cssi/SciSciGPT-SciSciNet huggingface dataset:
Minimal Model Fusion Rules From 2-Groups

The fusion rules for the (p,q)-minimal model representations of the Virasoro algebra are shown to come from the group \(G = \mathbb{Z}_2^{p + q - 5} \) in the following manner. There is a partition \(G = P_1 \cup \; \cdot \cdot \cdot \; \cup P_N \) into disjoint subsets and a bijection between \(\{ P_1 ,\;...,\;P_N \} \) and the sectors \(\{ S_1 ,\;...,\;S_N \} \) of the (p,q)-minimal model such that the fusion rules \(S_i * \;S_j = \sum\nolimits_k {D(S_i ,S_j ,S_k )S_k } \) correspond to \(P_i * \;P_j = \sum\nolimits_{k \in T(i,j)} {P_k } \) where \(T(i,j) = \{ k|\exists a \in P_i ,\;\exists b \in P_j ,a + b \in P_k \} \).
MethCP: Differentially Methylated Region Detection with Change Point Models.

Whole-genome bisulfite sequencing (WGBS) provides a precise measure of methylation across the genome, yet presents a challenge in identifying regions that are differentially methylated (DMRs) between different conditions. Many methods have been developed, which focus primarily on the setting of two-group comparison. We develop a DMR detecting method MethCP for WGBS data, which is applicable for a wide range of experimental designs beyond the two-group comparisons, such as time-course data. MethCP identifies DMRs based on change point detection, which naturally segments the genome and provides region-level differential analysis. For simple two-group comparison, we show that our method outperforms developed methods in accurately detecting the complete DM region on a simulated dataset and an Arabidopsis dataset. Moreover, we show that MethCP is capable of detecting wide regions with small effect sizes, which can be common in some settings but existing techniques are poor in detecting such DMRs. We also demonstrate the use of MethCP for time-course data on another dataset following methylation throughout seed germination in Arabidopsis.
Observation of Dynamical Fermionization

The wave function of a Tonks-Girardeau (T-G) gas of strongly interacting bosons in one dimension maps onto the absolute value of the wave function of a noninteracting Fermi gas. Although this fermionization makes many aspects of the two gases identical, their equilibrium momentum distributions are quite different. We observed dynamical fermionization, where the momentum distribution of a T-G gas evolves from bosonic to fermionic after its axial confinement is removed. The asymptotic momentum distribution after expansion in one dimension is the distribution of rapidities, which are the conserved quantities associated with many-body integrable systems. Our measurements agree well with T-G gas theory. We also studied momentum evolution after the trap depth is suddenly changed to a new nonzero value, and we observed the theoretically predicted bosonic-fermionic oscillations.
Convex Hierarchical Clustering for Graph-Structured Data

Convex clustering [6] is a recent stable alternative to hierarchical clustering. It formulates the recovery of progressively coalescing clusters as a regularized convex problem. While convex clustering was originally designed for handling Euclidean distances between data points, in a growing number of applications, the data is directly characterized by a similarity matrix or weighted graph. In this paper, we extend the robust hierarchical clustering approach to these broader classes of similarities. Having defined an appropriate convex objective, the crux of this adaptation lies in our ability to provide: (a) an efficient recovery of the regularization path and (b) an empirical demonstration of the use of our method. We address the first challenge through a proximal dual algorithm, for which we characterize both the theoretical efficiency and the empirical performance on a set of experiments. Finally, we highlight the potential of our method by showing its application to several real-life datasets, thus providing a natural extension to the current scope of applications of convex clustering.
RANKL-independent osteoclastogenesis in the SH3BP2 cherubism mice

Abstract Even though the receptor activator of the nuclear factor-κB ligand (RANKL) and its receptor RANK have an exclusive role in osteoclastogenesis, the possibility of RANK/RANKL-independent osteoclastogenesis has been the subject of a long-standing debate in bone biology. In contrast, it has been reported that calvarial injection of TNF-ɑ elicits significant osteoclastogenesis in the absence of RANK/RANKL in NF-κB2- and RBP-J-deficient mice, suggesting that inflammatory challenges and secondary gene manipulation are the prerequisites for RANK/RANKL-deficient mice to develop osteoclasts in vivo. Here we report that, even in the absence of RANKL (Rankl−/−), cherubism mice (Sh3bp2KI/KI) harboring the homozygous gain-of-function mutation in SH3-domain binding protein 2 (SH3BP2) develop tartrate-resistant acid phosphatase (TRAP)-positive multinucleated osteoclasts spontaneously. The Sh3bp2KI/KI Rankl−/− mice exhibit an increase in tooth exposure and a decrease in bone volume/total volume compared to Sh3bp2+/+ Rankl−/− mice. The multinucleated cells were stained positively for cathepsin K. Osteoclastic marker gene expression in bone and serum TRAP5b levels were elevated in Sh3bp2KI/KI Rankl−/− mice. Elevation of the serum TNF-ɑ levels suggested that TNF-ɑ is a driver for the RANKL-independent osteoclast formation in Sh3bp2KI/KI mice. Our results provide a novel mutant model that develops osteoclasts independent of RANKL and establish that the gain-of-function of SH3BP2 promotes osteoclastogenesis not only in the presence of RANKL but also in the absence of RANKL.
On the number of irreducible real-valued characters of a finite group

Abstract We prove that there exists an integer-valued function f on positive integers such that if a finite group G has at most k real-valued irreducible characters, then | G / Sol ( G ) | ≤ f ( k ) , where Sol ( G ) denotes the largest solvable normal subgroup of G. In the case k = 5 , we further classify G / Sol ( G ) . This partly answers a question of Iwasaki [15] on the relationship between the structure of a finite group and its number of real-valued irreducible characters.
3D Unknown View Tomography Via Rotation Invariants

In this paper, we study the problem of reconstructing a 3D point source model from a set of 2D projections at unknown view angles. Our method obviates the need to recover the projection angles by extracting a set of rotation-invariant features from the noisy projection data. From the features, we reconstruct the density map through a constrained nonconvex optimization. We show that the features have geometric interpretations in the form of radial and pairwise distances of the model. We further perform an ablation study to examine the effect of various parameters on the quality of the estimated features from the projection data. Our results showcase the potential of the proposed method in reconstructing point source models in various noise regimes.
Estimating the burden of United States workers exposed to infection or disease: A key factor in containing risk of COVID-19 infection.

Introduction With the global spread of COVID-19, there is a compelling public health interest in quantifying who is at increased risk of contracting disease. Occupational characteristics, such as interfacing with the public and being in close quarters with other workers, not only put workers at high risk for disease, but also make them a nexus of disease transmission to the community. This can further be exacerbated through presenteeism, the term used to describe the act of coming to work despite being symptomatic for disease. Quantifying the number of workers who are frequently exposed to infection and disease in the workplace, and understanding which occupational groups they represent, can help to prompt public health risk response and management for COVID-19 in the workplace, and subsequent infectious disease outbreaks. Methods To estimate the number of United States workers frequently exposed to infection and disease in the workplace, national employment data (by Standard Occupational Classification) maintained by the Bureau of Labor Statistics (BLS) was merged with a BLS O*NET survey measure reporting how frequently workers in each occupation are exposed to infection or disease at work. This allowed us to estimate the number of United States workers, across all occupations, exposed to disease or infection at work more than once a month. Results Based on our analyses, approximately 10% (14.4 M) of United States workers are employed in occupations where exposure to disease or infection occurs at least once per week. Approximately 18.4% (26.7 M) of all United States workers are employed in occupations where exposure to disease or infection occurs at least once per month. While the majority of exposed workers are employed in healthcare sectors, other occupational sectors also have high proportions of exposed workers. These include protective service occupations (e.g. police officers, correctional officers, firefighters), office and administrative support occupations (e.g. couriers and messengers, patient service representatives), education occupations (e.g. preschool and daycare teachers), community and social services occupations (community health workers, social workers, counselors), and even construction and extraction occupations (e.g. plumbers, septic tank installers, elevator repair). Conclusions The large number of persons employed in occupations with frequent exposure to infection and disease underscore the importance of all workplaces developing risk response plans for COVID-19. Given the proportion of the United States workforce exposed to disease or infection at work, this analysis also serves as an important reminder that the workplace is a key locus for public health interventions, which could protect both workers and the communities they serve.
Preparation of DPPC liposomes using probe-tip sonication: Investigating intrinsic factors affecting temperature phase transitions

Liposomes are an important tool and have gained much attention for their promise as an effective means of delivering small therapeutic compounds to targeted sites. In an effort to establish an effective method to produce liposomes from the lipid, dipalmitoyl-phosphatidylcholine or DPPC, we have found important aspects that must be taken into consideration. Here, we used probe-tip sonication to prepare liposomes on a batch scale. During this process we uncovered interesting steps in their preparation that altered the thermodynamic properties and phase transitions of the resulting liposome mixtures. Using differential scanning calorimetry to assess this we found that increasing the sonication time had the most dramatic effect on our sample, producing almost an entirely separate phase transition relative to the main phase transition. This result is consistent with reports from the current literature. We also highlight a smaller transition, which we attribute to traces of unincorporated lipid that seems to gradually disappear as the total lipid concentration decreases. Overall, sonication is an effective means of producing liposomes, but we cannot assert this method is optimal in producing them with precise physical properties. Here we highlight the physical effects at play during this process.
Cellular Heterogeneity of the Luteinizing Hormone Receptor and Its Significance for Cyclic GMP Signaling in Mouse Preovulatory Follicles

Meiotic arrest and resumption in mammalian oocytes are regulated by 2 opposing signaling proteins in the cells of the surrounding follicle: the guanylyl cyclase natriuretic peptide receptor 2 (NPR2), and the luteinizing hormone receptor (LHR). NPR2 maintains a meiosis-inhibitory level of cyclic guanosine 5'-monophosphate (cGMP) until LHR signaling causes dephosphorylation of NPR2, reducing NPR2 activity, lowering cGMP to a level that releases meiotic arrest. However, the signaling pathway between LHR activation and NPR2 dephosphorylation remains incompletely understood, due in part to imprecise information about the cellular localization of these 2 proteins. To investigate their localization, we generated mouse lines in which hemagglutinin epitope tags were added to the endogenous LHR and NPR2 proteins, and used immunofluorescence and immunogold microscopy to localize these proteins with high resolution. The results showed that the LHR protein is absent from the cumulus cells and inner mural granulosa cells, and is present in only 13% to 48% of the outer mural granulosa cells. In contrast, NPR2 is present throughout the follicle, and is more concentrated in the cumulus cells. Less than 20% of the NPR2 is in the same cells that express the LHR. These results suggest that to account for the LH-induced inactivation of NPR2, LHR-expressing cells send a signal that inactivates NPR2 in neighboring cells that do not express the LHR. An inhibitor of gap junction permeability attenuates the LH-induced cGMP decrease in the outer mural granulosa cells, consistent with this mechanism contributing to how NPR2 is inactivated in cells that do not express the LHR.
Weighted Aleksandrov estimates: PDE and stochastic versions

We prove several pointwise estimates for solutions of linear elliptic (parabolic) equations with measurable coefficients in smooth domains (cylinders) through the weighted $L_{d}$ ($L_{d+1}$)-norm of the free term. The weights allow the free term to blow up near the (latteral) boundary. We also present weighted estimates for occupation times of diffusion processes.
The Search for Close Binary Evolved Stars

We report on a search for short-period binary systems composed of pairs of evolved stars. The search is being carried out concurrently with a program to characterize the kinematical properties of two different samples of stars. Each sample has produced one close binary candidate for which further spectroscopic observations are planned. We also recapitulate the discovery of a close detached binary system composed of two cool DA white dwarfs, and we discuss the null results of Ha observations of the suspected white dwarf/brown dwarf system G 29–38.
Leveraging Union of Subspace Structure to Improve Constrained Clustering.

Many clustering problems in computer vision and other contexts are also classification problems, where each cluster shares a meaningful label. Subspace clustering algorithms in particular are often applied to problems that fit this description, for example with face images or handwritten digits. While it is straightforward to request human input on these datasets, our goal is to reduce this input as much as possible. We present a pairwise-constrained clustering algorithm that actively selects queries based on the union-of-subspaces model. The central step of the algorithm is in querying points of minimum margin between estimated subspaces; analogous to classifier margin, these lie near the decision boundary. We prove that points lying near the intersection of subspaces are points with low margin. Our procedure can be used after any subspace clustering algorithm that outputs an affinity matrix. We demonstrate on several datasets that our algorithm drives the clustering error down considerably faster than the state-of-the-art active query algorithms on datasets with subspace structure and is competitive on other datasets.
Understanding Competitive Pricing and Market Power in Wholesale Electricity Markets

Abstract Discussions of competition in restructured electricity markets have revealed many misunderstandings about the definition, diagnosis, and implications of market power, including the common myths that it is present in all markets and that it must be present in order for firms with significant fixed costs to remain profitable.
Chapter 6 – Developmental Physiology

Plant size and the environment limit the capacity of grapevines to grow shoots and fruit, and vines self-regulate to balance their annual vegetative and reproductive growth. Grapevine yield is made up of several components, namely the numbers of buds per vine, shoots per bud, clusters per shoot, and berries per cluster, in addition to berry weight. Yield formation occurs over two growing seasons, and developmental and environmental factors interact to determine the final crop through their variable impact on the different yield components. The berries’ physical and chemical composition at harvest determines their quality for fresh consumption or processing. Organic acids, tannin precursors, some minerals such as calcium, and some aroma precursors and green aroma components accumulate during early berry growth, while amino acids, flavonols, and other minerals such as potassium accumulate throughout berry development. Fruit ripening begins at veraison and is associated with berry softening, skin stiffening, accumulation of sugars, anthocyanins, tannins, and flavor and aroma components, increase in pH, and decrease in chlorophyll, organic acids, and green aroma components. Harvest fruit composition varies considerably both within and between vineyards and between years due to differences in fruit maturity and environmental impacts dominated by light, temperature, and water and nutrient status, with some variation also being due to crop load and rootstock.
Target-responsive vasoactive probes for ultrasensitive molecular imaging.

The ability to monitor molecules volumetrically throughout the body could provide valuable biomarkers for studies of healthy function and disease, but noninvasive detection of molecular targets in living subjects often suffers from poor sensitivity or selectivity. Here we describe a family of potent imaging probes that can be activated by molecules of interest in deep tissue, providing a basis for mapping nanomolar-scale analytes without the radiation or heavy metal content associated with traditional molecular imaging agents. The probes are reversibly caged vasodilators that induce responses detectable by hemodynamic imaging; they are constructed by combining vasoactive peptides with synthetic chemical appendages and protein blocking domains. We use this architecture to create ultrasensitive biotin-responsive imaging agents, which we apply for wide-field mapping of targets in rat brains using functional magnetic resonance imaging. We also adapt the sensor design for detecting the neurotransmitter dopamine, illustrating versatility of this approach for addressing biologically important molecules. Noninvasive detection of molecular targets in living subjects could provide valuable insights into healthy function and disease. Here, the authors develop vasoactive imaging probes which allow wide-field in vivo mapping of nanomolar-level molecular species in rat brain.
Tropical Pacific intensifies June extreme rainfall over Southwestern United States/Northwestern Mexico

Extreme rainfall over Southwestern United States/Northwestern Mexico (SWUNWM) has been mostly investigated during wet seasons, while no or little attention has been paid to extreme rainfall during dry seasons despite its vital importance for sustaining vegetation and ecosystems. Here we examine the top 1% rainfall over SWUNWM in June, the driest month on average, and assess how it is affected by the ocean with a 50 km-resolution global climate model. Comparing millennia-long simulations with and without the ocean, we find that the ocean does not change the pattern and magnitude of atmospheric circulation associated with June extreme rainfall, but significantly enhances rainfall intensity. This intensification is attributed to a larger variability of atmospheric moisture content enhanced mainly by the sea surface temperature (SST) in the tropical Pacific. The similarities in the atmospheric circulation associated with, and the temporal characteristics of, June extreme rainfall between the two simulations point to a dominant control of extreme rainfall dynamics by atmospheric intrinsic processes and atmosphere-land coupling. These modeling results imply that the predictability of occurrence of June extreme rainfall over SWUNWM is limited by atmospheric intrinsic dynamics and atmosphere-land coupling, while reliable predictions of its intensity likely require a faithful simulation of SST variability, especially in the tropical Pacific.
Sufficient Statistics Revisited

This article reviews and generalizes the sufficient statistics approach to policy evaluation. The idea of the approach is that the welfare effect of policy changes can be expressed in terms of esti...
Incentives Work: Getting Teachers to Come to School

We use a randomized experiment and a structural model to test whether monitoring and financial incentives can reduce teacher absence and increase learning in India. In treatment schools, teachers' attendance was monitored daily using cameras, and their salaries were made a nonlinear function of attendance. Teacher absenteeism in the treatment group fell by 21 percentage points relative to the control group, and the children's test scores increased by 0.17 standard deviations. We estimate a structural dynamic labor supply model and find that teachers respond strongly to financial incentives. Our model is used to compute cost-minimizing compensation policies. (JEL I21, J31, J45, O15)
HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity.

The human microbiome is a collection of microorganisms. They form complex communities and collectively affect host health. Recently, the advances in next-generation sequencing technology enable the high-throughput profiling of the human microbiome. This calls for a statistical model to construct microbial networks from the microbiome sequencing count data. As microbiome count data are high-dimensional and suffer from uneven sampling depth, over-dispersion, and zero-inflation, these characteristics can bias the network estimation and require specialized analytical tools. Here we propose a general framework, HARMONIES, a Hybrid Approach foR MicrobiOme Network Inferences via Exploiting Sparsity, to infer a sparse microbiome network. HARMONIES first utilizes a zero-inflated negative binomial (ZINB) distribution to model the skewness and excess zeros in the microbiome data, as well as incorporates a stochastic process prior for sample-wise normalization. This approach infers a sparse and stable network by imposing non-trivial regularizations based on the Gaussian graphical model. In comprehensive simulation studies, HARMONIES outperformed four other commonly used methods. When using published microbiome data from a colorectal cancer study, it discovered a novel community with disease-enriched bacteria. In summary, HARMONIES is a novel and useful statistical framework for microbiome network inference, and it is available at \href{https://github.com/shuangj00/HARMONIES}{https://github.com/shuangj00/HARMONIES}.
Spectral and Dynamic Consequences of Network Specialization

One of the hallmarks of real networks is the ability to perform increasingly complex tasks as their topology evolves. To explain this, it has been observed that as a network grows certain subsets o...
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE

Neural ordinary differential equations (NODEs) have recently attracted increasing attention; however, their empirical performance on benchmark tasks (e.g. image classification) are significantly inferior to discrete-layer models. We demonstrate an explanation for their poorer performance is the inaccuracy of existing gradient estimation methods: the adjoint method has numerical errors in reverse-mode integration; the naive method directly back-propagates through ODE solvers, but suffers from a redundantly deep computation graph when searching for the optimal stepsize. We propose the Adaptive Checkpoint Adjoint (ACA) method: in automatic differentiation, ACA applies a trajectory checkpoint strategy which records the forward-mode trajectory as the reverse-mode trajectory to guarantee accuracy; ACA deletes redundant components for shallow computation graphs; and ACA supports adaptive solvers. On image classification tasks, compared with the adjoint and naive method, ACA achieves half the error rate in half the training time; NODE trained with ACA outperforms ResNet in both accuracy and test-retest reliability. On time-series modeling, ACA outperforms competing methods. Finally, in an example of the three-body problem, we show NODE with ACA can incorporate physical knowledge to achieve better accuracy. We provide the PyTorch implementation of ACA: https://github.com/juntang-zhuang/torch-ACA.
Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer

Tracking algorithms such as the Kalman filter aim to improve inference performance by leveraging the temporal dynamics in streaming observations. However, the tracking regularizers are often based on the $\ell _p$ -norm which cannot account for important geometrical relationships between neighboring signal elements. We propose a practical approach to using the earth mover's distance (EMD) via the earth mover's distance dynamic filtering (EMD-DF) algorithm for causally tracking time-varying sparse signals when there is a natural geometry to the coefficient space that should be respected (e.g., meaningful ordering). Specifically, this letter presents a new Beckmann formulation that dramatically reduces computational complexity, as well as an evaluation of the performance and complexity of the proposed approach in imaging and frequency tracking applications with real and simulated neurophysiology data.
The $RO(C_2)$-graded cohomology of $C_2$-surfaces in $\underline{\mathbb{Z}/2}$-coefficients

A surface with an involution can be viewed as a $$C_2$$ -space where $$C_2$$ is the cyclic group of order two. Up to equivariant isomorphism, all involutions on surfaces were classified in Bujalance et al. (Math Z 211:461–478, 1992) and recently classified using equivariant surgery in Dugger (J Homotopy Relat Struct 14(4):919–992, 2019). We use the classification given in Dugger (2019) to compute the $$RO(C_2)$$ -graded Bredon cohomology of all $$C_2$$ -surfaces in constant $${\mathbb {Z}}/2$$ coefficients as modules over the cohomology of a point. We show the cohomology depends only on three numerical invariants in the nonfree case, and only on two numerical invariants in the free case.
Private Identity Agreement for Private Set Functionalities

Private set intersection and related functionalities are among the most prominent real-world applications of secure multiparty computation. While such protocols have attracted significant attention from the research community, other functionalities are often required to support a PSI application in practice. For example, in order for two parties to run a PSI over the unique users contained in their databases, they might first invoke a support functionality to agree on the primary keys to represent their users.
Economic policy uncertainty and short-term financing: The case of trade credit

Abstract We examine the impact of economic policy uncertainty on trade credit. We document a decline (increase) in accounts payable, receivable, and net credit during periods of high (low) policy uncertainty and that firms react quickly to changes in uncertainty. The relation is long-term and holds after controlling for endogeneity, non-policy economic and political uncertainties, and the Great Recession. Industry competitiveness, proxied by firm market power, moderates the impact of economic policy uncertainty on trade credit. Uncertainty about monetary and fiscal policies, taxes, and regulations are the major drivers of trade credit changes. The reduction in trade credit during periods of increasing uncertainty can be explained by financial distress, constraints, and relation-specific investment channels.
Thermoelectric transport control with metamaterial composites

The control of thermal and electric currents is important for many devices and applications. Being able to independently direct the two flows under simultaneous thermal and voltage gradients is, however, difficult when coupling via thermoelectric effects is present in the material. Here, we present a general computational scheme for the design of composites whose constituent materials follow a simple circuit theory of in-parallel and in-series connected transport properties capable of enhancing or inhibiting electric and thermal currents in a desired direction. We show that using the geometry of the flow, thermoelectric metamaterials for controlling currents can be designed using individual components. Controlling the thermoelectrically coupled electric and thermal currents in terms of different flow directions can be significantly optimized by exploring the dissimilarity of the transport properties of the different components.
Energy Shortage in Human and Mouse Models of SLC4A11-Associated Corneal Endothelial Dystrophies.

Purpose To elucidate the molecular events in solute carrier family 4 member 11 (SLC4A11)-deficient corneal endothelium that lead to the endothelial dysfunction that characterizes the dystrophies associated with SLC4A11 mutations, congenital hereditary endothelial dystrophy (CHED) and Fuchs endothelial corneal dystrophy 4. Methods Comparative transcriptomic analysis (CTA) was performed in primary human corneal endothelial cells (pHCEnC) and murine corneal endothelial cells (MCEnC) with normal and reduced levels of SLC4A11 (SLC4A11 KD pHCEnC) and Slc4a11 (Slc4a11-/- MCEnC), respectively. Validation of differentially expressed genes was performed using immunofluorescence staining of CHED corneal endothelium, as well as western blot and quantitative PCR analysis of SLC4A11 KD pHCEnC and Slc4a11-/- MCEnC. Functional analyses were performed to investigate potential functional changes associated with the observed transcriptomic alterations. Results CTA revealed inhibition of cell metabolism and ion transport function as well as mitochondrial dysfunction, leading to reduced adenosine triphosphate (ATP) production, in SLC4A11 KD pHCEnC and Slc4a11-/- MCEnC. Co-localization of SNARE protein STX17 with mitochondria marker COX4 was observed in CHED corneal endothelium, as was activation of AMPK-p53/ULK1 in both SLC4A11 KD pHCEnC and Slc4a11-/- MCEnC, providing additional evidence of mitochondrial dysfunction and mitophagy. Reduced Na+-dependent HCO3- transport activity and altered NH4Cl-induced membrane potential changes were observed in Slc4a11-/- MCEnC. Conclusions Reduced steady-state ATP levels and subsequent activation of the AMPK-p53 pathway provide a link between the metabolic functional deficit and transcriptome alterations, as well as evidence of insufficient ATP to maintain the Na+/K+-ATPase corneal endothelial pump as the cause of the edema that characterizes SLC4A11-associated corneal endothelial dystrophies.
On Benjamini--Schramm limits of congruence subgroups

A sequence of orbifolds corresponding to pairwise non-conjugate congruence lattices in a higher rank semisimple group over zero characteristic local fields is Benjamini-Schramm convergent to the universal cover.
Genome-wide mapping and prediction of plant architecture in a sorghum nested association mapping population

Modifying plant architecture is often necessary for yield improvement and climate adaptation, but we lack understanding of the genotype-phenotype map for plant morphology in sorghum. Here, we use a nested association mapping (NAM) population that captures global allelic diversity of sorghum to characterize the genetics of leaf erectness, leaf width (at two stages), and stem diameter. Recombinant inbred lines (n = 2200) were phenotyped in multiple environments (35,200 observations) and joint linkage mapping was performed with ∼93,000 markers. Fifty-four QTL of small to large effect were identified for trait BLUPs (9-16 per trait) each explaining 0.4-4% of variation across the NAM population. While some of these QTL colocalize with sorghum homologs of grass genes (e.g., those involved in transcriptional regulation of hormone synthesis [rice SPINDLY] and transcriptional regulation of development [rice Ideal plant architecture1]), most QTL did not colocalize with an a priori candidate gene (92%). Genomic prediction accuracy was generally high in five-fold cross-validation (0.65-0.83), and varied from low to high in leave-one-family-out cross-validation (0.04-0.61). The findings provide a foundation to identify the molecular basis of architecture variation in sorghum and establish genomic-enabled breeding for improved plant architecture.
Hybrid safe–strong rules for efficient optimization in lasso-type problems

Abstract The lasso model has been widely used for model selection in data mining, machine learning, and high-dimensional statistical analysis. However, with the ultrahigh-dimensional, large-scale data sets now collected in many real-world applications, it is important to develop algorithms to solve the lasso that efficiently scale up to problems of this size. Discarding features from certain steps of the algorithm is a powerful technique for increasing efficiency and addressing the Big Data challenge. This paper proposes a family of hybrid safe–strong rules (HSSR) which incorporate safe screening rules into the sequential strong rule (SSR) to remove unnecessary computational burden. Two instances of HSSR are presented, SSR-Dome and SSR-BEDPP, for the standard lasso problem. SSR-BEDPP is further extended to the elastic net and group lasso problems to demonstrate the generalizability of the hybrid screening idea. Extensive numerical experiments with synthetic and real data sets are conducted for both the standard lasso and the group lasso problems. Results show that the proposed hybrid rules can substantially outperform existing state-of-the-art rules.
ThirdEye: Triplet Based Iris Recognition without Normalization

Most iris recognition pipelines involve three stages: segmenting into iris/non-iris pixels, normalization the iris region to a fixed area, and extracting relevant features for comparison. Given recent advances in deep learning, it is prudent to ask which stages are required for accurate iris recognition. Lojez et al. (IWBF 2019) recently concluded that the segmentation stage is still crucial for good accuracy. We ask if normalization is beneficial?Towards answering this question, we develop a new iris recognition system called ThirdEye based on triplet convolutional neural networks (Schroff et al., ICCV 2015). ThirdEye directly uses segmented images without normalization.We observe equal error rates of 1.32%, 9.20%, and 0.59% on the ND-0405, UbirisV2, and IITD datasets respectively. For IITD, the most constrained dataset, this improves on the best prior work. However, for ND-0405 and UbirisV2, our equal error rate is slightly worse than prior systems. Our concluding hypothesis is that normalization is more important for less constrained environments.
An optimal separation of randomized and Quantum query complexity

We prove that for every decision tree, the absolute values of the Fourier coefficients of given order t≥1 sum to at most (cd/t)t/2(1+logn)(t−1)/2, where n is the number of variables, d is the tree depth, and c>0 is an absolute constant. This bound is essentially tight and settles a conjecture due to Tal (arxiv 2019; FOCS 2020). The bounds prior to our work degraded rapidly with t, becoming trivial already at t=√d. As an application, we obtain, for every integer k≥1, a partial Boolean function on n bits that has bounded-error quantum query complexity at most ⌈ k/2⌉ and randomized query complexity Ω(n1−1/k). This separation of bounded-error quantum versus randomized query complexity is best possible, by the results of Aaronson and Ambainis (STOC 2015). Prior to our work, the best known separation was polynomially weaker: O(1) versus Ω(n2/3−є) for any є>0 (Tal, FOCS 2020). As another application, we obtain an essentially optimal separation of O(logn) versus Ω(n1−є) for bounded-error quantum versus randomized communication complexity, for any є>0. The best previous separation was polynomially weaker: O(logn) versus Ω(n2/3−є) (implicit in Tal, FOCS 2020).
Ibeaconmap: Automated indoor space representation for beacon-based wayfinding

Traditionally, there have been few options for navigational aids for the blind and visually impaired (BVI) in large indoor spaces. Some recent indoor navigation systems allow users equipped with smartphones to interact with low cost Bluetooth-based beacons deployed strategically within the indoor space of interest to navigate their surroundings. A major challenge in deploying such beacon-based navigation systems is the need to employ a time and labor-expensive beacon planning process to identify potential beacon placement locations and arrive at a topological structure representing the indoor space. This work presents a technique called IBeaconMap for creating such topological structures to use with beacon-based navigation that only needs the floor plans of the indoor spaces of interest.
Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms

Orienting objects is a critical component in the automation of many packing and assembly tasks. We present an algorithm to orient novel objects given a depth image of the object in its current and desired orientation. We formulate a self-supervised objective for this problem and train a deep neural network to estimate the 3D rotation as parameterized by a quaternion, between these current and desired depth images. We then use the trained network in a proportional controller to re-orient objects based on the estimated rotation between the two depth images. Results suggest that in simulation we can rotate unseen objects with unknown geometries by up to 30° with a median angle error of 1.47° over 100 random initial/desired orientations each for 22 novel objects. Experiments on physical objects suggest that the controller can achieve a median angle error of 4.2° over 10 random initial/desired orientations each for 5 objects.
Hepatic NFAT signaling regulates the expression of inflammatory cytokines in cholestasis.

Background & Aims The nuclear factor of activated T-cells (NFAT) plays an important role in immune responses by regulating the expression of inflammatory genes. However, it is not known whether NFAT plays any role in the bile acid (BA)-induced hepatic inflammatory response. Thus, we aimed to examine the functional role of NFATc3 in cholestatic liver injury in mice and humans. Methods Gene and protein expression and cellular localization were assessed in primary hepatocyte cultures (mouse and human) and cholestatic liver tissues (murine models and patients with primary biliary cholangitis [PBC] or primary sclerosing cholangitis [PSC]) by quantitative PCR, western blot and immunohistochemistry. Specific NFAT inhibitors were used in vivo and in vitro. Gene reporter assays and ChIP-PCR were used to determine promoter activity. Results NFAT isoforms c1 and c3 were expressed in human and mouse hepatocytes. When treated with cholestatic levels of BAs, nuclear translocation of NFATc3 was increased in both human and mouse hepatocytes and was associated with elevated mRNA levels of IL-8, CXCL2, and CXCL10 in these cells. Blocking NFAT activation with pathway-specific inhibitors or knocking down Nfatc3 expression significantly decreased BA-driven induction of these cytokines in mouse hepatocytes. Nuclear expression of NFATc3/Nfatc3 protein was increased in cholestatic livers, both in mouse models (bile duct ligation or Abcb4-/- mice) and in patients with PBC and PSC in association with elevated tissue levels of Cxcl2 (mice) or IL-8 (humans). Gene reporter assays and ChIP-PCR demonstrated that the NFAT response element in the IL-8 promoter played a key role in BA-induced human IL-8 expression. Finally, blocking NFAT activation in vivo in Abcb4-/- mice reduced cholestatic liver injury. Conclusions NFAT plays an important role in BA-stimulated hepatic cytokine expression in cholestasis. Blocking hepatic NFAT activation may reduce cholestatic liver injury in humans. Lay summary Bile acid induces liver injury by stimulating the expression of inflammatory genes in hepatocytes through activation of the transcription factor NFAT. Blocking this activation in vitro (in hepatocyte cultures) and in vivo (in cholestatic mice) decreased the expression of inflammatory genes and reduced liver injury.
Ancient solutions to the Ricci flow in dimension 3

It follows from work of Perelman that any finite-time singularity of the Ricci flow on a compact $3$-manifold is modeled on an ancient $\varkappa$-solution. We prove that every non-compact ancient $\varkappa$-solution in dimension $3$ is isometric to a family of shrinking cylinders (or a quotient thereof), or to the Bryant soliton. This confirms a conjecture of Perelman.
Smectic Order in Double-Twist Cylinders

I propose a double-twist texture with local smectic order, which may have been seen in recent experiments [1]. As in the Renn-Lubensky TGB phase, the smectic order is broken only through a lattice of screw dislocations. A melted lattice of screw dislocations can produce a double-twist texture as can an unmelted lattice. In the latter case I show that geometry only allows for certain angles between smectic regions. I discuss the possibility of connecting these double-twist tubes together to form a smectic blue phase.
STAR: scaling transactions through asymmetric replication

In this paper, we present STAR, a new distributed in-memory database with asymmetric replication. By employing a single-node non-partitioned architecture for some replicas and a partitioned architecture for other replicas, STAR is able to efficiently run both highly partitionable workloads and workloads that involve cross-partition transactions. The key idea is a new phase-switching algorithm where the execution of single-partition and cross-partition transactions is separated. In the partitioned phase, single-partition transactions are run on multiple machines in parallel to exploit more concurrency. In the single-master phase, mastership for the entire database is switched to a single designated master node, which can execute these transactions without the use of expensive coordination protocols like two-phase commit. Because the master node has a full copy of the database, this phase-switching can be done at negligible cost. Our experiments on two popular benchmarks (YCSB and TPC-C) show that high availability via replication can coexist with fast serializable transaction execution in distributed in-memory databases, with STAR outperforming systems that employ conventional concurrency control and replication algorithms by up to one order of magnitude.
Gauged Floer Homology and Spectral Invariants

We define a version of spectral invariant in the vortex Floer theory for a GHamiltonian manifold M . This defines potentially new (partial) symplectic quasi-morphism and quasi-states when M//G is not semi-positive. We also establish a relation between vortex Hamiltonian Floer homology [37] and Woodward’s quasimap Floer homology [34] by constructing a closed-open string map between them. This yields applications to study non-displaceability problems of subsets in M//G.
Entanglement wedge reconstruction using the Petz map

At the heart of recent progress in AdS/CFT is the question of subregion duality, or entanglement wedge reconstruction: which part(s) of the boundary CFT are dual to a given subregion of the bulk? This question can be answered by appealing to the quantum error correcting properties of holography, and it was recently shown that robust bulk (entanglement wedge) reconstruction can be achieved using a universal recovery channel known as the twirled Petz map. In short, one can use the twirled Petz map to recover bulk data from a subset of the boundary. However, this map involves an averaging procedure over bulk and boundary modular time, and hence it can be somewhat intractable to evaluate in practice. We show that a much simpler channel, the Petz map, is sufficient for entanglement wedge reconstruction for any code space of fixed finite dimension — no twirling is required. Moreover, the error in the reconstruction will always be non-perturbatively small. From a quantum information perspective, we prove a general theorem extending the use of the Petz map as a general-purpose recovery channel to subsystem and operator algebra quantum error correction.
Why Random Reshuffling Beats Stochastic Gradient Descent

We analyze the convergence rate of the random reshuffling (RR) method, which is a randomized first-order incremental algorithm for minimizing a finite sum of convex component functions. RR proceeds in cycles, picking a uniformly random order (permutation) and processing the component functions one at a time according to this order, i.e., at each cycle, each component function is sampled without replacement from the collection. Though RR has been numerically observed to outperform its with-replacement counterpart stochastic gradient descent (SGD), characterization of its convergence rate has been a long standing open question. In this paper, we answer this question by providing various convergence rate results for RR and variants when the sum function is strongly convex. We first focus on quadratic component functions and show that the expected distance of the iterates generated by RR with stepsize $$\alpha _k=\varTheta (1/k^s)$$ for $$s\in (0,1]$$ converges to zero at rate $$\mathcal{O}(1/k^s)$$ (with $$s=1$$ requiring adjusting the stepsize to the strong convexity constant). Our main result shows that when the component functions are quadratics or smooth (with a Lipschitz assumption on the Hessian matrices), RR with iterate averaging and a diminishing stepsize $$\alpha _k=\varTheta (1/k^s)$$ for $$s\in (1/2,1)$$ converges at rate $$\varTheta (1/k^{2s})$$ with probability one in the suboptimality of the objective value, thus improving upon the $$\varOmega (1/k)$$ rate of SGD. Our analysis draws on the theory of Polyak–Ruppert averaging and relies on decoupling the dependent cycle gradient error into an independent term over cycles and another term dominated by $$\alpha _k^2$$ . This allows us to apply law of large numbers to an appropriately weighted version of the cycle gradient errors, where the weights depend on the stepsize. We also provide high probability convergence rate estimates that shows decay rate of different terms and allows us to propose a modification of RR with convergence rate $$\mathcal{O}(\frac{1}{k^2})$$ .
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion

In this letter, we study the deterministic sampling patterns for the completion of low-rank matrix, when corrupted with a sparse noise, also known as robust matrix completion. We extend the recent results on the deterministic sampling patterns in the absence of noise based on the geometric analysis on the Grassmannian manifold. A special case where each column has a certain number of noisy entries is considered, where our probabilistic analysis performs very efficiently. Furthermore, assuming that the rank of the original matrix is not given, we provide an analysis to determine if the rank of a valid completion is indeed the actual rank of the data corrupted with sparse noise by verifying some conditions.
Threshold of transverse mode coupling instability with arbitrary space charge

The threshold of the transverse mode coupling instability is calculated in framework of the square well model at arbitrary value of space charge tune shift. A new method of calculation is developed beyond the traditional expansion technique. The square, resistive, and exponential wakes are investigated. It is shown that the instability threshold goes up indefinitely when the tune shift increases. A comparison with conventional case of the parabolic potential well is performed.