Miracle-dz/UniFAR-FTUs huggingface dataset:
TeethGNN: Semantic 3D Teeth Segmentation With Graph Neural Networks

In this paper, we present TeethGNN, a novel 3D tooth segmentation method based on graph neural networks (GNNs). Given a mesh-represented 3D dental model in non-euclidean domain, our method outputs accurate and fine-grained separation of each individual tooth robust to scanning noise, foreign matters (e.g., bubbles, dental accessories, etc.), and even severe malocclusion. Unlike previous CNN-based methods that bypass handling non-euclidean mesh data by reshaping hand-crafted geometric features into regular grids, we explore the non-uniform and irregular structure of mesh itself in its dual space and exploit graph neural networks for effective geometric feature learning. To address the crowded teeth issues and incomplete segmentation that commonly exist in previous methods, we design a two-branch network, one of which predicts a segmentation label for each facet while the other regresses each facet an offset away from its tooth centroid. Clustering are later conducted on offset-shifted locations, enabling both the separation of adjoining teeth and the adjustment of incompletely segmented teeth. Exploiting GNN for directly processing mesh data frees us from extracting hand-crafted feature, and largely speeds up the inference procedure. Extensive experiments have shown that our method achieves the new state-of-the-art results for teeth segmentation and outperforms previous methods both quantitatively and qualitatively.
Changes in fatty acid in Tecoma stans grown in mine residues after compost amendment

Abstract Amendment tailing heaps with compost may deplete metal(loid)s concentration and improve the conditions for plant development. This research aimed to compare the Tecoma stans ability to grow on soil from the Sonora desert and mining waste (MW) after amendment with compost. Amendment the MW, with compost, decreased soluble As, Cd, Cu, Mn, Pb, and Zn up to 47, 33, 11, 34, 69, and 34%, respectively; increased ten times the leaves weight, and thirteen times the leaf area of the plants. Arsenic, Cd, Pb, Cu, and Zn in plants tissues decreased 27, 28, 27, 12, and 11%, respectively. The bioaccumulation and translocation factors were lower than one, so T. stans do not accumulate these elements. Polyunsaturated fatty acids 18:2ω6 and 18:3ω3 were increased, suggesting lower alteration of thylakoidal membrane integrity due to compost treatment. But, the amendment to the tailing was not enough to deplete the abiotic stress. NOVELTY STATEMENT Amendment mine tailing with vermicompost depletes changes in polyunsaturated fatty acid of Tecoma stans
Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network

Drug discovery pipelines typically involve high‐throughput screening of large amounts of compounds in a search of potential drugs candidates. As a chemical space of small organic molecules is huge, a “navigation” over it urges for fast and lightweight computational methods, thus promoting machine‐learning approaches for processing huge pools of candidates. In this contribution, we present a graph‐based deep neural network for prediction of protein‐drug binding affinity and assess its predictive power under thorough testing conditions. Within the suggested approach, both protein and drug molecules are represented as graphs and passed to separate graph sub‐networks, then concatenated and regressed towards a binding affinity. The neural network is trained on two binding affinity datasets—PDBbind and data imported from RCSB Protein Data Bank. In order to explore the generalization capabilities of the model we go beyond traditional random or leave‐cluster‐out techniques and demonstrate the need for more elaborate model performance assessment – six different strategies for test/train data partitioning (random, time‐ and property‐arranged, protein‐ and ligand‐clustered) with a k‐fold cross‐validation are engaged. Finally, we discuss the model performance in terms of a set of metrics for different split strategies and fold arrangement. Our code is available at https://github.com/SoftServeInc/affinity-by-GNN.
Resident microbes shape the vaginal epithelial glycan landscape

Epithelial cells are covered in carbohydrates (glycans). This glycan coat or “glycocalyx” interfaces directly with microbes, providing a protective barrier against potential pathogens. Bacterial vaginosis (BV) is a condition associated with adverse health outcomes in which bacteria reside in direct proximity to the vaginal epithelium. Some of these bacteria, including Gardnerella, produce glycosyl hydrolase enzymes. However, glycans of the human vaginal epithelial surface have not been studied in detail. Here, we elucidate key characteristics of the “normal” vaginal epithelial glycan landscape and analyze the impact of resident microbes on the surface glycocalyx. In human BV, glycocalyx staining was visibly diminished in electron micrographs compared to controls. Biochemical and mass spectrometric analysis showed that, compared to normal vaginal epithelial cells, BV cells were depleted of sialylated N- and O-glycans, with underlying galactose residues exposed on the surface. Treatment of primary epithelial cells from BV-negative women with recombinant Gardnerella sialidases generated BV-like glycan phenotypes. Exposure of cultured VK2 vaginal epithelial cells to recombinant Gardnerella sialidase led to desialylation of glycans and induction of pathways regulating cell death, differentiation, and inflammatory responses. These data provide evidence that vaginal epithelial cells exhibit an altered glycan landscape in BV and suggest that BV-associated glycosidic enzymes may lead to changes in epithelial gene transcription that promote cell turnover and regulate responses toward the resident microbiome. Description Resident bacteria damage carbohydrates on vaginal epithelial cell surfaces during clinical bacterial vaginosis. Editor’s summary During bacterial vaginosis (BV), an overgrowth of bacteria resides on affected epithelial surfaces, which are prone to increased shedding of cells. Agarwal et al. surveyed the glycans (carbohydrates) covering vaginal epithelial cells in human BV and found depletion of sialic acid residues, resulting in overall diminished protective glycocalyx. Treatment of healthy vaginal epithelial cells with recombinant Gardnerella sialidases recapitulated BV-like desialyated glycans and altered epithelial cell gene expression pathways regulating cell death, differentiation, and inflammatory responses. This work suggests that the action of bacterial sialidases on vaginal surfaces may underlie the aberrant epithelial pathophysiology observed in BV. -Catherine Charneski
Challenges and Opportunities Experienced by Performing Artists during COVID-19 Lockdown: Scoping Review

This scoping review synthesises published literature on the experiences of professional and amateur performing artists during COVID-19 and their perceptions of the challenges and opportunities faced. Six electronic databases were searched for published English-language articles containing primary data on this topic; twenty-one studies were reviewed. Themes included loss of work, financial impact, concerns about the future, psychological wellbeing, social connections, continuing creative pursuits, and inequalities. Participants reported both detrimental psychological effects of lockdown such as anxiety and sleep problems and positive effects including reduced stress and enjoyment of having more free time. Most continued creative pursuits throughout lockdown, most commonly shifting to online platforms. However, many barriers to creative pursuits were reported, including lack of technological expertise or equipment. Concerns were raised about inequality, in particular racial disparities in the financial impact of the pandemic and additional pressures faced by performers with disabilities; with insufficient funds to afford the equipment needed to shift to remote performing; and with additional caring responsibilities. It is important that performing artists have access to peer support; that education on digital technologies is incorporated into future performing arts education; and that inequities are addressed to ensure the needs of diverse communities are met.
Phase Continuity: Learning Derivatives of Phase Spectrum for Speech Enhancement

Modern neural speech enhancement models usually include various forms of phase information in their training loss terms, either explicitly or implicitly. However, these loss terms are typically designed to reduce the distortion of phase spectrum values at specific frequencies, which ensures they do not significantly affect the quality of the enhanced speech. In this paper, we propose an effective phase reconstruction strategy for neural speech enhancement that can operate in noisy environments. Specifically, we introduce a phase continuity loss that considers relative phase variations across the time and frequency axes. By including this phase continuity loss in a state-of-the-art neural speech enhancement system trained with reconstruction loss and a number of magnitude spectral losses, we show that our proposed method further improves the quality of enhanced speech signals over the baseline, especially when training is done jointly with a magnitude spectrum loss.
Variable elimination, graph reduction and efficient g-formula

We study efficient estimation of an interventional mean associated with a point exposure treatment under a causal graphical model represented by a directed acyclic graph without hidden variables. Under such a model, it may happen that a subset of the variables are uninformative in that failure to measure them neither precludes identification of the interventional mean nor changes the semiparametric variance bound for regular estimators of it. We develop a set of graphical criteria that are sound and complete for eliminating all the uninformative variables so that the cost of measuring them can be saved without sacrificing estimation efficiency, which could be useful when designing a planned observational or randomized study. Further, we construct a reduced directed acyclic graph on the set of informative variables only. We show that the interventional mean is identified from the marginal law by the g-formula (Robins, 1986) associated with the reduced graph, and the semiparametric variance bounds for estimating the interventional mean under the original and the reduced graphical model agree. This g-formula is an irreducible, efficient identifying formula in the sense that the nonparametric estimator of the formula, under regularity conditions, is asymptotically efficient under the original causal graphical model, and no formula with such property exists that only depends on a strict subset of the variables.
Mixed-Mode Oscillations in Coupled FitzHugh-Nagumo Oscillators: Blow-Up Analysis of Cusped Singularities

In this paper, we use geometric singular perturbation theory and blowup, as our main technical tool, to study the mixed-mode oscillations (MMOs) that occur in two coupled FitzHugh-Nagumo units with symmetric and repulsive coupling. In particular, we demonstrate that the MMOs in this model are not due to generic folded singularities, but rather due to singularities at a cusp -- not a fold -- of the critical manifold. Using blowup, we determine the number of SAOs analytically, showing -- as for the folded nodes -- that they are determined by the Weber equation and the ratio of eigenvalues. We also show that the model undergoes a (symmetric) saddle-node bifurcation in the desingularized reduced problem, which -- although resembling a folded saddle-node (type II) at this level -- also occurs on a cusp, and not a fold. We demonstrate that this bifurcation is associated with the emergence of an invariant cylinder, the onset of SAOs, as well as SAOs of increasing amplitude. We relate our findings with numerical computations and find excellent agreement.
Deep Learning Unresolved Lensed Lightcurves

Gravitationally lensed sources may have unresolved or blended multiple images, and for time varying sources the lightcurves from individual images can overlap. We use convolutional neural nets to both classify the lightcurves as due to unlensed, double, or quad lensed sources and fit for the time delays. Focusing on lensed supernova systems with time delays Δt ≳ 6 days, we achieve 100 per cent precision and recall in identifying the number of images and then estimating the time delays to σΔt ≈ 1 day, with a 1000 × speedup relative to our previous Monte Carlo technique. This also succeeds for flux noise levels $\sim 10{{\%}}$. For Δt ∈ [2, 6] days we obtain 94–98 per cent accuracy, depending on image configuration. We also explore using partial lightcurves where observations only start near maximum light, without the rise time data, and quantify the success.
A Partition-and-Merge Algorithm for Solving the Steiner Tree Problem in Large Graphs

The Steiner tree problem, which asks for a minimum weighted tree spanning a given set of terminal vertices in a given graph, is a classic problem arising in numerous practical applications. Many algorithms about this problem emerged in the past decade, especially presented in the 11th DIMACS Challenge in 2014 and the 3rd PACE Competition in 2018. In this paper, we present a novel partition-and-merge algorithm for effectively solving this NP-hard problem in large graphs. The algorithm first breaks the input graph into small fragments and then gradually builds up the graph in a level-by-level manner. Intuitively, the method aggregates information that is found by local search at each level into the final graph. We assess the algorithm on a wide range of benchmark instances, showing that the algorithm outperforms the winners of DIMACS and PACE challenges on large instances and competes favorably with them on small or middle-sized instances.
Observations of Bell Inequality Violations with Causal Isolation between Source and Detectors

We report the experimental observations of Bell inequality violations (BIV) in entangled photons causally separated by a rotating mirror. A Foucault mirror gating geometry is used to causally isolate the entangled photon source and detectors. We report an observed BIV of CHSH-S=2.30±0.07>2.00. This result rules out theories that explain correlations with traveling communication between source and detectors, including super-luminal and instantaneous communication.
Geometric energy transport and refrigeration with driven quantum dots

We study geometric energy transport in a slowly driven single-level quantum dot weakly coupled to electronic contacts and with strong onsite interaction, which can be either repulsive or attractive. Exploiting a recently discovered fermionic duality for the evolution operator of the master equation, we provide compact and insightful analytic expressions of energy pumping curvatures for any pair of driving parameters. This enables us to systematically identify and explain the pumping mechanisms for diﬀerent driving schemes, thereby also comparing energy and charge pumping. We determine the concrete impact of many-body interactions and show how particle-hole symmetry and fermionic duality manifest, both individually and in combination, as system-parameter symmetries of the energy pumping curvatures. Building on this transport analysis, we study the driven dot acting as a heat pump or refrigerator, where we ﬁnd that the sign of the onsite interaction plays a crucial role in the performance of these thermal machines.
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions

As deep learning (DL) efficacy grows, concerns for poor model explainability grow also. Attribution methods address the issue of explainability by quantifying the importance of an input feature for a model prediction. Among various methods, Integrated Gradients (IG) sets itself apart by claiming other methods failed to satisfy desirable axioms, while IG and methods like it uniquely satisfy said axioms. This paper comments on fundamental aspects of IG and its applications/extensions: 1) We identify key differences between IG function spaces and the supporting literature's function spaces which problematize previous claims of IG uniqueness. We show that with the introduction of an additional axiom, \textit{non-decreasing positivity}, the uniqueness claims can be established. 2) We address the question of input sensitivity by identifying function classes where IG is/is not Lipschitz in the attributed input. 3) We show that axioms for single-baseline methods have analogous properties for methods with probability distribution baselines. 4) We introduce a computationally efficient method of identifying internal neurons that contribute to specified regions of an IG attribution map. Finally, we present experimental results validating this method.
Reduced bandwidth: a qualitative strengthening of twin-width in minor-closed classes (and beyond)

In a reduction sequence of a graph, vertices are successively identified until the graph has one vertex. At each step, when identifying $u$ and $v$, each edge incident to exactly one of $u$ and $v$ is coloured red. Bonnet, Kim, Thomass\'e and Watrigant [J. ACM 2022] defined the twin-width of a graph $G$ to be the minimum integer $k$ such that there is a reduction sequence of $G$ in which every red graph has maximum degree at most $k$. For any graph parameter $f$, we define the reduced $f$ of a graph $G$ to be the minimum integer $k$ such that there is a reduction sequence of $G$ in which every red graph has $f$ at most $k$. Our focus is on graph classes with bounded reduced bandwidth, which implies and is stronger than bounded twin-width (reduced maximum degree). We show that every proper minor-closed class has bounded reduced bandwidth, which is qualitatively stronger than an analogous result of Bonnet et al.\ for bounded twin-width. In many instances, we also make quantitative improvements. For example, all previous upper bounds on the twin-width of planar graphs were at least $2^{1000}$. We show that planar graphs have reduced bandwidth at most $466$ and twin-width at most $583$. Our bounds for graphs of Euler genus $\gamma$ are $O(\gamma)$. Lastly, we show that fixed powers of graphs in a proper minor-closed class have bounded reduced bandwidth (irrespective of the degree of the vertices). In particular, we show that map graphs of Euler genus $\gamma$ have reduced bandwidth $O(\gamma^4)$. Lastly, we separate twin-width and reduced bandwidth by showing that any infinite class of expanders excluding a fixed complete bipartite subgraph has unbounded reduced bandwidth, while there are bounded-degree expanders with twin-width at most 6.
Compact High-Resolution Absolute-Gravity Gradiometer Based on Atom Interferometers

Wei Lyu, 2 Jia-Qi Zhong, ∗ Xiao-Wei Zhang, Wu Liu, 2 Lei Zhu, Wei-Hao Xu, 2 Xi Chen, Biao Tang, Jin Wang, 3, † and Ming-Sheng Zhan 3, ‡ State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China Wuhan Institute of Quantum Technology, Wuhan 430206, China (Dated: February 25, 2022)
Uncertainty-driven Planner for Exploration and Navigation

We consider the problems of exploration and pointgoal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constitute these tasks challenging. We argue that learning occupancy priors over indoor maps provides significant advantages towards addressing these problems. To this end, we present a novel planning framework that first learns to generate occupancy maps beyond the field-of-view of the agent, and second leverages the model uncertainty over the generated areas to formulate path selection policies for each task of interest. For pointgoal navigation the policy chooses paths with an upper confidence bound policy for efficient and traversable paths, while for exploration the policy maximizes model uncertainty over candidate paths. We perform experiments in the visually realistic environments of Matterport3D using the Habitat simulator and demonstrate: 1) Improved results on exploration and map quality metrics over competitive methods, and 2) The effectiveness of our planning module when paired with the state-of-the-art DD-PPO method for the point-goal navigation task.
Positive Trigonometric Polynomials on the Stability of Spatially Interconnected Systems

This paper is devoted to the stability analysis of spatially interconnected systems (SISs) via the sum-of-squares (SOS) decomposition of positive trigonometric polynomials. For each spatial direction of SISs, three types of interconnected structures are considered. Inspired by the idea of rational parameterization and robust stabilizability function, necessary and sufficient conditions are derived for establishing the stability of SISs with two different combined topologies respectively. For these results, the primary issue concerns the global or local positivity of trigonometric polynomials. SOS decomposition and generalized trace parameterization of positive trigonometric polynomials are utilized so that the addressed problems can be quantified by two semidefinite programs (SDPs). The proposed methods are applicable to all possible interconnected structures due to the assumption of spatial reversibility. Numerical examples are given to illustrate the efficiency of the derived theoretical results.
Numerical Simulations of a Superwalking Droplet

Rahil N. Valani1,∗ Tapio P. Simula, and Anja C. Slim School of Physics and Astronomy, Monash University, Victoria 3800, Australia School of Mathematics, Monash University, Victoria 3800, Australia School of Earth, Atmosphere and Environment, Monash University, Victoria 3800, Australia and Optical Sciences Centre, Swinburne University of Technology, Melbourne 3122, Australia (Dated: February 25, 2022)
TARexp: A Python Framework for Technology-Assisted Review Experiments

Technology-assisted review (TAR) is an important industrial application of information retrieval (IR) and machine learning (ML). While a small TAR research community exists, the complexity of TAR software and workflows is a major barrier to entry. Drawing on past open source TAR efforts, as well as design patterns from the IR and ML open source software, we present an open source Python framework for conducting experiments on TAR algorithms. Key characteristics of this framework are declarative representations of workflows and experiment plans, the ability for components to play variable numbers of workflow roles, and state maintenance and restart capabilities. Users can draw on reference implementations of standard TAR algorithms while incorporating novel components to explore their research interests. The framework is available at https://github.com/eugene-yang/tarexp.
Revisiting the cumulative incidence function with competing risks data

We consider estimation of the cumulative incidence function (CIF) in the competing risks Cox model. We study three methods. Methods 1 and 2 are existing methods while method 3 is a newly proposed method. Method 3 is constructed so that the sum of the CIF’s across all event types at the last observed event time is guaranteed, assuming no ties, to be equal to 1. The performance of the methods is examined via simulation, and they are illustrated on data from the field of computer code comprehension. The newly proposed method 3 exhibits performance comparable to that of methods 1 and 2 in terms of bias and variance, and better than that of Methods 1 and 2 in terms of confidence interval coverage rates.