cssi/SciSciGPT-SciSciNet huggingface dataset:
Cascading parallel fractures on Enceladus

Active eruptions from the south polar region of Saturn’s ~500-km-diameter moon Enceladus are concentrated along a series of lineaments known as the ‘tiger stripes’1,2, thought to be partially open fissures that connect to the liquid water ocean beneath the ice shell3,4. To date, no study simultaneously explains why the tiger stripes should be located only at the south pole, why there are multiple approximately parallel and regularly spaced fractures, what accounts for their spacing of about 35 km, and why similarly active fissures have not been observed on other icy bodies. Here we propose that secular cooling, which leads to a thickening of the ice shell and building of global tensile stresses5,6, causes the first fracture to form at one of the poles, where the ice shell is thinnest owing to tidal heating7. The tensile stresses are thereby relieved, preventing a similar failure at the opposite pole. The steadily erupting water ice loads the flanks of the open fissure, causing bending in the surrounding elastic plate and further tensile failure in bands parallel to the first fracture—a process that may be unique to Enceladus, where the gravity is too weak for compressive stresses to prevent fracture propagation through the thin ice shell. The sequence of fissures then cascades outwards until the loading becomes too weak or the background shell thickness becomes too great to permit through-going fractures. Enceladus’s tiger stripes at the south pole formed in cascade and spaced equally after the first fracture—probably Baghdad Sulcus—was created by the release of accumulated tensile stress, caused in turn by secular cooling.
Dorsal and Ventral Hippocampal Sharp-Wave Ripples Activate Distinct Nucleus Accumbens Networks

Memories of positive experiences link places, events, and reward outcomes. These memories recruit interactions between the hippocampus and nucleus accumbens (NAc). Both dorsal and ventral hippocampus (dH and vH) project to the NAc, but it remains unknown whether dH and vH act in concert or separately to engage NAc representations related to space and reward. We recorded simultaneously from the dH, vH, and NAc of rats during an appetitive spatial task and focused on hippocampal sharp-wave ripples (SWRs) to identify times of memory reactivation across brain regions. Here, we show that dH and vH awake SWRs occur asynchronously and activate distinct and opposing patterns of NAc spiking. Only NAc neurons activated during dH SWRs were tuned to task- and reward-related information. These temporally and anatomically separable hippocampal-NAc interactions point to distinct channels of mnemonic processing in the NAc, with the dH-NAc channel specialized for spatial task and reward information. VIDEO ABSTRACT.
Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning

Affective states have a critical role in driving performance and safety. They can degrade driver situation awareness and negatively impact cognitive processes, severely diminishing road safety. Therefore, detecting and assessing drivers' affective states is crucial in order to help improve the driving experience, and increase safety, comfort and well-being. Recent advances in affective computing have enabled the detection of such states. This may lead to empathic automotive user interfaces that account for the driver's emotional state and influence the driver in order to improve safety. In this work, we propose a multiview multi-task machine learning method for the detection of driver's affective states using physiological signals. The proposed approach is able to account for inter-drive variability in physiological responses while enabling interpretability of the learned models, a factor that is especially important in systems deployed in the real world. We evaluate the models on three different datasets containing real-world driving experiences. Our results indicate that accounting for drive-specific differences significantly improves model performance.
Optically Induced Localized Paramagnetic States in Amorphous Semiconductors

In addition to the previously reported paramagnetic hole center localized on the chalcogen, a conjugate optically induced electron center localized on an As atom is observed in glassy ${\mathrm{As}}_{2}$${\mathrm{S}}_{3}$ and ${\mathrm{As}}_{2}$${\mathrm{Se}}_{3}$. The chalcogen center is further identified as a hole localized in a nonbonding $p$ orbital. These optically induced centers are unique to the amorphous phase, and their density saturates at or below ${10}^{17}$ spins/${\mathrm{cm}}^{3}$.
Autonomous Robot Swarms for Off-World Construction and Resource Mining

Kick-starting the space economy requires identification of critical resources that can lower the cost of space transport, sustain logistic bases and communication relay networks between major nodes in the network. One important challenge with this space-economy is ensuring the low-cost transport of raw materials from one gravity-well to another. The escape delta-v of 11.2 km/s from Earth makes this proposition very expensive. Transporting materials from the Moon takes 2.4 km/s and from Mars 5.0 km/s. Based on these factors, the Moon and Mars have the potential to export material into this space economy. Water has been identified as a critical resource both to sustain human-life but also for use in propulsion, attitude-control, power, thermal storage and radiation protection systems.There is also important need for construction materials such as aluminum, iron/steel, and titanium. Based upon these important findings, we have developed an energy model to determine the feasibility of developing a mining base on the Moon and Mars. These mining base mine and principally exports water, aluminum, titanium and steel. Our designs for a mining base utilize renewable energy sources namely photovoltaics and solar-thermal concentrators to provide power to construct the base, keep it operational and export water and other resources using a Mass Driver. Using the energy model developed, we will determine the energy per Earth-day to export 100 tons each of water, titanium, aluminum and low-grade steel into escape velocity of the Moon and Mars. We perform a detailed comparison of the energy required for construction of similar bases on the Moon and Mars, in addition to the operating energy required for regolith excavation, processing, refining and finally transport off-the-body.
Pseudo-Deterministic Streaming.

A pseudo-deterministic algorithm is a (randomized) algorithm which, when run multiple times on the same input, with high probability outputs the same result on all executions. Classic streaming algorithms, such as those for finding heavy hitters, approximate counting, 𝓁_2 approximation, finding a nonzero entry in a vector (for turnstile algorithms) are not pseudo-deterministic. For example, in the instance of finding a nonzero entry in a vector, for any known low-space algorithm A, there exists a stream x so that running A twice on x (using different randomness) would with high probability result in two different entries as the output. In this work, we study whether it is inherent that these algorithms output different values on different executions. That is, we ask whether these problems have low-memory pseudo-deterministic algorithms. For instance, we show that there is no low-memory pseudo-deterministic algorithm for finding a nonzero entry in a vector (given in a turnstile fashion), and also that there is no low-dimensional pseudo-deterministic sketching algorithm for 𝓁_2 norm estimation. We also exhibit problems which do have low memory pseudo-deterministic algorithms but no low memory deterministic algorithm, such as outputting a nonzero row of a matrix, or outputting a basis for the row-span of a matrix. We also investigate multi-pseudo-deterministic algorithms: algorithms which with high probability output one of a few options. We show the first lower bounds for such algorithms. This implies that there are streaming problems such that every low space algorithm for the problem must have inputs where there are many valid outputs, all with a significant probability of being outputted.
RADAR: differential analysis of MeRIP-seq data with a random effect model

Epitranscriptome profiling using MeRIP-seq is a powerful technique for in vivo functional studies of reversible RNA modifications. We develop RADAR, a comprehensive analytical tool for detecting differentially methylated loci in MeRIP-seq data. RADAR enables accurate identification of altered methylation sites by accommodating variability of pre-immunoprecipitation expression level and post-immunoprecipitation count using different strategies. In addition, it is compatible with complex study design when covariates need to be incorporated in the analysis. Through simulation and real dataset analyses, we show that RADAR leads to more accurate and reproducible differential methylation analysis results than alternatives, which is available at https://github.com/scottzijiezhang/RADAR.
Radiative and Electroweak Penguin Decays at \(e^{+}e^{-}\)B-Factories

B-meson decays involving radiative and electroweak penguin processes are sensitive probes to new physics beyond the standard model. The Belle experiment recently reported measurements of the inclusive radiative decay \(B \rightarrow X^{}_{s} \gamma \), and exclusive radiative decays \(B \rightarrow K^{*} \gamma \) and \(B \rightarrow K^{0}_{S} \eta \gamma \). A lepton-flavor dependent measurements of angular observables for the decays \(B \rightarrow K^{*} \ell \ell \) by Belle hinted at possible deviation from lepton-flavor-universality. Any departure from lepton flavor universality is essentially accompanied by lepton flavor violation. Recently, lepton-flavor-violating decays \(B^{0} \rightarrow K^{*0} \mu ^{\pm } e^{\mp }\) are searched for at Belle, and stringent limits on their branching fractions are set. The BaBar experiment has searched for the decay \(B^{+} \rightarrow K^{+} \tau ^{+} \tau ^{-}\), which comprises third generation of the lepton family. The decays \(B \rightarrow h \nu \bar{\nu }\) are searched for at Belle and obtained upper limits for these decays are close to the standard model predictions.
A New Characterization of $\mathcal{V}$-Posets

Hasebe and Tsujie characterized the set of induced N-free and bowtie-free posets as a certain class of recursively defined subposets which they term “ $\mathcal {V}$ -posets”. Here we offer a new characterization of $\mathcal {V}$ -posets by introducing a property we refer to as autonomy. A poset $\mathcal {P}$ is said to be autonomous if there exists a directed acyclic graph D (with adjacency matrix U) whose transitive closure is $\mathcal {P}$ , with the property that any total ordering of the vertices of D so that Gaussian elimination of UTU proceeds without row swaps is a linear extension of $\mathcal {P}$ . Autonomous posets arise from the theory of pressing sequences in graphs, a problem with origins in computational evolutionary biology. The pressing sequences of a graph can be partitioned into families corresponding to posets; because of the interest in enumerating pressing sequences, we investigate when this partition has only one block, that is, when the pressing sequences are all linear extensions of a single autonomous poset. We also provide an efficient algorithm for recognition of autonomy using structural information and the forbidden subposet characterization, and we discuss a few open questions that arise in connection with these posets.
Improving the decision-making process of self-adaptive systems by accounting for tactic volatility

When self-adaptive systems encounter changes within their surrounding environments, they enact tactics to perform necessary adaptations. For example, a self-adaptive cloud-based system may have a tactic that initiates additional computing resources when response time thresholds are surpassed, or there may be a tactic to activate a specific security measure when an intrusion is detected. In real-world environments, these tactics frequently experience tactic volatility which is variable behavior during the execution of the tactic.Unfortunately, current self-adaptive approaches do not account for tactic volatility in their decision-making processes, and merely assume that tactics do not experience volatility. This limitation creates uncertainty in the decision-making process and may adversely impact the system's ability to effectively and efficiently adapt. Additionally, many processes do not properly account for volatility that may effect the system's Service Level Agreement (SLA). This can limit the system's ability to act proactively, especially when utilizing tactics that contain latency.To address the challenge of sufficiently accounting for tactic volatility, we propose a Tactic Volatility Aware (TVA) solution. Using Multiple Regression Analysis (MRA), TVA enables self-adaptive systems to accurately estimate the cost and time required to execute tactics. TVA also utilizes Autoregressive Integrated Moving Average (ARIMA) for time series forecasting, allowing the system to proactively maintain specifications.
A Real-Time Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes

Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run. However, as the simulations are computationally costly and time-consuming for predicting multiscale multi-physics phenomena in AM, physics-informed data-driven machine-learning systems for predicting the behavior of AM processes are immensely beneficial. Such models accelerate not only multiscale simulation tools but also empower real-time control systems using in-situ data. In this paper, we design and develop essential components of a scientific framework for developing a data-driven model-based real-time control system. Finite element methods are employed for solving time-dependent heat equations and developing the database. The proposed framework uses extremely randomized trees - an ensemble of bagged decision trees as the regression algorithm iteratively using temperatures of prior voxels and laser information as inputs to predict temperatures of subsequent voxels. The models achieve mean absolute percentage errors below 1% for predicting temperature profiles for AM processes. The code is made available for the research community at https://github.com/paularindam/ml-iter-additive.
Galois extensions and a Conjecture of Ogg

Let $N=pq$ be a product of two distinct primes. There is an isogeny $J_0(N)^{\rm new}\to J^N$ defined over $\mathbf{Q}$ between the new quotient of $J_0(N)$ and the Jacobian of the Shimura curve attached to the indefinite quaternion algebra of discriminant $N$. In the case when $p=2,3,5,7,13$, Ogg made predictions about the kernels of these isogenies. We show that Ogg's conjecture is not true in general. Afterwards, we propose a strategy for proving results toward Ogg's conjecture in certain situations. Finally, we discuss this strategy in detail for $N=5\cdot 13$.
Deep-learning-based prediction of late age-related macular degeneration progression

Both genetic and environmental factors influence the etiology of age-related macular degeneration (AMD), a leading cause of blindness. AMD severity is primarily measured by fundus images and recently developed machine learning methods can successfully predict AMD progression using image data. However, none of these methods have utilized both genetic and image data for predicting AMD progression. Here we jointly used genotypes and fundus images to predict an eye as having progressed to late AMD with a modified deep convolutional neural network (CNN). In total, we used 31,262 fundus images and 52 AMD-associated genetic variants from 1,351 subjects from the Age-Related Eye Disease Study (AREDS) with disease severity phenotypes and fundus images available at baseline and follow-up visits over a period of 12 years. Our results showed that fundus images coupled with genotypes could predict late AMD progression with an averaged area under the curve (AUC) value of 0.85 (95%CI: 0.83-0.86). The results using fundus images alone showed an averaged AUC of 0.81 (95%CI: 0.80-0.83). We implemented our model in a cloud-based application for individual risk assessment.
COPT: Coordinated Optimal Transport on Graphs

We introduce COPT, a novel distance metric between graphs defined via an optimization routine, computing a coordinated pair of optimal transport maps simultaneously. This gives an unsupervised way to learn general-purpose graph representation, applicable to both graph sketching and graph comparison. COPT involves simultaneously optimizing dual transport plans, one between the vertices of two graphs, and another between graph signal probability distributions. We show theoretically that our method preserves important global structural information on graphs, in particular spectral information, and analyze connections to existing studies. Empirically, COPT outperforms state of the art methods in graph classification on both synthetic and real datasets.
miR-206 knockout shows it is critical for myogenesis and directly regulates newly identified target mRNAs.

The muscle specific miRNA, miR-206, is important for the process of myogenesis; however, studying the function of miR-206 in muscle development and differentiation still proves challenging because ...
Differential Modes of Orphan Subunit Recognition for the WRB/CAML Complex.

Summary A large proportion of membrane proteins must be assembled into oligomeric complexes for function. How this process occurs is poorly understood, but it is clear that complex assembly must be tightly regulated to avoid accumulation of orphan subunits with potential cytotoxic effects. We interrogated assembly in mammalian cells by using the WRB/CAML complex, an essential insertase for tail-anchored proteins in the endoplasmic reticulum (ER), as a model system. Our data suggest that the stability of each subunit is differentially regulated. In WRB’s absence, CAML folds incorrectly, causing aberrant exposure of a hydrophobic transmembrane domain to the ER lumen. When present, WRB can correct the topology of CAML both in vitro and in cells. In contrast, WRB can independently fold correctly but is still degraded in the absence of CAML. We therefore propose that there are at least two distinct regulatory pathways for the surveillance of orphan subunits in the mammalian ER.
Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda

Organizations are rapidly deploying artificial intelligence (AI) systems to manage their workers. However, AI has been found at times to be unfair to workers. Unfairness toward workers has been ass...
Fast and robust ancestry prediction using principal component analysis

Motivation Population stratification (PS) is a major confounder in genome-wide association studies (GWAS) and can lead to false-positive associations. To adjust for PS, principal component analysis (PCA)-based ancestry prediction has been widely used. Simple projection (SP) based on principal component loadings and the recently developed data augmentation, decomposition and Procrustes (ADP) transformation, such as LASER and TRACE, are popular methods for predicting PC scores. However, the predicted PC scores from SP can be biased toward NULL. On the other hand, ADP has a high computation cost because it requires running PCA separately for each study sample on the augmented dataset. Results We develop and propose two alternative approaches: bias-adjusted projection (AP) and online ADP (OADP). Using random matrix theory, AP asymptotically estimates and adjusts for the bias of SP. OADP uses a computationally efficient online singular value decomposition algorithm, which can greatly reduce the computation cost of ADP. We carried out extensive simulation studies to show that these alternative approaches are unbiased and the computation speed can be 16-16 000 times faster than ADP. We applied our approaches to the UK Biobank data of 488 366 study samples with 2492 samples from the 1000 Genomes data as the reference. AP and OADP required 0.82 and 21 CPU hours, respectively, while the projected computation time of ADP was 1628 CPU hours. Furthermore, when inferring sub-European ancestry, SP clearly showed bias, unlike the proposed approaches. Availability and implementation The OADP and AP methods, as well as SP and ADP, have been implemented in the open-source Python software FRAPOSA, available at github.com/daviddaiweizhang/fraposa. Contact leeshawn@umich.edu. Supplementary information Supplementary data are available at Bioinformatics online.
A Fast and Simple Method for Detecting Identity-by-Descent Segments in Large-Scale Data.

Segments of identity by descent (IBD) are used in many genetic analyses. We present a method for detecting identical-by-descent haplotype segments in phased genotype data. Our method, called hap-IBD, combines a compressed representation of haplotype data, the positional Burrows-Wheeler transform, and multi-threaded execution to produce very fast analysis times. An attractive feature of hap-IBD is its simplicity: the input parameters clearly and precisely define the IBD segments that are reported, so that program correctness can be confirmed by users. We evaluate hap-IBD and four state-of-the-art IBD segment detection methods (GERMLINE, iLASH, RaPID, and TRUFFLE) using UK Biobank chromosome 20 data and simulated sequence data. We show that hap-IBD detects IBD segments faster and more accurately than competing methods, and that hap-IBD is the only method that can rapidly and accurately detect short 2–4 centiMorgan (cM) IBD segments in the full UK Biobank data. Analysis of 485,346 UK Biobank samples through the use of hap-IBD with 12 computational threads detects 231.5 billion autosomal IBD segments with length ≥2 cM in 24.4 h.
The Relative Gromov Width of Lagrangian Cobordisms between Legendrians

We obtain upper and lower bounds for the relative Gromov width of Lagrangian cobordisms between Legendrian submanifolds. Upper bounds arise from the existence of $J$-holomorphic disks with boundary on the Lagrangian cobordism that pass through the center of any given symplectically embedded ball. The areas of these disks --- and hence the sizes of these balls --- are controlled by a real-valued fundamental capacity, a quantity derived from the algebraic structure of filtered linearized Legendrian Contact Homology of the Legendrian at the top of the cobordism. Lower bounds come from explicit constructions that use neighborhoods of Reeb chords in the Legendrian ends. We also study relationships between the relative Gromov width and another quantitative measurement, the length of a Lagrangian cobordism.
Feedbacks and eddy diffusivity in an energy balance model of tropical rainfall shifts

Radiative feedbacks are known to strongly modify horizontal shifts of the intertropical convergence zone (ITCZ) produced by remote atmospheric energy sources. This study uses a one-dimensional moist energy balance model to understand how radiative feedbacks and the structure of an imposed eddy diffusivity can influence such ITCZ shifts. The Planck feedback is shown to damp ITCZ shifts more strongly for extratropical forcings than for tropical ones, because lower moisture content in cold regions makes the temperature response larger there. The water vapor feedback on ITCZ shifts is shown to be dominated by changes in the cross-equatorial asymmetry of the relative humidity of subtropical dry zones, with additional contributions by the changes in mixing ratio that occur at fixed relative humidity and by the meridional shift of the humid ITCZ. Finally, the ITCZ response is found to be highly sensitive to the meridional structure of the diffusivity; the ITCZ shift increases with the tropical diffusivity, even when the global mean diffusivity is fixed.
Rotational spectroscopy of PAHs: acenaphthene, acenaphthylene, and fluorene

Pure rotational spectra of the polycyclic aromatic hydrocarbons acenaphthene, ace- naphthylene and fluorene have been obtained by Fourier transform microwave spectroscopy of a molecular beam in the frequency range from 7 to 37GHz. Initial searches were guided by quan- tum chemical calculations performed at the B3LYP/cc-pVTZ level of theory. All three molecules exhibitb-type rotational spectra and are calculated to be significantly polar, of order 0.3-0.9 D. Very good agreement (to better than 1%) between the calculated equilibrium and experimen- tally derived ground state rotational constants is found. Selected transitions of acenaphthene and fluorene have also been measured at 3mm using conventional free space absorption spec- troscopy. The data presented here will be useful for deep radio astronomical searches for PAHs employing big radio telecopes.
Photovoltaic systems perspective

This paper summarizes the elements of photovoltaic power system and clarifies the terminology currently used. The relationship of system efficiency and cost is described particularly for the Balance of Photovoltaic System (BOPS) area. The current status of the BOPS development activity is described. The photovoltaic systems terminology is found to be on the road to standardization. Power conditioning, energy storage, and support structure are found to be BOPS cost and/or efficiency drivers. Although the current BOPS activity has identified low-cost/high-efficiency components, further development work is necessary.
Module Level Weighted Random Patterns

The paper describes a module level self-test architecture that uses weighted random patterns. A pseudorandom pattern generator (PRPG) is used to generate equally likely patterns that are then transformed to weighted patterns by a universal weighting generator. The module being tested is assumed to be composed of a number of chips all of which have been designed to support a scan test. The signature is collected by a multiple input signature register (MISR). Each scan latch in the module is fed by its near-optimal weight during test. In order to avoid any additional test pins, some of the existing signal pins are designated (demultiplexed) to perform a weight control function during test. This architecture can dramatically decrease the self-test time with only a small increase of hardware overhead.
Efficient Ladder-Style DenseNets for Semantic Segmentation of Large Images

Recent progress of deep image classification models provides great potential for improving related computer vision tasks. However, the transition to semantic segmentation is hampered by strict memory limitations of contemporary GPUs. The extent of feature map caching required by convolutional backprop poses significant challenges even for moderately sized Pascal images, while requiring careful architectural considerations when input resolution is in the megapixel range. To address these concerns, we propose a novel ladder-style DenseNet-based architecture which features high modelling power, efficient upsampling, and inherent spatial efficiency which we unlock with checkpointing. The resulting models deliver high performance and allow training at megapixel resolution on commodity hardware. The presented experimental results outperform the state-of-the-art in terms of prediction accuracy and execution speed on Cityscapes, VOC 2012, CamVid and ROB 2018 datasets. Source code at https://github.com/ivankreso/LDN .
Short paired-end reads trump long single-end reads for expression analysis

Typical experimental design advice for expression analyses using RNA-seq generally assumes that single-end reads provide robust gene-level expression estimates in a cost-effective manner, and that the additional benefits obtained from paired-end sequencing are not worth the additional cost. However, in many cases (e.g., with Illumina NextSeq and NovaSeq instruments), shorter paired-end reads and longer single-end reads can be generated for the same cost, and it is not obvious which strategy should be preferred. Using publicly available data, we test whether short-paired end reads can achieve more robust expression estimates and differential expression results than single-end reads of approximately the same total number of sequenced bases. At both the transcript and gene levels, 2 × 40 paired-end reads unequivocally provide expression estimates that are more highly correlated with 2 × 125 than 1 × 75 reads; in nearly all cases, those correlations are also greater than for 1 × 125, despite the greater total number of sequenced bases for the latter. Across an array of metrics, differential expression tests based upon 2 × 40 consistently outperform those using 1 × 75. Researchers seeking a cost-effective approach for gene-level expression analysis should prefer short paired-end reads over a longer single-end strategy. Short paired-end reads will also give reasonably robust expression estimates and differential expression results at the isoform level.
Investment in Fixed and Working Capital During Early Industrialization: Evidence From U.S. Manufacturing Firms

This paper utilizes a survey of U. S. manufacturing firms from 1832 to investigate the structure of manufacturing investment during early industrialization. The relative magnitudes of investments in fixed and working capital, and how they varied with firm size, location, and industry, are documented. This variation across industries in the composition of capital investments is indicative of a more general variation in factor intensities, and bears on the issues of why industries became concentrated in the regions they did, and the degrees to which they were adversely affected by the limited availability of long–term loans. Evidence that most manufacturing industries had quite modest investments in machinery and tools per unit of labor is also presented, serving to undercut the notion that the early period of industrialization was based on a proliferation of new, machinery–intensive technologies.
Diagonal Orbits in a Type A Double Flag Variety of Complexity One

We continue our study of the inclusion posets of diagonal SL(n)-orbit closures in a product of two partial flag varieties. We prove that, if the diagonal action is of complexity one, then the poset is isomorphic to one of the 28 posets that we determine explicitly. Furthermore, our computations show that the number of diagonal SL(n)-orbits in any of these posets is at most 10 for any positive integer n. This is in contrast with the complexity 0 case, where, in some cases, the resulting posets attain arbitrary heights.
Institutional Investors and Equity Prices

We analyze institutional investors' preferences for stocks and the implications that these preferences have for stock-market prices and returns. We find that -- a category including all managers with greater than $100 million under discretionary control -- have nearly doubled their share of the common-stock market from 1980 to 1996 most of this increase driven by the growth in holdings of the largest one-hundred institutions. Large institutions, when compared with other investors, prefer stocks that have greater market capitalizations, are more liquid, and have higher book-to-market ratios and lower returns for the previous year. We discuss how institutional preferences, when combined with the rising share of the market held by institutions, induce changes in the relative prices and returns of large stocks and small stocks. We provide evidence to support the in-sample implications for prices and realized returns and we derive out-of-sample predictions for expected returns.
Pharmacologic Activation of the G Protein-Coupled Estrogen Receptor Inhibits Pancreatic Ductal Adenocarcinoma.

Background & Aims Female sex is associated with lower incidence and improved clinical outcomes for most cancer types including pancreatic ductal adenocarcinoma (PDAC). The mechanistic basis for this sex difference is unknown. We hypothesized that estrogen signaling may be responsible, despite the fact that PDAC lacks classic nuclear estrogen receptors. Methods Here we used murine syngeneic tumor models and human xenografts to determine that signaling through the nonclassic estrogen receptor G protein-coupled estrogen receptor (GPER) on tumor cells inhibits PDAC. Results Activation of GPER with the specific, small molecule, synthetic agonist G-1 inhibited PDAC proliferation, depleted c-Myc and programmed death ligand 1 (PD-L1), and increased tumor cell immunogenicity. Systemically administered G-1 was well-tolerated in PDAC bearing mice, induced tumor regression, significantly prolonged survival, and markedly increased the efficacy of PD-1 targeted immune therapy. We detected GPER protein in a majority of spontaneous human PDAC tumors, independent of tumor stage. Conclusions These data, coupled with the wide tissue distribution of GPER and our previous work showing that G-1 inhibits melanoma, suggest that GPER agonists may be useful against a range of cancers that are not classically considered sex hormone responsive and that arise in tissues outside of the reproductive system.
Technical strategies for the study of catecholamines in man

Publisher Summary This chapter discusses the technical strategies for the study of catecholamines in man. MAO has been studied in human platelets and plasma. The platelet enzyme is located in mitochondria, and has many substrates, and inhibitor characteristics in common with mitochondrial MAOs of the B type found in other tissues. The purified platelet enzyme is electrophoretically homogenous, with a molecular weight of 235,000. The soluble plasma enzyme is a pyridoxal-containing enzyme which has different substrate specificities compared to platelet, and other tissue MAOs, and is inhibited by carbonyl reagents but is relatively insensitive to tissue MAO inhibitors such as pargyline, tranylcypromine, and iproniazid. Treatment with MAO inhibiting drugs under ordinary clinical conditions leads to marked inhibition of the human platelet, and brain enzymes, with MAO activity reduced 70–90% after several days of treatment. Studies comparing MAO activity in normal, and psychiatrically-ill monozygotic, and dizygotic twins, sibling pairs, and random unrelated pairs suggest a high order of genetic influence on platelet MAO activity.
Pigouvian Taxation with Administrative Costs

Abstract This paper examines how the optimal Pigouvian tax should be adjusted to reflect adminisrative costs. Several cases are examined, depending on whether the administrative costs are fixed per firm taxed or are a function of the amount of tax collected, and on whether such costs are borne by the government or by the taxed firm. In some cases the presence of administrative costs leads to an optimal tax greater than the external cost, while in other cases it leads to a tax less than the external cost.
PRICE, TOBACCO CONTROL POLICIES AND SMOKING AMONG YOUNG ADULTS

Cigarette smoking among youths and young adults remains a significant problem, even in the face of the antismoking campaign. One demonstratedly effective policy in reducing smoking among adults is increased cigarette taxation. Early research found that smoking by youths and young adults was about three times as sensitive to price than smoking by adults. However, recent research finds no difference in the price responsiveness of smoking with age. This paper provides new evidence on the effects of prices among young adults using data from early 1993 on 17,592 students in 140 U.S. colleges and universities.
Transgenic Overexpression of GPNMB Protects Against MPTP-Induced Neurodegeneration

Parkinson's disease (PD) is a progressive neurodegenerative disease highlighted by a marked loss of dopaminergic cell loss and motor disturbances. Currently, there are no drugs that slow the progression of the disease. A myriad of factors have been implicated in the pathogenesis and progression of PD including neuroinflammation. Although anti-inflammatory agents are being evaluated as potential disease-modifying therapies for PD, none has proven effective to date, suggesting that new and novel targets are needed. Glycoprotein nonmetastatic melanoma protein B (GPNMB) is a transmembrane glycoprotein that has recently been shown to reduce inflammation in astrocytes and to be increased in post-mortem PD brain samples. Here we show that transgenic overexpression of GPNMB protects against dopaminergic neurodegeneration in a 1-methyl-4-phenyl-1,2,3,6-tetrahydropridine mouse model of Parkinson's disease. Furthermore, GPNMB overexpression reduces gliosis and prevented microglial morphological changes following MPTP treatment compared with wild-type MPTP-treated mice. Additionally, recombinant GPNMB attenuates LPS-induced inflammation in primary mouse microglia. These results suggest a neuroprotective and anti-inflammatory role for GPNMB and warrant further investigation for GPNMB as a novel therapy for PD.
Multi-Property-Preserving Hash Domain Extension and the EMD Transform.

We point out that the seemingly strong pseudorandom oracle preserving (PRO-Pr) property of hash function domain-extension transforms defined and implemented by Coron et. al. [12] can actually weaken our guarantees on the hash function, in particular producing a hash function that fails to be even collision-resistant (CR) even though the compression function to which the transform is applied is CR. Not only is this true in general, but we show that all the transforms presented in [12] have this weakness. We suggest that the appropriate goal of a domain extension transform for the next generation of hash functions is to be multi-property preserving, namely that one should have a single transform that is simultaneously at least collision-resistance preserving, pseudorandom function preserving and PRO-Pr. We present an efficient new transform that is proven to be multi-property preserving in this sense.
Street-level Travel-time Estimation via Aggregated Uber Data.

Estimating temporal patterns in travel times along road segments in urban settings is of central importance to traffic engineers and city planners. In this work, we propose a methodology to leverage coarse-grained and aggregated travel time data to estimate the street-level travel times of a given metropolitan area. Our main focus is to estimate travel times along the arterial road segments where relevant data are often unavailable. The central idea of our approach is to leverage easy-to-obtain, aggregated data sets with broad spatial coverage, such as the data published by Uber Movement, as the fabric over which other expensive, fine-grained datasets, such as loop counter and probe data, can be overlaid. Our proposed methodology uses a graph representation of the road network and combines several techniques such as graph-based routing, trip sampling, graph sparsification, and least-squares optimization to estimate the street-level travel times. Using sampled trips and weighted shortest-path routing, we iteratively solve constrained least-squares problems to obtain the travel time estimates. We demonstrate our method on the Los Angeles metropolitan-area street network, where aggregated travel time data is available for trips between traffic analysis zones. Additionally, we present techniques to scale our approach via a novel graph pseudo-sparsification technique.
Statistically Efficient Off-Policy Policy Gradients

Policy gradient methods in reinforcement learning update policy parameters by taking steps in the direction of an estimated gradient of policy value. In this paper, we consider the statistically efficient estimation of policy gradients from off-policy data, where the estimation is particularly non-trivial. We derive the asymptotic lower bound on the feasible mean-squared error in both Markov and non-Markov decision processes and show that existing estimators fail to achieve it in general settings. We propose a meta-algorithm that achieves the lower bound without any parametric assumptions and exhibits a unique 3-way double robustness property. We discuss how to estimate nuisances that the algorithm relies on. Finally, we establish guarantees on the rate at which we approach a stationary point when we take steps in the direction of our new estimated policy gradient.
Fiat-Shamir for Repeated Squaring with Applications to PPAD-Hardness and VDFs

The Fiat-Shamir transform is a methodology for compiling a (public-coin) interactive proof system for a language L into a non-interactive argument system for L. Proving security of the Fiat-Shamir transform in the standard model, especially in the context of succinct arguments, is largely an unsolved problem. The work of Canetti et al. (STOC 2019) proved the security of the Fiat-Shamir transform applied to the Goldwasser-Kalai-Rothblum (STOC 2008) succinct interactive proof system under a very strong “optimal learning with errors” assumption. Achieving a similar result under standard assumptions remains an important open question.
Singularities and vanishing cycles in number theory over function fields

This article is an overview of the vanishing cycles method in number theory over function fields. We first explain how this works in detail in a toy example and then give three examples which are relevant to current research. The focus will be a general explanation of which sorts of problems this method can be applied to.
Bounds on Regularity of Quadratic Monomial Ideals

Abstract Castelnuovo-Mumford regularity is a measure of algebraic complexity of an ideal. Regularity of monomial ideals can be investigated combinatorially. We use a simple graph decomposition and results from structural graph theory to prove, improve and generalize many of the known bounds on regularity of quadratic square-free monomial ideals.
Waypoint Optimization Using Bayesian Optimization: A Case Study in Airborne Wind Energy Systems

We present a data-driven optimization framework that aims to address online adaptation of the flight path shape for an airborne wind energy system (AWE) that follows a repetitive path to generate power. Specifically, Bayesian optimization, which is a data-driven algorithm for finding the optimum of an unknown objective function, is utilized to solve the waypoint adaptation. To form a computationally efficient optimization framework, we describe each figure-8 flight via a compact set of parameters, termed as basis parameters. We model the underlying objective function by a Gaussian Process (GP). Bayesian optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent basis parameters. Once a path is generated using Bayesian optimization, a path following mechanism is used to track the generated figure-8 flight. The proposed framework is validated on a simplified 2-dimensional model that mimics the key behaviors of a 3-dimensional AWE system. We demonstrate the capability of the proposed framework in a simulation environment for a simplified 2-dimensional AWE system model.
Steady-State Analysis of Light-Harvesting Energy Transfer Driven by Incoherent Light: From Dimers to Networks.

The question of how quantum coherence facilitates energy transfer has been intensively debated in the scientific community. Since natural and artificial light-harvesting units operate under the sta...
Turbulence closure modeling with data-driven techniques: physical compatibility and consistency considerations

A recent thrust in turbulence closure modeling research is to incorporate machine learning (ML) elements, such as neural networks, for the purpose of enhancing the predictive capability to a broader class of flows. Such a turbulence closure framework entails solving a system of equations comprised of ML functionals coupled with traditional (physics-based - PB) elements. While combining closure elements from fundamentally different ideologies can lead to unprecedented progress, there are many critical challenges that must be overcome. This study examines three such challenges: (i) Physical compatibility (or lack thereof) between ML and PB constituents of the modeling system of equations; (ii) Internal (self) consistency of the ML training process; and (iii) Formulation of an optimal objective (or loss) function for training. These issues are critically important for generalization of the ML-enhanced methods to predictive computations of complex engineering flows. Training and implementation strategies in current practice that may lead to significant incompatibilities and inconsistencies are identified. Using the simple test case of turbulent channel flow, key deficiencies are highlighted and proposals for mitigating them are investigated. Compatibility constraints are evaluated and it is demonstrated that an iterative training procedure can help ensure certain degree of consistency. In summary, this work develops foundational tenets to guide development of ML-enhanced turbulence closure models.
Plethystic formulas for permutation enumeration

Abstract We prove several general formulas for the distributions of various permutation statistics over any set of permutations whose quasisymmetric generating function is a symmetric function. Our formulas involve certain kinds of plethystic substitutions on quasisymmetric generating functions, and the permutation statistics we consider include the descent number, peak number, left peak number, and the number of up-down runs. We apply these results to cyclic permutations, involutions, and derangements, and more generally, to derive formulas for counting all permutations by the above statistics jointly with the number of fixed points and jointly with cycle type. A number of known formulas are recovered as special cases of our results, including formulas of Desarmenien–Foata, Gessel–Reutenauer, Stembridge, Fulman, Petersen, Diaconis–Fulman–Holmes, Zhuang, and Athanasiadis.
A new Topology of a High-Voltage-Gain DC-DC Converter Based on Modified Greinacher Voltage Multiplier

This paper proposes a new topology of a non-isolated two-phase interleaved boost dc-dc converters with modified Greinacher voltage multiplier cells (VMCs) in continuous conduction mode (CCM). The proposed topology can quickly achieve a high gain with low stress on components and proper duty cycle. Moreover, this topology requires a small inductance size in CCM while maintaining the continuity of the input current due to the interleaved technique used in this converter. Because of these features, this topology can be used in many renewable applications such as PV, DC microgrids, etc. The design procedures and component selection are presented in this paper. Furthermore, the proposed converter was simulated to convert an input voltage of 20 V to output voltage of 400 V with an output power of 200 W. Finally, experimental results have been provided to confirm the analysis and simulation results.