cssi/SciSciGPT-SciSciNet huggingface dataset:
A delay efficient multiclass packet scheduler for heterogeneous M2M uplink

The sensory traffic in Machine-to-Machine (M2M) communications has fairly heterogeneous service delay requirements. Therefore, we study the delay-performance of a heterogeneous M2M uplink from the sensors to a M2M application server (AS) via M2M aggregators (MA). We classify the heterogeneous M2M traffic aggregated at AS into multiple Periodic Update (PU) and Event Driven (ED) classes. The PU arrivals are periodic and need to be processed by a prespecified firm service deadline whereas the ED arrivals are random with firm or soft real-time or non real-time service requirements. We use step and sigmoidal functions to represent the service utility for PU and ED packets respectively. We propose a delay efficient multiclass packet scheduling heuristic that aims to maximize a proportionally fair system utility metric. Specifically, the proposed scheduler prioritizes service to ED data while ensuring that the PU packets meet their service deadline. It also minimizes successive PU failures for critical applications by penalizing their occurrences. Furthermore, the failed PU packets are immediately cleared from the system so as to reduce network congestion. Using extensive simulations, we show that the proposed scheduler outperforms popular packet schedulers and the performance gap increases with heterogeneity in latency requirements and with greater penalty for PU failures in critical applications.
Adapting Neural Text Classification for Improved Software Categorization

Software Categorization is the task of organizing software into groups that broadly describe the behavior of the software, such as "editors" or "science." Categorization plays an important role in several maintenance tasks, such as repository navigation and feature elicitation. Current approaches attempt to cast the problem as text classification, to make use of the rich body of literature from the NLP domain. However, as we will this paper, algorithms are generally not applicable off-the-shelf to source code; we found that they work well when high-level project descriptions are available, but suffer very large performance penalties when classifying sourcecode and comments only. We propose a set of adaptations to a state-of-the-art neural classification algorithm and perform two evaluations: one with reference data from Debian end-user programs, and one with a set of C/C++ libraries that we hired professional programmers to annotate. We show that our proposed approach achieves performance exceeding that of previous software classification techniques as well as a state-of-the-art neural text classification technique.
On ramification in transcendental extensions of local fields

Abstract Let L / K be an extension of complete discrete valuation fields, and assume that the residue field of K is perfect and of positive characteristic. The residue field of L is not assumed to be perfect. In this paper, we prove a formula for the Swan conductor of the image of a character χ ∈ H 1 ( K , Q / Z ) in H 1 ( L , Q / Z ) for χ sufficiently ramified. Further, we define generalizations ψ L / K ab and ψ L / K AS of the classical ψ-function and prove a formula for ψ L / K ab ( t ) for sufficiently large t ∈ R .
Quantitative form of certain k-plane transform inequalities

Abstract Let d ≥ 2 and 1 ≤ k ≤ d − 1 . The k-plane transform satisfies some L p → L q dilation-invariant inequality. In this case the best constant and the extremizers are explicitly known. We give a quantitative form of the inequality with respect to these extremizers, that works for k = d − 1 and for k ≠ d − 1 while restricted to radial functions.
Causal inference by quantile regression kink designs

Abstract The quantile regression kink design (QRKD) is proposed by empirical researchers, but its causal interpretation remains unknown. We show that the QRKD estimand measures a weighted average of heterogeneous marginal effects at respective conditional quantiles of outcome given a designed kink point. We also derive limit processes for the QRKD estimator to conduct statistical inference on heterogeneous treatment effects using the QRKD. Applying our methods to the Continuous Wage and Benefit History Project (CWBH) data, we find heterogeneous positive causal effects of unemployment insurance benefits on unemployment durations. These effects are larger for individuals with longer unemployment durations.
On Estimating L_2^2 Divergence

We give a comprehensive theoretical characterization of a nonparametric estimator for the L 2 divergence between two continuous distributions. We first bound the rate of convergence of our estimator, showing that it is p n-consistent provided the densities are suciently smooth. In this smooth regime, we then show that our estimator is asymptotically normal, construct asymptotic confi=
OptNet: differentiable optimization as a layer in neural networks

This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end train-able deep networks. These layers encode constraints and complex dependencies between the hidden states that traditional convolutional and fully-connected layers often cannot capture. In this paper, we explore the foundations for such an architecture: we show how techniques from sensitivity analysis, bilevel optimization, and implicit differentiation can be used to exactly differentiate through these layers and with respect to layer parameters; we develop a highly efficient solver for these layers that exploits fast GPU-based batch solves within a primal-dual interior point method, and which provides backpropagation gradients with virtually no additional cost on top of the solve; and we highlight the application of these approaches in several problems. In one notable example, we show that the method is capable of learning to play mini-Sudoku (4x4) given just input and output games, with no a priori information about the rules of the game; this highlights the ability of our architecture to learn hard constraints better than other neural architectures.
An integral equation for distorted wave amplitudes

We derive a new integral equation that allows the calculation of the scattering or annihilation amplitude of two particles subjected to two potentials when the corresponding amplitude for one potential only is known. We assume that scattering or annihilation occurs through one of the potentials, while the other potential affects the particle wave functions. Our expression is valid for any choice of the distorting potential and for any particle model. Our technique does not require the expansion of the amplitude into partial waves, and allows the study of models that are generally difficult to solve by means of the Schroedinger equation.
Shrinkage Function And Its Applications In Matrix Approximation

The shrinkage function is widely used in matrix low-rank approximation, compressive sensing, and statistical estimation. In this article, an elementary derivation of the shrinkage function is given. In addition, applications of the shrinkage function are demonstrated in solving several well-known problems, together with a new result in matrix approximation.
Fast growth of the vorticity gradient in symmetric smooth domains for 2D incompressible ideal flow

Abstract We construct initial data for the two-dimensional Euler equation in a bounded smooth symmetric domain such that the gradient of vorticity in L ∞ grows as a double exponential in time, for all time. Our construction is based on the recent result by Kiselev and Sverak [9] .
On the second rigidity theorem of Huneke and Wiegand

In 2007 Huneke and Wiegand announced in an erratum that one of the conclusions of their depth formula theorem is flawed due to an incorrect convention for the depth of the zero module. Since then, the deleted claim has remained unresolved. In this paper we give examples to prove that the deleted claim is false, in general. Moreover, we point out several places in the literature which relied upon this deleted claim or the initial argument from 2007.
A note on discreteness of F-jumping numbers

Suppose that $R$ is a ring essentially of finite type over a perfect field of characteristic $p > 0$ and that $a \subseteq R$ is an ideal. We prove that the set of $F$-jumping numbers of $\tau_b(R; a^t)$ has no limit points under the assumption that $R$ is normal and $Q$-Gorenstein -- we do \emph{not} assume that the $Q$-Gorenstein index is not divisible by $p$. Furthermore, we also show that the $F$-jumping numbers of $\tau_b(R; \Delta, a^t)$ are discrete under the more general assumption that $K_R + \Delta$ is $\bR$-Cartier.
Variable Annealing Length and Parallelism in Simulated Annealing

In this paper, we propose: (a) a restart schedule for an adaptive simulated annealer, and (b) parallel simulated annealing, with an adaptive and parameter-free annealing schedule. The foundation of our approach is the Modified Lam annealing schedule, which adaptively controls the temperature parameter to track a theoretically ideal rate of acceptance of neighboring states. A sequential implementation of Modified Lam simulated annealing is almost parameter-free. However, it requires prior knowledge of the annealing length. We eliminate this parameter using restarts, with an exponentially increasing schedule of annealing lengths. We then extend this restart schedule to parallel implementation, executing several Modified Lam simulated annealers in parallel, with varying initial annealing lengths, and our proposed parallel annealing length schedule. To validate our approach, we conduct experiments on an NP-Hard scheduling problem with sequence-dependent setup constraints. We compare our approach to fixed length restarts, both sequentially and in parallel. Our results show that our approach can achieve substantial performance gains, throughout the course of the run, demonstrating our approach to be an effective anytime algorithm.
The edit distance function and symmetrization

The edit distance between two graphs on the same labeled vertex set is the size of the symmetric difference of the edge sets. The distance between a graph, G , and a hereditary property, ℋ, is the minimum of the distance between G and each G' ∈ℋ. The edit distance function of ℋ is a function of p ∈[0,1] and is the limit of the maximum normalized distance between a graph of density p and ℋ. This paper utilizes a method due to Sidorenko [ Combinatorica 13 (1), pp. 109-120], called "symmetrization", for computing the edit distance function of various hereditary properties. For any graph H , Forb( H ) denotes the property of not having an induced copy of H . This paper gives some results regarding estimation of the function for an arbitrary hereditary property. This paper also gives the edit distance function for Forb( H ), where H is a cycle on 9 or fewer vertices.
Piercing Axis-Parallel Boxes

Let $\mathcal{F}$ be a finite family of axis-parallel boxes in $\mathbb{R}^d$ such that $\mathcal{F}$ contains no $k+1$ pairwise disjoint boxes. We prove that if $\mathcal{F}$ contains a subfamily $\mathcal{M}$ of $k$ pairwise disjoint boxes with the property that for every $F\in \mathcal{F}$ and $M\in \mathcal{M}$ with $F \cap M \neq \emptyset$, either $F$ contains a corner of $M$ or $M$ contains $2^{d-1}$ corners of $F$, then $\mathcal{F}$ can be pierced by $O(k)$ points. One consequence of this result is that if $d=2$ and the ratio between any of the side lengths of any box is bounded by a constant, then $\mathcal{F}$ can be pierced by $O(k)$ points. We further show that if for each two intersecting boxes in $\mathcal{F}$ a corner of one is contained in the other, then $\mathcal{F}$ can be pierced by at most $O(k\log\log(k))$ points, and in the special case where $\mathcal{F}$ contains only cubes this bound improves to $O(k)$.
Classification of unital simple Leavitt path algebras of infinite graphs

We prove that if E and F are graphs with a finite number of vertices and an infinite number of edges, if K is a field, and if LK(E) and LK(F) are simple Leavitt path algebras, then LK(E) is Morita equivalent to LK(F) if and only if K0alg(LK(E))≅K0alg(LK(F)) and the graphs E and F have the same number of singular vertices, and moreover, in this case one may transform the graph E into the graph F using basic moves that preserve the Morita equivalence class of the associated Leavitt path algebra. We also show that when K is a field with no free quotients, the condition that E and F have the same number of singular vertices may be replaced by K1alg(LK(E))≅K1alg(LK(F)), and we produce examples showing this cannot be done in general. We describe how we can combine our results with a classification result of Abrams, Louly, Pardo, and Smith to get a nearly complete classification of unital simple Leavitt path algebras — the only missing part is determining whether the “sign of the determinant condition” is necessary in the finite graph case. We also consider the Cuntz splice move on a graph and its effect on the associated Leavitt path algebra.
MULTINETS, RESONANCE VARIETIES, AND PENCILS OF PLANE CURVES

Dedicated to Joseph H.M. Steenbrink on the occasion of his sixtieth birthday Abstract. We show that a line arrangement in the complex projective plane supports a nontrivial resonance variety if and only if it is the underlying ar- rangement of a "multinet," a multi-arrangement with a partition into three or more equinumerous classes which have equal multiplicities at each inter-class intersection point, and satisfy a connectivity condition. We also prove that this combinatorial structure is equivalent to the existence of a pencil of plane curves, also satisfying a connectivity condition, whose singular fibers include at least three products of lines, which comprise the arrangement. We derive numerical conditions which impose restrictions on the number of classes, and the line and point multiplicities that can appear in multinets, and allow us to detect whether the associated pencils yield nonlinear fiberings of the comple- ment.
Learning Combinatorial Functions from Pairwise Comparisons

A large body of work in machine learning has focused on the problem of learning a close approximation to an underlying combinatorial function, given a small set of labeled examples. However, for real-valued functions, cardinal labels might not be accessible, or it may be difficult for an expert to consistently assign real-valued labels over the entire set of examples. For instance, it is notoriously hard for consumers to reliably assign values to bundles of merchandise. Instead, it might be much easier for a consumer to report which of two bundles she likes better. With this motivation in mind, we consider an alternative learning model, wherein the algorithm must learn the underlying function up to pairwise comparisons, from pairwise comparisons. In this model, we present a series of novel algorithms that learn over a wide variety of combinatorial function classes. These range from graph functions to broad classes of valuation functions that are fundamentally important in microeconomic theory, the analysis of social networks, and machine learning, such as coverage, submodular, XOS, and subadditive functions, as well as functions with sparse Fourier support.
Spin structures and superstrings

In superstring theory spin structures are present on both the 2-dimensional worldsheet and 10-dimensional spacetime. We present a new proposal for the B-field in superstring theory and demonstrate its interaction with worldsheet spin structures. Our formulation generalizes to orientifolds, where various twistings appear. A special case of the orientifold worldsheet B-field amplitude is a KO-theoretic construction of the Z/8Z-valued Kervaire invariant on pin-minus surfaces.
ESTIMATES FOR J-CURVES AS SUBMANIFOLDS

In this paper, we develop some basic analytic tools to study compactness properties of J-curves (i.e. pseudoholomorphic curves) when regarded as submanifolds. Incorporating techniques from the theory of minimal surfaces, we derive an inhomogeneous mean curvature equation for such curves by establishing an extrinsic monotonicity principle for nonnegative functions f satisfying Δf ≥ -c2 f, we show that curves locally parametrized as a graph over a coordinate tangent plane have all derivatives a priori bounded in terms of curvature and ambient geometry, and we thus establish ϵ-regularity for the square length of their second fundamental forms. These results are all provided for J-curves either with or without Lagrangian boundary and hold in almost all Hermitian manifolds of arbitrary even dimension (i.e. Riemannian manifolds for which the almost complex structure is an isometry).
Leading Digits of Mersenne Numbers

It has long been known that sequences such as the powers of 2 and the factorials satisfy Benford’s Law; that is, leading digits in these sequences occur with frequencies given by P(d)= log 10(1+1/d...
A Ramsey Property of Random Regular and $k$-out Graphs

In this note we consider a Ramsey property of random $d$-regular graphs, $\mathcal{G}(n,d)$. Let $r\ge 2$ be fixed. Then w.h.p. the edges of $\mathcal{G}(n, 2r)$ can be colored such that every monochromatic component has size $o(n)$. On the other hand, there exists a constant $\gamma > 0$ such that w.h.p., every $r$-coloring of the edges of $\mathcal{G}(n, 2r+1)$ must contain a monochromatic cycle of length at least $\gamma n$. We prove an analogous result for random $k$-out graphs.
The use of item scores and response times to detect examinees who may have benefited from item preknowledge

According to Wollack and Schoenig (2018, The Sage encyclopedia of educational research, measurement, and evaluation. Thousand Oaks, CA: Sage, 260), benefiting from item preknowledge is one of the three broad types of test fraud that occur in educational assessments. We use tools from constrained statistical inference to suggest a new statistic that is based on item scores and response times and can be used to detect examinees who may have benefited from item preknowledge for the case when the set of compromised items is known. The asymptotic distribution of the new statistic under no preknowledge is proved to be a simple mixture of two χ2 distributions. We perform a detailed simulation study to show that the Type I error rate of the new statistic is very close to the nominal level and that the power of the new statistic is satisfactory in comparison to that of the existing statistics for detecting item preknowledge based on both item scores and response times. We also include a real data example to demonstrate the usefulness of the suggested statistic.
Communication-Avoiding Cholesky-QR2 for Rectangular Matrices

Scalable QR factorization algorithms for solving least squares and eigenvalue problems are critical given the increasing parallelism within modern machines. We introduce a more general parallelization of the CholeskyQR2 algorithm and show its effectiveness for a wide range of matrix sizes. Our algorithm executes over a 3D processor grid, the dimensions of which can be tuned to trade-off costs in synchronization, interprocessor communication, computational work, and memory footprint. We implement this algorithm, yielding a code that can achieve a factor of Θ(P^1/6) less interprocessor communication on P processors than any previous parallel QR implementation. Our performance study on Intel Knights-Landing and Cray XE supercomputers demonstrates the effectiveness of this CholeskyQR2 parallelization on a large number of nodes. Specifically, relative to ScaLAPACK's QR, on 1024 nodes of Stampede2, our CholeskyQR2 implementation is faster by 2.6x-3.3x in strong scaling tests and by 1.1x-1.9x in weak scaling tests.
Did the ACA Medicaid Expansion Save Lives

We estimate the effect of the Affordable Care Act Medicaid expansion on county-level mortality in the first four years following expansion using restricted-access microdata covering all deaths in the United States. To adjust for pre-expansion differences in mortality rates between treatment and control, we use a propensity-score weighting model together with techniques from machine learning to match counties in expansion and non-expansion states. We find a reduction in all-cause mortality in ages 20 to 64 equaling 11.36 deaths per 100,000 individuals, a 3.6 percent decrease. This estimate is largely driven by reductions in mortality in counties with higher pre-expansion uninsured rates and for causes of death likely to be influenced by access to healthcare. A cost-benefit analysis shows that the improvement in welfare due to mortality responses may offset the entire net-of-transfers expenditure associated with the expansion.
Effect of Bar-code Technology on the Safety of Medication Administration

ABSTRACT Hospital patients are at risk of serious medication errors that may result in patient harm. Such errors commonly occur during the ordering of medication, drug administration, or the transcription and dispensing stages. Previous studies have shown that use of bar-code technology can prevent errors by providing automatic verification of a patient's identity and the medication to be administered. Bar-code technology is usually implemented in conjunction with an electronic medication administration system (eMAR). Bar-code eMAR records physician and pharmacy medication orders electronically; its implementation may reduce medication errors and protect patients from potential adverse events. Use of this technology has been increasing, but there is only limited and mixed evidence of its effectiveness. This prospective study evaluated the effectiveness of bar-code eMAR technology to reduce administration and transcription errors, as well as lower potential adverse drug events. Over a 9-month period in 2005 in a tertiary academic medical center, error rates in units that were using this technology were compared with the rates in units that were not. The 2 primary study outcomes were errors in timing, involving early and late administration of drugs, and errors unrelated to timing. Identified errors were reviewed by 2 members of a medical panel to confirm the presence of an error and the potential for adverse drug events and patient harm. A total of 7318 medication administrations were observed on patient units implementing bar-code eMAR and 6723 medication administrations on patient units that did not. Transcription orders were reviewed for 1283 units that used the bar-code eMAR and for 1799 that did not use this technology. On units not using the bar-code eMAR, there was an error rate of 11.5% (776/6723) for nontiming medication-administration errors, whereas on units with the bar-code eMAR, the error rate for nontiming-errors was 6.8% (495/7318). The difference represents a 41.4% relative reduction in the rate of nontiming errors (P < 0.001). Compared with units without bar-code eMAR, use of this technology reduced the rate of potential adverse drug events because of nontiming administration errors from 3.1% to 1.6%, representing a relative reduction of 50.8% (P < 0.001). The overall rate of timing errors decreased from 16.7% without the bar-code eMAR to 12.2% with its use, a reduction of 27.3% (P = 0.001), but there was no significant difference in the incidence of potential adverse drug events due to timing errors between the comparison groups. The rate of transcription errors was 6.1% on units that did not use the bar-code eMAR; no transcription errors were found in units using it.
Student conceptual resources for understanding mechanical wave propagation

In certain contexts, student ideas about mechanical wave propagation that have formerly been reported as difficulties can be viewed as resources that are consistent with formal physics.
Global existence for systems of quasilinear wave equations in (1 + 4)-dimensions

Abstract Hormander proved global existence of solutions for sufficiently small initial data for scalar wave equations in ( 1 + 4 ) -dimensions of the form □ u = Q ( u , u ′ , u ″ ) where Q vanishes to second order and ( ∂ u 2 Q ) ( 0 , 0 , 0 ) = 0 . Without the latter condition, only almost global existence may be guaranteed. The first author and Sogge considered the analog exterior to a star-shaped obstacle. Both results relied on writing the lowest order terms u ∂ α u = 1 2 ∂ α u 2 and as such do not immediately generalize to systems. The current study remedies such and extends both results to the case of multiple speed systems.
On nonlocal systems with jump processes of finite range and with decays

We study the following system of equations $$ L_i(u_i) = H_i(u_1,\cdots,u_m) \quad \text{in} \ \ \mathbb R^n , $$ when $m\ge 1$, $u_i: \mathbb R^n \to \mathbb R$ and $H=(H_i)_{i=1}^m$ is a sequence of general nonlinearities. The nonlocal operator $L_i$ is given by $$L_i(f (x)):= \lim_{\epsilon\to 0} \int_{\mathbb R^n \setminus B_\epsilon(x) } [f(x) - f(z)] J_i(z-x) dz,$$ for a sequence of even, nonnegative and measurable jump kernels $J_i$. We prove a Poincare inequality for stable solutions of the above system for a general jump kernel $J_i$. In particular, for the case of scalar equations, that is when $m=1$, it reads \begin{equation*}\label{} \iint_{ \mathbb R^{2n}} \mathcal A_y(\nabla_x u) [\eta^2(x)+\eta^2(x+y)] J(y) dx dy \le \iint_{ \mathbb R^{2n}} \mathcal B_y(\nabla_x u) [ \eta(x) - \eta(x+y) ] ^2 J(y) d x dy , \end{equation*} for any $\eta \in C_c^1(\mathbb R^{n})$ and for some nonnegative $ \mathcal A_y(\nabla_x u)$ and $ \mathcal B_y(\nabla_x u)$. This is a counterpart of the celebrated inequality derived by Sternberg and Zumbrun in \cite{sz} for semilinear elliptic equations that is used extensively in the literature to establish De Giorgi type results, to study phase transitions and to prove regularity properties. We then apply this inequality to finite range jump processes and to jump processes with decays to prove De Giorgi type results in two dimensions. In addition, we show that whenever $H_i(u)\ge 0$ or $\sum_{i=1}^m u_i H_i(u)\le 0$ then Liouville theorems hold for each $u_i$ in one and two dimensions. Lastly, we provide certain energy estimates under various assumptions on the jump kernel $J_i$ and a Liouville theorem for the quotient of partial derivatives of $u$.
Neural Task Graphs: Generalizing to Unseen Tasks From a Single Video Demonstration

Our goal is to generate a policy to complete an unseen task given just a single video demonstration of the task in a given domain. We hypothesize that to successfully generalize to unseen complex tasks from a single video demonstration, it is necessary to explicitly incorporate the compositional structure of the tasks into the model. To this end, we propose Neural Task Graph (NTG) Networks, which use conjugate task graph as the intermediate representation to modularize both the video demonstration and the derived policy. We empirically show NTG achieves inter-task generalization on two complex tasks: Block Stacking in BulletPhysics and Object Collection in AI2-THOR. NTG improves data efficiency with visual input as well as achieve strong generalization without the need for dense hierarchical supervision. We further show that similar performance trends hold when applied to real-world data. We show that NTG can effectively predict task structure on the JIGSAWS surgical dataset and generalize to unseen tasks.
Psychosocial moderation of polygenic risk for cannabis involvement: the role of trauma exposure and frequency of religious service attendance.

Cannabis use and disorders (CUD) are influenced by multiple genetic variants of small effect and by the psychosocial environment. However, this information has not been effectively incorporated into studies of gene–environment interaction (GxE). Polygenic risk scores (PRS) that aggregate the effects of genetic variants can aid in identifying the links between genetic risk and psychosocial factors. Using data from the Pasman et al. GWAS of cannabis use (meta-analysis of data from the International Cannabis Consortium and UK Biobank), we constructed PRS in the Collaborative Study on the Genetics of Alcoholism (COGA) participants of European (N: 7591) and African (N: 3359) ancestry. The primary analyses included only individuals of European ancestry, reflecting the ancestral composition of the discovery GWAS from which the PRS was derived. Secondary analyses included the African ancestry sample. Associations of PRS with cannabis use and DSM-5 CUD symptom count (CUDsx) and interactions with trauma exposure and frequency of religious service attendance were examined. Models were adjusted for sex, birth cohort, genotype array, and ancestry. Robustness models were adjusted for cross-term interactions. Higher PRS were associated with a greater likelihood of cannabis use and with CUDsx among participants of European ancestry (p < 0.05 and p < 0.1 thresholds, respectively). PRS only influenced cannabis use among those exposed to trauma (R2: 0.011 among the trauma exposed vs. R2: 0.002 in unexposed). PRS less consistently influenced cannabis use among those who attend religious services less frequently; PRS × religious service attendance effects were attenuated when cross-term interactions with ancestry and sex were included in the model. Polygenic liability to cannabis use was related to cannabis use and, less robustly, progression to symptoms of CUD. This study provides the first evidence of PRS × trauma for cannabis use and demonstrates that ignoring important aspects of the psychosocial environment may mask genetic influences on polygenic traits.
A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin.

The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance. Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy. Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context. We analyzed human homologs of yeast genes exhibiting gene-doxorubicin interaction in cancer pharmacogenomics data to predict causality for differential gene expression associated with doxorubicin cytotoxicity in cancer cells. This analysis suggested conserved cellular responses to doxorubicin due to influences of homologous recombination, sphingolipid homeostasis, telomere tethering at nuclear periphery, actin cortical patch localization, and other gene functions. Warburg status alters the genetic network required for yeast to buffer doxorubicin toxicity. Integration of yeast phenomic and cancer pharmacogenomics data suggests evolutionary conservation of gene-drug interaction networks and provides a new experimental approach to model their influence on chemotherapy response. Thus, yeast phenomic models could aid the development of precision oncology algorithms to predict efficacious cytotoxic drugs for cancer, based on genetic and metabolic profiles of individual tumors.
SSS: Scalable Key-Value Store with External Consistent and Abort-free Read-only Transactions

We present SSS, a scalable transactional key-value store deploying a novel distributed concurrency control that provides external consistency for all transactions, never aborts read-only transactions due to concurrency, all without specialized hardware. SSS ensures the above properties without any centralized source of synchronization. SSS's concurrency control uses a combination of vector clocks and a new technique, called snapshot-queuing, to establish a single serialization order where transactions are guaranteed to read from the latest non-concurrent transaction externally visible to clients. We compare SSS against high performance key-value stores, Walter, ROCOCO, and a two-phase commit baseline. SSS outperforms 2PC-baseline by as much as 7x using 20 nodes; and ROCOCO by as much as 2.2x with long read-only transactions using 15 nodes.
Universal Behavior of the Corners of Orbital Beta Processes

There is a unique unitarily-invariant ensemble of $N\times N$ Hermitian matrices with a fixed set of real eigenvalues $a_1 > \dots > a_N$. The joint eigenvalue distribution of the $(N - 1)$ top-left principal submatrices of a random matrix from this ensemble is called the orbital unitary process. There are analogous matrix ensembles of symmetric and quaternionic Hermitian matrices that lead to the orbital orthogonal and symplectic processes, respectively. By extrapolation, on the dimension of the base field, of the explicit density formulas, we define the orbital beta processes. We prove the universal behavior of the virtual eigenvalues of the smallest $m$ principal submatrices, when $m$ is independent of $N$ and in such a way that the rescaled empirical measures converge weakly. The limiting object is the Gaussian beta corners process. As a byproduct of our approach, we prove a theorem on the asymptotics of multivariate Bessel functions.
Comparison and experimental validation of predictive models for soft, fiber-reinforced actuators:

Successful soft robot modeling approaches appearing in the recent literature have been based on a variety of distinct theories, including traditional robotic theory, continuum mechanics, and machin...
In-vitro and in-vivo characterization of a multi-stage enzyme-responsive nanoparticle-in-microgel pulmonary drug delivery system.

Although the lung is an obvious target for site-specific delivery of many therapeutics for respiratory airway diseases such as asthma, COPD, and cystic fibrosis, novel strategies are needed to avoid key physiologic barriers for efficient delivery and controlled release of therapeutics to the lungs. Specifically, deposition into the deep lung requires particles with a 1-5μm aerodynamic diameter; however, particles with a geometric diameter less than 6μm are rapidly cleared by alveolar macrophages. Additionally, epithelial, endothelial, and fibroblast cells prefer smaller (< 300nm) nanoparticles for efficient endocytosis. Here we address these contradictory design requirements by using a nanoparticle-inside-microgel system (Nano-in-Microgel). Using an improved maleimide-thiol based Michael Addition during (water-in-oil) Emulsion (MADE) method, we fabricated both trypsin-responsive and neutrophil elastase-responsive polymeric Nano-in-Microgel to show the versatility of the system in easily exchanging enzyme-responsive crosslinkers for disease-specific proteases. By varying the initial macromer concentration, from 20 to 50% w/v, the size distribution means ranged from 4-8μm, enzymatic degradation of the microgels is within 30min, and in vitro macrophage phagocytosis is lower for the higher % w/v. We further demonstrated that in vivo lung delivery of the multi-stage carriers through the pulmonary route yields particle retention up to several hours and followed by clearance within in naive mice. Our results provide a further understanding of how enzymatically-degradable multi-stage polymeric carriers can be used for pulmonary drug delivery.
Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning

A key challenge for autonomous driving is safe trajectory planning in cluttered, urban environments with dynamic obstacles, such as pedestrians, bicyclists, and other vehicles. A reliable prediction of the future environment, including the behavior of dynamic agents, would allow planning algorithms to proactively generate a trajectory in response to a rapidly changing environment. We present a novel framework that predicts the future occupancy state of the local environment surrounding an autonomous agent by learning a motion model from occupancy grid data using a neural network. We take advantage of the temporal structure of the grid data by utilizing a convolutional long-short term memory network in the form of the PredNet architecture. This method is validated on the KITTI dataset and demonstrates higher accuracy and better predictive power than baseline methods.
Network-based Fake News Detection: A Pattern-driven Approach

Fake news gains has gained significant momentum, strongly motivating the need for fake news research. Many fake news detection approaches have thus been proposed, where most of them heavily rely on news content. However, networkbased clues revealed when analyzing news propagation on social networks is an information that has hardly been comprehensively explored or used for fake news detection. We bridge this gap by proposing a network-based pattern-driven fake news detection approach. We aim to study the patterns of fake news in social networks, which refer to the news being spread, spreaders of the news and relationships among the spreaders. Empirical evidence and interpretations on the existence of such patterns are provided based on social psychological theories. These patterns are then represented at various network levels (i.e., node-level, ego-level, triad-level, community-level and the overall network) for being further utilized to detect fake news. The proposed approach enhances the explainability in fake news feature engineering. Experiments conducted on real-world data demonstrate that the proposed approach can outperform the state of the arts.
The Geography of Prescription Pharmaceuticals Supplied to the U.S.: Levels, Trends and Implications

Prescription pharmaceuticals are frequently used consumer products whose manufacturing location is commonly held as a trade secret by firms and US regulatory agencies. Here we use previously non-publicly available data to describe levels and trends in the manufacturing locations of the most commonly used prescription pharmaceuticals, off-patent generic drugs, intended to be consumed by Americans. We find that the base ingredients required for the manufacturing of these prescription drugs are overwhelmingly and increasingly manufactured in non-domestic locations, specifically India and China. The manufacturing of finished prescription drugs for the American market is more equally split between domestic and foreign locations, but is increasingly foreign as well. The American reliance on non-domestic manufacturing of prescription drugs is important for stakeholders to appreciate, given current quality and pricing concerns and their potential susceptibility to interruptions in supply due to natural disasters, pandemics, and international trade negotiations. We discuss implications of these levels and trends for current domestic and international policy discussions.
A fast solver for the narrow capture and narrow escape problems in the sphere

Abstract We present an efficient method to solve the narrow capture and narrow escape problems for the sphere. The narrow capture problem models the equilibrium behavior of a Brownian particle in the exterior of a sphere whose surface is reflective, except for a collection of small absorbing patches. The narrow escape problem is the dual problem: it models the behavior of a Brownian particle confined to the interior of a sphere whose surface is reflective, except for a collection of small patches through which it can escape. Mathematically, these give rise to mixed Dirichlet/Neumann boundary value problems of the Poisson equation. They are numerically challenging for two main reasons: (1) the solutions are non-smooth at Dirichlet-Neumann interfaces, and (2) they involve adaptive mesh refinement and the solution of large, ill-conditioned linear systems when the number of small patches is large. By using the Neumann Green's functions for the sphere, we recast each boundary value problem as a system of first-kind integral equations on the collection of patches. A block-diagonal preconditioner together with a multiple scattering formalism leads to a well-conditioned system of second-kind integral equations and a very efficient approach to discretization. This system is solved iteratively using GMRES. We develop a hierarchical, fast multipole method-like algorithm to accelerate each matrix-vector product. Our method is insensitive to the patch size, and the total cost scales with the number N of patches as O ( N log ⁡ N ) , after a precomputation whose cost depends only on the patch size and not on the number or arrangement of patches. We demonstrate the method with several numerical examples, and are able to achieve highly accurate solutions with 100 000 patches in one hour on a 60-core workstation. For that case, adaptive discretization of each patch would lead to a dense linear system with about 360 million degrees of freedom. Our preconditioned system uses only 13.6 million “compressed” degrees of freedom and a few dozen GMRES iterations.
A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures

Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.
Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery

Abstract Variable Rate Irrigation (VRI) considers spatial variability in soil and plant characteristics to optimize irrigation management in agricultural fields. The advent of unmanned aircraft systems (UAS) creates an opportunity to utilize high-resolution (spatial and temporal) imagery into irrigation management due to decreasing costs, ease of operation, and reduction of regulatory constraints. This research aimed to evaluate the use of UAS data for VRI, and to quantify the potential of VRI in terms of relative crop and water response. Irrigation treatments were: (1) VRI using Landsat imagery (VRI-L), (2) VRI using UAS imagery (VRI-U), (3) uniform (U), and (4) rainfed (R). An updated remote-sensing-based evapotranspiration and water balance model, incorporating soil water measurements, was used to make prescriptions for the VRI treatments at a field site in eastern Nebraska. In 2017, the mean prescribed seasonal irrigation depth (Ip) for VRI-L was significantly greater (α = 0.05) than the Ip for U for soybean. In 2018, Ip for soybean was greatest for VRI-U treatment followed by the U and VRI-L treatments, with all being significantly different from each other. No significant differences in Ip for maize were observed in 2017 or 2018. In all crop-year combinations, the VRI and U treatments had significantly greater evapotranspiration (ET) than the R treatment. Yield differences among treatments were not significant (except for rainfed maize compared to VRI-L in 2017). For maize in 2017, IWUE for VRI-L was comparable to the U treatment. The UAS imagery was a better match for the scale of crop management than Landsat imagery, particularly for thermal data. The multispectral UAS data was successfully used in the crop coefficient ET model for real-time irrigation, but using UAS to determine accurate canopy temperatures for surface energy balance modeling remains a challenge.
Regularly varying random fields

Abstract We study the extremes of multivariate regularly varying random fields. The crucial tools in our study are the tail field and the spectral field, notions that extend the tail and spectral processes of Basrak and Segers (2009). The spatial context requires multiple notions of extremal index, and the tail and spectral fields are applied to clarify these notions and other aspects of extremal clusters. An important application of the techniques we develop is to the Brown–Resnick random fields.
A Change in the Frame: From Absenteeism to Attendance

School attendance is important for student long-term academic and career success. However, in the U.S., our current practice often disenfranchises more at-risk students than it helps. Students slated for suspension and expulsion are often recipients of these practices. This manuscript offers a recommended change in how we frame student absenteeism and attendance using attendance markers and conceptual information by identifying the discrepancies, proposing options, and recommending a new way to actively leverage attendance data (not absenteeism data) for proactive student support. Particular attention is paid to how excused and unexcused absences and in-school suspensions are treated. An emerging pivot program, the Evaluation and Support Program, engages students while they receive school services, community support, and complete consequences is discussed as a possible, promising intervention.
Hormone Therapy for Postmenopausal Women

Key Clinical Points Hormone Therapy for Postmenopausal Women Women younger than 60 years of age or within 10 years after the onset of menopause who have symptomatic menopausal hot flashes or night ...
Risk Premium Shocks Can Create Inefficient Recessions

We develop an equilibrium theory of business cycles driven by spikes in risk premiums that depress business demand for capital and labor. Aggregate shocks increase firms’ uninsurable idiosyncratic risk and raise risk premiums. We show that risk shocks can create quantitatively realistic recessions, with contractions in employment, consumption, and investment. Business cycles are inefficient—output and employment fall too much during recessions, compared to the constrained-efficient allocation, and consumption should rise. Optimal policy involves stimulating employment and consumption during recessions.
Sign patterns of orthogonal matrices and the strong inner product property

Abstract A new condition, the strong inner product property, is introduced and used to construct sign patterns of row orthogonal matrices. Using this property, infinite families of sign patterns allowing row orthogonality are found. These provide insight into the underlying combinatorial structure of row orthogonal matrices. Algorithmic techniques for verifying that a matrix has the strong inner product property are also presented. These techniques lead to a generalization of the strong inner product property and can be easily implemented using various software.