as a result of their weak atmospheric and strong surface cold traps compared to the slow rotating case. These results elucidate the nature of the water cycle on arid rocky exoplanets and will aid interpretation of atmospheric observations in the future.
Modular flow of excited states

We develop new techniques for studying the modular and the relative modular flows of general excited states. We show that the class of states obtained by acting on the vacuum (or any cyclic and separating state) with invertible operators from the algebra of a region is dense in the Hilbert space. This enables us to express the modular and the relative modular operators, as well as the relative entropies of generic excited states in terms of the vacuum modular operator and the operator that creates the state. In particular, the modular and the relative modular flows of any state can be expanded in terms of the modular flow of operators in vacuum. We illustrate the formalism with simple examples including states close to the vacuum, and coherent and squeezed states in generalized free field theory.
Two-­photon fluorescence background rejection by differential aberration imaging

We present a simple and robust way to reject out-of-focus background when performing deep two-photon excited fluorescence (TPEF) imaging in thick tissue. The technique is based on the use of a deformable mirror (DM) to introduce illumination aberrations that preferentially degrade TPEF signal while leaving TPEF background relatively unchanged. A subtraction of aberrated from unaberrated images leads to background rejection. We present a heuristic description of our technique, which we corroborate with experiment. Images of a labeled mouse olfactory bulb are compared with standard TPEF microscopy images, demonstrating significant out of focus TPEF background rejection with our technique. Finally we improve our technique by developing a faster aberration modulation mechanism that performs background subtraction line by line rather than frame by frame. In this manner, the overall image acquisition rate of our technique is the same as that of a standard TPEF microscope.
Large-Scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification

Deep AUC Maximization (DAM) is a new paradigm for learning a deep neural network by maximizing the AUC score of the model on a dataset. Most previous works of AUC maximization focus on the perspective of optimization by designing efficient stochastic algorithms, and studies on generalization performance of large-scale DAM on difficult tasks are missing. In this work, we aim to make DAM more practical for interesting real-world applications (e.g., medical image classification). First, we propose a new margin-based min-max surrogate loss function for the AUC score (named as AUC min-max-margin loss or simply AUC margin loss for short). It is more robust than the commonly used AUC square loss, while enjoying the same advantage in terms of large-scale stochastic optimization. Second, we conduct extensive empirical studies of our DAM method on four difficult medical image classification tasks, namely (i) classification of chest x-ray images for identifying many threatening diseases, (ii) classification of images of skin lesions for identifying melanoma, (iii) classification of mammogram for breast cancer screening, and (iv) classification of microscopic images for identifying tumor tissue. Our studies demonstrate that the proposed DAM method improves the performance of optimizing cross-entropy loss by a large margin, and also achieves better performance than optimizing the existing AUC square loss on these medical image classification tasks. Specifically, our DAM method has achieved the 1st place on Stanford CheXpert competition on Aug. 31, 2020. To the best of our knowledge, this is the first work that makes DAM succeed on large-scale medical image datasets. We also conduct extensive ablation studies to demonstrate the advantages of the new AUC margin loss over the AUC square loss on benchmark datasets. The proposed method is implemented in our open-sourced library LibAUC (this http URL).
A Systematic Review of the Protective Effect of Prior SARS-CoV-2 Infection on Repeat Infection.

We systematically reviewed studies to estimate the risk of SARS-CoV-2 reinfection among those previously infected with SARS-CoV-2. For this systematic review, we searched scientific publications on...
Space Shuttle Columbia Post‐Accident Analysis and Investigation

Although the loss of the Space Shuttle Columbia and its crew was tragic, the circumstances offered a unique opportunity to examine a multitude of components which had experienced one of the harshest environments ever encountered by engineered materials: a break up at a velocity in excess of Mach 18 and an altitude exceeding 200,000 feet (63 KM), resulting in a debris field 645 miles/1,038 KM long and 10 miles/16 KM wide. Various analytical tools were employed to ascertain the sequence of events leading to the disintegration of the Orbiter and to characterize the features of the debris. The testing and analyses all indicated that a breach in a left wing reinforced carbon/carbon composite leading edge panel was the access point for hot gasses generated during re-entry to penetrate the structure of the vehicle and compromise the integrity of the materials and components in that area of the Shuttle.
Sub-Riemannian Limit of the Differential Form Heat Kernels of Contact Manifolds

We study the behavior of the heat kernel of the Hodge Laplacian on a contact manifold endowed with a family of Riemannian metrics that blow-up the directions transverse to the contact distribution. We apply this to analyze the behavior of global spectral invariants such as the eta-invariant and the determinant of the Laplacian. In particular we prove that contact versions of the relative eta-invariant and the relative analytic torsion are equal to their Riemannian analogues and hence topological.
Constructed Measures and Causal Inference: Towards a New Model of Measurement for Psychosocial Constructs.

Psychosocial constructs can only be assessed indirectly, and measures are typically formed by a combination of indicators that are thought to relate to the construct. Reflective and formative measurement models offer different conceptualizations of the relation between the indicators and what is sometimes conceived of as a univariate latent variable supposed to correspond in some way to the construct. I argue that the empirical implications of reflective and formative models will often be violated by data since the causally relevant constituents will generally be multivariate, not univariate. These empirical implications can be formally tested but factor analysis is not adequate to do so. I argue that formative models misconstrue the relationship between the constructed measures and the underlying reality by which causal processes operate, but that reflective models misconstrue the nature of the underlying reality itself by typically presuming that the constituents of it that are causally efficacious are unidimensional. I discuss the ensuing problems arising from these misconstruals. I propose a causal interpretation of associations between constructed measures and various outcomes that is applicable to both reflective and formative models and is applicable even if the usual assumptions of these models are violated. I put forward an outline for a new model of the process of measure construction and discuss the practical implications of these observations and proposals for the provision of definitions, the selection of items, item-by-item analyses, the construction of measures, and the interpretation of the associations of these measures with subsequent outcomes.
Discovering and Validating AI Errors With Crowdsourced Failure Reports

AI systems can fail to learn important behaviors, leading to real-world issues like safety concerns and biases. Discovering these systematic failures often requires significant developer attention, from hypothesizing potential edge cases to collecting evidence and validating patterns. To scale and streamline this process, we introduce crowdsourced failure reports, end-user descriptions of how or why a model failed, and show how developers can use them to detect AI errors. We also design and implement Deblinder, a visual analytics system for synthesizing failure reports that developers can use to discover and validate systematic failures. In semi-structured interviews and think-aloud studies with 10 AI practitioners, we explore the affordances of the Deblinder system and the applicability of failure reports in real-world settings. Lastly, we show how collecting additional data from the groups identified by developers can improve model performance.
Symmetry finder: A method for hunting symmetry in neutrino oscillation

Symmetry in neutrino oscillation serves for a better understanding of the physical properties of the phenomenon. We present a systematic way of finding symmetry in neutrino oscillation, which we call symmetry finder (SF). By extending the known framework in vacuum into a matter environment, we derive the SF equation, a powerful machinery for identifying symmetry in the system. After learning lessons on symmetry in the Zaglauer-Schwarzer system with matter equivalent to the vacuum symmetry, we apply the SF method to the [P. B. Denton et al., Compact perturbative expressions for neutrino oscillations in matter, J. High Energy Phys. 06 (2016) 051.] (DMP) perturbation theory to first order. We show that the method is so powerful that we uncover the eight reparametrization symmetries with the $1\ensuremath{\leftrightarrow}2$ state exchange in DMP, denoted as IA, IB, $\dots{}$, IVB, all new except for IA. The transformations consist of both fundamental and dynamical variables, indicating their equal importance. It is also shown that all the symmetries discussed in this paper can be understood as the Hamiltonian symmetries, which ensures their all-order validity and applicability to varying density matter.
Short-chain fatty acids activate acetyltransferase p300.

Short-chain fatty acids (SCFAs) acetate, propionate, and butyrate are produced in large quantities by the gut microbiome and contribute to a wide array of physiological processes. While the underlying mechanisms are largely unknown, many effects of SCFAs have been traced to changes in the cell's epigenetic state. Here, we systematically investigate how SCFAs alter the epigenome. Using quantitative proteomics of histone modification states, we identified rapid and sustained increases in histone acetylation after addition of butyrate or propionate, but not acetate. While decades of prior observations would have suggested that hyperacetylation induced by SCFAs are attributed to inhibition of histone deacetylases (HDACs), we found that propionate and butyrate instead activate the acetyltransferase p300. Propionate and butyrate are rapidly converted to the corresponding acyl-CoAs which are then used by p300 to catalyze auto-acylation of the autoinhibitory loop, activating the enzyme for histone/protein acetylation. This data challenges the long-held belief that SCFAs mainly regulate chromatin by inhibiting HDACs, and instead reveals a previously unknown mechanism of HAT activation that can explain how an influx of low levels of SCFAs alters global chromatin states.
Guaranteeing completely positive quantum evolution

In open quantum systems, it is known that if the system and environment are in a product state, the evolution of the system is given by a linear completely positive (CP) Hermitian map. CP maps are a subset of general linear Hermitian maps, which also include non completely positive (NCP) maps. NCP maps can arise in evolutions such as non-Markovian evolution, where the CP divisibility of the map (writing the overall evolution as a composition of CP maps) usually fails. Positive but NCP maps are also useful as entanglement witnesses. In this paper, we focus on transforming NCP maps to CP maps through composition with the asymmetric depolarizing map. We use separate asymmetric depolarizing maps acting on the individual subsystems. Previous work have looked at structural physical approximation (SPA), which is a CP approximation of a NCP map using a mixture of the NCP map with a completely depolarizing map. We prove that the composition can always be made CP without completely depolarizing in any direction. It is often possible to depolarize less in some directions. We give the general proof by using the Choi matrix and an isomorphism from a maximally entangled two qudit state to qubits. We also give measures that describe the amount of disturbance the depolarization introduces to the original map. Given our measures, we show that asymmetric depolarization has many advantages over SPA in preserving the structure of the original NCP map. Finally, we give some examples. For some measures and examples, completely depolarizing (while not necessary) in some directions can give a better approximation than keeping the depolarizing parameters bounded by the required depolarization if symmetric depolarization is used.
Chromosome-specific telomere lengths and the minimal functional telomere revealed by nanopore sequencing.

We developed a method to tag telomeres and measure telomere length by nanopore sequencing in the yeast S. cerevisiae Nanopore allows long-read sequencing through the telomere, subtelomere and into unique chromosomal sequence, enabling assignment of telomere length to a specific chromosome end. We observed chromosome end specific telomere lengths that were stable over 120 cell divisions. These stable chromosome-specific telomere lengths may be explained by slow clonal variation or may represent a new biological mechanism that maintains equilibrium unique to each chromosome end. We examined the role of RIF1 and TEL1 in telomere length regulation and found that TEL1 is epistatic to RIF1 at most telomeres, consistent with the literature. However, at telomeres that lack subtelomeric Y' sequences, tel1Δ rif1Δ double mutants had a very small, but significant, increase in telomere length compared to the tel1Δ single mutant, suggesting an influence of Y' elements on telomere length regulation. We sequenced telomeres in a telomerase-null mutant (est2Δ) and found the minimal telomere length to be around 75 bp. In these est2Δ mutants there were apparent telomere recombination events at individual telomeres before the generation of survivors, and these events were significantly reduced in est2Δ rad52Δ double mutants. The rate of telomere shortening in the absence of telomerase was similar across all chromosome ends at about 5 bp per generation. This new method gives quantitative, high resolution telomere length measurement at each individual chromosome end, and suggests possible new biological mechanisms regulating telomere length.
A Hidden Structural Variation in a Known IRD Gene: A Cautionary Tale of Two New Disease Candidate Genes.

Rod cone dystrophy (RCD), also known as retinitis pigmentosa, is an inherited condition leading to vision loss, affecting 1/3500 people. Over 270 genes are known to be implicated in the inherited retinal degenerations (IRDs), yet genetic diagnosis for ~30% IRD of patients remains elusive despite advances in sequencing technologies. The goal of this study was to determine the genetic causality in a family with Rod-cone dystrophy (RCD). Family members were given a full ophthalmic exam at the Retinal Service at MEE and consented to genetic testing. Whole exome sequencing (WES) was performed and variants of interest were Sanger validated. Functional assays were conducted in zebrafish along with splicing assays in relevant cell lines to determine the impact on retinal function. WES identified variants in two potential candidate genes that segregated with disease: GNL3 (G Protein Nucleolar 3) c.1187+3A>C and c.1568-8C>A; and PDE4DIP (Phosphodiester 4D Interacting Protein) c.3868G>A (p.Glu1290Lys) and c.4603G>A (p.Ala1535Thr). Both genes were promising candidates based on their retinal involvement (development and interactions with IRD-associated proteins), however the functional assays did not validate either gene. Subsequent WES reanalysis with an updated bioinformatics pipeline and widened search parameters led to the detection of a 94bp duplication in PRPF31 (pre-mRNA Processing Factor 31) c.73_266dup (p.Asp56GlyfsTer33) as the causal variant. Our study demonstrates the importance of thorough functional characterization of new disease candidate genes, and the value of reanalyzing NGS sequence data, which in our case led to identification of a hidden pathogenic variant in a known IRD gene.
Supported Decisions as the Patient's Own?

Peterson, Karlawish and Largent (2021) offer a defense of supported decision making in health care for people with dynamic and diminishing capacity. They are to be warmly commended for bringing sup...
Using Sociolinguistic Variables to Reveal Changing Attitudes Towards Sexuality and Gender.

Individuals signal aspects of their identity and beliefs through linguistic choices. Studying these choices in aggregate allows us to examine large-scale attitude shifts within a population. Here, we develop computational methods to study word choice within a sociolinguistic lexical variable—alternate words used to express the same concept—in order to test for change in the United States towards sexuality and gender. We examine two variables: i) referents to significant others, such as the word “partner” and ii) referents to an indefinite person, both of which could optionally be marked with gender. The linguistic choices in each variable allow us to study increased rates of acceptances of gay marriage and gender equality, respectively. In longitudinal analyses across Twitter and Reddit over 87M messages, we demonstrate that attitudes are changing but that these changes are driven by specific demographics within the United States. Further, in a quasi-causal analysis, we show that passages of Marriage Equality Acts in different states are drivers of linguistic change.