cssi/SciSciGPT-SciSciNet huggingface dataset:
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns

Graph neural networks (GNNs) have achieved tremendous success on multiple graph-based learning tasks by fusing network structure and node features. Modern GNN models are built upon iterative aggregation of neighbor's/proximity features by message passing. Its prediction performance has been shown to be strongly bounded by assortative mixing in the graph, a key property wherein nodes with similar attributes mix/connect with each other. We observe that real world networks exhibit heterogeneous or diverse mixing patterns and the conventional global measurement of assortativity, such as global assortativity coefficient, may not be a representative statistic in quantifying this mixing. We adopt a generalized concept, node-level assortativity, one that is based at the node level to better represent the diverse patterns and accurately quantify the learnability of GNNs. We find that the prediction performance of a wide range of GNN models is highly correlated with the node level assortativity. To break this limit, in this work, we focus on transforming the input graph into a computation graph which contains both proximity and structural information as distinct type of edges. The resulted multi-relational graph has an enhanced level of assortativity and, more importantly, preserves rich information from the original graph. We then propose to run GNNs on this computation graph and show that adaptively choosing between structure and proximity leads to improved performance under diverse mixing. Empirically, we show the benefits of adopting our transformation framework for semi-supervised node classification task on a variety of real world graph learning benchmarks.
When and Where Is It Cheaper to Issue Inflation-Linked Debt?

I compare the direct issuance costs of inflation-linked debt (the liquidity premium) with nominal government debt (the inflation risk premium) in developed countries. On average, it is cheaper to issue nominal debt at medium maturities (5–10 years) and inflation-linked debt at long maturities (20 or more years), although results vary somewhat based on whether survey-based or statistical inflation expectations are used. Issuance costs exhibit pronounced time and cross-country variation. Lower inflation-linked debt issuance costs are associated with more countercyclical inflation and higher proportions of inflation-linked debt. International inflation-linked zero-coupon yields are available as an Internet Appendix to this paper. (JEL E31, E43, G12, G15, H30, H63)Received November 22, 2018; editorial decision March 9, 2021 by Editor Jeffrey Pontiff. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
Using Pilot Jobs and CernVM File System for Simplified Use of Containers and Software Distribution

High Energy Physics (HEP) experiments entail an abundance of computing resources, i.e. sites, to run simulations and analyses by processing data. This requirement is fulfilled by local batch farms, grid sites, private/commercial clouds, and supercomputing centers via High Throughput Computing (HTC). The growing needs of such experiments and resources being prone to trends of heterogeneity make it difficult for physicists to handle these resources directly. Additionally, HEP collaborations heavily rely on data and software releases, typically in the order of tens of gigabytes, while conducting simulations and analyses. Hence, aspects of scalability, reliability, and maintenance become crucial with regards to the distribution of the necessary data and software stack. The GlideinWMS [4] framework helps with the resource management problem by using pilot jobs, aka Glideins, to provision reliable elastic virtual clusters. Glideins are submitted to unreliable heterogeneous resources which are validated and customized by the Glideins to make the worker nodes available for end-user job execution. On the other hand, the CernVM File System (CernVM-FS or CVMFS) [1] helps with data distribution. It is a write-once, read-everywhere filesystem used to deploy scientific software to thousands of nodes on a worldwide distributed computing infrastructure. CVMFS is based on the Hyper Text Transfer Protocol and has been widely used within the particle physics community for (1) distributing experiment software and data such as calibrations, and (2) facilitating containerization by efficiently hosting container images along with providing containerization software, especially Singularity [3] GlideinWMS relies on CVMFS installed locally on the computing resources to satisfy the experiments' software needs. This requires system administrators' effort to install and maintain CVMFS at the sites and limits the use of sites, especially HPC resources, that do not have CVMFS installed. This poster presents a solution, taking advantage of Glideins to provide CVMFS at most sites without the need for a local installation. Doing so expands the pool of resources available for HEP experiments and reduces the effort of system administrators for current resources. Additionally, the proposed solution allows GlideinWMS to also start Singularity [3], a containerization software that can run unprivileged, on sites where neither CVMFS nor Singularity are available, including HPC sites. The benefits provided by this solution are: (1) lower overhead for site administrators in that they have less software to install, (2) an expanded pool of resources that run user jobs with easy access to software and data provided by CVMFS, thus making life easier for the scientists, and (3) improved flexibility to use HPC resources by enabling GlideinWMS pilot jobs to support HPC sites.
Nutrition as Medicine, Food Sensitivities, and Chronic Disease: A Mini Curriculum Shaped by Transformative Learning Theory

Nutrition-related diseases are preventable twenty-first-century health problems. Students report being underprepared for nutritional therapeutics. We developed a mini, spiral curriculum shaped by transformative learning theory and centered on nutrition as medicine, food sensitivities, and chronic disease to kick-start a shift in cognition, attitudes, and skills. Seven demographically representative students participated in the 3-week curriculum. Visual, textual, and verbal data were captured and analyzed qualitatively. We found the curriculum evoked paradigm transformation that persisted 6 months later and facilitated opportunities for our cohort to self-identify learning gaps and consider how they related to their current and future goals for patient care.
Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction

This paper examines a combined supervised-unsupervised framework involving dictionary-based blind learning and deep supervised learning for MR image reconstruction from under-sampled k-space data. A major focus of the work is to investigate the possible synergy of learned features in traditional shallow reconstruction using adaptive sparsity-based priors and deep prior-based reconstruction. Specifically, we propose a framework that uses an unrolled network to refine a blind dictionary learning-based reconstruction. We compare the proposed method with strictly supervised deep learning-based reconstruction approaches on several datasets of varying sizes and anatomies. We also compare the proposed method to alternative approaches for combining dictionary-based methods with supervised learning in MR image reconstruction. The improvements yielded by the proposed framework suggest that the blind dictionary-based approach preserves fine image details that the supervised approach can iteratively refine, suggesting that the features learned using the two methods are complementary.
An Operator theoretic approach to the convergence of rearranged Fourier series

This article studies the rearrangement problem for Fourier series introduced by P. L. Ulyanov, who posed the question if every continuous function on the torus admits a rearrangement of its Fourier coefficients such that the rearranged partial sums of the Fourier series converge uniformly to the function. The main theorem here gives several new equivalences to this problem in terms of the convergence of the rearranged Fourier series in the strong (equivalently in this case, weak) operator topologies on $${\cal B}({L_2}(\mathbb{T}))$$ ). Additionally, a new framework for further investigation is introduced by considering convergence for subspaces of L2, which leads to many methods for attempting to prove or disprove Ulyanov’s problem. In this framework, we provide characterizations of unconditional convergence of the Fourier series in the SOT and WOT. These considerations also give rise to some interesting questions regarding weaker versions of the rearrangement problem Along the way, we consider some interesting questions related to the classical theory of trigonometric polynomials. All of the results here admit natural extensions to arbitrary dimensions.
Sample Efficient Reinforcement Learning with REINFORCE

Policy gradient methods are among the most effective methods for large-scale reinforcement learning, and their empirical success has prompted several works that develop the foundation of their global convergence theory. However, prior works have either required exact gradients or state-action visitation measure based mini-batch stochastic gradients with a diverging batch size, which limit their applicability in practical scenarios. In this paper, we consider classical policy gradient methods that compute an approximate gradient with a single trajectory or a fixed size mini-batch of trajectories under soft-max parametrization and log-barrier regularization, along with the widely-used REINFORCE gradient estimation procedure. By controlling the number of "bad" episodes and resorting to the classical doubling trick, we establish an anytime sub-linear high probability regret bound as well as almost sure global convergence of the average regret with an asymptotically sub-linear rate. These provide the first set of global convergence and sample efficiency results for the well-known REINFORCE algorithm and contribute to a better understanding of its performance in practice.
Spectral Efficiency vs Complexity in Downlink Algorithms for Reconfigurable Intelligent Surfaces

Reconfigurable Intelligent Surfaces (RIS) are an emerging technology that can be used to reconfigure the propagation environment to improve cellular communication link rates. RIS, which are thin metasurfaces composed of discrete elements, passively manipulate incident electromagnetic waves through controlled reflective phase tuning. In this paper, we investigate co-design of the multiantenna basestation precoder and multielement RIS phase shifts. The downlink narrowband transmission uses sub-6 GHz frequency bands, and the user equipment is equipped with a single antenna. Subject to the non-convex constraints due to the RIS phase shifts, we maximize the spectral efficiency or its equivalent channel power proxy. Our contributions in improving RIS-aided links include (1) design of gradient ascent co-design algorithms, (2) asymptotic (Big O) computational complexity analysis of proposed and considered prior algorithms, and (3) comparison of seven co-design algorithms in spectral efficiency vs. computational complexity.
Mesoscopic photogrammetry with an unstabilized phone camera

We present a feature-free photogrammetric technique that enables quantitative 3D mesoscopic (mm-scale height variation) imaging with tens-of-micron accuracy from sequences of images acquired by a smartphone at close range (several cm) under freehand motion without additional hardware. Our end-to-end, pixel-intensity-based approach jointly registers and stitches all the images by estimating a coaligned height map, which acts as a pixel-wise radial deformation field that orthorectifies each camera image to allow plane-plus-parallax registration. The height maps themselves are reparameterized as the output of an untrained encoder-decoder convolutional neural network (CNN) with the raw camera images as the input, which effectively removes many reconstruction artifacts. Our method also jointly estimates both the camera’s dynamic 6D pose and its distortion using a nonparametric model, the latter of which is especially important in mesoscopic applications when using cameras not designed for imaging at short working distances, such as smartphone cameras. We also propose strategies for reducing computation time and memory, applicable to other multi-frame registration problems. Finally, we demonstrate our method using sequences of multi-megapixel images captured by an un-stabilized smartphone on a variety of samples (e.g., painting brushstrokes, circuit board, seeds).
Comparison of Characteristic Schemes for Three-Dimensional, Steady, Isentropic Flow

The accuracy and order of three bicharacteristic schemes based on the numerical method of characteristics for three-dimensional, steady, isentropic, supersonic flow were compared based on numerical calculations. Comparisons were made for source flows and for flows in contoured axisymmetric nozzles. The first two methods were tetrahedral line networks, differing only in the manner in which the cross derivatives were evaluated. The third method was the pentahedral line network proposed by Butler. All three methods were numerically stable and produced efficient and accurate solutions. A discussion of the relative merits and disadvantages of each method is presented.
An Efficient High-Order Meshless Method for Advection-Diffusion Equations on Time-Varying Irregular Domains

Abstract We present a high-order radial basis function finite difference (RBF-FD) framework for the solution of advection-diffusion equations on time-varying domains. Our framework is based on a generalization of the recently developed Overlapped RBF-FD method that utilizes a novel automatic procedure for computing RBF-FD weights on stencils in variable-sized regions around stencil centers. This procedure eliminates the overlap parameter δ, thereby enabling tuning-free assembly of RBF-FD differentiation matrices on moving domains. In addition, our framework utilizes a simple and efficient procedure for updating differentiation matrices on moving domains tiled by node sets of time-varying cardinality. Finally, advection-diffusion in time-varying domains is handled through a combination of rapid node set modification, a new high-order semi-Lagrangian method that utilizes the new tuning-free overlapped RBF-FD method, and a high-order time-integration method. The resulting framework has no tuning parameters and has O ( N log ⁡ N ) time complexity. We demonstrate high-orders of convergence for advection-diffusion equations on time-varying 2D and 3D domains for both small and large Peclet numbers. We also present timings that verify our complexity estimates. Finally, we utilize our method to solve a coupled 3D problem motivated by models of platelet aggregation and coagulation, once again demonstrating high-order convergence rates on a moving domain.
Key Homomorphic PRFs and Their Applications.

A pseudorandom function F : K ×X → Y is said to be key homomorphic if given F (k1, x) and F (k2, x) there is an efficient algorithm to compute F (k1 ⊕ k2, x), where ⊕ denotes a group operation on k1 and k2 such as xor. Key homomorphic PRFs are natural objects to study and have a number of interesting applications: they can simplify the process of rotating encryption keys for encrypted data stored in the cloud, they give one round distributed PRFs, and they can be the basis of a symmetric-key proxy re-encryption scheme. Until now all known constructions for key homomorphic PRFs were only proven secure in the random oracle model. We construct the first provably secure key homomorphic PRFs in the standard model. Our main construction is based on the learning with errors (LWE) problem. In the proof of security we need a variant of LWE where query points are non-uniform and we show that this variant is as hard as the standard LWE. We also construct key homomorphic PRFs based on the decision linear assumption in groups with an `-linear map. We leave as an open problem the question of constructing standard model key homomorphic PRFs from more general assumptions.
An order-aware dataflow model for parallel Unix pipelines

We present a dataflow model for modelling parallel Unix shell pipelines. To accurately capture the semantics of complex Unix pipelines, the dataflow model is order-aware, i.e., the order in which a node in the dataflow graph consumes inputs from different edges plays a central role in the semantics of the computation and therefore in the resulting parallelization. We use this model to capture the semantics of transformations that exploit data parallelism available in Unix shell computations and prove their correctness. We additionally formalize the translations from the Unix shell to the dataflow model and from the dataflow model back to a parallel shell script. We implement our model and transformations as the compiler and optimization passes of a system parallelizing shell pipelines, and use it to evaluate the speedup achieved on 47 pipelines.
Negative Bubbles and the Market for "Dreams": "Lemons" in the Looking Glass

I extend Akerlof's (1970) adverse selection model, where uninformed participants withdraw from the market, and show that rather than collapse, "lemons" can, and often do, lead to a negative bubble. A mirror image of his model, where uninformed participants pursue "dreams" (for example, purchasing lottery tickets), can lead to a positive bubble that ultimately causes informed experts to withdraw. When the supply of assets is unlimited, a positive bubble can persist when there is systematic favorable -- but incorrect -- information (as might result from advertising), a preference for skewness, or an optimistic bias on the part of uniformed participants. I also argue that, because prices of antiques, collectibles, and other objects d'art are typically based primarily on sentiment, such assets trade persistently in a positive bubble.
On the local stability of semidefinite relaxations

We consider a parametric family of quadratically constrained quadratic programs and their associated semidefinite programming (SDP) relaxations. Given a nominal value of the parameter at which the SDP relaxation is exact, we study conditions (and quantitative bounds) under which the relaxation will continue to be exact as the parameter moves in a neighborhood around the nominal value. Our framework captures a wide array of statistical estimation problems including tensor principal component analysis, rotation synchronization, orthogonal Procrustes, camera triangulation and resectioning, essential matrix estimation, system identification, and approximate GCD. Our results can also be used to analyze the stability of SOS relaxations of general polynomial optimization problems.
Algebras from Congruences

We present a functorial construction which, starting from a congruence $$\alpha $$ of finite index in an algebra $$\mathbf {A}$$ , yields a new algebra $$\mathbf {C}$$ with the following properties: the congruence lattice of $$\mathbf {C}$$ is isomorphic to the interval of congruences between 0 and $$\alpha $$ on $$\mathbf {A}$$ , this isomorphism preserves higher commutators and TCT types, and $$\mathbf {C}$$ inherits all idempotent Maltsev conditions from $$\mathbf {A}$$ . As applications of this construction, we first show that supernilpotence is decidable for congruences of finite algebras in varieties that omit type $$\mathbf {1}$$ . Secondly, we prove that the subpower membership problem for finite algebras with a cube term can be effectively reduced to membership questions in subdirect products of subdirectly irreducible algebras with central monoliths. As a consequence, we obtain a polynomial time algorithm for the subpower membership problem for finite algebras with a cube term in which the monolith of every subdirectly irreducible section has a supernilpotent centralizer.
NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces

Abstract External control of agent-based models is vital for complex adaptive systems research. Often these experiments require vast numbers of simulation runs and are computationally expensive. NetLogo is the language of choice for most agent-based modelers but lacks direct API access through Python. NL4Py is a Python package for the parallel execution of NetLogo simulations via Python, designed for speed, scalability, and simplicity of use. NL4Py provides access to the large number of open-source machine learning and analytics libraries of Python and enables convenient and efficient parallelization of NetLogo simulations with minimal coding expertise by domain scientists.
Cold ion beam in a storage ring as a platform for large-scale quantum computers and simulators: Challenges and directions for research and development

The purpose of this paper is to evaluate the possibility of constructing a large-scale storage-ring-type ion-trap system capable of storing, cooling, and controlling a large number of ions as a platform for scalable quantum computing (QC) and quantum simulations. In such a trap, the ions form a crystalline beam moving along a circular path with a constant velocity determined by the frequency and intensity of the cooling lasers. In this paper, we consider a large leap forward in terms of the number of ions that serve as qubits in QC, from fewer than 100 available in state of the art linear ion-trap devices today to an order of $1{0}^{5}$ crystallized ions in the storage-ring setup. This new trap design unifies two different concepts: the storage rings of charged particles and the linear ion traps used for QC and mass spectrometry. In this paper, we use the language of particle accelerators to discuss the ion state and dynamics. We outline the differences between the above concepts, analyze challenges of the large ring with a revolving chain of ions, and propose goals for the research and development required to enable future quantum computers with 1000 times more qubits than available today. The challenge of creating such a large-scale quantum system while maintaining the necessary coherence of the qubits and the high fidelity of quantum logic operations is significant. Performing analog quantum simulations may be an achievable initial goal for such a device. Quantum calculations and simulations of complex quantum systems will move forward both the fundamental science and the applied research. Nuclear and particle physics, many-body quantum systems, lattice gauge theories, and nuclear structure calculations are just a few examples in which a large-scale quantum simulation system will become a very powerful tool to move forward our understanding of nature.
Role of Polycomb Repressive Complex 2 in Regulation of Human Transcription Factor Gene Expression

Control of gene expression is now recognized as a central issue in the field of molecular biology. We now know the sequences of many genomes including that of the human genome, and we know the nature of many pathways involved in control of gene expression. It remains difficult, however, to look at the DNA sequences surrounding a particular gene and tell which methods of regulatory control are in use. I have been pursuing the idea that progress might be made by comparing the regulatory regions of paired gene populations in which one population is strongly expressed and the other weakly. Here I report the results obtained with human genes encoding transcription factors (TF). In this population, broadly expressed genes are strongly expressed while tissue targeted TF expression is suppressed in most tissues. The results demonstrated that the promoter region of broadly expressed TF genes is enriched in binding sites for POLR2A, a component of RNA polymerase II while promoters of tissue targeted genes are enriched in EZH2, a subunit of polycomb repressive complex 2 (PRC2). It was rare to observe promoters with binding sites for both POLR2A and EZH2. The findings are interpreted to indicate that strong expression of broadly expressed TF genes is due to the presence of RNA polymerase II at the promoter while weak expression of tissue targeted promoters results from the presence of PRC2. Finally, transcription factor families were compared in the proportion of broadly expressed and tissue targeted genes they contain. The results demonstrated that most families possess both broadly expressed and tissue targeted members. For instance, this was the case with 16 of 20 TF families examined. The results are interpreted to indicate that while individual TFs such as EZH2 may be specific for broadly expressed or tissue targeted genes, this is not a property of most TF families.
Multimodal Clustering Networks for Self-Supervised Learning From Unlabeled Videos

Multimodal self-supervised learning is getting more and more attention as it allows not only to train large networks without human supervision but also to search and retrieve data across various modalities. In this context, this paper proposes a self-supervised training framework that learns a common multimodal embedding space that, in addition to sharing representations across different modalities, enforces a grouping of semantically similar instances. To this end, we extend the concept of instance-level contrastive learning with a multimodal clustering step in the training pipeline to capture semantic similarities across modalities. The resulting embedding space enables retrieval of samples across all modalities, even from unseen datasets and different domains. To evaluate our approach, we train our model on the HowTo100M dataset and evaluate its zero-shot retrieval capabilities in two challenging domains, namely text-to-video retrieval, and temporal action localization, showing state-of-the-art results on four different datasets.
Ideals of étale groupoid algebras and Exel’s Effros–Hahn conjecture

We extend to arbitrary commutative base rings a recent result of Demeneghi that every ideal of an ample groupoid algebra over a field is an intersection of kernels of induced representations from isotropy groups, with a much shorter proof, by using the author's Disintegration Theorem for groupoid representations. We also prove that every primitive ideal is the kernel of an induced representation from an isotropy group; however, we are unable to show, in general, that it is the kernel of an irreducible induced representation. If each isotropy group is finite (e.g., if the groupoid is principal) and if the base ring is Artinian (e.g., a field), then we can show that every primitive ideal is the kernel of an irreducible representation induced from isotropy.
Computing all identifiable functions of parameters for ODE models

Abstract Parameter identifiability is a structural property of an ODE model for recovering the values of parameters from the data (i.e., from the input and output variables). This property is a prerequisite for meaningful parameter identification in practice. In the presence of nonidentifiability, it is important to find all functions of the parameters that are identifiable. The existing algorithms check whether a given function of parameters is identifiable or, under the solvability condition, find all identifiable functions. However, this solvability condition is not always satisfied, which presents a challenge. Our first main result is an algorithm that computes all identifiable functions without any additional assumptions, which is the first such algorithm as far as we know. Our second main result concerns the identifiability from multiple experiments (with generically different inputs and initial conditions among the experiments). For this problem, we prove that the set of functions identifiable from multiple experiments is what would actually be computed by input–output equation-based algorithms (whether or not the solvability condition is fulfilled), which was not known before. We give an algorithm that not only finds these functions but also provides an upper bound for the number of experiments to be performed to identify these functions. We provide an implementation of the presented algorithms.
Predicting Mechanical Ventilation Requirement and Mortality in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study

In this study, we aimed to predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and random forest (RF) machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using radiomic features extracted from patients' CXRs. Deep learning (DL) approaches were also explored for the clinical outcome prediction task and a novel radiomic embedding framework was introduced. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic classification models had mean area under the receiver operating characteristic curve (mAUCs) of 0.78 ± 0.05 (sensitivity = 0.72 ± 0.07, specificity = 0.72 ± 0.06) and 0.78 ± 0.06 (sensitivity = 0.70 ± 0.09, specificity = 0.73 ± 0.09), compared with expert scores mAUCs of 0.75 ± 0.02 (sensitivity = 0.67 ± 0.08, specificity = 0.69 ± 0.07) and 0.79 ± 0.05 (sensitivity = 0.69 ± 0.08, specificity = 0.76 ± 0.08) for mechanical ventilation requirement and mortality prediction, respectively. Classifiers using both expert severity scores and radiomic features for mechanical ventilation (mAUC = 0.79 ± 0.04, sensitivity = 0.71 ± 0.06, specificity = 0.71 ± 0.08) and mortality (mAUC = 0.83 ± 0.04, sensitivity = 0.79 ± 0.07, specificity = 0.74 ± 0.09) demonstrated improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances in which the inclusion of radiomic features in DL improves model predictions over DL alone. The models proposed in this study and the prognostic information they provide might aid physician decision making and efficient resource allocation during the COVID-19 pandemic.
Rapid extraction and kinetic analysis of protein complexes from single cells.

Abstract Proteins contribute to cell biology by forming dynamic, regulated interactions, and measuring these interactions is a foundational approach in biochemistry. We present a rapid, quantitative in vivo assay for protein-protein interactions, based on optical cell lysis followed by time-resolved single-molecule analysis of protein complex binding to an antibody-coated substrate. We show that our approach has better reproducibility, higher dynamic range, and lower background than previous single-molecule pull-down assays. Furthermore, we demonstrate that by monitoring cellular protein complexes over time after cell lysis, we can measure the dissociation rate constant of a cellular protein complex, providing information about binding affinity and kinetics. Our dynamic single-cell, single-molecule pull-down method thus approaches the biochemical precision that is often sought from in vitro assays while being applicable to native protein complexes isolated from single cells in vivo.
Modulation of tactile feedback for the execution of dexterous movement.

Although dexterity relies on the constant transmission of sensory information, unchecked feedback can be disruptive. Yet how somatosensory feedback from the hands is regulated and whether this modu...
Square root p-adic L-functions I: Construction of a one-variable measure

The Ichino–Ikeda conjecture, and its generalization to unitary groups by N. Harris, gives explicit formulas for central critical values of a large class of Rankin–Selberg tensor products. The latter conjecture has been proved in full generality and applies to L-values of the form L(12,BC(π)× BC(π′)), where π and π′ are cohomological automorphic representations of unitary groups U(V) and U(V′), respectively. Here V and V′ are hermitian spaces over a CM field, V of dimension n, V′ of codimension 1 in V, and BC denotes the twisted base change to GL(n)× GL(n−1). This paper contains the first steps toward constructing a p-adic interpolation of the normalized square roots of these L-values, generalizing the construction in my paper with Tilouine on triple product L-functions. It will be assumed that the CM field is imaginary quadratic, π is a holomorphic representation and π′ varies in an ordinary Hida family (of antiholomorphic forms). The construction of the measure attached to π uses recent work of Eischen, Fintzen, Mantovan, and Varma.
SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings

Temporal Knowledge Graphs store events in the form of subjects, relations, objects, and timestamps which are often represented by dynamic heterogeneous graphs. Event forecasting is a critical and challenging task in Temporal Knowledge Graph reasoning that predicts the subject or object of an event in the future. To obtain temporal embeddings multi-step away in the future, existing methods learn generative models that capture the joint distribution of the observed events. To reduce the high computation costs, these methods rely on unrealistic assumptions of independence and approximations in training and inference. In this work, we propose SeDyT, a discriminative framework that performs sequence modeling on the dynamic entity embeddings to solve the multi-step event forecasting problem. SeDyT consists of two components: a Temporal Graph Neural Network that generates dynamic entity embeddings in the past and a sequence model that predicts the entity embeddings in the future. Compared with the generative models, SeDyT does not rely on any heuristic-based probability model and has low computation complexity in both training and inference. SeDyT is compatible with most Temporal Graph Neural Networks and sequence models. We also design an efficient training method that trains the two components in one gradient descent propagation. We evaluate the performance of SeDyT on five popular datasets. By combining temporal Graph Neural Network models and sequence models, SeDyT achieves an average of 2.4% MRR improvement when not using the validation set and more than 10% MRR improvement when using the validation set.
Cassini Attitude Control Flight Software: From Development to In-Flight Operation

The Cassini Attitude and Articulation Control Subsystem (AACS) Flight Software (FSW) has achieved its intended design goals by successfully guiding and controlling the Cassini-Huygens planetary mission to Saturn and its moons. This paper describes an overview of AACS FSW details from early design, development, implementation, and test to its fruition of operating and maintaining spacecraft control over an eleven year prime mission. Starting from phases of FSW development, topics expand to FSW development methodology, achievements utilizing in-flight autonomy, and summarize lessons learned during flight operations which can be useful to FSW in current and future spacecraft missions.
Quantifying the propagation of parametric uncertainty on flux balance analysis.

Abstract Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g mmol−1, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.
Extensive High-Accuracy Thermochemistry and Group Additivity Values for Halocarbon Combustion Modeling

Standard enthalpies, entropies, and heat capacities are calculated for 16,813 halocarbons using an automated high-fidelity thermochemistry workflow. This workflow generates conformers at density functional tight binding (DFTB) level, optimizes geometries, calculates harmonic frequencies, and performs 1D hindered rotor scans at DFT level, and computes electronic energies at G4 level. The computed enthalpies of formation for 400 molecules show good agreement with literature references, but the majority of the calculated species have no reference in the literature. Thus, this work presents the most accurate thermochemistry for many halocarbons to date. This new data set is used to train an extensive ensemble of group additivity values and hydrogen bond increment groups within the Reaction Mechanism Generator (RMG) framework. On average, the new group values estimate standard enthalpies for halogenated hydrocarbons within 3 kcal/mol of their G4 values. A significant contribution towards automated mechanism generation of halocarbon combustion, this research provides thermochemical data for thousands of novel halogenated species and presents a self-consistent set of halogen group additivity values.
Predictors of Mortality and Outcomes of Liver Transplant in Spur Cell Hemolytic Anemia

Spur cell hemolytic anemia (SCHA) is a rare, acquired, nonimmune hemolytic anemia of decompensated cirrhosis. Data describing prognostic impact, outcomes of liver transplant, and clinical hematologic characteristics of SCHA are absent or limited. We performed a multicenter, 24-year observational cohort study of patients with SCHA, retrospectively analyzing hepatic and hematologic parameters, independent predictors of mortality, and long-term outcomes of liver transplant. Sixty-nine patients with SCHA met eligibility for inclusion. The median (interquartile range) age was 53 (42-59) years; 46.4% were female, and 11 (15.9%) received liver transplant. Thirty-nine patients (56.5%) were red blood celltransfusion-dependent. All 11 patients undergoing transplant had rapid and complete resolution of SCHA, with improvement in median hematocrit from 22.1% to 34.6% post-transplant (p = .001) and excellent post-transplant outcomes. In multivariable logistic models adjusting for age, sex, etiology of cirrhosis, active/recent variceal bleeding, and Child-Turcotte-Pugh score, transfusion dependence had an odds ratio (OR) for 90-day mortality of 9.14 (95% CI, 2.46-34.00) and reduced pre-transfusion hematocrit had an OR of 4.73 (95% CI, 1.42-15.82) per 6% decrease; increased red cell transfusion requirement, reduced hemoglobin, increased lactate dehydrogenase, and increased indirect bilirubin were also independently predictive of higher 90-day mortality. Model for end-stage liver disease (MELD)-Na and Child-Turcotte-Pugh scores consistently significantly underestimated 90-day mortality, with standardized mortality ratios (SMRs) >1 across all scores/classes [MELD-Na 20-29, SMR 2.42 (1.18-4.44); Child-Turcotte-Pugh class B, SMR 4.46 (1.64-9.90)]. In conclusion, SCHA is associated with substantial excess mortality than predicted by MELD-Na or Child-Turcotte-Pugh scores and uniformly resolves with liver transplant, without recurrence. Multiple parameters of hemolytic anemia severity independently predict higher 90-day mortality.
Visual Goal-Step Inference using wikiHow.

Understanding what sequence of steps are needed to complete a goal can help artificial intelligence systems reason about human activities. Past work in NLP has examined the task of goal-step inference for text. We introduce the visual analogue. We propose the Visual Goal-Step Inference (VGSI) task, where a model is given a textual goal and must choose which of four images represents a plausible step towards that goal. With a new dataset harvested from wikiHow consisting of 772,277 images representing human actions, we show that our task is challenging for state-of-the-art multimodal models. Moreover, the multimodal representation learned from our data can be effectively transferred to other datasets like HowTo100m, increasing the VGSI accuracy by 15 - 20%. Our task will facilitate multimodal reasoning about procedural events.
Thermal stress around a smooth cavity in a plate subjected to uniform heat flux

The two-dimensional thermoelastic problem of an adiabatic cavity in an infinite isotropic homogeneous medium subjected to uniform heat flux is studied, where the shape of the cavity is characterize...
A Stringent Upper Limit on Dark Matter Self-Interaction Cross Section from Cluster Strong Lensing

We analyze strongly lensed images in 8 galaxy clusters to measure their dark matter density profiles in the radial region between 10 kpc and 100 kpc, and use this to constrain the self-interaction cross section of dark matter (DM) particles. We infer the mass profiles of the central DM halos, bright central galaxies, key member galaxies, and DM subhalos for the member galaxies for all 8 clusters using the QLens code. The inferred DM halo surface densities are fit to a self-interacting dark matter (SIDM) model, which allows us to constrain the self-interaction cross section. When our full method is applied to mock data generated from two clusters in the Illustris-TNG simulation, we find results consistent with no dark matter self-interactions as expected. For the eight observed clusters with maximum circular velocities in the range of 1400 to 2000 km/s, we find that the upper limit for the self-interaction cross section per unit mass of dark matter is 0.065 $cm^2/g$ at the 95% confidence level.
Introduction to Electronics: Dc/Ac Circuits

I. FUNDAMENTALS OF ELECTRICITY/ELECTRONICS. 1. Survey of Electronics. 2. Electricity/Electronic Units and Notations. 3. Electrical Properties. 4. Electrical Quantities. 5. Resistors. 6. Ohm's Law, Work, Energy, and Power. II. DC CIRCUITS. 7. Series DC Circuits. 8. Parallel DC Circuits. 9. Series-Parallel DC Circuits. 10. Circuit Measurement. 11. Kirchhoff's Laws, Voltage, and Current Dividers. 12. Network Theorems. 13. Conductors, Cells, and Supplemental Components. III. MAGNETISM AND MAGNETIC DEVICES. 14. Magnetism and Magnetic Quantities. 15. Electromagnetic Induction. IV. AC AND TRANSFORMERS. 16. Alternating Current (AC) 17. AC Test Equipment. 18. Transformers. V. CAPACITORS, INDUCTORS, AND TRANSIENT RESPONSE. 19. Capacitors and Capacitance. 20. Inductors and Inductance. 21. RC and RL Transient Responses. VI. AC REACTIVE CIRCUITS. 22. Capacitive and Inductive Reactance. 23. Complex Numbers for AC Analysis. 24. RCL AC Circuit Analysis. 25. Resonance. VII. AC CIRCUIT APPLICATIONS. 26. Coupling and Filter Circuits.
Nutrition at the End of Life: Ethical Issues

Because of the importance of food as an integral part of life, health-care providers need to be well versed in the issues surrounding nutrition and hydration in terminal illness in order to assist patients and their families in treatment decisions. Case law in the United States considers enteral nutrition to be medical treatment and as such recognizes a patient’s right to refuse artificial nutritional support. The decision about feeding should be consistent with the overall goals of care: for example, curative treatment, rehabilitative treatment or palliative treatment.
A Second Catalog of Orbiting Astronomical Observatory 2 Filter Photometry: Ultraviolet Photometry of 614 Stars

Ultraviolet photometry from the Wisconsin Experiment Package on the Orbiting Astronomical Observatory 2 (OAO 2) is presented for 614 stars. Previously unpublished magnitudes from 12 filter bandpasses with wavelengths ranging from 1330 to 4250 A have been placed on the white dwarf model atmosphere absolute flux scale. The fluxes were converted to magnitudes using V = 0 for F(V) = 3.46 × 10-9 ergs cm-2 s-1 A-1, or mλ = -2.5 log Fλ - 21.15. This second catalog effectively doubles the amount of OAO 2 photometry available in the literature and includes many objects too bright to be observed with modern space observatories.
A characterization of invariant affine connections

In [1] Ambrose and Singer gave a necessary and sufficient condition (Theorem 3 here) for a simply connected complete Riemannian manifold to admit a transitive group of motions. Here we shall give a simple proof of a more general theorem—Theorem 1 (the proof of Theorem 1 became suggestive to us after we noted that the T x of [1] is just the a x of [6] when X is restricted to p 0, see [6], p. 539). In fact after introducing, below, the notion of one affine connection A on a manifold being rigid with respect to another affine connection B on M and making some observations concerning such a relationship, Theorem 1 is seen to be a reformulation of Theorem 2.
Identification and characterization of a new Escherichia coli gene that is a dosage-dependent suppressor of a dnaK deletion mutation.

Abstract We report the isolation and characterization of a previously unidentified Escherichia coli gene that suppresses the temperature-sensitive growth and filamentation of a dnaK deletion mutant strain. Introduction of a multicopy plasmid carrying this wild-type gene into a dnaK deletion mutant strain rescued the temperature-sensitive growth of the dnaK deletion mutant strain at 40.5 degrees C and the filamentation, fully at 37 degrees C and partially at 40.5 degrees C. However, the inability of dnaK mutant cells to support bacteriophage lambda growth was not suppressed. This gene was also able to suppress the temperature-sensitive growth of a grpE280 mutant strain at 41 degrees C. Filamentation of the grpE280 mutant strain was suppressed at 37 degrees C but not at 41 degrees C. The dnaK suppressor gene, designated dksA, maps near the mrcB gene (3.7 min on the E. coli chromosome). DNA sequence analysis and in vivo experiments showed that dksA encodes a 17,500-Mr polypeptide. Gene disruption experiments indicated that dksA is not an essential gene.
Knowing‐‘wh’, Mention‐Some Readings, and Non‐Reducibility

This article presents a new criticisms of reductive approaches to knowledge-‘wh’ (i.e., those approaches on which whether one stands in the knowledge-‘wh’ relation to a question is determined by whether one stands in the knowledge-‘that’ relation to some answer(s) to the question). It argues in particular that the truth of a knowledge-‘wh’ attribution like ‘Janna knows where she can buy an Italian newspaper’ depends not only on what Janna knows about the availability of Italian newspapers, but on what she believes about the matter. This dependence of Janna's knowledge-‘wh’ on her (possibly false) beliefs is incompatible with the reductive approach.