cssi/SciSciGPT-SciSciNet huggingface dataset:
Gut microbiota has a widespread and modifiable effect on host gene regulation

Variation in gut microbiome is associated with wellness and disease in humans, and yet the molecular mechanisms by which this variation affects the host are not well understood. A likely mechanism is that of changing gene regulation in interfacing host epithelial cells. Here, we treated colonic epithelial cells with live microbiota from five healthy individuals and quantified induced changes in transcriptional regulation and chromatin accessibility in host cells. We identified over 5,000 host genes that change expression, including 588 distinct associations between specific taxa and host genes. The taxa with the strongest influence on gene expression alter the response of genes associated with complex traits. Using ATAC-seq, we showed that a subset of these changes in gene expression are associated with changes in host chromatin accessibility and transcription factor binding induced by exposure to gut microbiota. We then created a manipulated microbial community with titrated doses of Collinsella, demonstrating that manipulation of the composition of the microbiome under both natural and controlled conditions leads to distinct and predictable gene expression profiles in host cells. Taken together, our results suggest that specific microbes play an important role in regulating expression of individual host genes involved in human complex traits. The ability to fine-tune the expression of host genes by manipulating the microbiome suggests future therapeutic routes.IMPORTANCE The composition of the gut microbiome has been associated with various aspects of human health, but the mechanism of this interaction is still unclear. We utilized a cellular system to characterize the effect of the microbiome on human gene expression. We showed that some of these changes in expression may be mediated by changes in chromatin accessibility. Furthermore, we validate the role of a specific microbe and show that changes in its abundance can modify the host gene expression response. These results show an important role of gut microbiota in regulating host gene expression and suggest that manipulation of microbiome composition could be useful in future therapies.
Addressing the fundamental tension of PCGML with discriminative learning

Procedural content generation via machine learning (PCGML) is typically framed as the task of fitting a generative model to full-scale examples of a desired content distribution. This approach presents a fundamental tension: the more design effort expended to produce detailed training examples for shaping a generator, the lower the return on investment from applying PCGML in the first place. In response, we propose the use of discriminative models, which capture the validity of a design rather the distribution of the content, trained on positive and negative example design fragments. Through a modest modification of WaveFunctionCollapse, a commercially-adopted PCG approach that we characterize as using elementary machine learning, we demonstrate a new mode of control for learning-based generators. We demonstrate how an artist might craft a focused set of additional positive and negative design fragments by critique of the generator's previous outputs. This interaction mode bridges PCGML with mixed-initiative design assistance tools by working with a machine to define a space of valid designs rather than just one new design.
Weak membrane interactions allow Rheb to activate mTORC1 signaling without major lysosome enrichment

Stable localization of the Rheb GTPase to lysosomes is thought to be required for activation of mTOR complex 1 (mTORC1) signaling. However, the lysosome targeting mechanisms for Rheb remain unclear. We therefore investigated the relationship between Rheb subcellular localization and mTORC1 activation. Surprisingly, we found that Rheb was undetectable at lysosomes. Nonetheless, functional assays in knockout human cells revealed that farnesylation of the C-terminal CaaX motif on Rheb was essential for Rheb-dependent mTORC1 activation. Although farnesylated Rheb exhibited partial endoplasmic reticulum (ER) localization, constitutively targeting Rheb to ER membranes did not support mTORC1 activation. Further systematic analysis of Rheb lipidation revealed that weak, nonselective, membrane interactions support Rheb-dependent mTORC1 activation without the need for a specific lysosome targeting motif. Collectively, these results argue against stable interactions of Rheb with lysosomes and instead that transient membrane interactions optimally allow Rheb to activate mTORC1 signaling.
Support theorem for an SPDE with multiplicative noise driven by a cylindrical Wiener process on the real line

We prove a Stroock–Varadhan’s type support theorem for a stochastic partial differential equation on the real line with a noise term driven by a cylindrical Wiener process on $$L_2 ({\mathbb {R}})$$ . The main ingredients of the proof are V. Mackevicius’s approach to support theorem for diffusion processes and N.V. Krylov’s $$L_p$$ -theory of SPDEs.
Semi-supervised Multi-task Learning with Chest X-Ray Images

Discriminative models that require full supervision are inefficacious in the medical imaging domain when large labeled datasets are unavailable. By contrast, generative modeling—i.e., learning data generation and classification—facilitates semi-supervised training with limited labeled data. Moreover, generative modeling can be advantageous in accomplishing multiple objectives for better generalization. We propose a novel multi-task learning model for jointly learning a classifier and a segmentor, from chest X-ray images, through semi-supervised learning. In addition, we propose a new loss function that combines absolute KL divergence with Tversky loss (KLTV) to yield faster convergence and better segmentation performance. Based on our experimental results using a novel segmentation model, an Adversarial Pyramid Progressive Attention U-Net (APPAU-Net), we hypothesize that KLTV can be more effective for generalizing multi-tasking models while being competitive in segmentation-only tasks.
Evaluating Measurement-Based Dynamic Load Modeling Techniques and Metrics

Wide-area data and algorithms in large power systems are creating new opportunities for implementation of measurement-based dynamic load modeling techniques. These techniques improve the accuracy of dynamic load models, which are an integral part of transient stability analysis. Measurement-based load modeling techniques commonly assume response error is correlated to system or model accuracy. Response error is the difference between simulation output and phasor measurement units (PMUs) samples. This paper investigates similarity measures, output types, simulation time spans, and disturbance types used to generate response error and the correlation of the response error to system accuracy. This paper aims to address two hypotheses: 1) can response error determine the total system accuracy? and 2) can response error indicate if a dynamic load model being used at a bus is sufficiently accurate? The results of the study show only specific combinations of metrics yield statistically significant correlations, and there is a lack of pattern of combinations of metrics that deliver significant correlations. Less than 20% of all simulated tests in this study resulted in statistically significant correlations. These outcomes highlight concerns with common measurement-based load modeling techniques, raising awareness to the importance of careful selection and validation of similarity measures and response output metrics. Naive or untested selection of metrics can deliver inaccurate and misleading results.
Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents

Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg- Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose an elegant answer based on the following insight: we can view Legg-Hutter agents as candidates in an election, whose voters are environments, letting each environment vote (via its rewards) which agent (if either) is more intelligent. This leads to an abstract family of comparators simple enough that we can prove some structural theorems about them. It is an open question whether these structural theorems apply to more practical intelligence measures.
STUDY OF SEVERAL ERROR SOURCES IN A LASER RAMAN CLOCK.

We are investigating the development of a cesium clock using a laser excited resonance Raman interaction in place of direct microwave excitation. Such a scheme, employing only semiconductor laser excitation and exploiting fiber optic and integrated optic technology together with a simple atomic beam design, may lead to the development of smaller, lighter and perhaps cheaper atomic clocks. So far, we have been studying a sodium Raman clock which consists of a sodium atomic beam, a dye laser, and an acousto-optic frequency shifter, used for the generation of the second laser frequency. Recent performance showed a stability of 1 x 10-l for a 5000 second averaging time. This compares favorably with commercial cesium clocks, when difference in atom transit time and transition frequency are taken into consideration. In this paper, we describe the results of a study of long term error sources unique to the Raman clock, which include: laser beam misalignment effects, errors caused by the laser frequency being slightly off-resonance with the intermediate state, laser intensity effects, and so on.
Scenario Forecasting of Residential Load Profiles

Load forecasting is an integral part of power system operations and planning. Due to rising penetrations of rooftop PV, electric vehicles and demand response programs, forecasting the load of individual and a small group of households is becoming increasingly important. Forecasting the load accurately, however, is considerably more difficult when only a few households are included. A way to mitigate this challenge is to provide a set of scenarios instead of one point forecast, so an operator or utility can consider the full range of behaviors. This paper proposes a novel scenario forecasting approach for residential load using flow-based conditional generative models. Compared to existing scenario forecasting methods, our approach can generate scenarios that are not only able to infer possible future realizations of residential load from the observed historical data but also realistic enough to cover a wide range of behaviors. Particularly, the flow-based models utilize a flow of reversible transformations to maximize the value of conditional density function of future load given the past observations. In order to better capture the complex temporal dependency of the forecasted future load, we extend the structure design for the reversible transformations in flow-based conditional generative models by strengthening the coupling between the output and the conditioning input. The simulation results show the flow-based designs outperform existing methods in scenario forecasting for residential load by providing both more accurate and more diverse scenarios.
Deep Closest Point: Learning Representations for Point Cloud Registration

Point cloud registration is a key problem for computer vision applied to robotics, medical imaging, and other applications. This problem involves finding a rigid transformation from one point cloud into another so that they align. Iterative Closest Point (ICP) and its variants provide simple and easily-implemented iterative methods for this task, but these algorithms can converge to spurious local optima. To address local optima and other difficulties in the ICP pipeline, we propose a learning-based method, titled Deep Closest Point (DCP), inspired by recent techniques in computer vision and natural language processing. Our model consists of three parts: a point cloud embedding network, an attention-based module combined with a pointer generation layer to approximate combinatorial matching, and a differentiable singular value decomposition (SVD) layer to extract the final rigid transformation. We train our model end-to-end on the ModelNet40 dataset and show in several settings that it performs better than ICP, its variants (e.g., Go-ICP, FGR), and the recently-proposed learning-based method PointNetLK. Beyond providing a state-of-the-art registration technique, we evaluate the suitability of our learned features transferred to unseen objects. We also provide preliminary analysis of our learned model to help understand whether domain-specific and/or global features facilitate rigid registration.
An explicit Abelian surface with maximal Galois action

We construct an explicit example of a genus 2 curve C over a number field K such that the adelic Galois representation arising from the action of Gal(K¯/K) on the Jacobian of C has image GSp4(Z).
Targetable Tetrazine-Based Dynamic Nuclear Polarization Agents for Biological Systems.

Dynamic nuclear polarization (DNP) has shown great promise as a tool to enhance the nuclear magnetic resonance (NMR) signals of proteins in the cellular environment. As the sensitivity increases, the ability to select and efficiently polarize a specific macromolecule over the cellular background has become desirable. Here, we address this need and present a tetrazine-based DNP polarization agent that can be targeted selectively to proteins containing the unnatural amino acid (UAA) norbornene-lysine. The UAA can be introduced efficiently by genetic means in the cellular milieu. Our approach is bio-orthogonal and easily adaptable to any protein of interest. We illustrate the scope of our methodology and investigate the DNP polarization transfer mechanisms in several biological systems. Our results shed light on the complex polarization transfer pathways in targeted DNP and ultimately pave the way to selective DNP-enhanced NMR spectroscopy in both bacterial and mammalian cells.
Stresses in Layered Elastic Solids

General expressions for stress are derived for two-layer systems subjected to surface line or strip load; equations developed may be used to calculate stresses in supporting layered media for embankments, continuous footings, pavement systems, compressor-station foundation mats, canals, etc, if supporting media can be approximately represented as two-layer plane strain system.
Experimental Demonstration of Probabilistic Spin Logic by Magnetic Tunnel Junctions

Recently proposed probabilistic spin logic (PSL) has offered promising solutions to novel computing applications, including some that have previously been covered by quantum computing. Several task implementations, including invertible logic gate, have been simulated numerically. Here, we report an experimental demonstration of a magnetic tunnel junction (MTJ) based hardware implementation of PSL. The probabilistic bit (p-bit) is the basic element of PSL. In our hardware implementation of a p-bit, two biasing methods, magnetic field and voltage, were used to better tune the characteristics of the MTJ random fluctuations. This addresses the potential system-wide speed limitations that result from the unavoidable device-to-device variation in MTJ fluctuation rates. With the p-bit hardware implementation demonstrated, we built three p-bits and connected them through a resistor network to implement an example PSL, an invertible AND gate, which performs exactly as expected.
Global-Scale Observations and Modeling of Far-Ultraviolet Airglow During Twilight

The NASA Global-scale Observations of the Limb and Disk (GOLD) ultraviolet imaging spectrograph performs observations of upper-atmosphere airglow from the sunlit disk and limb of the Earth, in order to infer quantities such as the composition and temperature of the thermosphere. To interpret the measurements, the observational and solar illumination geometry must be considered. We use forward models of upper atmosphere density and composition, photoelectron impact, airglow emissions, radiative transfer, and line-of-sight integration, to describe the expected observations, and here test those calculations against observations near the terminator, and near the limb. On the night side of the terminator, broad regions of faint airglow are seen, particularly near the winter solstice. These are caused by photoelectrons that were transported along field lines from magnetically conjugate areas in the other hemisphere, where those areas are still illuminated. We perform model calculations to demonstrate that this process is the source of the emission, and obtain good agreement with its morphology and intensity. In some regions, the observed emissions are not as intense as the model simulations. Some of the reductions in electron flux are explained by changes in magnetic field strength; in other cases, particularly at high magnetic latitude, the cause is unknown, but must occur along extended field lines as they reach into the plasma sheet.
The Role of Specific Ion Effects in Ion Transport: The Case of Nitrate and Thiocyanate

The selective transport of trivalent rare earth metals from aqueous to organic environments with the help of amphiphilic “extractants” is an industrially important process. When the amphiphilic ext...
Drug Effects on the Thyroid.

Drug Effects on the Thyroid The list of medications, many commonly used, that adversely affect thyroid function or the interpretation of standard thyroid tests is growing. This review addresses dru...
Thermoelectrically pumped light-emitting diodes operating above unity efficiency

A heated semiconductor light-emitting diode at low forward bias voltage V<k(B)T/q is shown to use electrical work to pump heat from the lattice to the photon field. Here the rates of both radiative and nonradiative recombination have contributions at linear order in V. As a result the device's wall-plug (i.e., power conversion) efficiency is inversely proportional to its output power and diverges as V approaches zero. Experiments directly confirm for the first time that this behavior continues beyond the conventional limit of unity electrical-to-optical power conversion efficiency.
Pass-Through of Own and Rival Cost Shocks: Evidence from the U.S. Fracking Boom

In imperfectly competitive settings, a firm's price depends on its own costs as well as those of its competitors. We demonstrate that this has important implications for the estimation and interpretation of pass-through. Leveraging a large input cost shock resulting from the fracking boom, we isolate price responses to firm-specific, regional and industry-wide input cost shocks in the US oil refining industry. The pass-through of these components vary from near zero to full pass-through, reconciling seemingly disparate results from the literature. We illustrate the policy implications of rival cost pass-through in the context of a tax on refinery carbon emissions.
Structural basis of DNA targeting by a transposon-encoded CRISPR-Cas system

Bacteria use adaptive immune systems encoded by CRISPR and Cas genes to maintain genomic integrity when challenged by pathogens and mobile genetic elements1–3. Type I CRISPR–Cas systems typically target foreign DNA for degradation via joint action of the ribonucleoprotein complex Cascade and the helicase–nuclease Cas34,5, but nuclease-deficient type I systems lacking Cas3 have been repurposed for RNA-guided transposition by bacterial Tn7-like transposons6,7. How CRISPR- and transposon-associated machineries collaborate during DNA targeting and insertion remains unknown. Here we describe structures of a TniQ–Cascade complex encoded by the Vibrio cholerae Tn6677 transposon using cryo-electron microscopy, revealing the mechanistic basis of this functional coupling. The cryo-electron microscopy maps enabled de novo modelling and refinement of the transposition protein TniQ, which binds to the Cascade complex as a dimer in a head-to-tail configuration, at the interface formed by Cas6 and Cas7 near the 3′ end of the CRISPR RNA (crRNA). The natural Cas8–Cas5 fusion protein binds the 5′ crRNA handle and contacts the TniQ dimer via a flexible insertion domain. A target DNA-bound structure reveals critical interactions necessary for protospacer-adjacent motif recognition and R-loop formation. This work lays the foundation for a structural understanding of how DNA targeting by TniQ–Cascade leads to downstream recruitment of additional transposase proteins, and will guide protein engineering efforts to leverage this system for programmable DNA insertions in genome-engineering applications. Cryo-electron microscopy structures of the TniQ–Cascade complex encoded by the Vibrio cholerae Tn6677 transposon reveal the mechanistic basis of the functional association of CRISPR- and transposon-associated machineries.
Quantification of reactive oxygen species production by the red fluorescent proteins KillerRed, SuperNova and mCherry.

Abstract Fluorescent proteins can generate reactive oxygen species (ROS) upon absorption of photons via type I and II photosensitization mechanisms. The red fluorescent proteins KillerRed and SuperNova are phototoxic proteins engineered to generate ROS and are used in a variety of biological applications. However, their relative quantum yields and rates of ROS production are unclear, which has limited the interpretation of their effects when used in biological systems. We cloned and purified KillerRed, SuperNova, and mCherry - a related red fluorescent protein not typically considered a photosensitizer - and measured the superoxide (O2•-) and singlet oxygen (1O2) quantum yields with irradiation at 561 nm. The formation of the O2•--specific product 2-hydroxyethidium (2-OHE+) was quantified via HPLC separation with fluorescence detection. Relative to a reference photosensitizer, Rose Bengal, the O2•- quantum yield (ΦO2•-) of SuperNova was determined to be 1.5 × 10-3, KillerRed was 0.97 × 10-3, and mCherry 1.2 × 10-3. At an excitation fluence of 916.5 J/cm2 and matched absorption at 561 nm, SuperNova, KillerRed and mCherry made 3.81, 2.38 and 1.65 μM O2•-/min, respectively. Using the probe Singlet Oxygen Sensor Green (SOSG), we ascertained the 1O2 quantum yield (Φ1O2) for SuperNova to be 22.0 × 10-3, KillerRed 7.6 × 10-3, and mCherry 5.7 × 10-3. These photosensitization characteristics of SuperNova, KillerRed and mCherry improve our understanding of fluorescent proteins and are pertinent for refining their use as tools to advance our knowledge of redox biology.
Observe Before Play: Multi-Armed Bandit with Pre-Observations.

We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to pre-observe arm rewards before playing an arm in each round. Apart from the usual trade-off between exploring new arms to find the best one and exploiting the arm believed to offer the highest reward, we encounter an additional dilemma: pre-observing more arms gives a higher chance to play the best one, but incurs a larger cost. For the single-player setting, we design an Observe-Before-Play Upper Confidence Bound (OBP-UCB) algorithm for K arms with Bernoulli rewards, and prove a T-round regret upper bound O(K2log T). In the multi-player setting, collisions will occur when players select the same arm to play in the same round. We design a centralized algorithm, C-MP-OBP, and prove its T-round regret relative to an offline greedy strategy is upper bounded in O(K4/M2log T) for K arms and M players. We also propose distributed versions of the C-MP-OBP policy, called D-MP-OBP and D-MP-Adapt-OBP, achieving logarithmic regret with respect to collision-free target policies. Experiments on synthetic data and wireless channel traces show that C-MP-OBP and D-MP-OBP outperform random heuristics and offline optimal policies that do not allow pre-observations.
Hopfield Neural Network Flow: A Geometric Viewpoint

We provide gradient flow interpretations for the continuous-time continuous-state Hopfield neural network (HNN). The ordinary and stochastic differential equations associated with the HNN were introduced in the literature as analog optimizers and were reported to exhibit good performance in numerical experiments. In this work, we point out that the deterministic HNN can be transcribed into Amari’s natural gradient descent, and thereby uncover the explicit relation between the underlying Riemannian metric and the activation functions. By exploiting an equivalence between the natural gradient descent and the mirror descent, we show how the choice of activation function governs the geometry of the HNN dynamics. For the stochastic HNN, we show that the so-called “diffusion machine,” while not a gradient flow itself, induces a gradient flow when lifted in the space of probability measures. We characterize this infinite-dimensional flow as the gradient descent of certain free energy with respect to a Wasserstein metric that depends on the geodesic distance on the ground manifold. Furthermore, we demonstrate how this gradient flow interpretation can be used for fast computation via recently developed proximal algorithms.
Beyond Wealth and Health: The Social Environment as a Protective Factor for Cognitive Development of Children in Nicaragua

ABSTRACTWe examine the contributions of the environmental context on cognitive development in a representative sample of children (24–59 month-olds) in Nicaragua. Multivariate regression models rev...
ω-Cen - an Ultra Compact Dwarf Galaxy?

We study the merging of star clusters out of cluster aggregates similar to Knot S in the Antennae on orbits close to the one of ω Cen by carrying out high resolution numerical N -body simulations. We want to constrain the parameter space which is able to produce merger objects with similar properties as ω Cen.
Value of Troponin Testing for Detection of Heart Disease in Previously Healthy Children.

Background Troponin levels are frequently obtained in pediatric patients, but the benefit remains unclear. Methods and Results This retrospective study included 1993 patients aged 0 to 21 years wit...
Energy equality for the Navier–Stokes equations in weak-in-time Onsager spaces

Onsager's conjecture for the 3D Navier–Stokes equations concerns the validity of energy equality of weak solutions with regards to their smoothness. In this note, we establish the energy equality for weak solutions in a large class of function spaces. These conditions are weak-in-time with optimal space regularity and therefore weaker than previous classical results. Heuristics using intermittency argument and divergence-free counterexamples are given, indicating the possible sharpness of our conditions.
Persistent problems in colorectal cancer reporting

Tumor stage, as determined by the Tumor, Node, Metastasis (TNM) staging system, is the single most influential factor determining treatment decisions and outcome among patients with colorectal cancer. Several stage-related elements in pathology reports consistently pose diagnostic challenges: recognition of serosal penetration by tumor (ie, pT3 vs pT4a), evaluation of regional lymph nodes, distinction between tumor deposits and effaced lymph nodes, and assessment of tumor stage in the neoadjuvant setting. This article discusses each of these issues in detail and provides practical tips regarding colorectal cancer staging.
Loss of a conserved MAPK causes catastrophic failure in assembly of a specialized cilium-like structure in Toxoplasma gondii

Primary cilia are important organizing centers that control diverse cellular processes. Apicomplexan parasites like Toxoplasma gondii have a specialized cilium-like structure called the conoid that organizes the secretory and invasion machinery critical for the parasites' lifestyle. The proteins that initiate the biogenesis of this structure are largely unknown. We identified the Toxoplasma orthologue of the conserved kinase ERK7 as essential to conoid assembly. Parasites in which ERK7 has been depleted lose their conoids late during maturation and are immotile and thus unable to invade new host cells. This is the most severe phenotype to conoid biogenesis yet reported, and is made more striking by the fact that ERK7 is not a conoid protein, as it localizes just basal to the structure. ERK7 has been recently implicated in ciliogenesis in metazoan cells, and our data suggest that this kinase has an ancient and central role in regulating ciliogenesis throughout Eukaryota.
Model for the electrical conductivity in dense plasma mixtures

Abstract A new density functional theory, average atom based model for the electrical conductivity of dense plasmas with a mixture of ion species, containing no adjustable parameters, is presented. The model takes the temperature, mass density and relative abundances of the species as input. It takes into account partial ionization, ionic structure, and core-valence orthogonality, and uses quantum mechanical calculations of cross sections. Comparison to an existing high fidelity but computationally expensive method reveals good agreement. The new model is computationally efficient and can reach high temperatures. A new mixing rule is also presented that gives reasonably accurate conductivities for high temperature plasma mixtures.
Nanomonitors: Electrical Immunoassays for Protein Biomarker Profiling

The objective of this research is to develop a “point-of-care” device for early disease diagnosis through protein biomarker characterization. Here we present label-free, high sensitivity detection of proteins with the use of electrical immunoassays that we call Nanomonitors. The basis of the detection principle lies in the formation of an electrical double layer and its perturbations caused by proteins trapped in a nanoporous alumina membrane over a microelectrode array platform. High sensitivity and rapid detection of two inflammatory biomarkers, C-reactive protein (CRP) and Myeloperoxidase (MPO) in pure and clinical samples through label-free electrical detection were achieved. The performance metrics achieved by this device makes it suitable as a “lab-on-a-chip” device for protein biomarker profiling and hence early disease diagnosis.
Urbanization and Its Discontents

American cities have experienced a remarkable renaissance over the past 40 years, but in recent years, cities have experienced considerable discontent. Anger about high housing prices and gentrification has led to protests. The urban wage premium appears to have disappeared for less skilled workers. The cities of the developing world are growing particularly rapidly, but in those places, the downsides of density are acute. In this essay, I review the causes of urban discontent and present a unified explanation for this unhappiness. Urban resurgence represents private sector success, and the public sector typically only catches up to urban change with a considerable lag. Moreover, as urban machines have been replaced by governments that are more accountable to empowered residents, urban governments do more to protect insiders and less to enable growth. The power of insiders can be seen in the regulatory limits on new construction and new businesses, the slow pace of school reform and the unwillingness to embrace congestion pricing.
Gap distribution of Farey fractions under some divisibility constraints

For a given positive integer l we show the existence of the limiting gap distribution measure for the sets of Farey fractions aq of order Q with l ∤ a, and respectively with (q, l) = 1, as Q → ∞.
Polarizing Front Ends for Robust Cnns

The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their "excessive linearity." In this paper, we propose a bottom-up strategy for attenuating adversarial perturbations using a nonlinear front end which polarizes and quantizes the data. We observe that ideal polarization can be utilized to completely eliminate perturbations, develop algorithms to learn approximately polarizing bases for data, and investigate the effectiveness of the proposed strategy on the MNIST and Fashion MNIST datasets.
Developments in Lorentz and CPT Violation

This talk at the CPT'19 meeting outlines a few recent developments in Lorentz and CPT violation, with particular attention to results obtained by researchers at the Indiana University Center for Spacetime Symmetries.
Evolutionary dynamics of culturally transmitted, fertility-reducing traits

Human populations in many countries have undergone a phase of demographic transition, characterized by a major reduction in fertility at a time of increased resource availability. A key stylized fact is that the reduction in fertility is preceded by a reduction in mortality and a consequent increase in population density. Various theories have been proposed to account for the demographic transition process, including maladaptation, increased parental investment in fewer offspring, and cultural evolution. None of these approaches, including formal cultural evolutionary models of the demographic transitions, have addressed a possible direct causal relationship between a reduction in mortality and the subsequent decline in fertility. We provide mathematical models in which low mortality favours the cultural selection of low-fertility traits. This occurs because reduced mortality slows turnover in the model, which allows the cultural transmission advantage of low-fertility traits to outrace their reproductive disadvantage. For mortality to be a crucial determinant of outcome, a cultural transmission bias is required where slow reproducers exert higher social influence. Computer simulations of our models that allow for exogenous variation in the death rate can reproduce the central features of the demographic transition process, including substantial reductions in fertility within only one to three generations. A model assuming continuous evolution of reproduction rates through imitation errors predicts fertility to fall below replacement levels if death rates are sufficiently low. This can potentially explain the very low preferred family sizes in Western Europe.
AI Feynman: A physics-inspired method for symbolic regression

A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality, and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult physics-based test set, we improve the state-of-the-art success rate from 15 to 90%.
Kelly Cache Networks

We study networks of M/M/1 queues in which nodes act as caches that store objects. Exogenous requests for objects are routed towards nodes that store them; as a result, object traffic in the network is determined not only by demand but, crucially, by where objects are cached. We determine how to place objects in caches to attain a certain design objective, such as, e.g., minimizing network congestion or retrieval delays. We show that for a broad class of objectives, including minimizing both the expected network delay and the sum of network queue lengths, this optimization problem can be cast as an NP-hard submodular maximization problem. We show that so-called continuous greedy algorithm attains a ratio arbitrarily close to $1-1/e\approx 0.63$ using a deterministic estimation via a power series; this drastically reduces execution time over prior art, which resorts to sampling. Finally, we show that our results generalize, beyond M/M/1 queues, to networks of M/M/ $k$ and symmetric M/D/1 queues.
Methane, Monsoons, and Modulation of Millennial-Scale Climate

Earths orbital geometry exerts a profound influence on climate by regulating changes in incoming solar radiation. Superimposed on orbitally-paced climate change, Pleistocene records reveal substantial millennial-scale variability characterized by trends, tipping points, and rapid swings. However, the extent to which orbital forcing modulates the amplitude and timing of these millennial variations is unclear. Here we isolate the magnitude of millennial-scale variability (MMV) in two well-dated records, both linked to precession cycles (19,000-23,000-year periodicity): atmospheric methane and Chinese speleothem δ¹⁸O, where the latter is commonly interpreted as a proxy for Asian monsoon intensity. At the millennial timescale (1,000-10,000 years), we find a fundamental decoupling wherein precession directly modulates the MMV of methane but not that of speleothem-δ¹⁸O. We hypothesize that mid-to-high latitude insolation modulates the MMV of atmospheric methane, but feedbacks internal to the Earth-climate system modulate the strength of millennial-scale monsoonal circulation.
Chapter 3 – Theory of Subband Decomposition

The chapter describes the development of the theory of subband filter banks. Subbabnd decomposition is a method of multiresolution signal decomposition. Data compression is the driving motivation for subband signal coding. The basic objective is to divide the signal frequency band into a set of uncorrelated frequency bands by filtering and then to encode each of these subbands using a bit allocation rationale matched to the signal energy in that subband. The actual coding of the subband signal can be done using waveform encoding techniques, such as pulse-code modulation (PCM), differential PCM (DPCM), or vector quantization. The subband coder achieves energy compaction by filtering serial data, whereas transform coding utilizes block transformation. If the subbands have little spillover from adjacent bands (as would be the case if the subband filters have sharp cutoffs), the quantization noise in a given band is confined largely to that band. This permits separation, band-by-band allocation of bits, and control of this noise in each subband.
24 – Energy economics

Publisher Summary This chapter discusses the economics of energy, which refers to the study of how societies use energy resources to produce valuable commodities and distribute them among different people.. In this context, the chapter takes a look at the comparative costs of electricity from nuclear plants and from coal or oil plants. Half the cost of electric power is for generation. Electricity from plants using coal or nuclear fuel is comparable in cost, with a tradeoff between capital costs and fuel costs. The nuclear industry has several opportunities for improvements including license extension, but is faced with the challenges of electricity restructuring. Several advanced reactor concepts are being promoted to preserve the nuclear option. The chapter then takes a look at the expected use for nuclear power in the future and examines the choices of nuclear power research and development that must be made for judicious use of energy sources. Finally, the chapter describes the concept of long-range role of nuclear power.
Fuzzy Logic Traction Controllers and their Effect on Longitudinal Vehicle Platoon Systems

SUMMARY This paper presents two fuzzy logic traction controllers and investigates their effect on longitudinal platoon systems. A fuzzy logic approach is appealing for traction control because of the nonlinearity and time-varying uncertainty involved in traction control systems The fuzzy logic traction controllers we present regulate brake torque to control wheel slip, which is the normalized difference between wheel and vehicle speed. One fuzzy controller estimates the peak slip corresponding to the maximum tire-road adhesion coefficient and regulates wheel slip at the peak slip. The controller is attractive because of its ability to maximize acceleration and deceleration regardless of road condition. However, we find through simulations the controller's performance degrades in the presence of time-varying uncertainties. The other fuzzy logic controller regulates wheel slip at any desired value. Through simulations we find the controller robust against changing road conditions and uncertainties. The targ...
High Resolution Turbulence Treatment of F/A-18 Tail Buffet

Unsteady tail loads of the F/A-18 are computed using various turbulence models at an angle of attack consistent with buffet induced by leading-edge extension vortex breakdown. Comparison of these industry-standard Reynolds-averaged Navier-Stokes turbulence models with the detached-eddy simulation hybrid approach reveals the inadequacies of Reynolds-averaged Navier-Stokes methods and the ability of detached-eddy simulation to reproduce the observed unsteadiness at these conditions. Computed vortex breakdown position and frequencies of the detached-eddy simulation method are shown to be accurate by comparison to flight-test and experimental results. Finally, comparison of the detached-eddy simulation unsteady tail pressures with flight-test tail pressures reveal the ability of the method to accurately reproduce F/A-18 tail buffet loads and its readiness for inclusion in a fluid-structure interaction method.
On the light-front wave functions of quarkonia

The light-front wave functions of hadrons allow us to calculate a wide range of physical observables; however, the wave functions themselves cannot be measured. We discuss recent results for quarkonia obtained in basis light-front quantization using an effective Hamiltonian with a confining model in both the transverse and longitudinal directions and with explicit one-gluon exchange. In particular, we focus on the numerical convergence of the basis expansion, as well as the asymptotic behavior of the light-front wave functions. We also illustrate that, for mesons with unequal quark masses, the maxima of the light-front wave functions depend in a non-trivial way on the valence quark-mass difference.
The Litigious Plaintiff Hypothesis: Case Selection and Resolution

A central feature of the litigation process that affects case outcomes is the selection of cases for litigation. In this study, we present a theoretical framework for understanding the operation of this suit selection process and its relationship to the underlying distribution of potential claims and claimants. We implement the model empirically by assuming that individuals vary more in their litigiousness (inverse costs of litigation) than do corporations. This assumption, coupled with the case selection process we present, yields clear predictions on trial rates as a function of whether the plaintiff and defendant were individuals or corporations. The model also yields a prediction on the plaintiff's win rate in lawsuits as a function of the plaintiff's identity. Our empirical analysis, using data on over 200,000 federal civil litigations, yields results that are generally consistent with the theory. Lawsuits where the plaintiff is an individual are found to have higher trial rates and lower plaintiff win rates.
The optimal use of fines and imprisonment

Abstract This paper examines the use of fines and imprisonment to deter individuals from engaging in harmful activities. These sanctions are analyzed separately as well as together, first for identical risk-neutral individuals and then for two groups of risk-neutral individuals who differ by wealth. When fines are used alone and individuals are identical, the optimal fine and probability of apprehension are such that there is some ‘underdeterrence’. If individuals differ by wealth, then the optimal fine for the high wealth group exceeds the fine for the low wealth group. When imprisonment is used alone and individuals are identical, the optimal imprisonment term and probability may be such that there is either underdeterrence or overdeterrence. If individuals differ by wealth, the optimal imprisonment term for the high wealth group may be longer or shorter than the term for the low wealth group. When fines and imprisonment are used together, it is desirable to use the fine to its maximum feasible extent before possibly supplementing it with an imprisonment term. The effects of risk aversion of these results are also discussed.
Characteristic Polynomials for Random Band Matrices Near the Threshold

The paper continues (Shcherbina and Shcherbina in Commun Math Phys 351:1009–1044, 2017); Shcherbina in Commun Math Phys 328:45–82, 2014) which study the behaviour of second correlation function of characteristic polynomials of the special case of $$n\times n$$ one-dimensional Gaussian Hermitian random band matrices, when the covariance of the elements is determined by the matrix $$J=(-W^2\triangle +1)^{-1}$$. Applying the transfer matrix approach, we study the case when the bandwidth W is proportional to the threshold $$\sqrt{n}$$.
BAD to the bone: Big Active Data at its core

Virtually, all of today’s Big Data systems are passive in nature, responding to queries posted by their users. Instead, we are working to shift Big Data platforms from passive to active. In our view, a Big Active Data (BAD) system should continuously and reliably capture Big Data while enabling timely and automatic delivery of relevant information to a large pool of interested users, as well as supporting retrospective analyses of historical information. While various scalable streaming query engines have been created, their active behavior is limited to a (relatively) small window of the incoming data. To this end, we have created a BAD platform that combines ideas and capabilities from both Big Data and Active Data (e.g., publish/subscribe, streaming engines). It supports complex subscriptions that consider not only newly arrived items but also their relationships to past, stored data. Further, it can provide actionable notifications by enriching the subscription results with other useful data. Our platform extends an existing open-source Big Data Management System, Apache AsterixDB, with an active toolkit. The toolkit contains features to rapidly ingest semistructured data, share execution pipelines among users, manage scaled user data subscriptions, and actively monitor the state of the data to produce individualized information for each user. This paper describes the features and design of our current BAD data platform and demonstrates its ability to scale without sacrificing query capabilities or result individualization.
Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations.

Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (PESs) are showing great promise for reducing the computational cost of accurate molecular simulations, but at present, they are not applicable to varying electronic states, and in particular, they are not well suited for molecular systems in which the local electronic structure is sensitive to the medium to long-range electronic environment. With this issue as the focal point, we present a new machine learning approach called "BpopNN" for obtaining efficient approximations to DFT PESs. Conceptually, the methodology is based on approaching the true DFT energy as a function of electron populations on atoms; in practice, this is realized with available density functionals and constrained DFT (CDFT). The new approach creates approximations to this function with neural networks. These approximations thereby incorporate electronic information naturally into a ML approach, and optimizing the model energy with respect to populations allows the electronic terms to self-consistently adapt to the environment, as in DFT. We confirm the effectiveness of this approach with a variety of calculations on LinHn clusters.