cssi/SciSciGPT-SciSciNet huggingface dataset:
Development and Pilot Screen of Novel High Content Assay for Down Regulators of Expression of Heterogenous Nuclear Ribonuclear Protein H2.

Background/aims Despite recent advances in melanoma drug discovery, the average overall survival of patients with late-stage metastatic melanoma is approximately 3 years, suggesting a need for new approaches and melanoma therapeutic targets. Previously we identified heterogeneous nuclear ribonucleoprotein H2 as a potential target of anti-melanoma compound 2155-14 (Palrasu et al., Cell Physiol Biochem 2019;53:656-686). In the present study, we endeavored to develop an assay to enable a high throughput screening campaign to identify drug-like molecules acting via down regulation of heterogeneous nuclear ribonucleoprotein H2 that can be used for melanoma therapy and research. Methods We established a cell-based platform using metastatic melanoma cell line WM266-4 expressing hnRNPH2 conjugated with green fluorescent protein to enable assay development and screening. High Content Screening assay was developed and validated in 384 well plate format, followed by miniaturization to 1,536 well plate format. Results All plate-based QC parameters were acceptable: %CV = 6.7±0.3, S/B = 21±2.1, Z' = 0.75±0.04. Pilot screen of FDA-approved drug library (n=1,400 compounds) demonstrated hit rate of 0.5%. Two compounds demonstrated pharmacological response and were authenticated by western blot analysis. Conclusion We developed a highly robust HTS-amenable high content screening assay capable of monitoring down regulation of hnRNPH2. This assay is thus capable of identifying authentic down regulators of hnRNPH1 and 2 in a large compound collection and, therefore, is amenable to a large-scale screening effort.
LDLE: Low Distortion Local Eigenmaps

We present Low Distortion Local Eigenmaps (LDLE), a manifold learning technique which constructs a set of low distortion local views of a dataset in lower dimension and registers them to obtain a global embedding. The local views are constructed using the global eigenvectors of the graph Laplacian and are registered using Procrustes analysis. The choice of these eigenvectors may vary across the regions. In contrast to existing techniques, LDLE is more geometric and can embed manifolds without boundary as well as non-orientable manifolds into their intrinsic dimension.
Four times out of Europe: serial invasions of the winter moth, Operophtera brumata, to North America

Reconstructing the geographic origins of non-native species is important for studying the factors that influence invasion success, however; these analyses can be constrained by the amount of diversity present in the native and invaded regions, and by changes in the genetic background of the invading population following bottlenecks and/or hybridization events. Here we explore the geographical origins of the invasive winter moth (Operopthera brumata L.) that has caused widespread defoliation to forests, orchards, and crops in Nova Scotia, British Columbia, Oregon, and the northeastern United States. It is not known whether these represent independent introductions to North America, or a "stepping stone" spread among regions. Using a combination of Bayesian assignment and approximate Bayesian computation methods, we analysed a population genetic data set of 24 microsatellite loci. We estimate that winter moth was introduced to North America on at least four occasions, with the Nova Scotian and British Columbian populations probably being introduced from France and Sweden, respectively; the Oregonian population probably being introduced from either the British Isles or northern Fennoscandia; and the population in the northeastern United States probably being introduced from somewhere in Central Europe. We discuss the impact of genetic bottlenecks on analyses meant to determine region of origin.
Spectral Threshold for Extremal Cyclic Edge-Connectivity

The universal cyclic edge-connectivity of a graph G is the least k such that there exists a set of k edges whose removal disconnects G into components where every component contains a cycle. We show that for graphs of minimum degree at least 3 and girth g at least 4, the universal cyclic edge-connectivity is bounded above by $$(\Delta -2)g$$ where $$\Delta $$ is the maximum degree. We then prove that if the second eigenvalue of the adjacency matrix of a d-regular graph of girth $$g\ge 4$$ is sufficiently small, then the universal cyclic edge-connectivity is $$(d-2)g$$ , providing a spectral condition for when this upper bound on universal cyclic edge-connectivity is tight.
PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics

As applications of deep learning (DL) continue to seep into critical scientific use-cases, the importance of performing uncertainty quantification (UQ) with DL has become more pressing than ever before. In scientific applications, it is also important to inform the learning of DL models with knowledge of physics of the problem to produce physically consistent and generalized solutions. This is referred to as the emerging field of physics-informed deep learning (PIDL). We consider the problem of developing PIDL formulations that can also perform UQ. To this end, we propose a novel physics-informed GAN architecture, termed PID-GAN, where the knowledge of physics is used to inform the learning of both the generator and discriminator models, making ample use of unlabeled data instances. We show that our proposed PID-GAN framework does not suffer from imbalance of generator gradients from multiple loss terms as compared to state-of-the-art. We also empirically demonstrate the efficacy of our proposed framework on a variety of case studies involving benchmark physics-based PDEs as well as imperfect physics. All the code and datasets used in this study have been made available on this link: https://github.com/arkadaw9/PID-GAN.
Low-Resolution Face Recognition In Resource-Constrained Environments

Abstract Although Deep Neural Networks (DNNs) have achieved tremendous success in the face recognition task, utilizing them in resource-constrained environments with limited networking and computing is challenging. Such environments often demand a small model capable of being effectively trained on a small number of labeled training data, with low training complexity, and low-resolution input images. To address these challenges, we adopt an emerging machine learning methodology called Successive Subspace Learning (SSL) to propose LRFRHop, a high-performance data-efficient low-resolution face recognition model for resource-constrained environments. SSL offers an explainable non-parametric feature extraction submodel that flexibly trades the model size for the verification performance. Its training complexity is significantly lower than DNN-based models since it is trained in a one-pass feedforward manner without backpropagation. Furthermore, active learning can be conveniently incorporated to reduce the labeling cost. We demonstrate the effectiveness of LRFRHop by conducting experiments on the LFW and the CMU Multi-PIE datasets.
Every $BT_1$ group scheme appears in a Jacobian

Let $p$ be a prime number and let $k$ be an algebraically closed field of characteristic $p$. A $BT_1$ group scheme over $k$ is a finite commutative group scheme which arises as the kernel of $p$ on a $p$-divisible (Barsotti--Tate) group. Our main result is that every $BT_1$ scheme group over $k$ occurs as a direct factor of the $p$-torsion group scheme of the Jacobian of an explicit curve defined over $\mathbb{F}_p$. We also treat a variant with polarizations. Our main tools are the Kraft classification of $BT_1$ group schemes, a theorem of Oda, and a combinatorial description of the de Rham cohomology of Fermat curves.
Lifting isometries of orbit spaces

Given an orthogonal representation of a compact group, we show that any element of the connected component of the isometry group of the orbit space lifts to an equivariant isometry of the original Euclidean space. Corollaries include a simple formula for this connected component, which applies to ``most'' representations.
Untrained networks for compressive lensless photography

Compressive lensless imagers enable novel applications in an extremely compact device, requiring only a phase or amplitude mask placed close to the sensor. They have been demonstrated for 2D and 3D microscopy, single-shot video, and single-shot hyperspectral imaging; in each case, a compressive-sensing-based inverse problem is solved in order to recover a 3D data-cube from a 2D measurement. Typically, this is accomplished using convex optimization and hand-picked priors. Alternatively, deep learning-based reconstruction methods offer the promise of better priors, but require many thousands of ground truth training pairs, which can be difficult or impossible to acquire. In this work, we propose an unsupervised approach based on untrained networks for compressive image recovery. Our approach does not require any labeled training data, but instead uses the measurement itself to update the network weights. We demonstrate our untrained approach on lensless compressive 2D imaging, single-shot high-speed video recovery using the camera’s rolling shutter, and single-shot hyperspectral imaging. We provide simulation and experimental verification, showing that our method results in improved image quality over existing methods.
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models

Model parallelism has become a necessity for training modern large-scale deep language models. In this work, we identify a new and orthogonal dimension from existing model parallel approaches: it is possible to perform pipeline parallelism within a single training sequence for Transformer-based language models thanks to its autoregressive property. This enables a more fine-grained pipeline compared with previous work. With this key idea, we design TeraPipe, a high-performance token-level pipeline parallel algorithm for synchronous model-parallel training of Transformer-based language models. We develop a novel dynamic programming-based algorithm to calculate the optimal pipelining execution scheme given a specific model and cluster configuration. We show that TeraPipe can speed up the training by 5.0x for the largest GPT-3 model with 175 billion parameters on an AWS cluster with 48 p3.16xlarge instances compared with state-of-the-art model-parallel methods.
Adversarial Linear Contextual Bandits with Graph-Structured Side Observations.

This paper studies the adversarial graphical contextual bandits, a variant of adversarial multi-armed bandits that leverage two categories of the most common side information: contexts and side observations. In this setting, a learning agent repeatedly chooses from a set of K actions after being presented with a d-dimensional context vector. The agent not only incurs and observes the loss of the chosen action, but also observes the losses of its neighboring actions in the observation structures, which are encoded as a series of feedback graphs. This setting models a variety of applications in social networks, where both contexts and graph-structured side observations are available. Two efficient algorithms are developed based on EXP3. Under mild conditions, our analysis shows that for undirected feedback graphs the first algorithm, EXP3-LGC-U, achieves a sub-linear regret with respect to the time horizon and the average independence number of the feedback graphs. A slightly weaker result is presented for the directed graph setting as well. The second algorithm, EXP3-LGC-IX, is developed for a special class of problems, for which the regret is the same for both directed as well as undirected feedback graphs. Numerical tests corroborate the efficiency of proposed algorithms.
JPL Innovation Foundry

Abstract Space science missions are increasingly challenged today: in ambition, by increasingly sophisticated hypotheses tested; in development, by the increasing complexity of advanced technologies; in budgeting, by the decline of flagship-class mission opportunities; in management, by expectations for breakthrough science despite a risk-averse programmatic climate; and in planning, by increasing competition for scarce resources. How are the space-science missions of tomorrow being formulated? The paper describes the JPL Innovation Foundry, created in 2011, to respond to this evolving context. The Foundry integrates methods, tools, and experts that span the mission concept lifecycle. Grounded in JPL's heritage of missions, flight instruments, mission proposals, and concept innovation, the Foundry seeks to provide continuity of support and cost-effective, on-call access to the right domain experts at the right time, as science definition teams and Principal Investigators mature mission ideas from “cocktail napkin” to PDR. The Foundry blends JPL capabilities in proposal development and concurrent engineering, including Team X, with new approaches for open-ended concept exploration in earlier, cost-constrained phases, and with ongoing research and technology projects. It applies complexity and cost models, project-formulation lessons learned, and strategy analyses appropriate to each level of concept maturity. The Foundry is organizationally integrated with JPL formulation program offices; staffed by JPL's line organizations for engineering, science, and costing; and overseen by senior Laboratory leaders to assure experienced coordination and review. Incubation of each concept is tailored depending on its maturity and proposal history, and its highest-leverage modeling and analysis needs.
New paradigms and new challenges [modeling and simulation]

This paper lays out provocative assertions about major challenges for the modeling and simulation community. One relates to building M&S for the purpose of assisting the search for strategies that are flexible, adaptive, and robust despite uncertainty. A key aspect of this search is exploratory analysis, coupled with selective zoom. These, in turn, require multi resolution modeling with sound models (albeit, with uncertain data). But sound models must be adaptive models, since humans are adaptive. And rigorous analysis with adaptive models, such as those involving agents, requires new methods and attitudes, as well as new tools.
RAW-C: Relatedness of Ambiguous Words in Context (A New Lexical Resource for English)

Most words are ambiguous—-i.e., they convey distinct meanings in different contexts—-and even the meanings of unambiguous words are context-dependent. Both phenomena present a challenge for NLP. Recently, the advent of contextualized word embeddings has led to success on tasks involving lexical ambiguity, such as Word Sense Disambiguation. However, there are few tasks that directly evaluate how well these contextualized embeddings accommodate the more continuous, dynamic nature of word meaning—-particularly in a way that matches human intuitions. We introduce RAW-C, a dataset of graded, human relatedness judgments for 112 ambiguous words in context (with 672 sentence pairs total), as well as human estimates of sense dominance. The average inter-annotator agreement (assessed using a leave-one-annotator-out method) was 0.79. We then show that a measure of cosine distance, computed using contextualized embeddings from BERT and ELMo, correlates with human judgments, but that cosine distance also systematically underestimates how similar humans find uses of the same sense of a word to be, and systematically overestimates how similar humans find uses of different-sense homonyms. Finally, we propose a synthesis between psycholinguistic theories of the mental lexicon and computational models of lexical semantics.
Algebraic Generalization of Venn Diagram

It is easy to deal with a Venn Diagram for 1 ≤ n ≤ 3 sets. When n gets larger, the picture becomes more complicated, that's why we thought at the following codification. That’s why we propose an easy and systematic algebraic way of dealing with the representation of intersections and unions of many sets.
Designing experiments informed by observational studies

The increasing availability of passively observed data has yielded a growing methodological interest in "data fusion." These methods involve merging data from observational and experimental sources to draw causal conclusions -- and they typically require a precarious tradeoff between the unknown bias in the observational dataset and the often-large variance in the experimental dataset. We propose an alternative approach to leveraging observational data, which avoids this tradeoff: rather than using observational data for inference, we use it to design a more efficient experiment. We consider the case of a stratified experiment with a binary outcome, and suppose pilot estimates for the stratum potential outcome variances can be obtained from the observational study. We extend results from Zhao et al. (2019) in order to generate confidence sets for these variances, while accounting for the possibility of unmeasured confounding. Then, we pose the experimental design problem as one of regret minimization, subject to the constraints imposed by our confidence sets. We show that this problem can be converted into a convex minimization and solved using conventional methods. Lastly, we demonstrate the practical utility of our methods using data from the Women's Health Initiative.
Stability and the index of biharmonic hypersurfaces in a Riemannian manifold

In this paper, we give an explicit second variation formula for a biharmonic hypersurface in a Riemannian manifold similar to that of a minimal hypersurface. We then use the second variation formula to compute the normal stability index of the known biharmonic hypersurfaces in a Euclidean sphere and to prove the nonexistence of unstable proper biharmonic hypersurface in a Euclidean space or a hyperbolic space, which adds another special case to support Chen’s conjecture on biharmonic submanifolds.
On Guessing: An Alternative Adjusted Positive Learning Estimator and Comparing Probability Misspecification With Monte Carlo Simulations.

Practitioners in the sciences have used the “flow” of knowledge (post-test score minus pre-test score) to measure learning in the classroom for the past 50 years. Walstad and Wagner, and Smith and ...
Information-Theoretic Performance Limitations of Feedback Control: Underlying Entropic Laws and Generic $\mathcal{L}_{p}$ Bounds

In this paper, we utilize information theory to study the fundamental performance limitations of generic feedback systems, where both the controller and the plant may be any causal functions/mappings while the disturbance can be with any distributions. More specifically, we obtain fundamental $\mathcal{L}_{p}$ bounds on the control error, which are shown to be completely characterized by the conditional entropy of the disturbance, based upon the entropic laws that are inherent in any feedback systems. We also discuss the generality and implications (in, e.g., fundamental limits of learning-based control) of the obtained bounds.
In-situ thermal transport measurement of flowing fluid using modulated photothermal radiometry

Abstract In situ thermal transport measurement of flowing fluid could be useful for the characterization and diagnosis of practical thermal systems such as fluid heat exchangers and thermal energy storage systems. Despite abundant reports on the ex-situ thermal conductivity measurement of stagnant fluids, a suitable technique for the thermal conductivity measurement of flowing fluid has been rarely reported. This paper presents the thermal conductivity measurement of flowing fluid within a pipe using a non-contact modulated photothermal radiometry (MPR) technique, where the surface of the pipe is heated by an intensity-modulated laser and the heat diffuses into the fluid with suitable modulation frequency. We design a tube section with small wall thickness suitable for the MPR measurements to maximize the sensitivity of the thermal response to the fluid properties while minimizing the lateral heat spreading effect. Intrinsic thermal conductivity of different fluids was obtained within a proper range of frequency and flow velocity where the forced convection effect is negligible. The forced convection effect became prominent at high flowing velocity and at low modulation frequency, leading to higher effective thermal conductivity of the fluid. It is found that the intrinsic thermal conductivity could be obtained when the flow velocity is less than 100 mm/sec and ReD1/2Pr1/3 R e D is the Reynolds number and P r is the Prandtl number.
The polycomb group protein EZH2 impairs DNA repair in breast epithelial cells

1064 The Polycomb Group Protein EZH2 is a transcriptional repressor involved in controlling cellular memory and has been linked to aggressive and metastatic breast cancer. Here we report that EZH2 decreases the expression of five RAD51 paralog proteins involved in homologous recombination repair (HR) of DNA double strand breaks (DSB): RAD51B/RAD51L1, RAD51C/RAD51L2, RAD51D/RAD51L3, XRCC2 and XRCC3, but did not affect the levels of DMC1 a gene which only functions in meiosis. EZH2 overexpression impaired the formation of RAD51 repair foci at sites of DNA breaks. Overexpression of EZH2 resulted in decreased cell survival and clonogenic capacity following DNA damage induced independently by etoposide and ionizing radiation. We suggest that EZH2 may contribute to breast tumorigenesis by specific down-regulation of RAD51-like proteins and impairment of HR repair. We provide mechanistic insights into the function of EZH2 in mammalian cells and uncover a link between EZH2, a regulator of homeotic gene expression, and HR DNA repair. Our study paves the way to explore blockade of EZH2 overexpression as a novel approach for prevention and treatment of breast cancer.
The Evolution from Life Insurance to Financial Engineering

Since the mid-1980s, the share of household net worth intermediated by US financial institutions has shifted from defined benefit plans to life insurers and defined contribution plans. Life insurers have primarily grown through variable annuities, which are mutual funds with longevity insurance, a potential tax advantage, and minimum return guarantees. The minimum return guarantees change the primary function of life insurers from traditional insurance to financial engineering. Variable annuity insurers are exposed to interest and equity risk mismatch and their stock returns were especially low during the COVID-19 crisis. We consider regulatory changes, such as more detailed financial disclosure and standardized stress tests, to monitor potential risk mismatch and to ensure stability of the insurance sector.
Between Shor and Steane: A Unifying Construction for Measuring Error Syndromes.

Fault-tolerant quantum error correction requires the measurement of error syndromes in a way that minimizes correlated errors on the quantum data. Steane and Shor ancilla are two well-known methods for fault-tolerant syndrome extraction. In this Letter, we find a unifying construction that generates a family of ancilla blocks that interpolate between Shor and Steane. This family increases the complexity of ancilla construction in exchange for reducing the rounds of measurement required to fault tolerantly measure the error. We then apply this construction to the toric code of size $L\ifmmode\times\else\texttimes\fi{}L$ and find that blocks of size $m\ifmmode\times\else\texttimes\fi{}m$ can be used to decode errors in $O(L/m)$ rounds of measurements. Our method can be applied to any Calderbank-Shor-Steane code and presents a new direction for optimizing fault-tolerant quantum computation.
Fatigue and Fracture Estimation for Metallic Components: Some Current Methods and Future Developments

Current approaches widely used in fatigue and fracture evaluations of mechanical components are summarized. Metallic alloys, and fatigue and fracture behavior in the absence of environmental effects such as corrosion or elevated temperatures, are considered. Certain shortcomings in the approaches are then discussed, followed by samples of recent work to improve the capabilities to make fatigue and fracture assessments. Finally, future developments that could further enhance those capabilities are suggested.
The influence of dietary patterns on outcomes in a Bayesian choice task.

Background and aims Previous research has investigated the impact of diet on cognition, but the focus has often been on general cognition. This paper reports on a preregistered cross-sectional study aimed at testing for specific executive function differences across individuals who self-reported one of four distinct dietary patterns: No Diet, No Sugar, Vegetarian, or Mediterranean Diet pattern. Our hypotheses were aimed at testing whether adherence to a specialty diet improved decision making relative to those who reported following No Diet. Methods We administered an incentivized Bayesian choice task to all participants. The task involved multiple components of information-existing information (base rate odds) as well as new information (sample draw evidence)-to allow a test of how these information components were used in making probability assessments, and how this may differ by self-reported dietary pattern. Sample size, hypotheses, and analysis plans were all determined ex ante and registered on the Open Science Framework. Multi-variate linear and non-linear estimation methods were used to analyze the data. Results Our data failed to support our pre-registered hypotheses. In fact, we found some evidence that self-reported adherence to a specialty No Sugar Diet was associated with a reduced decision accuracy and was connected to an increased imbalance in how the participant weighted the two available sources of information when making choices. Conclusion Our results suggest that decision making is nuanced among dietary groups, but that short-term incentivized decisions in an ecologically valid field setting are likely not improved solely by following promoted dietary patterns such as the Mediterranean or Vegetarian diets.
Examining Gender Differences in Predictors of Financial Satisfaction: Evidence from Taiwan

Using an integrative model of top-down and bottom-up influences on financial satisfaction, this study examines gender differences in the predictors of financial satisfaction in Taiwan. Using the 2016 wave of the Panel Study of Family Dynamics (PSFD), gender differences in the extent to which top-down (trait positive and negative affect) and bottom-up (demographic, financial, and social support) factors predicted financial satisfaction were explored within three Taiwanese social contexts: all adults (n=3,593), working adults (n=2,713), and married working adults (n=1,306). Varied gender differences were observed across all three social contexts. In particular, income (more strongly associated among men than women), education (more strongly associated among women than men), and trait negative affect (more strongly associated among women than men) tended to predict financial satisfaction differently by gender. Trait positive affect predicted financial satisfaction regardless of gender. The present analysis suggests traditional breadwinner ideologies continue to influence financial satisfaction assessment in Taiwan.
Biochemical Basis for the Time-of-Day Effect on Glufosinate Efficacy against Amaranthus palmeri

Glufosinate, a glutamine synthetase (GS) inhibitor, often provides variable weed control depending on environmental conditions such as light, temperature and humidity at the time of application. Midday applications normally provide improved efficacy compared to applications at dawn or dusk. We investigated the biochemical basis for the time-of-day effect on glufosinate efficacy in Amaranthus palmeri. GS1/GS2 gene expression and GS1/GS2 protein abundance were assessed in different parts (young leaves, old leaves, and roots) of plants incubated in the dark compared to those in the light. The turnover of GS total activity was also evaluated overtime following glufosinate treatment at midday compared to dusk application. The results suggest that GS in A. palmeri is less expressed and less abundant in the dark compared to in the light. Midday application of glufosinate under intense light conditions following application provide full control of A. palmeri plants. Consequently, these plants are unable to recover GS activity by de novo protein synthesis. Full activity of GS is required for complete inhibition by the irreversible inhibitor glufosinate. Therefore, glufosinate applications should always be performed in the middle of the day when sunlight is intense, to prevent weed escapes from the herbicide treatment.
Evaluation of a Recessive Male Floret Color in Cultivated Northern Wild Rice (Zizania palustris L.) for Pollen-Mediated Gene Flow Studies

Abstract Northern wild rice (NWR; Zizania palustris L.) is a wind-pollinated, annual, aquatic grass that grows naturally in the Great Lakes Region (GLR) of the United States and Canada, and is also cultivated in flooded paddies, predominantly in California and Minnesota. A better understanding of pollen-mediated gene flow is needed within the species for both conservation and breeding efforts as cultivation occurs within the species natural range and spatially-isolated, paddy structures are limited within breeding programs. Widely cited pollen travel research in NWR demonstrated that pollen could travel at least 3200m. However, a population segregating for male sterility was used as the pollen recipient in the study and was determined to not be adequate for NWR pollen travel studies. Here, we present the characterization of a recessive white male floret (WMF) population in contrast to the dominant, purple male floret (PMF) color of cultivated NWR along with estimates of pollen-mediated gene flow in a cultivated paddy setting. Studies conducted in 2018 and 2019 revealed that the primary amount of pollen-mediated gene flow occurred within the first 7m from the PMF donor source with no gene flow detected past 63m. These results suggest that the likelihood of pollen-mediated gene flow between cultivated NWR and natural stands remains low. We also identified a strong linkage between male floret, auricle, and culm color. This study demonstrates that the WMF trait is an excellent candidate for use in pollen-mediated gene flow studies in NWR.
Gastric neurons in the nucleus tractus solitarius are selective to the orientation of gastric electrical stimulation.

Gastric electrical stimulation (GES) is a bioelectric intervention for gastroparesis, obesity, and other functional gastrointestinal disorders. In a potential mechanism of action, GES activates the nerve endings of vagal afferent neurons and induces the vago-vagal reflex through the nucleus tractus solitarius (NTS) in the brainstem. However, it is unclear where and how to stimulate in order to optimize the vagal afferent responses. To address this question with electrophysiology in rats, we applied mild electrical currents to two serosal targets on the distal forestomach with dense distributions of vagal intramuscular arrays that innervated the circular and longitudinal smooth muscle layers. During stimulation, we recorded single and multi-unit responses from gastric neurons in NTS and evaluated how the recorded responses depended on the stimulus orientation and amplitude. We found that NTS responses were highly selective to the stimulus orientation for a range of stimulus amplitudes. The strongest responses were observed when the applied current flowed in the same direction as the intramuscular arrays in parallel with the underlying smooth muscle fibers. Our results suggest that gastric neurons in NTS may encode the orientation-specific activity of gastric smooth muscles relayed by vagal afferent neurons. This finding suggests that the orientation of GES is critical to effective engagement of vagal afferents and should be considered in light of the structural phenotypes of vagal terminals in the stomach.
The Value of Planning for Infinite-Horizon Model Predictive Control

Model Predictive Control (MPC) is a classic tool for optimal control of complex, real-world systems. Although it has been successfully applied to a wide range of challenging tasks in robotics, it is fundamentally limited by the prediction horizon, which, if too short, will result in myopic decisions. Recently, several papers have suggested using a learned value function as the terminal cost for MPC. If the value function is accurate, it effectively allows MPC to reason over an infinite horizon. Unfortunately, Reinforcement Learning (RL) solutions to value function approximation can be difficult to realize for robotics tasks. In this paper, we suggest a more efficient method for value function approximation that applies to goal-directed problems, like reaching and navigation. In these problems, MPC is often formulated to track a path or trajectory returned by a planner. However, this strategy is brittle in that unexpected perturbations to the robot will require replanning, which can be costly at runtime. Instead, we show how the intermediate data structures used by modern planners can be interpreted as an approximate value function. We show that that this value function can be used by MPC directly, resulting in more efficient and resilient behavior at runtime.
Public good provision with participation costs

Esteban and Ray (2001) develop a model with an increasing marginal cost of contribution and overturn the Olson hypothesis that large groups are unable to provide themselves with a rival public good. Pecorino and Temimi (2008) consider fixed, but avoidable, participation costs in this framework. They conclude that public good provision must fall to zero in a large group if the degree of rivalry is sufficiently high, because the individual payoff goes to zero at the interior equilibrium. Thus, individuals prefer a corner solution of zero contributions to incurring the fixed participation cost. However, when the degree of rivalry is sufficiently low, they conclude that provision of the public good will rise without bound. We show that this latter conclusion is incorrect, and that any degree of rivalry will lead to a total breakdown in public good provision in a sufficiently large group. Not including the fixed participation cost, the difference in the payoff to an individual when she contributes to the public good and when she does not contribute goes to zero in a large group. Thus, in a sufficiently large group, she will not be willing to incur the participation costs. The same result also applies to the Morgan (2000) lottery mechanism.
An explicit characteristic-based immersed boundary method for compressible flows

Abstract In this work, characteristic-based volume penalization is combined with a high-order energy-stable finite difference framework for both the compressible Navier–Stokes and Euler equations. The method is purely explicit where free parameters are chosen based on the limitations of the underlying discretization. This results in a computationally efficient method that avoids increased stiffness associated with the boundary treatment and consequently does not rely on iterative solvers for improved stability. To improve robustness in the presence of strong discontinuities (e.g. shocks), a regularized Heaviside function is imposed on the volume penalization terms near the interface. Unlike other immersed boundary methods applied to compressible flows, the proposed method does not require modifications to the underlying computational stencil. The method is demonstrated on a series of inviscid and viscous, subsonic and supersonic flows.
Understanding How Encoder-Decoder Architectures Attend

Encoder-decoder networks with attention have proven to be a powerful way to solve many sequence-to-sequence tasks. In these networks, attention aligns encoder and decoder states and is often used for visualizing network behavior. However, the mechanisms used by networks to generate appropriate attention matrices are still mysterious. Moreover, how these mechanisms vary depending on the particular architecture used for the encoder and decoder (recurrent, feed-forward, etc.) are also not well understood. In this work, we investigate how encoder-decoder networks solve different sequence-to-sequence tasks. We introduce a way of decomposing hidden states over a sequence into temporal (independent of input) and input-driven (independent of sequence position) components. This reveals how attention matrices are formed: depending on the task requirements, networks rely more heavily on either the temporal or input-driven components. These findings hold across both recurrent and feed-forward architectures despite their differences in forming the temporal components. Overall, our results provide new insight into the inner workings of attention-based encoder-decoder networks.
Geometric and algebraic approaches to quantum theory

Abstract We show how to formulate physical theory taking as a starting point the set of states (geometric approach). We discuss the relation of this formulation to the conventional approach to classical and quantum mechanics and the theory of complex systems. The equations of motion and the formulas for probabilities of physical quantities are analyzed. A heuristic proof of decoherence in our setting is used to justify the formulas for probabilities. We show that any physical theory can be obtained from classical theory if we restrict the set of observables. This remark can be used to construct models with any prescribed group of symmetries; one can hope that this construction leads to new interesting models that cannot be build in the conventional framework. The geometric approach can be used to formulate quantum theory in terms of Jordan algebras, generalizing the algebraic approach to quantum theory. The scattering theory can be formulated in geometric approach.
Dynamic Influence Maximization

We initiate a systematic study on $\mathit{dynamic}$ $\mathit{influence}$ $\mathit{maximization}$ (DIM). In the DIM problem, one maintains a seed set $S$ of at most $k$ nodes in a dynamically involving social network, with the goal of maximizing the expected influence spread while minimizing the amortized updating cost. We consider two involution models. In the $\mathit{incremental}$ model, the social network gets enlarged over time and one only introduces new users and establishes new social links, we design an algorithm that achieves $(1-1/e-\epsilon)$-approximation to the optimal solution and has $k \cdot\mathsf{poly}(\log n, \epsilon^{-1})$ amortized running time, which matches the state-of-art offline algorithm with only poly-logarithmic overhead. In the $\mathit{fully}$ $\mathit{dynamic}$ model, users join in and leave, influence propagation gets strengthened or weakened in real time, we prove that under the Strong Exponential Time Hypothesis (SETH), no algorithm can achieve $2^{-(\log n)^{1-o(1)}}$-approximation unless the amortized running time is $n^{1-o(1)}$. On the technical side, we exploit novel adaptive sampling approaches that reduce DIM to the dynamic MAX-k coverage problem, and design an efficient $(1-1/e-\epsilon)$-approximation algorithm for it. Our lower bound leverages the recent developed distributed PCP framework.
The importance of residue-level filtering and the Top2018 best-parts dataset of high-quality protein residues.

We have curated a high-quality, "best parts" reference dataset of about 3 million protein residues in about 15,000 PDB-format coordinate files, each containing only residues with good electron density support for a physically acceptable model conformation. The resulting pre-filtered data typically contains the entire core of each chain, in quite long continuous fragments. Each reference file is a single protein chain, and the total set of files were selected for low redundancy, high resolution, good MolProbity score, and other chain-level criteria. Then each residue was critically tested for adequate local map quality to firmly support its conformation, which must also be free of serious clashes or covalent-geometry outliers. The resulting Top2018 pre-filtered datasets have been released on the Zenodo online web service and are freely available for all uses under a Creative Commons license. Currently, one dataset is residue-filtered on mainchain plus Cβ atoms, and a second dataset is full-residue filtered; each is available at 4 different sequence-identity levels. Here, we illustrate both statistics and examples that show the beneficial consequences of residue-level filtering. That process is necessary because even the best of structures contain a few highly disordered local regions with poor density and low-confidence conformations that should not be included in reference data. Therefore the open distribution of these very large, pre-filtered reference datasets constitutes a notable advance for structural bioinformatics and the fields that depend upon it. This article is protected by copyright. All rights reserved.
Crystal structure of the potato leafroll virus coat protein and implications for viral assembly.

Luteoviruses, poleroviruses, and enamoviruses are insect-transmitted, agricultural pathogens that infect a wide array of plants, including staple food crops. Previous cryo-electron microscopy studies of virus-like particles show that luteovirid viral capsids are built from a structural coat protein that organizes with T = 3 icosahedral symmetry. Here, we present the crystal structure of a truncated version of the coat protein monomer from potato leafroll virus at 1.80-A resolution. In the crystal lattice, monomers pack into flat sheets that preserve the two-fold and three-fold axes of icosahedral symmetry and show minimal structural deviations when compared to the full-length subunits of the assembled virus-like particle. These observations have important implications in viral assembly and maturation and suggest that the CP N-terminus and its interactions with RNA play an important role in generating capsid curvature.
The generalized method of characteristics for waveform relaxation analysis of lossy coupled transmission lines

The transient response of lossy coupled transmission lines is simulated by iterative waveform relaxation analyses of equivalent disjoint networks constructed with congruence transformers, fast-Fourier-transform waveform generators, and characteristic impedances synthesized by the Pade approximation. A two-order reduction of central-processing-unit time and a one-order savings in computer memory have been achieved. A lossy directional coupler is simulated for illustration. >
A thermodynamical formulation for dispersed multiphase turbulent flows—1

Abstract Turbulent flows of dispersed multiphase solid-fluid mixtures are considered. From the global equations of balance for each phase and via a special ensemble-averaging technique, the local conservation laws for the mean motions are developed. Particular attentions are given to the averaged form of the Clausius-Duhem inequality and the fluctuation energies of the fluid phase and the particulate constituents. The thermodynamics of the mixture in the turbulent state is studied. The concept of coldness of turbulence for each phase is introduced, the free energy function is discussed and several thermodynamical relationships are established. Based on the averaged entropy inequality, constitutive equations for the stresses, energy and heat fluxes of various species are developed. It is shown that the model contains the recently developed turbulence models for dilute two-phase flows and dense granular flows as special limiting cases.
What the Logs Can Tell You: Mediation to Implement Feedback in Training

The problem addressed by Mediation to Implement Feedback in Training (MIFT) is to customize the feedback from training exercises by exploiting knowledge about the training scenario, training objectives, and specific student/teacher needs. We achieve this by inserting an intelligent mediation layer into the information flow from observations collected during training exercises to the display and user interface. Knowledge about training objectives, scenarios, and tasks is maintained in the mediating layer. A designer constraint is that domain experts must be able to extend mediators by adding domain-specific knowledge that supports additional aggregations, abstractions, and views of the results of training exercises. The MIFT mediation concept is intended to be integrated with existing military training exercise management tools and reduce the cost of developing and maintaining separate feedback and evaluation tools for every training simulator and every set of customer needs. The MIFT Architecture is designed as a set of independently reusable components which interact with each other through standardized formalisms such as the Knowledge Interchange Format (KIF) and Knowledge Query and Manipulation Language (KQML).
EMMA: Early Metal Microstructure Archive

EMMA stands for Early Metal Microstructure Archive and was initiated by the University of Mary Washington Historic Preservation Department. It is intended as an online resource for the promotion of understanding and interpretation of archaeological and historical metal artifacts. Contributions of metallographic images from interested professionals and academics from around the world accompanied with a description of each image are submitted and approved by an EMMA administrator. An EMMA administrator has the ability to approve, edit, view, and deny an image submission. If the administrator has approved the image and description the image is now viewable in the public archives and is searchable through the search functionality of the site and can be viewed by anybody via the World Wide Web. The content of the poster will be a description of the project accompanied with screen shots of the software. Also, the poster will contain a use case diagram of the system itself which will enhance the description of the system and some other models of the system such as a data flow diagram and subsystem model.
The effects of environment on the cool phase of the interstellar medium in galaxies

The atomic and molecular gas properties of many galaxies are altered by external influences. Ionization by the cosmic ultraviolet background radiation can account for sharp cutoffs observed in the HI disks of spirals. Tidal interactions between galaxies strip outer disk gas and cause gas clouds to collide more frequently, leading to large radial inflows of gas and enhanced rates of star formation. And in many clusters, interactions between gas in the intracluster medium (ICM) and gas in a galaxy’s interstellar medium (ISM) lead to stripping of outer disk gas, and may perturb the remaining ISM sufficiently to trigger star formation.
A clinical and histological study of the attachment of grafted tissue to bone and teeth.

Clinical success has frequently been reported with the use of various types of tissue grafts. The criteria used in judging the postoperative results successful were, (1) the area resisted a probe and (2) there was an adequate zone of attached gingiva in areas that previously had none or very little. The clinical successes reported provided the impetus for this study. This paper reviews the investigations carried out in these areas and also presents histological evidence of healing of these grafts.