cssi/SciSciGPT-SciSciNet huggingface dataset:
Expanding theory testing in general relativity: LIGO and parametrized theories

Abstract The multiple detections of gravitational waves by LIGO (the Laser Interferometer Gravitational-Wave Observatory), operated by Caltech and MIT, have been acclaimed as confirming Einstein's prediction, a century ago, that gravitational waves propagating as ripples in spacetime would be detected. Yunes and Pretorius (2009) investigate whether LIGO's template-based searches encode fundamental assumptions, especially the assumption that the background theory of general relativity is an accurate description of the phenomena detected in the search. They construct the parametrized post-Einsteinian (ppE) framework in response, which broadens those assumptions and allows for wider testing under more flexible assumptions. Their methods are consistent with work on confirmation and testing found in Carnap (1936), Hempel (1969), and Stein (1992, 1994), with the following principles in common: that confirmation is distinct from testing, and that, counterintuitively, revising a theory's formal basis can make it more broadly empirically testable. These views encourage a method according to which theories can be made abstract, to define families of general structures for the purpose of testing. With the development of the ppE framework and related approaches, multi-messenger astronomy is a catalyst for deep reasoning about the limits and potential of the theoretical framework of general relativity.
Data-driven correction reduced order models for the quasi-geostrophic equations: a numerical investigation

This paper investigates the recently introduced data-driven correction reduced order model (DDC-ROM) in the numerical simulation of the quasi-geostrophic equations. The DDC-ROM uses available data ...
Design and Evaluation of an RDMA-aware Data Shuffling Operator for Parallel Database Systems

The commoditization of high-performance networking has sparked research interest in the RDMA capability of this hardware. One-sided RDMA primitives, in particular, have generated substantial excitement due to the ability to directly access remote memory from within an application without involving the TCP/IP stack or the remote CPU. This article considers how to leverage RDMA to improve the analytical performance of parallel database systems. To shuffle data efficiently using RDMA, one needs to consider a complex design space that includes (1) the number of open connections, (2) the contention for the shared network interface, (3) the RDMA transport function, and (4) how much memory should be reserved to exchange data between nodes during query processing. We contribute eight designs that capture salient tradeoffs in this design space as well as an adaptive algorithm to dynamically manage RDMA-registered memory. We comprehensively evaluate how transport-layer decisions impact the query performance of a database system for different generations of InfiniBand. We find that a shuffling operator that uses the RDMA Send/Receive transport function over the Unreliable Datagram transport service can transmit data up to 4× faster than an RDMA-capable MPI implementation in a 16-node cluster. The response time of TPC-H queries improves by as much as 2×.
Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure.

Though large multilocus genomic data sets have led to overall improvements in phylogenetic inference, they have posed the new challenge of addressing conflicting signals across the genome. In particular, ancestral population structure, which has been uncovered in a number of diverse species, can skew gene tree frequencies, thereby hindering the performance of species tree estimators. Here we develop a novel maximum likelihood method, termed TASTI (Taxa with Ancestral structure Species Tree Inference), that can infer phylogenies under such scenarios, and find that it has increasing accuracy with increasing numbers of input gene trees, contrasting with the relatively poor performances of methods not tailored for ancestral structure. Moreover, we propose a supertree approach that allows TASTI to scale computationally with increasing numbers of input taxa. We use genetic simulations to assess TASTI’s performance in the three- and four-taxon settings and demonstrate the application of TASTI on a six-species Afrotropical mosquito data set. Finally, we have implemented TASTI in an open-source software package for ease of use by the scientific community.
Learning Partial Differential Equations From Data Using Neural Networks

We develop a framework for estimating unknown partial differential equations (PDEs) from noisy data, using a deep learning approach. Given noisy samples of a solution to an unknown PDE, our method interpolates the samples using a neural network, and extracts the PDE by equating derivatives of the neural network approximation. Our method applies to PDEs which are linear combinations of user-defined dictionary functions, and generalizes previous methods that only consider parabolic PDEs. We introduce a regularization scheme that prevents the function approximation from overfitting the data and forces it to be a solution of the underlying PDE. We validate the model on simulated data generated by the known PDEs and added Gaussian noise, and we study our method under different levels of noise. We also compare the error of our method with a Cramer-Rao lower bound for an ordinary differential equation (ODE). Our results indicate that our method outperforms other methods in estimating PDEs, especially in the low signal-to-noise (SNR) regime.
Maximal Tests in Minimal Gravity

Recent tests have generated impressive reach in the gravity sector of the Standard-Model Extension. This contribution to the CPT'19 proceedings summarizes this progress and maps the structure of work in the gravity sector.
Near-Field Radiative Heat Transfer between Dissimilar Materials Mediated by Coupled Surface Phonon- and Plasmon-Polaritons

Near-field radiative heat transfer (NFRHT) between dissimilar materials supporting surface polaritons in the infrared is of critical importance for applications such as photonic thermal rectificati...
A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning

Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work explores the feasibility of using the D-Wave as a sampler for a machine learning task. We describe a hybrid method that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave for image generation. Our method overcomes two key limitations in the 2000-qubit D-Wave processor, namely the limited number of qubits available to accommodate typical problem sizes for fully connected quantum objective functions, and samples that are binary pixel representations. As a consequence of these limitations we are able to show how we achieved nearly a 22-fold compression factor of grayscale 28 x 28 sized images to binary 6 x 6 sized images with a lossy recovery of the original 28 x 28 grayscale images. We further show how generating samples from the D-Wave after training the RBM, resulted in 28 x 28 images that were variations of the original input data distribution, as opposed to recreating the training samples. We evaluated the quality of this method by using a downstream classification method. We formulated a MNIST classification problem using a deep convolutional neural network that used samples from the quantum RBM to train the MNIST classifier and compared the results with a MNIST classifier trained with the original MNIST training data set, as well as a MNIST classifier trained using classical RBM samples. We also explored using a secondary dataset, the MNIST Fashion dataset and demonstrate the first quantum-generated fashion. Our hybrid autoencoder approach indicates advantage for RBM results relative to the use of a current RBM classical computer implementation for image-based machine learning and even more promising results for the next generation D-Wave quantum system. Our method for compression and image mappings is not constrained to RBMs, the autoencoder part of this method could be coupled with other quantum-based algorithms.
Fracture assessment of ductile crack growth in weld material from a full-thickness clad RPV shell segment

Recent studies indicate that the onset of stable ductile tearing leads to crack-tip fields ahead of the growing crack and crack-tip profiles that differ from those of a stationary crack. Stable ductile tearing exposes additional volumes of material to elevated stresses as the crack advances, which alters the sampling of potential cleavage initiation sites on the microstructural level. Also, measured cleavage fracture toughness values for these specimens will be influenced by changes in crack-tip constraint conditions that occur with prior stable crack growth. Fracture analysis techniques for inclusion of ductile crack growth in finite-element analyses were evaluated through applications to a full-thickness clad beam specimen containing a shallow crack in material for which metallurgical conditions are prototypic of those found in reactor pressure vessels (RPVs) at beginning of life. The beam specimen, which experienced a significant amount of precleavage stable ductile tearing, was fabricated from a section of an RPV wall (removed from a canceled nuclear plant) that includes weld, plate, and clad material. Effects of precleavage tearing on estimates of fracture toughness were investigated using continuum damage models.
Minimum Spanning Tree

A minimum spanning tree (MST) connects all the vertices of a weighted graph by existing edges whose total weight is minimum. It has been the object of intensive study due to its importance in combinatorial optimization. In this paper, we discuss the formulation of the MST problem, present some of the most commonly used algorithms for its construction, and discuss several of its applications in statistics, probability, and data analysis. Keywords: algorithms; multivariate data analysis; optimization; statistical applications
Making a Case for an Integrated Faculty-Designed and Faculty Operated Study Abroad Center

This essay argues that the most effective Study Abroad Centers have the active and committed participation of faculty from across their institutions. However, the structures of of these institutions do not always make this possible in all aspects of programming and implementation. This essay demonstrates that the most important characterizes of a thriving Study Abroad Center are high faculty involvement and shared governance.
Go or No Go: Learning to Persuade in an Early-Stage Student Entrepreneurship Program

Background: Early-stage accelerator programs teach new entrepreneurs how to identify and exploit venture opportunities. In doing so, they implicitly teach these new entrepreneurs how to develop and iterate claims. But since this function of teaching persuasion has been implicit and generally unsystematic, it is unclear how well it works. Literature review: We review related literature on the venture development process, value propositions, and logic orientation (Goods-Dominant vs. Service-Dominant Logic). Research questions: 1. Does an entrepreneurship training program implicitly teach new entrepreneurs to make and iterate persuasive claims? 2. How effectively does it do so, and how can it improve? Research methodology: We examine one such accelerator program via a qualitative case study. In this case study, we collected interviews, observations, and artifacts, then analyzed them with thematic coding. Results/discussion: All teams had received previous entrepreneurship training and mentoring. However, they differed in their problem and logic orientations as well as their stage in the venture development process. These differences related to the extent to which they iterated value propositions in the program. Conclusions: We conclude with recommendations for improving how accelerator programs can better train new entrepreneurs to communicate and persuade.
Potassium Dihydrogen Phosphate (KH2PO4, KDP) and Three of Its Isomorphs

Publisher Summary Potassium Dihydrogen Phosphate (KDP) is the best known; most extensively studied, and most widely used crystal in laser technology. Crystals of KDP are used for frequency up conversion operation in high-power laser systems. KDP is also the primary nonlinear optical material used at the major laser facilities. It is noted that for each application of these nonlinear crystals, a precise knowledge is required of the crystal's refractive index as a function of wavelength. In addition, crystal temperature has a sensitive effect on the indices within the precision range of interest. Kirby and DeShazer exhibit that the index values vary from about 6 x 10-5 to 60 x 10-5 in the visible region for KDP crystals from different crystal growers. These differences are correlated with deviations in the crystal lattice parameters and it is speculated that crystalline defects incorporated during the crystal-growth process are the cause of the lattice-parameter variations. The measured values of Zernike and Kirby and DeShazer are fit separately to both a Herzberger-type and a modified Sellmeier-type dispersion formula.
Chapter 5 – Output: Getting Results from Computers

As we have seen, computers are becoming more and more important in our lives and are generating an increasing portion of the information we use. This information from computers is known as output. Output can appear in various forms. It may be in readable form on paper or computer terminal screens, or in secondary storage such as tape and disk, for later use. In this chapter we consider these and many other popular means of generating computer outputs. Secondary storage is discussed in Chapter 6.
Can a Unilateral Carbon Tax Reduce Emissions Elsewhere

Abstract One country or sector that tries to reduce greenhouse gas emissions may fear that other countries or sectors will get a competitive advantage and increase emissions. Computable general equilibrium (CGE) models such as Elliott et al. (2010a, 2010b) indicate that 15–25% of abatement might be offset by this “leakage.” Yet the Fullerton et al. (2012) simple two-sector analytical general equilibrium model shows an offsetting term with negative leakage. In this paper, we use a full CGE model with many countries and many goods to measure effects in a way that allows for this negative leakage term. We vary elasticities of substitution and confirm the analytical model's prediction that whether this negative leakage term offsets the positive leakage terms depends on the ability of consumers to substitute into the untaxed good relative to the ability of firms to substitute from carbon emissions into labor or capital.
Chapter 9 – Advanced File Input and Output

This chapter covers lower-level file input/output statements that require opening and closing the file. Functions that can read the entire file at once as well as those that require reading one line at a time in a conditional loop are introduced, and examples that demonstrate the differences in their use are shown. Writing formatted text to files is demonstrated. Additionally, techniques for reading from and writing to spreadsheet files are introduced. Finally, the use of special binary files in MATLAB that can store variable names and their contents is demonstrated.
Chapter 9 – Principles Used in Measurement

This chapter describes the principles that are common to different measurement applications. Laser-based measurements are capable of being used for a variety of different measurements. The same physical principles recur in various applications. Some of the important applications of lasers involve their use for measurement of various quantities like: distance, product dimension, and angular rotation rate. The Michelson interferometer finds a useful application by producing an interference pattern between two beams of light that traverse different paths. One of the interference effects that is employed in measurement is the phenomenon of a beat note. If two light beams of slightly different frequency interfere, the interference fringes do not remain fixed in time, but are in motion. At a given position in space, the motion of the interference pattern produces a varying intensity. Many measurement applications involve a shift in the frequency of part of the beam from a laser. Subsequent recombination of the original beam and the frequency shifted beam yields the beat note, the frequency of which can be measured, providing a measurement of the parameter that caused the shift.
Deep Learning Methods for Mean Field Control Problems With Delay

We consider a general class of mean field control problems described by stochastic delayed differential equations of McKean-Vlasov type. Two numerical algorithms are provided based on deep learning techniques, one is to directly parameterize the optimal control using neural networks, the other is based on numerically solving the McKean-Vlasov forward anticipated backward stochastic differential equation (MV-FABSDE) system. In addition, we establish a necessary and sufficient stochastic maximum principle for this class of mean field control problems with delay based on the differential calculus on function of measures, as well as existence and uniqueness results for the associated MV-FABSDE system.
Modifications to the relationship between the magnetic field and weak Zeeman features in the spectra of astrophysical masers

Numerical solutions are presented for the equations of radiative transfer for astrophysical masers in a magnetic field to describe the observed weak circular polarization of H2O and SiO masers. Previously unrecognized intensity-dependent effects are found to produce circular polarization which resembles that of the usual Zeeman effect. Astrophysical H2O, SiO, and perhaps other masers are likely to be radiatively saturated with rates for stimulated emission that are within 1-2 orders of magnitude of the Zeeman frequency. Under these conditions, the circular polarization found here tends to be larger (but can be smaller) and may be of opposite sign from that given by the standard treatment for polarized Zeeman spectra.
Textile Masks and Surface Covers—A Spray Simulation Method and a “Universal Droplet Reduction Model” Against Respiratory Pandemics

The main form of COVID-19 transmission is via "oral-respiratory droplet contamination" (droplet: very small drop of liquid) produced when individuals talk, sneeze, or cough. In hospitals, health-care workers wear facemasks as a minimum medical "droplet precaution" to protect themselves. Due to the shortage of masks during the pandemic, priority is given to hospitals for their distribution. As a result, the availability/use of medical masks is discouraged for the public. However, for asymptomatic individuals, not wearing masks in public could easily cause the spread of COVID-19. The prevention of "environmental droplet contamination" (EnvDC) from coughing/sneezing/speech is fundamental to reducing transmission. As an immediate solution to promote "public droplet safety," we assessed household textiles to quantify their potential as effective environmental droplet barriers (EDBs). The synchronized implementation of a universal "community droplet reduction solution" is discussed as a model against COVID-19. Using a bacterial-suspension spray simulation model of droplet ejection (mimicking a sneeze), we quantified the extent by which widely available clothing fabrics reduce the dispersion of droplets onto surfaces within 1.8 m, the minimum distance recommended for COVID-19 "social distancing." All textiles reduced the number of droplets reaching surfaces, restricting their dispersion to <30 cm, when used as single layers. When used as double-layers, textiles were as effective as medical mask/surgical-cloth materials, reducing droplet dispersion to <10 cm, and the area of circumferential contamination to ~0.3%. The synchronized implementation of EDBs as a "community droplet reduction solution" (i.e., face covers/scarfs/masks and surface covers) will reduce COVID-19 EnvDC and thus the risk of transmitting/acquiring COVID-19.
An Inexact Interior-Point Lagrangian Decomposition Algorithm with Inexact Oracles

We combine the Lagrangian dual decomposition, barrier smoothing, path-following, and proximal Newton techniques to develop a new inexact interior-point Lagrangian decomposition method to solve a broad class of constrained composite convex optimization problems. Our method allows one to approximately solve the primal subproblems (called the slave problems), which leads to inexact oracles (i.e., inexact function value, gradient, and Hessian) of the smoothed dual problem (called the master problem). By appropriately controlling the inexact computation in both the slave and master problems, we can still establish a polynomial-time iteration complexity of our algorithm and recover primal solutions. We illustrate the performance of our method through two numerical examples and compare it with existing methods.
Weakly Supervised Prostate Tma Classification Via Graph Convolutional Networks

Histology-based grade classification is clinically important for many cancer types in stratifying patients into distinct treatment groups. In prostate cancer, the Gleason score is a grading system used to measure the aggressiveness of prostate cancer from the spatial organization of cells and the distribution of glands. However, the subjective interpretation of Glea-son score often suffers from large interobserver and intraob-server variability. Previous work in deep learning-based objective Gleason grading requires manual pixel-level annotation. In this work, we propose a weakly-supervised approach for grade classification in tissue micro-arrays (TMA) using graph convolutional networks (GCNs), in which we model the spatial organization of cells as a graph to better capture the proliferation and community structure of tumor cells. We learn the morphometry of each cell using a contrastive predictive coding (CPC)-based self-supervised approach. Using five-fold cross-validation we demonstrate that our method can achieve a 0.9637 ± 0.0131 AUC using only TMA-level labels. Our method also demonstrates a 36.36% improvement in AUC over standard GCNs with texture features and a 15.48% improvement over GCNs with VGG19 features. Our proposed pipeline can be used to objectively stratify low and high-risk cases, reducing inter- and intra-observer variability and pathologist workload.
Are frequent acquirers more entrenched

Abstract We do not find a significant influence of management entrenchment on the firm's propensity for frequent acquisition, in contrast to the existing studies that show entrenched managers are more likely to become frequent acquirers. We show that entrenched management is less likely to engage in frequent acquisitions because of low strategic managerial ability. Strategic managerial ability is positively associated with the firm's propensity for frequent acquisition. Entrenched acquirers have lower strategic managerial ability; and lower ability acquirers are more likely to be entrenched. Reducing acquisition frequency exacerbates management entrenchment. Frequent acquisitions further enhance strategic managerial ability, and high-ability management is likely to be more acquisitive. Frequent acquirers are 40% less likely to be entrenched compared to non-frequent acquirers. Our results are consistent with the notion that the market for corporate control effectively disciplines frequent acquirers such that their management are less likely to be entrenched. Entrenched acquirers suffer a loss in firm value, which further supports that the market for corporate control is effective in penalizing entrenched management. Frequent acquirers, often being in the market for targets, are disciplined by the market for corporate control. Frequent acquisitions appear to be driven by strategic managerial ability, rather than by management entrenchment.
How to Construct Quantum Random Functions.

In the presence of a quantum adversary, there are two possible definitions of security for a pseudorandom function. The first, which we call standard-security, allows the adversary to be quantum, but requires queries to the function to be classical. The second, quantum-security, allows the adversary to query the function on a quantum superposition of inputs, thereby giving the adversary a superposition of the values of the function at many inputs at once. Existing techniques for proving the security of pseudorandom functions fail when the adversary can make quantum queries. We give the first quantum-security proofs for pseudorandom functions by showing that some classical constructions of pseudorandom functions are quantum-secure. Namely, we show that the standard constructions of pseudorandom functions from pseudorandom generators or pseudorandom synthesizers are secure, even when the adversary can make quantum queries. We also show that a direct construction from lattices is quantum-secure. To prove security, we develop new tools to prove the indistinguishability of distributions under quantum queries. In light of these positive results, one might hope that all standard-secure pseudorandom functions are quantum-secure. To the contrary, we show a separation: under the assumption that standard-secure pseudorandom functions exist, there are pseudorandom functions secure against quantum adversaries making classical queries, but insecure once the adversary can make quantum queries.
Bottom-Up Fabrication of Plasmonic Nanoantenna-Based High-throughput Multiplexing Biosensors for Ultrasensitive Detection of microRNAs Directly from Cancer Patients' Plasma

There is an unmet need in clinical point-of-care (POC) cancer diagnostics for early state disease detection, which would greatly increase patient survival rates. Currently available analytical tech...
Broad Bracketing for Low Probability Events

Individuals tend to underprepare for rare, catastrophic events because of biases in risk perception. A simple form of broad bracketing—presenting the cumulative probability of loss over a longer time horizon—has the potential to alleviate these barriers to accurate risk perception and increase protective actions such as purchasing flood insurance. However, it is an open question whether broad bracketing effects last over time: There is evidence that descriptive probability information is ignored when decisions are based on “experience” (repeatedly and in the face of feedback), which characterizes many protective decisions. Across six incentive-compatible experiments with high stakes, we find that the broad bracketing effect does not disappear or change size when decisions are made from experience. We also advance our understanding of the mechanisms underlying broad bracketing, finding that, while cumulative probability size is a strong driver of the effect, this is dampened for larger brackets leading people to be less sensitive to probability size.
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search. Using similar building blocks, SpineNet models outperform ResNet-FPN models by 3%+ AP at various scales while using 10-20% fewer FLOPs. In particular, SpineNet-190 achieves 52.1% AP on COCO, attaining the new state-of-the-art performance for single model object detection without test-time augmentation. SpineNet can transfer to classification tasks, achieving 5% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset. Code is at: https://github.com/tensorflow/tpu/tree/master/models/official/detection.
Fiedler Regularization: Learning Neural Networks with Graph Sparsity

We introduce a novel regularization approach for deep learning that incorporates and respects the underlying graphical structure of the neural network. Existing regularization methods often focus on dropping/penalizing weights in a global manner that ignores the connectivity structure of the neural network. We propose to use the Fiedler value of the neural network's underlying graph as a tool for regularization. We provide theoretical support for this approach via spectral graph theory. We list several useful properties of the Fiedler value that makes it suitable in regularization. We provide an approximate, variational approach for fast computation in practical training of neural networks. We provide bounds on such approximations. We provide an alternative but equivalent formulation of this framework in the form of a structurally weighted L1 penalty, thus linking our approach to sparsity induction. We performed experiments on datasets that compare Fiedler regularization with traditional regularization methods such as dropout and weight decay. Results demonstrate the efficacy of Fiedler regularization.
Low-Resolution Overhead Thermal Tripwire for Occupancy Estimation

Smart buildings use occupancy sensing for various tasks ranging from energy-efficient HVAC and lighting to space-utilization analysis and emergency response. We propose a people counting system which uses a low-resolution thermal sensor. Unlike previous people-counting systems based on thermal sensors, we use an overhead tripwire configuration at entryways to detect and track transient entries or exits. We develop two distinct people counting algorithms for this configuration. To evaluate our algorithms, we have collected and labeled a low-resolution thermal video dataset using the proposed system. The dataset, the first of its kind, is public and available for download 1 . We also propose new evaluation metrics that are more suitable for systems that are subject to drift and jitter.
Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web

The World Wide Web contains vast quantities of textual information in several forms: unstructured text, template-based semi-structured webpages (which present data in key-value pairs and lists), and tables. Methods for extracting information from these sources and converting it to a structured form have been a target of research from the natural language processing (NLP), data mining, and database communities. While these researchers have largely separated extraction from web data into different problems based on the modality of the data, they have faced similar problems such as learning with limited labeled data, defining (or avoiding defining) ontologies, making use of prior knowledge, and scaling solutions to deal with the size of the Web. In this tutorial we take a holistic view toward information extraction, exploring the commonalities in the challenges and solutions developed to address these different forms of text. We will explore the approaches targeted at unstructured text that largely rely on learning syntactic or semantic textual patterns, approaches targeted at semi-structured documents that learn to identify structural patterns in the template, and approaches targeting web tables which rely heavily on entity linking and type information. While these different data modalities have largely been considered separately in the past, recent research has started taking a more inclusive approach toward textual extraction, in which the multiple signals offered by textual, layout, and visual clues are combined into a single extraction model made possible by new deep learning approaches. At the same time, trends within purely textual extraction have shifted toward full-document understanding rather than considering sentences as independent units. With this in mind, it is worth considering the information extraction problem as a whole to motivate solutions that harness textual semantics along with visual and semi-structured layout information. We will discuss these approaches and suggest avenues for future work.
MRFMap: Online Probabilistic 3D Mapping using Forward Ray Sensor Models.

Traditional dense volumetric representations for robotic mapping make simplifying assumptions about sensor noise characteristics due to computational constraints. We present a framework that, unlike conventional occupancy grid maps, explicitly models the sensor ray formation for a depth sensor via a Markov Random Field and performs loopy belief propagation to infer the marginal probability of occupancy at each voxel in a map. By explicitly reasoning about occlusions our approach models the correlations between adjacent voxels in the map. Further, by incorporating learnt sensor noise characteristics we perform accurate inference even with noisy sensor data without ad-hoc definitions of sensor uncertainty. We propose a new metric for evaluating probabilistic volumetric maps and demonstrate the higher fidelity of our approach on simulated as well as real-world datasets.
Reproducible and Efficient Benchmarks for Hyperparameter Optimization of Neural Machine Translation Systems

Hyperparameter selection is a crucial part of building neural machine translation (NMT) systems across both academia and industry. Fine-grained adjustments to a model’s architecture or training rec...
Final Design of the Boundary Layer Transition (BOLT) Flight Experiment

To improve scientific knowledge of boundary-layer transition physics on increasingly complex geometries, the Air Force Research Laboratory/Air Force Office of Scientific Research has sponsored the ...
A Converse Result on Convergence Time for Opportunistic Wireless Scheduling

This paper proves an impossibility result for stochastic network utility maximization for multi-user wireless systems, including multi-access and broadcast systems. Every time slot an access point observes the current channel states and opportunistically selects a vector of transmission rates. Channel state vectors are assumed to be independent and identically distributed with an unknown probability distribution. The goal is to learn to make decisions over time that maximize a concave utility function of the running time average transmission rate of each user. Recently it was shown that a stochastic Frank-Wolfe algorithm converges to utility-optimality with an error of O(log(T)/T), where T is the time the algorithm has been running. An existing Ω(1/T) converse is known. The current paper improves the converse to Ω(log(T)/T), which matches the known achievability result. The proof uses a reduction from the opportunistic scheduling problem to a Bernoulli estimation problem. Along the way, it refines a result on Bernoulli estimation.
A unique case of hemolytic‐uremic syndrome secondary to enteropathogenic E Coli

Causative factors of HUS due to infection are not limited to classic EHEC and Shigella infection. Understanding the effects of EPEC-related HUS and its complications is imperative for early diagnosis and treatment to mitigate long-term sequelae.
Broadening of the drumhead-mode spectrum due to in-plane thermal fluctuations of two-dimensional trapped ion crystals in a Penning trap

The authors theoretically and numerically investigate the in-plane motion of two-dimensional ion crystals stored in Penning traps. They find that the thermal fluctuations in the in-plane positions of ions, described by normal modes whose potential energy is several hundred times larger than their kinetic energy, strongly contribute to the spectral broadening of the transverse drumhead motion.
How Can Released State Test Items Support Interim Assessment Purposes in an Educational Crisis

State testing programs regularly release previously administered test items to the public. We provide an open-source recipe for state, district, and school assessment coordinators to combine these items flexibly to produce scores linked to established state score scales. These would enable estimation of student score distributions and achievement levels. We discuss how educators can use resulting scores to estimate achievement distributions at the classroom and school level. We emphasize that any use of such tests should be tertiary, with no stakes for students, educators, and schools, particularly in the context of a crisis like the COVID-19 pandemic. These tests and their results should also be lower in priority than assessments of physical, mental, and social–emotional health, and lower in priority than classroom and district assessments that may already be in place. We encourage state testing programs to release all the ingredients for this recipe to support low-stakes, aggregate-level assessments. This is particularly urgent during a crisis where scores may be declining and gaps increasing at unknown rates.
UBR-box containing protein, UBR5, is over-expressed in human lung adenocarcinoma and is a potential therapeutic target

N-end rule ubiquitination pathway is known to be disrupted in many diseases, including cancer. UBR5, an E3 ubiquitin ligase, is mutated and/or overexpressed in human lung cancer cells suggesting its pathological role in cancer. We determined expression of UBR5 protein in multiple lung cancer cell lines and human patient samples. Using immunoprecipitation followed by mass spectrometry we determined the UBR5 interacting proteins. The impact of loss of UBR5 for lung adenocarcinoma cell lines was analyzed using cell viability, clonogenic assays and in vivo xenograft models in nude mice. Additional Western blot analysis was performed to assess the loss of UBR5 on downstream signaling. Statistical analysis was done by one-way ANOVA for in vitro studies and Wilcoxon paired t-test for in vivo tumor volumes. We show variability of UBR5 expression levels in lung adenocarcinoma cell lines and in primary human patient samples. To gain better insight into the role that UBR5 may play in lung cancer progression we performed unbiased interactome analyses for UBR5. Data indicate that UBR5 has a wide range of interacting protein partners that are known to be involved in critical cellular processes such as DNA damage, proliferation and cell cycle regulation. We have demonstrated that shRNA-mediated loss of UBR5 decreases cell viability and clonogenic potential of lung adenocarcinoma cell lines. In addition, we found decreased levels of activated AKT signaling after the loss of UBR5 in lung adenocarcinoma cell lines using multiple means of UBR5 knockdown/knockout. Furthermore, we demonstrated that loss of UBR5 in lung adenocarcinoma cells results in significant reduction of tumor volume in nude mice. These findings demonstrate that deregulation of the N-end rule ubiquitination pathway plays a crucial role in the etiology of some human cancers, and blocking this pathway via UBR5-specific inhibitors, may represent a unique therapeutic target for human cancers.
Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy

In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.
The Soft $\mathcal{S}$-Matrix in Gravity

The gravitational $$ \mathcal{S} $$ -matrix defined with an infrared (IR) cutoff factorizes into hard and soft factors. The soft factor is universal and contains all the IR and collinear divergences. Here we show, in a momentum space basis, that the intricate expression for the soft factor is fully reproduced by two boundary currents, which live on the celestial sphere. The first of these is the supertranslation current, which generates spacetime supertranslations. The second is its symplectic partner, the Goldstone current for spontaneously broken supertranslations. The current algebra has an off-diagonal level structure involving the gravitational cusp anomalous dimension and the logarithm of the IR cutoff. It is further shown that the gravitational memory effect is contained as an IR safe observable within the soft $$ \mathcal{S} $$ -matrix.
German Business Mobilization against Right-Wing Populism:

Why do some business associations mobilize, engage in collective action, and take public stands against the populist right while others do not? This article examines business mobilization against t...
The emergence of phase separation as an organizing principle in bacteria.

Abstract Recent investigations in bacteria suggest that membraneless organelles play a crucial role in the subcellular organization of bacterial cells. However, the biochemical functions and assembly mechanisms of these compartments have not yet been completely characterized. This article assesses the current methodologies used in the study of membraneless organelles in bacteria, highlights the limitations in determining the phase of complexes in cells that are typically an order of magnitude smaller than a eukaryotic cell, and identifies gaps in our current knowledge about the functional role of membraneless organelles in bacteria. Liquid-liquid phase separation (LLPS) is one proposed mechanism for membraneless organelle assembly. Overall, we outline the framework to evaluate LLPS in vivo in bacteria, we describe the bacterial systems with proposed LLPS activity, and we comment on the general role LLPS plays in bacteria and how it may regulate cellular function. Lastly, we provide an outlook for super-resolution microscopy and single-molecule tracking as tools to assess condensates in bacteria.
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator

Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data. Variational auto-encoders (VAEs) with binary latent variables as hashing codes provide state-of-the-art performance in terms of precision for document retrieval. We propose a pairwise loss function with discrete latent VAE to reward within-class similarity and between-class dissimilarity for supervised hashing. Instead of solving the optimization relying on existing biased gradient estimators, an unbiased low-variance gradient estimator is adopted to optimize the hashing function by evaluating the non-differentiable loss function over two correlated sets of binary hashing codes to control the variance of gradient estimates. This new semantic hashing framework achieves superior performance compared to the state-of-the-arts, as demonstrated by our comprehensive experiments.
Informing urban climate planning with high resolution data: the Hestia fossil fuel CO 2 emissions for Baltimore, Maryland

Cities contribute more than 70% of global anthropogenic carbon dioxide (CO2) emissions and are leading the effort to reduce greenhouse gas (GHG) emissions through sustainable planning and development. However, urban greenhouse gas mitigation often relies on self-reported emissions estimates that may be incomplete and unverifiable via atmospheric monitoring of GHGs. We present the Hestia Scope 1 fossil fuel CO2 (FFCO2) emissions for the city of Baltimore, Maryland—a gridded annual and hourly emissions data product for 2010 through 2015 (Hestia-Baltimore v1.6). We also compare the Hestia-Baltimore emissions to overlapping Scope 1 FFCO2 emissions in Baltimore’s self-reported inventory for 2014. The Hestia-Baltimore emissions in 2014 totaled 1487.3 kt C (95% confidence interval of 1158.9–1944.9 kt C), with the largest emissions coming from onroad (34.2% of total city emissions), commercial (19.9%), residential (19.0%), and industrial (11.8%) sectors. Scope 1 electricity production and marine shipping were each generally less than 10% of the city’s total emissions. Baltimore’s self-reported Scope 1 FFCO2 emissions included onroad, natural gas consumption in buildings, and some electricity generating facilities within city limits. The self-reported Scope 1 FFCO2 total of 1182.6 kt C was similar to the sum of matching emission sectors and fuels in Hestia-Baltimore v1.6. However, 20.5% of Hestia-Baltimore’s emissions were in sectors and fuels that were not included in the self-reported inventory. Petroleum use in buildings were omitted and all Scope 1 emissions from industrial point sources, marine shipping, nonroad vehicles, rail, and aircraft were categorically excluded. The omission of petroleum combustion in buildings and categorical exclusions of several sectors resulted in an underestimate of total Scope 1 FFCO2 emissions in Baltimore’s self-reported inventory. Accurate Scope 1 FFCO2 emissions, along with Scope 2 and 3 emissions, are needed to inform effective urban policymaking for system-wide GHG mitigation. We emphasize the need for comprehensive Scope 1 emissions estimates for emissions verification and measuring progress towards Scope 1 GHG mitigation goals using atmospheric monitoring.
Kctd15 regulates nephron segment development by repressing Tfap2a activity.

A functional vertebrate kidney relies on structural units called nephrons, which are epithelial tubules with a sequence of segments each expressing a distinct repertoire of solute transporters. The transcriptional codes driving regional specification, solute transporter program activation, and terminal differentiation of segment populations remain poorly understood. Here, we demonstrate that the KCTD15 paralogs, kctd15a and kctd15b, function in concert to restrict distal early (DE)/thick ascending limb (TAL) segment lineage assignment in the developing zebrafish pronephros by repressing Tfap2a activity. During renal ontogeny, expression of these factors co-localized with tfap2a in distal tubule precursors. kctd15 loss primed nephron cells to adopt distal fates by driving slc12a1, kcnj1a.1, and stc1 expression. These phenotypes were resultant of Tfap2a hyperactivity, where kctd15a/b-deficient embryos exhibited increased abundance of this transcription factor. Interestingly, tfap2a reciprocally promoted kctd15 transcription, unveiling a circuit of autoregulation operating in nephron progenitors. Concomitant kctd15b knockdown with tfap2a overexpression further expanded the DE population. Our study reveals that a transcription factor-repressor feedback module employs tight regulation of Tfap2a and Kctd15 kinetics to control nephron segment fate choice and differentiation during kidney development.