cssi/SciSciGPT-SciSciNet huggingface dataset:
The histone demethylase KDM5 controls developmental timing in Drosophila by promoting prothoracic gland endocycles

In Drosophila, the larval prothoracic gland integrates nutritional status with developmental signals to regulate growth and maturation through the secretion of the steroid hormone ecdysone. While the nutritional signals and cellular pathways that regulate prothoracic gland function are relatively well studied, the transcriptional regulators that orchestrate the activity of this tissue remain less characterized. Here, we show that lysine demethylase 5 (KDM5) is essential for prothoracic gland function. Indeed, restoring kdm5 expression only in the prothoracic gland in an otherwise kdm5 null mutant animal is sufficient to rescue both the larval developmental delay and the pupal lethality caused by loss of KDM5. Our studies show that KDM5 functions by promoting the endoreplication of prothoracic gland cells, a process that increases ploidy and is rate limiting for the expression of ecdysone biosynthetic genes. Molecularly, we show that KDM5 activates the expression of the receptor tyrosine kinase torso, which then promotes polyploidization and growth through activation of the MAPK signaling pathway. Taken together, our studies provide key insights into the biological processes regulated by KDM5 and expand our understanding of the transcriptional regulators that coordinate animal development.
Program synthesis by type-guided abstraction refinement

We consider the problem of type-directed component-based synthesis where, given a set of (typed) components and a query type, the goal is to synthesize a term that inhabits the query. Classical approaches based on proof search in intuitionistic logics do not scale up to the standard libraries of modern languages, which span hundreds or thousands of components. Recent graph reachability based methods proposed for Java do scale, but only apply to monomorphic data and components: polymorphic data and components infinitely explode the size of the graph that must be searched, rendering synthesis intractable. We introduce type-guided abstraction refinement (TYGAR), a new approach for scalable type-directed synthesis over polymorphic datatypes and components. Our key insight is that we can overcome the explosion by building a graph over abstract types which represent a potentially unbounded set of concrete types. We show how to use graph reachability to search for candidate terms over abstract types, and introduce a new algorithm that uses proofs of untypeability of ill-typed candidates to iteratively refine the abstraction until a well-typed result is found. We have implemented TYGAR in H+, a tool that takes as input a set of Haskell libraries and a query type, and returns a Haskell term that uses functions from the provided libraries to implement the query type. Our support for polymorphism allows H+ to work with higher-order functions and type classes, and enables more precise queries due to parametricity. We have evaluated H+ on 44 queries using a set of popular Haskell libraries with a total of 291 components. H+ returns an interesting solution within the first five results for 32 out of 44 queries. Our results show that TYGAR allows H+ to rapidly return well-typed terms, with the median time to first solution of just 1.4 seconds. Moreover, we observe that gains from iterative refinement over exhaustive enumeration are more pronounced on harder queries.
Activity and Kinematics of M and L Dwarfs

I discuss observations of two traditional age indicators: chromospheric activity and kinematics, in late-M and L dwarfs near the hydrogen-burning limit. The frequency and strength of chromospheric activity disappears rapidly as a function of temperature over spectral types M8-L4. There is evidence that young late-M and L dwarfs have weaker activity than older ones (the opposite of the traditional stellar age-activity relation). The kinematics of L dwarfs confirm that lithium L dwarfs are younger than non-lithium dwarfs.
Elliptic stable envelope for Hilbert scheme of points in the plane

We find an explicit formula for the elliptic stable envelope in the case of the Hilbert scheme of points on a complex plane. The formula has a structure of a sum over trees in Young diagrams. In the limit we obtain the formulas for the stable envelope in equivariant K-theory (with arbitrary slope) and equivariant cohomology.
Comparative genomics of six Juglans species reveals disease-associated gene family contractions.

Juglans (walnuts), the most speciose genus in the walnut family (Juglandaceae), represents most of the family's commercially valuable fruit and wood-producing trees. It includes several species used as rootstock for their resistance to various abiotic and biotic stressors. We present the full structural and functional genome annotations of six Juglans species and one outgroup within Juglandaceae (Juglans regia, J. cathayensis, J. hindsii, J. microcarpa, J. nigra, J. sigillata and Pterocarya stenoptera) produced using BRAKER2 semi-unsupervised gene prediction pipeline and additional tools. For each annotation, gene predictors were trained using 19 tissue-specific J. regia transcriptomes aligned to the genomes. Additional functional evidence and filters were applied to multi-exonic and mono-exonic putative genes to yield between 27 000 and 44 000 high-confidence gene models per species. Comparison of gene models to the BUSCO embryophyta dataset suggested that, on average, genome annotation completeness was 85.6%. We utilized these high-quality annotations to assess gene family evolution within Juglans, and among Juglans and selected Eurosid species. We found notable contractions in several gene families in J. hindsii, including disease resistance-related wall-associated kinase (WAK), Catharanthus roseus receptor-like kinase (CrRLK1L) and others involved in abiotic stress response. Finally, we confirmed an ancient whole-genome duplication that took place in a common ancestor of Juglandaceae using site substitution comparative analysis.
Network inference in systems biology: recent developments, challenges, and applications

One of the most interesting, difficult, and potentially useful topics in computational biology is the inference of gene regulatory networks (GRNs) from expression data. Although researchers have been working on this topic for more than a decade and much progress has been made, it remains an unsolved problem and even the most sophisticated inference algorithms are far from perfect. In this paper, we review the latest developments in network inference, including state-of-the-art algorithms like PIDC, Phixer, and more. We also discuss unsolved computational challenges, including the optimal combination of algorithms, integration of multiple data sources, and pseudo-temporal ordering of static expression data. Lastly, we discuss some exciting applications of network inference in cancer research, and provide a list of useful software tools for researchers hoping to conduct their own network inference analyses.
Spectral stability of hydraulic shock profiles

Abstract By reduction to a generalized Sturm–Liouville problem, we establish spectral stability of hydraulic shock profiles of the Saint-Venant equations for inclined shallow-water flow, over the full parameter range of their existence, for both smooth-type profiles and discontinuous-type profiles containing subshocks. Together with work of Mascia–Zumbrun and Yang–Zumbrun, this yields linear and nonlinear H 2 ∩ L 1 → H 2 stability with sharp rates of decay in L p , p ≥ 2 , the first complete stability results for large-amplitude shock profiles of a hyperbolic relaxation system.
Empirical patterns of environmental variation favor adaptive transgenerational plasticity.

Effects of parental environment on offspring traits have been well known for decades. Interest in this transgenerational form of phenotypic plasticity has recently surged due to advances in our understanding of its mechanistic basis. Theoretical research has simultaneously advanced by predicting the environmental conditions that should favor the adaptive evolution of transgenerational plasticity. Yet whether such conditions actually exist in nature remains largely unexplored. Here, using long-term climate data, we modeled optimal levels of transgenerational plasticity for an organism with a one-year life cycle at a spatial resolution of 4 km2 across the continental United States. Both annual temperature and precipitation levels were often autocorrelated, but the strength and direction of these autocorrelations varied considerably even among nearby sites. When present, such environmental autocorrelations render offspring environments statistically predictable based on the parental environment, a key condition for the adaptive evolution of transgenerational plasticity. Results of our optimality models were consistent with this prediction: High levels of transgenerational plasticity were favored at sites with strong environmental autocorrelations, and little-to-no transgenerational plasticity was favored at sites with weak or nonexistent autocorrelations. These results are among the first to show that natural patterns of environmental variation favor the evolution of adaptive transgenerational plasticity. Furthermore, these findings suggest that transgenerational plasticity is likely variable in nature, depending on site-specific patterns of environmental variation.
AI and Holistic Review: Informing Human Reading in College Admissions

College admissions in the United States is carried out by a human-centered method of evaluation known as holistic review, which typically involves reading original narrative essays submitted by each applicant. The legitimacy and fairness of holistic review, which gives human readers significant discretion over determining each applicant's fitness for admission, has been repeatedly challenged in courtrooms and the public sphere. Using a unique corpus of 283,676 application essays submitted to a large, selective, state university system between 2015 and 2016, we assess the extent to which applicant demographic characteristics can be inferred from application essays. We find a relatively interpretable classifier (logistic regression) was able to predict gender and household income with high levels of accuracy. Findings suggest that data auditing might be useful in informing holistic review, and perhaps other evaluative systems, by checking potential bias in human or computational readings.
Metropolized Knockoff Sampling

Model-X knockoffs is a wrapper that transforms essentially any feature importance measure into a variable selection algorithm, which discovers true effects while rigorously controlling the expected...
Ballot permutations and odd order permutations

Abstract A permutation π is ballot if, for all k , the word π 1 ⋯ π k has at least as many ascents as it has descents. Let b ( n ) denote the number of ballot permutations of size n , and let p ( n ) denote the number of permutations which have odd order in the symmetric group S n . Callan conjectured that b ( n ) = p ( n ) for all n , which was proved by Bernardi, Duplantier, and Nadeau. We propose a refinement of Callan’s original conjecture. Let b ( n , d ) denote the number of ballot permutations with d descents. Let p ( n , d ) denote the number of odd order permutations with M ( π ) = d , where M ( π ) is a certain statistic related to the cyclic descents of π . We conjecture that b ( n , d ) = p ( n , d ) for all n and d . We prove this stronger conjecture for the cases d = 1 , 2 , 3 , and d = ⌊ ( n − 1 ) ∕ 2 ⌋ , and in each of these cases we establish formulas for b ( n , d ) involving Eulerian numbers and Eulerian–Catalan numbers.
Chromatic neuronal jamming in a primitive brain

Jamming models developed in inanimate matter have been widely used to describe cell packing in tissues1–7, but predominantly neglect cell diversity, despite its prevalence in biology. Most tissues, animal brains in particular, comprise a mix of many cell types, with mounting evidence suggesting that neurons can recognize their respective types as they organize in space8–11. How cell diversity revises the current jamming paradigm is unknown. Here, in the brain of the flatworm planarian Schmidtea mediterranea, which exhibits remarkable tissue plasticity within a simple, quantifiable nervous system12–16, we identify a distinct packing state, ‘chromatic’ jamming. Combining experiments with computational modelling, we show that neurons of distinct types form independent percolating networks barring any physical contact. This jammed state emerges as cell packing configurations become constrained by cell type-specific interactions and therefore may extend to describe cell packing in similarly complex tissues composed of multiple cell types. An imaging study of planarian flatworm brains demonstrates that densely packed neural tissues seem to have packing configurations commensurate with a jammed state.
Polycysteine-encoding leaderless short ORFs function as cysteine-responsive attenuators of operonic gene expression in mycobacteria

Genome-wide transcriptomic analyses have revealed abundant expressed short open reading frames (ORFs) in bacteria. Whether these short ORFs, or the small proteins they encode, are functional remains an open question. One quarter of mycobacterial mRNAs are leaderless, beginning with a 5'-AUG or GUG initiation codon. Leaderless mRNAs often encode unannotated short ORFs as the first gene of a polycistronic transcript. Here, we show that polycysteine-encoding leaderless short ORFs function as cysteine-responsive attenuators of operonic gene expression. Detailed mutational analysis shows that one polycysteine short ORF controls expression of the downstream genes. Our data indicate that ribosomes stalled in the polycysteine tract block mRNA structures that otherwise sequester the ribosome-binding site of the 3'gene. We assessed endogenous proteomic responses to cysteine limitation in Mycobacterium smegmatis using mass spectrometry. Six cysteine metabolic loci having unannotated polycysteine-encoding leaderless short ORF architectures responded to cysteine limitation, revealing widespread cysteine-responsive attenuation in mycobacteria. Individual leaderless short ORFs confer independent operon-level control, while their shared dependence on cysteine ensures a collective response mediated by ribosome pausing. We propose the term ribulon to classify ribosome-directed regulons. Regulon-level coordination by ribosomes on sensory short ORFs illustrates one utility of the many unannotated short ORFs expressed in bacterial genomes.
Analysis of Spectral Methods for Phase Retrieval With Random Orthogonal Matrices

Phase retrieval refers to algorithmic methods for recovering a signal from its phaseless measurements. There has been recent interest in understanding the performance of local search algorithms that work directly on the non-convex formulation of the problem. Due to the non-convexity of the problem, the success of these local search algorithms depends heavily on their starting points. The most widely used initialization scheme is the spectral method, in which the leading eigenvector of a data-dependent matrix is used as a starting point. Recently, the performance of the spectral initialization was characterized accurately for measurement matrices with independent and identically distributed entries. This paper aims to obtain the same level of knowledge for isotropically random column-orthogonal matrices, which are substantially better models for practical phase retrieval systems. Towards this goal, we consider the asymptotic setting in which the number of measurements $m$ , and the dimension of the signal, $n$ , diverge to infinity with $m/n = \delta \in (1,\infty )$ , and obtain a simple expression for the overlap between the spectral estimator and the true signal vector.
Associations of 3-year-olds' block-building complexity with later spatial and mathematical skills.

Block-building skills at age 3 are related to spatial skills at age 5 and spatial skills in grade school are linked to later success in science, technology, engineering, and mathematics (STEM) fiel...
Disentangling Controllable Object Through Video Prediction Improves Visual Reinforcement Learning

In many vision-based reinforcement learning (RL) problems, the agent controls a movable object in its visual field, e.g., the player’s avatar in video games and the robotic arm in visual grasping and manipulation. Leveraging action-conditioned video prediction, we propose an end-to-end learning frame-work to disentangle the controllable object from the observation signal. The disentangled representation is shown to be useful for RL as additional observation channels to the agent. Experiments on a set of Atari games with the popular Double DQN algorithm demonstrate improved sample efficiency and game performance (from 222.8% to 261.4% measured in normalized game scores, with prediction bonus reward).
Filipino and American Teachers: Their Differences in Psychological Needs, Performance, and Culture

This study addresses the dearth of literature about the Filipino international teachers whose presence continues to pervade the K-12 education system of the United States. Through quantitative analyses and the self-determination theory framework, it seeks to answer how basic psychological needs are addressed in higher education institutions set in different cultural settings and whether culture has a role in Filipino international teachers’ performance. The findings of this study may have an implication on home country’s teacher education, US teacher recruitment, and in-service professional development of international teachers. The ultimate goal of this study is how to fully prepare and support international teachers for the best of the American youth.
Frequentist properties of Bayesian inequality tests

Abstract Bayesian and frequentist criteria fundamentally differ, but often posterior and sampling distributions agree asymptotically. For the corresponding single-draw experiment, we characterize the frequentist size of a certain Bayesian hypothesis test of (possibly nonlinear) inequalities. If the null hypothesis is that the parameter lies in a specified half-space, then the Bayesian test’s size equals α ; if the null hypothesis is a subset of a half-space, then size is above α ; otherwise, size may be equal to, above, or below α . Rejection probabilities at certain points are also characterized. Two examples illustrate our results: translog cost function curvature and ordinal distribution relationships.
The Skin Microbiota and Itch: Is There a Link?

Itch is an unpleasant sensation that emanates primarily from the skin. The chemical mediators that drive neuronal activity originate from a complex interaction between keratinocytes, inflammatory cells, nerve endings and the skin microbiota, relaying itch signals to the brain. Stress also exacerbates itch via the skin–brain axis. Recently, the microbiota has surfaced as a major player to regulate this axis, notably during stress settings aroused by actual or perceived homeostatic challenge. The routes of communication between the microbiota and brain are slowly being unraveled and involve neurochemicals (i.e., acetylcholine, histamine, catecholamines, corticotropin) that originate from the microbiota itself. By focusing on itch biology and by referring to the more established field of pain research, this review examines the possible means by which the skin microbiota contributes to itch.
Analysis of Covariance With Qualitative Data

This paper deals with data that has a group structure. A simple example in the context of a linear regression model is E(yitlx, 1S, ar) = P'xit + ai (i = 1, ...,9 N; t = 1, ... T), where there are T observations within each of N groups. The ai are group specific parameters. Our primary concern is with the estimation of f3, a parameter vector common to all groups. The role of the ai is to control for group specific effects; i.e. for omitted variables that are constant within a group. The regression function that does not condition on the group will not in general identify 1: E(yitlx, 13) 0 1'xit. In this case there is an omitted variable bias. An important application is generated by longitudinal or panel data, in which there are two or more observations on each individual. Then the group is the individual, and the ai capture individual differences. If these person effects are correlated with x, then a regression function that fails to control for them will not identify f. In another important application the group is a family, with observations on two or more siblings within the family. Then the ai capture omitted variables that are family specific, and they give a concrete representation to family background. We shall assume that observations from different groups are independent. Then the ai are incidental parameters (Neyman and Scott (1948)), and 0, which is common to the independent sampling units, is a vector of structural parameters. In the application to sibling data, T is small, typically T= 2, whereas there may be a large number of families. Small T and large N are also characteristic of many of the currently available longitudinal data sets. So a basic statistical issue is to develop an estimator for j that has good properties in this case. In particular, the estimator ought to be consistent as N -> ac for fixed T. It is well-known that analysis of covariance in the linear regression model does have this consistency property. The problem of finding consistent estimators in other models is non-trivial, however, since the number of incidental parameters is increasing with sample size. We shall work with the following probability model: Yit is a binary variable with
Correction to: An Observational View of Relationships Between Moisture Aggregation, Cloud, and Radiative Heating Profiles

The article “An observational view of relationships between moisture aggregation, cloud and radiative heating profiles”, written by Matthew D. Lebsock, Tristan S. L’Ecuyer and Robert Pincus, was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 31 October 2017 without open access.
Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To solve this stochastic economic dispatch, the conventional Monte Carlo method is prohibitively time consuming for medium- and large-scale power systems. To overcome this problem, we propose in this paper a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Based on the dimension-reduction results obtained by the Karhunen-Loeve expansion, a Gaussian-process emulator is constructed. This surrogate allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus system reveal that the proposed method has an excellent performance as compared to the traditional Monte Carlo method.
Parameter estimation for one-sided heavy-tailed distributions

Abstract Stable subordinators, and more general subordinators possessing power law probability tails, have been widely used in the context of subdiffusions, where particles get trapped or immobile in a number of time periods, called constant periods. The lengths of the constant periods follow a one-sided distribution which involves a parameter between 0 and 1 and whose first moment does not exist. This paper constructs an estimator for the parameter, applying the method of moments to the number of observed constant periods in a fixed time interval. The resulting estimator is asymptotically unbiased and consistent, and it is well-suited for situations where multiple observations of the same subdiffusion process are available. We present supporting numerical examples and an application to market price data for a low-volume stock.
4 – Hyperbolic Equations

Publisher Summary This chapter describes the properties of hyperbolic equations. Initial value or propagation problems are described by parabolic and hyperbolic equations. Hyperbolic equations arise in transport, wave mechanics, gas dynamics, vibrations, and other areas. The concepts of domains of dependence, and influence, and of characteristics are fundamental to all hyperbolic equations. The extent to which initial and boundary conditions determine unique solutions can be deduced for a large number of cases from the three theorems that are described for the two simultaneous first-order quasilinear equations. No boundary interference or other obstruction is present in the considered domain. It is quite possible that unanticipated boundaries, such as shock waves, flame fronts, and other discontinuities, may appear within the solution domain. The discontinuities of properties are propagated by their own special laws and represent boundaries among regions where different equations must be solved.
Trade liberalization, exit, and productivity improvements: Evidence from Chilean plants

This paper empirically investigates the effects of liberalized trade on plant productivity in the case of Chile. Chile presents an interesting setting to study this relationship since it underwent a massive trade liberalization that significantly exposed its plants to competition from abroad during the late 1970s and early 1980s. Methodologically, I approach this question in two steps. In the first step, I estimate a production function to obtain a measure of plant productivity. I estimate the production function semiparametrically to correct for the presence of selection and simultaneity biases in the estimates of the input coefficients required to construct a productivity measure. I explicitly incorporate plant exit in the estimation to correct for the selection problem induced by liquidated plants. These methodological aspects are important in obtaining a reliable plant-level productivity measure based on consistent estimates of the input coefficients. In the second step, I identify the impact of trade on plants' productivity in a regression framework allowing variation in productivity over time and across traded- and nontraded-goods sectors. Using plant-level panel data on Chilean manufacturers, I find evidence of within plant productivity improvements that can be attributed to a liberalized trade for the plants in the import-competing sector. In many cases, aggregate productivity improvements stem from the reshuffling of resources and output from less to more efficient producers.
Vulnerable window of yield strength for swelling-driven fracture of phase-transforming battery materials

Despite numerous experimental and theoretical investigations of the mechanical behavior of high-capacity Si and Ge Li-ion battery anodes, our basic understanding of swelling-driven fracture in these materials remains limited. Existing theoretical studies have provided insights into elasto-plastic deformations caused by large volume change phase transformations, but have not modeled fracture explicitly beyond Griffith’s criterion. Here, we use a multi-physics phase-field approach to model self-consistently anisotropic phase transformation, elasto-plastic deformation, and crack initiation and propagation during lithiation of Si nanopillars. Our computational results reveal that fracture occurs within a “vulnerable window” inside the two-dimensional parameter space of yield strength and fracture energy and highlight the importance of taking into account the surface localization of plastic deformation to accurately predict the magnitude of tensile stresses at the onset of fracture. They further demonstrate how the increased robustness of hollow nanopillars can be understood as a direct effect of anode geometry on the size of this vulnerable window. Those insights provide an improved theoretical basis for designing next-generation mechanically stable phase-transforming battery materials undergoing large volume changes.
Q-systems with boundary parameters

Q-systems provide an efficient way of solving Bethe equations. We formulate here Q-systems for both the isotropic and anisotropic open Heisenberg quantum spin-1/2 chains with diagonal boundary magnetic fields. We check these Q-systems using novel Wronskian-type formulas (relating the fundamental Q-function and its dual) that involve the boundary parameters.
mRNA stem-loops can pause the ribosome by hindering A-site tRNA binding.

Although the elongating ribosome is an efficient helicase, certain mRNA stem-loop structures are known to impede ribosome movement along mRNA and stimulate programmed ribosome frameshifting via mechanisms that are not well understood. Using biochemical and single-molecule Forster resonance energy transfer (smFRET) experiments, we studied how frameshift-inducing stem-loops from E. coli dnaX mRNA and the gag-pol transcript of Human Immunodeficiency Virus (HIV) perturb translation elongation. We find that upon encountering the ribosome, the stem-loops strongly inhibit A-site tRNA binding and ribosome intersubunit rotation that accompanies translation elongation. Electron cryo-microscopy (cryo-EM) reveals that the HIV stem-loop docks into the A site of the ribosome. Our results suggest that mRNA stem-loops can transiently escape the ribosome helicase by binding to the A site. Thus, the stem-loops can modulate gene expression by sterically hindering tRNA binding and inhibiting translation elongation.
Mining Environment Assumptions for Cyber-Physical System Models

Many complex cyber-physical systems can be modeled as heterogeneous components interacting with each other in real-time. We assume that the correctness of each component can be specified as a requirement satisfied by the output signals produced by the component, and that such an output guarantee is expressed in a real-time temporal logic such as Signal Temporal Logic (STL). In this paper, we hypothesize that a large subset of input signals for which the corresponding output signals satisfy the output requirement can also be compactly described using an STL formula that we call the environment assumption. We propose an algorithm to mine such an environment assumption using a supervised learning technique. Essentially, our algorithm treats the environment assumption as a classifier that labels input signals as good if the corresponding output signal satisfies the output requirement, and as bad otherwise. Our learning method simultaneously learns the structure of the STL formula as well as the values of the numeric constants appearing in the formula.1 To achieve this, we combine a procedure to systematically enumerate candidate Parametric STL (PSTL) formulas, with a decision-tree based approach to learn parameter values. We demonstrate experimental results on real world data from several domains including transportation and health care.
Is Non-Neutrality Profitable for the Stakeholders of the Internet Market?

We consider a system in which there exists two ISPs, one “big” Content Provider (CP), and a continuum of End-Users (EUs). One of the ISPs is neutral and the other is non-neutral. We consider that the CP can differentiate between ISPs by controlling the quality of the content she is offering on each one. We also consider that EUs have different levels of innate preferences for ISPs. We formulate a sequential game, and explicitly characterize all the possible Sub-game Perfect Nash Equilibria (SPNE) of the game. We prove that if an SPNE exists, it would be one of the five possible strategies each of which we explicitly characterize. We prove that when EUs have sufficiently low innate preferences for ISPs, a unique SPNE exists in which the neutral ISP would be driven out of the market. We also prove that when these preferences are sufficiently high, there exists a unique SPNE with a non-neutral outcome in which both ISPs are active. Numerical results reveal that the neutral ISP receives a lower payoff and the non-neutral ISP receives a higher payoff (most of the time) in a non-neutral scenario. However, we identify scenarios in which the non-neutral ISP loses payoff by adopting non-neutrality. We also show that a non-neutral regime yields a higher welfare for EUs in comparison to a neutral one if the market power of the non-neutral ISP is small, the sensitivity of EUs (respectively, the CP) to the quality is low (respectively, high), or a combinations of these factors.
Training Mixed-Objective Pointing Decoders for Block-Level Optimization in Search Recommendation

Related or ideal follow-up suggestions to a web query in search engines are often optimized based on several different parameters -- relevance to the original query, diversity, click probability etc. One or many rankers may be trained to score each suggestion from a candidate pool based on these factors. These scorers are usually pairwise classification tasks where each training example consists of a user query and a single suggestion from the list of candidates. We propose an architecture that takes all candidate suggestions associated with a given query and outputs a suggestion block. We discuss the benefits of such an architecture over traditional approaches and experiment with further enforcing each individual metric through mixed-objective training.
Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding

Deep Bayesian latent variable models have enabled new approaches to both model and data compression. Here, we propose a new algorithm for compressing latent representations in deep probabilistic models, such as variational autoencoders, in post-processing. The approach thus separates model design and training from the compression task. Our algorithm generalizes arithmetic coding to the continuous domain, using adaptive discretization accuracy that exploits estimates of posterior uncertainty. A consequence of the "plug and play" nature of our approach is that various rate-distortion trade-offs can be achieved with a single trained model, eliminating the need to train multiple models for different bit rates. Our experimental results demonstrate the importance of taking into account posterior uncertainties, and show that image compression with the proposed algorithm outperforms JPEG over a wide range of bit rates using only a single machine learning model. Further experiments on Bayesian neural word embeddings demonstrate the versatility of the proposed method.
The Implicit and Explicit Regularization Effects of Dropout

Dropout is a widely-used regularization technique, often required to obtain state-of-the-art for a number of architectures. This work demonstrates that dropout introduces two distinct but entangled regularization effects: an explicit effect (also studied in prior work) which occurs since dropout modifies the expected training objective, and, perhaps surprisingly, an additional implicit effect from the stochasticity in the dropout training update. This implicit regularization effect is analogous to the effect of stochasticity in small mini-batch stochastic gradient descent. We disentangle these two effects through controlled experiments. We then derive analytic simplifications which characterize each effect in terms of the derivatives of the model and the loss, for deep neural networks. We demonstrate these simplified, analytic regularizers accurately capture the important aspects of dropout, showing they faithfully replace dropout in practice.
Deformation and Failure Modes of Rapidly Solidified, Ultra-Fine Grain, AMX602 and ZAXE1711 Magnesium Alloys

This paper characterizes microstructure, mechanical properties, and the plastic flow and fracture behaviors of the high strength and high ductility, ultra-fine grain (UFG), magnesium alloys AMX602 (Mg-5.4Al-0.39Mn-l.77Ca) and ZAXE1711 (Mg-7.28Al-1.20La-1.18Ca-0.93Zn). Rapidly solidified processed (RSP) powders produced by Spinning Water Atomization (SWAP) were formed into compacts that were subsequently extruded into flat bars. Specific characteristics of the mechanical properties, and deformation and failure modes and features are revealed in response to loading conditions in tension, compression, shear, and impact. The material behaviors are shown with numerical and stress-strain test data, and power law hardening constitutive plastic flow parameters. Fracture modes and features are shown with optical and SEM microscopy. The ZAXE1711 alloy reveals lower yield strength, a high rate of strain hardening, greater ultimate strength and impact toughness, and more homogeneous less localized plastic flow and fracture.
Structured clustering of the glycosphingolipid GM1 is required for membrane curvature induced by cholera toxin

AB5 bacterial toxins and polyomaviruses induce membrane curvature as a mechanism to facilitate their entry into host cells. How membrane bending is accomplished is not yet fully understood but has been linked to the simultaneous binding of the pentameric B subunit to multiple copies of glycosphingolipid receptors. Here, we probe the toxin membrane binding and internalization mechanisms by using a combination of superresolution and polarized localization microscopy. We show that cholera toxin subunit B (CTxB) can induce membrane curvature only when bound to multiple copies of its glycosphingolipid receptor, GM1, and the ceramide structure of GM1 is likely not a determinant of this activity as assessed in model membranes. A mutant CTxB capable of binding only a single GM1 fails to generate curvature either in model membranes or in cells, and clustering the mutant CTxB-single-GM1 complexes by antibody cross-linking does not rescue the membrane curvature phenotype. We conclude that both the multiplicity and specific geometry of GM1 binding sites are necessary for the induction of membrane curvature. We expect this to be a general rule of membrane behavior for all AB5 toxins and polyomaviruses that bind glycosphingolipids to invade host cells.
Cohorting KPC+ Klebsiella pneumoniae (KPC-Kp)-positive patients: A genomic exposé of cross-colonization hazards in a long-term acute-care hospital (LTACH).

Objective:Cohorting patients who are colonized or infected with multidrug-resistant organisms (MDROs) protects uncolonized patients from acquiring MDROs in healthcare settings. The potential for cross transmission within the cohort and the possibility of colonized patients acquiring secondary isolates with additional antibiotic resistance traits is often neglected. We searched for evidence of cross transmission of KPC+ Klebsiella pneumoniae (KPC-Kp) colonization among cohorted patients in a long-term acute-care hospital (LTACH), and we evaluated the impact of secondary acquisitions on resistance potential.Design:Genomic epidemiological investigation.Setting:A high-prevalence LTACH during a bundled intervention that included cohorting KPC-Kp–positive patients.Methods:Whole-genome sequencing (WGS) and location data were analyzed to identify potential cases of cross transmission between cohorted patients.Results:Secondary KPC-Kp isolates from 19 of 28 admission-positive patients were more closely related to another patient’s isolate than to their own admission isolate. Of these 19 cases, 14 showed strong genomic evidence for cross transmission (<10 single nucleotide variants or SNVs), and most of these patients occupied shared cohort floors (12 patients) or rooms (4 patients) at the same time. Of the 14 patients with strong genomic evidence of acquisition, 12 acquired antibiotic resistance genes not found in their primary isolates.Conclusions:Acquisition of secondary KPC-Kp isolates carrying distinct antibiotic resistance genes was detected in nearly half of cohorted patients. These results highlight the importance of healthcare provider adherence to infection prevention protocols within cohort locations, and they indicate the need for future studies to assess whether multiple-strain acquisition increases risk of adverse patient outcomes.
Ando–Choi–Effros liftings for regular maps between Banach lattices

The Ando–Choi–Effros lifting theorem provides conditions under which a bounded linear mapping taking values in a quotient space can be lifted through the quotient map. We prove two versions of said theorem for regular maps between Banach lattices. Our conditions mirror the classical ones, but additionally taking into account the order structure.
Factorial Design of Experiments for Optimization of Photocatalytic Degradation of Tartrazine by Zinc Oxide (ZnO) Nanorods with Different Aspect Ratios

Abstract The photocatalytic degradation of the azo dye tartrazine using zinc oxide (ZnO) as photocatalyst under ultraviolet light was investigated using a 24 factorial design. The variables studied were the aspect ratio of ZnO nanorods, the ZnO dosage (400 or 700 mg/L), the initial pH (7 or 10) of the tartrazine solution, and the H2O2 volume (0 or 30 μL). These variables were considered aiming to maximize the tartrazine removal efficiency and the pseudo-1st-order rate constant of the removal process. The ZnO aspect ratio was tuned by varying the Lewis base during the synthesis, hexamethylenetetramine (HMTA) was used to prepare ZnO with a low aspect ratio (ZnO_LowAR), and NaOH was used to prepare ZnO with a high aspect ratio (ZnO_HighAR). The microstructural characterizations indicated that ZnO_LowAR and ZnO_HighAR nanorods have similar structural, textural, and optical properties. The only exception was the dimensions of the nanorods obtained, which could result in differences in the facets exposed on each type of nanorod surface. The factorial design revealed that the ZnO aspect ratio, the initial pH of the tartrazine solution, and the H2O2 volume all have significant primary effects. In contrast, the ZnO dosage is not significant neither in the tartrazine removal efficiency nor in the pseudo-1st-order rate constant. Statistical models considering the coefficients of the significant interactions were obtained, leading to consistent predicted results in comparison to the results experimentally obtained. The conditions leading to highest removal efficiency (∼92 %) and pseudo-1st-order rate constant (3.81 × 10−2 min−1) were carried out with ZnO_HighAR, initial pH 7, and without H2O2, which outperformed the TiO2 P-25 under the same conditions.
Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions

Global climate models represent small-scale processes such as convection using subgrid models known as parameterizations, and these parameterizations contribute substantially to uncertainty in climate projections. Machine learning of new parameterizations from high-resolution model output is a promising approach, but such parameterizations have been prone to issues of instability and climate drift, and their performance for different grid spacings has not yet been investigated. Here we use a random forest to learn a parameterization from coarse-grained output of a three-dimensional high-resolution idealized atmospheric model. The parameterization leads to stable simulations at coarse resolution that replicate the climate of the high-resolution simulation. Retraining for different coarse-graining factors shows the parameterization performs best at smaller horizontal grid spacings. Our results yield insights into parameterization performance across length scales, and they also demonstrate the potential for learning parameterizations from global high-resolution simulations that are now emerging. Machine learning has been used to represent small-scale processes, such as clouds, in atmospheric models but this can lead to instability in simulations of climate. Here, the authors demonstrate a use of machine learning in an atmospheric model that leads to stable simulations of climate at a range of grid spacings.
Learning Task-Driven Control Policies via Information Bottlenecks

This paper presents a reinforcement learning approach to synthesizing task-driven control policies for robotic systems equipped with rich sensory modalities (e.g., vision or depth). Standard reinforcement learning algorithms typically produce policies that tightly couple control actions to the entirety of the system's state and rich sensor observations. As a consequence, the resulting policies can often be sensitive to changes in task-irrelevant portions of the state or observations (e.g., changing background colors). In contrast, the approach we present here learns to create a task-driven representation that is used to compute control actions. Formally, this is achieved by deriving a policy gradient-style algorithm that creates an information bottleneck between the states and the task-driven representation; this constrains actions to only depend on task-relevant information. We demonstrate our approach in a thorough set of simulation results on multiple examples including a grasping task that utilizes depth images and a ball-catching task that utilizes RGB images. Comparisons with a standard policy gradient approach demonstrate that the task-driven policies produced by our algorithm are often significantly more robust to sensor noise and task-irrelevant changes in the environment.
Optimizing the accuracy of cortical volumetric analysis in traumatic brain injury

ABSTRACT Cortical volumetric analysis is widely used to study the anatomic basis of neurological deficits in patients with traumatic brain injury (TBI). However, patients with TBI-related lesions are often excluded from MRI analyses because cortical lesions may compromise the accuracy of reconstructed surfaces upon which volumetric measurements are based. We developed a FreeSurfer-based lesion correction method and tested its impact on cortical volume measures in 87 patients with chronic moderate-to-severe TBI. We reconstructed cortical surfaces from T1-weighted MRI scans, then manually labeled and removed vertices on the cortical surfaces where lesions caused inaccuracies. Next, we measured the surface area of lesion overlap with seven canonical brain networks and the percent volume of each network affected by lesions. • The lesion correction method revealed that cortical lesions in patients with TBI are preferentially located in the limbic and default mode networks (95.7% each), with the limbic network also having the largest average surface area (4.4+/−3.7%) and percent volume affected by lesions (12.7+/−9.7%). • The method has the potential to improve the accuracy of cortical volumetric measurements and permit inclusion of patients with lesioned brains in MRI analyses. • The method also provides new opportunities to elucidate network-based mechanisms of neurological deficits in patients with TBI.
Structural Equation Modeling: Latent Growth Curve Analysis†

In this chapter, we use an example of panel data on the longitudinal development of reading in children to introduce the latent growth curve analysis. Latent growth modeling originates in a simple mapping of the standard multilevel model for change onto the general model available for covariance structure analysis (CSA). Here, we illustrate how the latent growth modeling approach can be enacted by using the CSA Y-measurement model to contain the level-1 individual growth model and the CSA structural model to contain the level-2 model for interindividual differences in change. Because this mapping is possible, all fixed and random parameters in the multilevel model for change can be estimated by fitting the corresponding CSA model to data using standard structural equation modeling software (such as LISREL). Latent growth curve analysis is important because it capitalizes on the intrinsic flexibility of the general CSA model to test interesting and complex hypotheses about change, many of which are either more difficult or impossible to test using standard multilevel modeling approaches.