cssi/SciSciGPT-SciSciNet huggingface dataset:
Nonlocal Flocking Dynamics: Learning the Fractional Order of PDEs from Particle Simulations

Flocking refers to collective behavior of a large number of interacting entities, where the interactions between discrete individuals produce collective motion on the large scale. We employ an agent-based model to describe the microscopic dynamics of each individual in a flock, and use a fractional partial differential equation (fPDE) to model the evolution of macroscopic quantities of interest. The macroscopic models with phenomenological interaction functions are derived by applying the continuum hypothesis to the microscopic model. Instead of specifying the fPDEs with an ad hoc fractional order for nonlocal flocking dynamics, we learn the effective nonlocal influence function in fPDEs directly from particle trajectories generated by the agent-based simulations. We demonstrate how the learning framework is used to connect the discrete agent-based model to the continuum fPDEs in one- and two-dimensional nonlocal flocking dynamics. In particular, a Cucker–Smale particle model is employed to describe the microscale dynamics of each individual, while Euler equations with nonlocal interaction terms are used to compute the evolution of macroscale quantities. The trajectories generated by the particle simulations mimic the field data of tracking logs that can be obtained experimentally. They can be used to learn the fractional order of the influence function using a Gaussian process regression model implemented with the Bayesian optimization. We show in one- and two-dimensional benchmarks that the numerical solution of the learned Euler equations solved by the finite volume scheme can yield correct density distributions consistent with the collective behavior of the agent-based system solved by the particle method. The proposed method offers new insights into how to scale the discrete agent-based models to the continuum-based PDE models, and could serve as a paradigm on extracting effective governing equations for nonlocal flocking dynamics directly from particle trajectories.
Precision Mass and Density Measurement of Individual Optically Levitated Microspheres

For a number of applications such as precision force-sensing experiments, characterization of an optically levitated test mass is critical to understanding both expected signals and possible backgrounds. Often it is difficult to probe or retrieve optically trapped particles $i\phantom{\rule{0}{0ex}}n$ $s\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}u$. Using electrostatic colevitation and a specially designed mechanical probe, optically trapped microspheres are weighed against gravity, then $i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}v\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}l\phantom{\rule{0}{0ex}}l\phantom{\rule{0}{0ex}}y$ retrieved and imaged with electron microscopy to measure their size. One observation: Particles grown via the St\"ober process are likely much more porous than typically assumed, and thus have reduced density and sensitivity to gravitylike forces.
Transfusion: Understanding Transfer Learning for Medical Imaging

Transfer learning from natural image datasets, particularly ImageNet, using standard large models and corresponding pretrained weights has become a de-facto method for deep learning applications to medical imaging. However, there are fundamental differences in data sizes, features and task specifications between natural image classification and the target medical tasks, and there is little understanding of the effects of transfer. In this paper, we explore properties of transfer learning for medical imaging. A performance evaluation on two large scale medical imaging tasks shows that surprisingly, transfer offers little benefit to performance, and simple, lightweight models can perform comparably to ImageNet architectures. Investigating the learned representations and features, we find that some of the differences from transfer learning are due to the over-parametrization of standard models rather than sophisticated feature reuse. We isolate where useful feature reuse occurs, and outline the implications for more efficient model exploration. We also explore feature independent benefits of transfer arising from weight scalings.
Positioning to Win: A Dynamic Role Assignment and Formation Positioning System.

This paper presents a dynamic role assignment and formation positioning system used by the 2011 RoboCup 3D simulation league champion UT Austin Villa. This positioning system was a key component in allowing the team to win all 24 games it played at the competition during which the team scored 136 goals and conceded none. The positioning system was designed to allow for decentralized coordination among physically realistic simulated humanoid soccer playing robots in the partially observable, non-deterministic, noisy, dynamic, and limited communication setting of the RoboCup 3D simulation league simulator. Although the positioning system is discussed in the context of the RoboCup 3D simulation environment, it is not domain specific and can readily be employed in other RoboCup leagues as it generalizes well to many realistic and real-world multiagent systems.
Distributed approximate Newton algorithms and weight design for constrained optimization

Abstract Motivated by economic dispatch and linearly-constrained resource allocation problems, this paper proposes a class of novel distributedapprox − Newton algorithms that approximate the standard Newton optimization method. We first develop the notion of an optimal edge weighting for the communication graph over which agents implement the second-order algorithm, and propose a convex approximation for the nonconvex weight design problem. We next build on the optimal weight design to develop a discrete distributed approx-Newton algorithm which converges linearly to the optimal solution for economic dispatch problems with unknown cost functions and relaxed local box constraints. For the full box-constrained problem, we develop a continuous distributed approx-Newton algorithm which is inspired by first-order saddle-point methods and rigorously prove its convergence to the primal and dual optimizers. A main property of each of these distributed algorithms is that they only require agents to exchange constant-size communication messages, which lends itself to scalable implementations. Simulations demonstrate that the distributedapprox − Newton algorithms with our weight design have superior convergence properties compared to existing weighting strategies for first-order saddle-point and gradient descent methods.
Making Recursive Bayesian Inference Accessible

Bayesian models provide recursive inference naturally because they can formally reconcile new data and existing scientific information. However, popular use of Bayesian methods often avoids priors ...
Scalable Robust Adaptive Control from the System Level Perspective

We will present a new general framework for robust and adaptive control that allows for distributed and scalable learning and control of large systems of interconnected linear subsystems. The control method is demonstrated for a linear time-invariant system with bounded parameter uncertainties, disturbances and noise. The presented scheme continuously collects measurements to reduce the uncertainty about the system parameters and adapts dynamic robust controllers online in a stable and performance-improving way. A key enabler for our approach is choosing a time-varying dynamic controller implementation, inspired by recent work on System Level Synthesis [1]. We leverage a new robustness result for this implementation to propose a general robust adaptive control algorithm. In particular, the algorithm allows us to impose communication and delay constraints on the controller implementation and is formulated as a sequence of robust optimization problems that can be solved in a distributed manner. The proposed control methodology performs particularly well when the interconnection between systems is sparse and the dynamics of local regions of subsystems depend only on a small number of parameters. As we will show on a five-dimensional exemplary chain-system, the algorithm can utilize system structure to efficiently learn and control the entire system while respecting communication and implementation constraints. Moreover, although current theoretical results require the assumption of small initial uncertainties to guarantee robustness, we will present simulations that show good closed-loop performance even in the case of large uncertainties, which suggests that this assumption is not critical for the presented technique and future work will focus on providing less conservative guarantees.
Natural Gas Flow Equations: Uniqueness and an MI-SOCP Solver

The critical role of gas fired-plants to compensate renewable generation has increased the operational variability in natural gas networks (GN). Towards developing more reliable and efficient computational tools for GN monitoring, control, and planning, this work considers the task of solving the nonlinear equations governing steady-state flows and pressures in GNs. It is first shown that if the gas flow equations are feasible, they enjoy a unique solution. To the best of our knowledge, this is the first result proving uniqueness of the steady-state gas flow solution over the entire feasible domain of gas injections. To find this solution, we put forth a mixed-integer second-order cone program (MI-SOCP)-based solver relying on a relaxation of the gas flow equations. This relaxation is provably exact under specific network topologies. Unlike existing alternatives, the devised solver does not need proper initialization or knowing the gas flow directions beforehand, and can handle gas networks with compressors. Numerical tests on tree and meshed networks indicate that the relaxation is exact even when the derived conditions are not met.
Financial Market Risk Perceptions and the Macroeconomy

We propose a novel measure of risk perceptions: the price of volatile stocks (PVSt), defined as the book-to-market ratio of low-volatility stocks minus the book-to-market ratio of high-volatility stocks. PVSt is high when perceived risk directly measured from surveys and option prices is low. When perceived risk is high according to our measure, safe asset prices are high, risky asset prices are low, the cost of capital for risky firms is high, and real investment is forecast to decline. Perceived risk as measured by PVSt falls after positive macroeconomic news. These declines are predictably followed by upward revisions in investor risk perceptions. Our results suggest that risk perceptions embedded in stock prices are an important driver of the business cycle and are not fully rational.
Overt Attention toward Appetitive Cues Enhances Their Subjective Value, Independent of Orbitofrontal Cortex Activity.

ABSTRACT Neural representations of value underlie many behaviors that are crucial for survival. Previously, we found that value representations in primate orbitofrontal cortex (OFC) are modulated by attention, specifically, by overt shifts of gaze towards or away from reward-associated visual cues (McGinty et al., 2016). Here, we investigate the influence of overt attention on behavior, by asking how gaze shifts correlate with reward anticipatory responses, and whether activity in OFC mediates this correlation. Macaque monkeys viewed Pavlovian-conditioned appetitive cues on a visual display, while the fraction of time they spent looking towards or away from the cues was measured using an eye tracker. Also measured during cue presentation were the monkeys’ reward anticipation, indicated by conditioned licking responses (CRs), and single neuron activity in OFC. In general, gaze allocation predicted subsequent licking responses: the longer the monkeys spent looking at a cue at a given time point in a trial, the more likely they were to produce an anticipatory CR later in that trial, as if the subjective value of the cue were increased. To address neural mechanisms, mediation analysis measured the extent to which the gaze-CR correlation could be statistically explained by the concurrently recorded firing of OFC neurons. The resulting mediation effects were indistinguishable from chance. Therefore, while overt attention may increase the subjective value of reward-associated cues (as revealed by anticipatory behaviors), the underlying mechanism remains unknown, as does the functional significance of gaze-driven modulation of OFC value signals. SIGNIFICANCE STATEMENT: Recent studies of human decision-making suggest a link between gaze and value: longer fixation of gaze upon a given item appears to accentuate its subjective value (its likelihood of being chosen), relative to items that are fixated less. The chief contribution of this study is novel evidence suggesting that gaze also modulates subjective value in simple appetitive conditioning, in an animal model whose gaze behavior closely resembles our own. It is therefore possible that the effects of gaze on value may apply to many forms of motivated behavior. With respect to the neural mechanisms by which gaze influences conditioned responses, our data appear to rule out a role for the OFC, though additional studies are necessary to confirm this finding.
Observed changes in Brewer-Dobson circulation for 1980-2018

Previous work has examined the Brewer-Dobson circulation (BDC) changes for 1980–2009 based on satellite Microwave Sounding Unit (MSU/AMSU) lower-stratospheric temperature (TLS) observations and ERA-Interim reanalysis data. Here we examine the BDC changes for the longer period now available (1980-2018), which also allows analysis of both the ozone depletion (1980-1999) and ozone healing (2000-2018) periods. We provide observational evidence that the annual mean BDC accelerated for 1980-1999 but decelerated for 2000-2018, with the changes largely driven by the Southern Hemisphere (SH), which might be partly contributed by the effects of ozone depletion and healing. We also show that the annual mean BDC has accelerated in the last 40 years (at the 90% confidence level) with a relative strengthening of ~1.7% per decade. This overall acceleration was driven by both Northern Hemisphere (NH) (40%) and SH (60%) cells. Significant SH radiative warming is also identified in September for 2000-2018 after excluding the year 2002 when a very rare SH stratospheric sudden warming occurred, supporting the view that healing of the Antarctic ozone layer has now begun to occur during the month of September.
Plug-and-Play Priors for Reconstruction-based Placental Image Registration

This paper presents a novel deformable registration framework, leveraging an image prior specified through a denoising function, for severely noise-corrupted placental images. Recent work on plug-and-play (PnP) priors has shown the state-of-the-art performance of reconstruction algorithms under such priors in a range of imaging applications. Integration of powerful image denoisers into advanced registration methods provides our model with a flexibility to accommodate datasets that have low signal-to-noise ratios (SNRs). We demonstrate the performance of our method under a wide variety of denoising models in the context of diffeomorphic image registration. Experimental results show that our model substantially improves the accuracy of spatial alignment in applications of 3D in-utero diffusion-weighted MR images (DW-MRI) that suffer from low SNR and large spatial transformations.
Mapping Native R-Loops Genome-wide Using a Targeted Nuclease Approach.

Summary R-loops are three-stranded DNA:RNA hybrids that are implicated in many nuclear processes. While R-loops may have physiological roles, the formation of stable, aberrant R-loops has been observed in neurological disorders and cancers. Current methods to assess their genome-wide distribution rely on affinity purification, which is plagued by large input requirements, high noise, and poor sensitivity for dynamic R-loops. Here, we present MapR, a method that utilizes RNase H to guide micrococcal nuclease to R-loops, which are subsequently cleaved, released, and identified by sequencing. MapR detects R-loops formed at promoters and active enhancers that are likely to form transient R-loops due to the low transcriptional output of these regulatory elements and the short-lived nature of enhancer RNAs. MapR is as specific as existing techniques and more sensitive, allowing for genome-wide coverage with low input material in a fraction of the time.
Investigation of Stress Field and Fracture Development During Shale Maturation Using Analog Rock Systems

The emergence of hydrocarbons within shale as a major recoverable resource has sparked interest in fluid transport through these tight mudstones. Recent studies suggest the importance to recovery of microfracture networks that connect localized zones with large organic content to the inorganic matrix. This paper presents a joint modeling and experimental study to examine the onset, formation, and evolution of microfracture networks as shale matures. Both the stress field and fractures are simulated and imaged. A novel laboratory-scale, phase-field fracture propagation model was developed to characterize the material failure mechanisms that play a significant role during the shale maturation process. The numerical model developed consists of coupled solid deformation, pore pressure, and fracture propagation mechanisms. Benchmark tests were conducted to validate model accuracy. Laboratory-grade gelatins with varying Young’s modulus were used as scaled-rock analogs in a two-dimensional Hele-Shaw cell apparatus. Yeast within the gelatin generates gas in a fashion analogous to hydrocarbon formation as shale matures. These setups allow study and visualization of host rock elastic-brittle fracture and fracture network propagation mechanisms. The experimental setup was fitted to utilize photoelasticity principles coupled with birefringence properties of gelatin to explore visually the stress field of the gelatin as the fracture network developed. Stress optics image analysis and linear elastic fracture mechanics (LEFM) principles for crack propagation were used to monitor fracture growth for each gelatin type. Observed and simulated responses suggest gas diffusion within and deformation of the gelatin matrix as predominant mechanisms for energy dissipation depending on gelatin strength. LEFM, an experimental estimation of principal stress development with fracture growth, at different stages was determined for each gelatin rheology. The interplay of gas diffusion and material deformation determines the resulting frequency and pattern of fractures. Results correlate with Young’s modulus. Experimental and computed stress fields reveal that fractures resulting from internal gas generation are similar to, but not identical to, type 1 opening mode.
CNTNAP2 is targeted to endosomes by the polarity protein PAR3

A decade of genetic studies has established contactin-associated protein-like 2 (CNTNAP2) as a prominent susceptibility gene associated with multiple neurodevelopmental disorders. The development and characterization of Cntnap2 knockout models in multiple species have bolstered this claim by establishing clear connections with certain endophenotypes. Despite these remarkable in vivo findings, CNTNAP2's molecular functions are relatively unexplored, highlighting the need to identify novel protein partners. Here, we characterized an interaction between CNTNAP2 and partitioning-defective 3 (PAR3)-a polarity molecule isolated in a yeast two-hybrid screen with CNTNAP2's C-terminus. We provide evidence that the two proteins interact via PDZ domain-mediated binding, that CNTNAP2+ /PAR3+ complexes are largely associated with clathrin-coated endocytic vesicles in heterologous cells and that PAR3 causes an enlargement of CNTNAP2 puncta size. Live imaging and fluorescence recovery after photobleaching (FRAP) reveals that PAR3 limits the mobility of CNTNAP2. Finally, overexpression of PAR3 but not a PAR3 mutant lacking all PDZ domains (PAR3∆PDZall) can cluster endogenous CNTNAP2 in primary neurons. Collectively, we conclude that PAR3 regulates CNTNAP2 spatial localization.
Dirac type operators on the quantum solid torus with global boundary conditions

Abstract We define a noncommutative space we call the quantum solid torus. It is an example of a noncommutative manifold with a noncommutative boundary. We study quantum Dirac type operators subject to Atiyah-Patodi-Singer like boundary conditions on the quantum solid torus. We show that such operators have compact inverse, which means that the corresponding boundary value problem is elliptic.
Protecting patient privacy in survival analyses.

Objective Survival analysis is the cornerstone of many healthcare applications in which the "survival" probability (eg, time free from a certain disease, time to death) of a group of patients is computed to guide clinical decisions. It is widely used in biomedical research and healthcare applications. However, frequent sharing of exact survival curves may reveal information about the individual patients, as an adversary may infer the presence of a person of interest as a participant of a study or of a particular group. Therefore, it is imperative to develop methods to protect patient privacy in survival analysis. Materials and methods We develop a framework based on the formal model of differential privacy, which provides provable privacy protection against a knowledgeable adversary. We show the performance of privacy-protecting solutions for the widely used Kaplan-Meier nonparametric survival model. Results We empirically evaluated the usefulness of our privacy-protecting framework and the reduced privacy risk for a popular epidemiology dataset and a synthetic dataset. Results show that our methods significantly reduce the privacy risk when compared with their nonprivate counterparts, while retaining the utility of the survival curves. Discussion The proposed framework demonstrates the feasibility of conducting privacy-protecting survival analyses. We discuss future research directions to further enhance the usefulness of our proposed solutions in biomedical research applications. Conclusion The results suggest that our proposed privacy-protection methods provide strong privacy protections while preserving the usefulness of survival analyses.
Computing Robust Inverse Kinematics Under Uncertainty

Robotic tasks, like reaching a pre-grasp configuration, are specified in the end effector space or task space, whereas, robot motion is controlled in joint space. Because of inherent actuation errors in joint space, robots cannot achieve desired configurations in task space exactly. Furthermore, different inverse kinematics (IK) solutions map joint space error set to task space differently. Thus for a given task with a prescribed error tolerance, all IK solutions will not be guaranteed to successfully execute the task. Any IK solution that is guaranteed to execute a task (possibly with high probability) irrespective of the realization of the joint space error is called a robust IK solution. In this paper we formulate and solve the robust inverse kinematics problem for redundant manipulators with actuation uncertainties (errors). We also present simulation and experimental results on a $7$-DoF redundant manipulator for two applications, namely, a pre-grasp positioning and a pre-insertion positioning scenario. Our results show that the robust IK solutions result in higher success rates and also allows the robot to self-evaluate how successful it might be in any application scenario.
DNA methylation associated with postpartum depressive symptoms overlaps findings from a genome-wide association meta-analysis of depression

Perinatal depressive symptoms have been linked to adverse maternal and infant health outcomes. The etiology associated with perinatal depressive psychopathology is poorly understood, but accumulating evidence suggests that understanding inter-individual differences in DNA methylation (DNAm) patterning may provide insight regarding the genomic regions salient to the risk liability of perinatal depressive psychopathology. Genome-wide DNAm was measured in maternal peripheral blood using the Infinium MethylationEPIC microarray. Ninety-two participants (46% African-American) had DNAm samples that passed all quality control metrics, and all participants were within 7 months of delivery. Linear models were constructed to identify differentially methylated sites and regions, and permutation testing was utilized to assess significance. Differentially methylated regions (DMRs) were defined as genomic regions of consistent DNAm change with at least two probes within 1 kb of each other. Maternal age, current smoking status, estimated cell-type proportions, ancestry-relevant principal components, days since delivery, and chip position served as covariates to adjust for technical and biological factors. Current postpartum depressive symptoms were measured using the Edinburgh Postnatal Depression Scale. Ninety-eight DMRs were significant (false discovery rate < 5%) and overlapped 92 genes. Three of the regions overlap loci from the latest Psychiatric Genomics Consortium meta-analysis of depression. Many of the genes identified in this analysis corroborate previous allelic, transcriptomic, and DNAm association results related to depressive phenotypes. Future work should integrate data from multi-omic platforms to understand the functional relevance of these DMRs and refine DNAm association results by limiting phenotypic heterogeneity and clarifying if DNAm differences relate to the timing of onset, severity, duration of perinatal mental health outcomes of the current pregnancy or to previous history of depressive psychopathology.
Capital Punishment and the Morality of Human Rights

According to the morality of human rights, every human being has inherent dignity and is inviolable. In a paper I posted on SSRN last month, I inquired whether there is a nonreligious ground for the morality of human rights. Here is the URL for that paper: https://ssrn.com/abstract=685550. In this paper, I pursue the implications of the morality of human rights for the issue of capital punishment. Should we who affirm the morality of human rights, because we affirm it, want the law to protect human beings from - by giving them a right to be free from - capital punishment. One of the most prominent and powerful voices against capital punishment in recent years was that of Pope John Paul II, whose position was more radical - more oppositionist - than than the official position of the Roman Catholic Church. In this paper, I present John Paul II's position and then explain why I am unable to embrace it. I then present an alternative position - an alternative reason why we who affirm the morality of human rights should oppose capital punishment.
ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest

Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data.
Improved Bounds on the Spectral Gap Above Frustration-Free Ground States of Quantum Spin Chains

Nachtergaele obtained explicit lower bounds for the spectral gap above many frustration free quantum spin chains by using the ‘martingale method’. We present simple improvements to his main bounds which allow one to obtain a sharp lower bound for the spectral gap above the spin-1/2 ferromagnetic XXZ chain. As an illustration of the method, we also calculate a lower bound for the spectral gap of the AKLT model, which is about 1/3 the size of the expected gap.
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint.

We shed new insights on the two commonly used updates for the online k-PCA problem, namely, Krasulina's and Oja's updates. We show that Krasulina's update corresponds to a projected gradient descent step on the Stiefel manifold of orthonormal k-frames, while Oja's update amounts to a gradient descent step using the unprojected gradient. Following these observations, we derive a more implicit form of Krasulina's k-PCA update, i.e. a version that uses the information of the future gradient as much as possible. Most interestingly, our implicit Krasulina update avoids the costly QR-decomposition step by bypassing the orthonormality constraint. A related update, called the Sanger's rule, can be seen as an explicit approximation of our implicit update. We show that the new update in fact corresponds to an online EM step applied to a probabilistic k-PCA model. The probabilistic view of the update allows us to combine multiple models in a distributed setting. We show experimentally that the implicit Krasulina update yields superior convergence while being significantly faster. We also give strong evidence that the new update can benefit from parallelism and is more stable w.r.t. tuning of the learning rate.
Fluctuating light experiments and semi-automated plant phenotyping enabled by self-built growth racks and simple upgrades to the IMAGING-PAM.

Over the last years, several plant science labs have started to employ fluctuating growth light conditions to simulate natural light regimes more closely. Many plant mutants reveal quantifiable effects under fluctuating light despite being indistinguishable from wild-type plants under standard constant light. Moreover, many subtle plant phenotypes become intensified and thus can be studied in more detail. This observation has caused a paradigm shift within the photosynthesis research community and an increasing number of scientists are interested in using fluctuating light growth conditions. However, high installation costs for commercial controllable LED setups as well as costly phenotyping equipment can make it hard for small academic groups to compete in this emerging field. We show a simple do-it-yourself approach to enable fluctuating light growth experiments. Our results using previously published fluctuating light sensitive mutants, stn7 and pgr5, confirm that our low-cost setup yields similar results as top-prized commercial growth regimes. Moreover, we show how we increased the throughput of our Walz IMAGING-PAM, also found in many other departments around the world. We have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets. We provide detailed instructions on how to build and functionally test each setup. With material costs well below USD$1000, it is possible to setup a fluctuating light rack including a constant light control shelf for comparison. This allows more scientists to perform experiments closer to natural light conditions and contribute to an emerging research field. A small addition to the IMAGING-PAM hardware not only increases sample throughput but also enables larger-scale plant phenotyping with automated data analysis.
Odor coding of nestmate recognition in the eusocial ant Camponotus floridanus

In eusocial ants, aggressive behaviors require the ability to discriminate between chemical signatures such as cuticular hydrocarbons that distinguish nestmate friends from non-nestmate foes. It has been suggested that a mismatch between a chemical signature (label) and the internal, neuronal representation of the colony odor (template) leads to aggression between non-nestmates. Moreover, a definitive demonstration that odorant receptors are responsible for the processing of the chemical signals that regulate nestmate recognition has thus far been lacking. To address these issues, we have developed an aggression-based bioassay incorporating highly selective modulators that target odorant receptor functionality to characterize their role in nestmate recognition in the formicine ant Camponotus floridanus. Electrophysiological studies were used to show that exposure to either a volatilized antagonist or an agonist eliminated or dramatically altered signaling, respectively. Administration of these compounds to adult workers significantly reduced aggression between non-nestmates without altering aggression levels between nestmates. These studies provide direct evidence that odorant receptors are indeed necessary and sufficient for mediating aggression towards non-nestmates. Furthermore, our observations support a hypothesis in which rejection of non-nestmates depends on the precise decoding of chemical signatures present on non-nestmates as opposed to the absence of any information or the active acceptance of familiar signatures.
The future of coal-fired power generation in Southeast Asia

Abstract As both China and India are moving away from coal in the power sector, Southeast Asia has emerged as arguably the most important region for future growth in coal-fired power generation. Here we use data from the January 2017 edition of the Global Coal Plant Tracker to understand coal’s fortunes in the region. More specifically, we manipulate three key policy levers – attrition rate, capacity factor, and plant lifespan – to provide a timely update on the future of coal in Southeast Asia. Using improved estimates of the attrition rate for planned power plants and capacity factors in different countries, we find that status-quo trends in Southeast Asian coal-fired power generation would significantly hamper the region’s ability to contribute to global efforts to limit warming to 1.5 or 2.0 degrees Celsius in line with international agreements, namely the 2009 Copenhagen and 2016 Paris Agreements. Though meeting these targets may be possible if policymakers limit the entry into service of new coal power capacity and reduce use of existing units, qualitative evidence suggests that there may be a lack of political will to do so.
A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency.

Definition Extraction (DE) is one of the well-known topics in Information Extraction that aims to identify terms and their corresponding definitions in unstructured texts. This task can be formalized either as a sentence classification task (i.e., containing term-definition pairs or not) or a sequential labeling task (i.e., identifying the boundaries of the terms and definitions). The previous works for DE have only focused on one of the two approaches, failing to model the inter-dependencies between the two tasks. In this work, we propose a novel model for DE that simultaneously performs the two tasks in a single framework to benefit from their inter-dependencies. Our model features deep learning architectures to exploit the global structures of the input sentences as well as the semantic consistencies between the terms and the definitions, thereby improving the quality of the representation vectors for DE. Besides the joint inference between sentence classification and sequential labeling, the proposed model is fundamentally different from the prior work for DE in that the prior work has only employed the local structures of the input sentences (i.e., word-to-word relations), and not yet considered the semantic consistencies between terms and definitions. In order to implement these novel ideas, our model presents a multi-task learning framework that employs graph convolutional neural networks and predicts the dependency paths between the terms and the definitions. We also seek to enforce the consistency between the representations of the terms and definitions both globally (i.e., increasing semantic consistency between the representations of the entire sentences and the terms/definitions) and locally (i.e., promoting the similarity between the representations of the terms and the definitions). The extensive experiments on three benchmark datasets demonstrate the effectiveness of our approach.1
Relative geometric assembly and mapping cones Part II: Chern characters and the Novikov property

We study Chern characters and the assembly mapping for free actions using the framework of geometric K-homology. The focus is on the relative groups associated with a group homomorphism phi: Gamma(1) -> Gamma(2) along with applications to Novikov type properties. In particular, we prove a relative strong Novikov property for homomorphisms of hyperbolic groups and a relative strong l(1)-Novikov property for polynomially bounded homomorphisms of groups with polynomially bounded cohomology in C. As a corollary, relative higher signatures on a manifold with boundary W, with pi(1)(partial derivative W) -> pi(1) (W) belonging to the class above, are homotopy invariant.
Automated ablation of dental composite using an IR pulsed laser coupled to a plume emission spectral feedback system

Dental composites are used as restorative materials for filling cavities, shaping, and covering teeth for esthetic purposes, and as adhesives. Dentists spend more time replacing existing restorations that fail than they do placing new restorations. Tooth colored restorations are difficult to differentiate from the surrounding tooth structure making them challenging to remove without damaging healthy tooth structure. Previous studies have demonstrated that CO 2 lasers in conjunction with spectral feedback can be used to selectively remove composite from tooth surfaces. The purpose of this study is to assemble a system that is feasible for clinical use incorporating a spectral feedback system, a scanning system, articulating arm and a clinical handpiece and then evaluate the performance of that system on extracted teeth. In addition, the selectivity of composite removal was analyzed using a high-speed optical coherence tomography system that is suitable for clinical use. The system was capable of rapidly removing composite from small preparations on tooth occlusal surfaces with a mean loss of enamel of less than 20-μm.
Knots with exactly 10 sticks

We prove that the knots 13n592 and 15n41,127 both have stick number 10. These are the first non-torus prime knots with more than 9 crossings for which the exact stick number is known.
Protein Secondary Structure Detection in Intermediate-Resolution Cryo-EM Maps using Deep Learning

Although structures determined at near-atomic resolution are now routinely reported by cryo-electron microscopy (cryo-EM), many density maps are determined at an intermediate resolution, and extracting structure information from these maps is still a challenge. We report a computational method, Emap2sec, that identifies the secondary structures of proteins (α-helices, β-sheets and other structures) in EM maps at resolutions of between 5 and 10 A. Emap2sec uses a three-dimensional deep convolutional neural network to assign secondary structure to each grid point in an EM map. We tested Emap2sec on EM maps simulated from 34 structures at resolutions of 6.0 and 10.0 A, as well as on 43 maps determined experimentally at resolutions of between 5.0 and 9.5 A. Emap2sec was able to clearly identify the secondary structures in many maps tested, and showed substantially better performance than existing methods. Emap2sec uses a deep convolutional neural network to assign protein secondary structures in intermediate-resolution (5–10 A) cryo-EM maps.
Finding all Borcherds product paramodular cusp forms of a given weight and level

We present an algorithm to compute all Borcherds product paramodular cusp forms of a specified weight and level, describing its implementation in some detail and giving examples of its use.
A Contingency Theory of Representational Complexity in Organizations

A long-standing question in the organizations literature is whether firms are better off by using simple or complex representations of their task environment. We address this question by developing...
U.S. Political Corruption and Audit Fees

Using data on corruption convictions from the U.S. Department of Justice, we find that auditors charge higher fees when a firm is headquartered in a more corrupt district. This result is robust to ...
NeuroPath2Path: Classification and elastic morphing between neuronal arbors using path-wise similarity

Neuron shape and connectivity affect function. Modern imaging methods have proven successful at extracting morphological information. One potential path to achieve analysis of this morphology is through graph theory. Encoding by graphs enables the use of high throughput informatic methods to extract and infer brain function. However, the application of graph-theoretic methods to neuronal morphology comes with certain challenges in term of complex subgraph matching and the difficulty in computing intermediate shapes in between two imaged temporal samples. Here we report a novel, efficacious graph-theoretic method that rises to the challenges. The morphology of a neuron, which consists of its overall size, global shape, local branch patterns, and cell-specific biophysical properties, can vary significantly with the cell’s identity, location, as well as developmental and physiological state. Various algorithms have been developed to customize shape based statistical and graph related features for quantitative analysis of neuromorphology, followed by the classification of neuron cell types using the features. Unlike the classical feature extraction based methods from imaged or 3D reconstructed neurons, we propose a model based on the rooted path decomposition from the soma to the dendrites of a neuron and extract morphological features from each constituent path. We hypothesize that measuring the distance between two neurons can be realized by minimizing the cost of continuously morphing the set of all rooted paths of one neuron to another. To validate this claim, we first establish the correspondence of paths between two neurons using a modified Munkres algorithm. Next, an elastic deformation framework that employs the square root velocity function is established to perform the continuous morphing, which, as an added benefit, provides an effective visualization tool. We experimentally show the efficacy of NeuroPath2Path, NeuroP2P, over the state of the art.
GLEE: Geometric Laplacian Eigenmap Embedding

Graph embedding seeks to build a low-dimensional representation of a graph $G$ . This low-dimensional representation is then used for various downstream tasks. One popular approach is Laplacian Eigenmaps (LE), which constructs a graph embedding based on the spectral properties of the Laplacian matrix of $G$ . The intuition behind it, and many other embedding techniques, is that the embedding of a graph must respect node similarity: similar nodes must have embeddings that are close to one another. Here, we dispose of this distance-minimization assumption. Instead, we use the Laplacian matrix to find an embedding with geometric properties instead of spectral ones, by leveraging the so-called simplex geometry of $G$ . We introduce a new approach, Geometric Laplacian Eigenmap Embedding, and demonstrate that it outperforms various other techniques (including LE) in the tasks of graph reconstruction and link prediction.
Lowness for isomorphism, countable ideals, and computable traceability

We show that every countable ideal of degrees that are low for isomorphism is contained in a principal ideal of degrees that are low for isomorphism by adapting an exact pair construction. We further show that within the hyperimmune-free degrees, lowness for isomorphism is entirely independent of computable traceability.
Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing

Community detection plays a key role in understanding graph structure. However, several recent studies showed that community detection is vulnerable to adversarial structural perturbation. In particular, via adding or removing a small number of carefully selected edges in a graph, an attacker can manipulate the detected communities. However, to the best of our knowledge, there are no studies on certifying robustness of community detection against such adversarial structural perturbation. In this work, we aim to bridge this gap. Specifically, we develop the first certified robustness guarantee of community detection against adversarial structural perturbation. Given an arbitrary community detection method, we build a new smoothed community detection method via randomly perturbing the graph structure. We theoretically show that the smoothed community detection method provably groups a given arbitrary set of nodes into the same community (or different communities) when the number of edges added/removed by an attacker is bounded. Moreover, we show that our certified robustness is tight. We also empirically evaluate our method on multiple real-world graphs with ground truth communities.
Learning and Interpreting Potentials for Classical Hamiltonian Systems

We consider the problem of learning an interpretable potential energy function from a Hamiltonian system’s trajectories. We address this problem for classical, separable Hamiltonian systems. Our approach first constructs a neural network model of the potential and then applies an equation discovery technique to extract from the neural potential a closed-form algebraic expression. We demonstrate this approach for several systems, including oscillators, a central force problem, and a problem of two charged particles in a classical Coulomb potential. Through these test problems, we show close agreement between learned neural potentials, the interpreted potentials we obtain after training, and the ground truth. In particular, for the central force problem, we show that our approach learns the correct effective potential, a reduced-order model of the system.
The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19

We examine the net benefits of social distancing to slow the spread of COVID-19 in USA. Social distancing saves lives but imposes large costs on society due to reduced economic activity. We use epidemiological and economic forecasting to perform a rapid benefit–cost analysis of controlling the COVID-19 outbreak. Assuming that social distancing measures can substantially reduce contacts among individuals, we find net benefits of about $5.2 trillion in our benchmark case. We examine the magnitude of the critical parameters that might imply negative net benefits, including the value of statistical life and the discount rate. A key unknown factor is the speed of economic recovery with and without social distancing measures in place. A series of robustness checks also highlight the key role of the value of mortality risk reductions and discounting in the analysis and point to a need for effective economic stimulus when the outbreak has passed.