cssi/SciSciGPT-SciSciNet huggingface dataset:
Federal Reserve Intervention and Systemic Risk during Financial Crises

Abstract I examine the relation between Federal Reserve emergency actions and aggregate U.S. systemic risk during the Global Financial Crisis (GFC) and the COVID-19 crisis. I divide these actions in to three categories: lender of last resort (LLR), liquidity provision, and open market operations (OMO). Evidence suggests that during the GFC, liquidity provision and OMO was related * to reduced systemic risk, while evidence on LLR actions is mixed. Further, I find that Federal Reserve actions were related to increased stability in other G8 financial systems during the GFC, and that after the GFC, facilities that remained operational were no longer related to aggregate systemic risk. I do not find a relation between Federal Reserve actions and systemic risk during the COVID-19 crisis. Together, these findings can inform actions and policy decisions in future financial crises.
Antinoise in U.S. equity markets

There are many well documented behavioral biases in financial markets. Yet, analyzing U.S. equities reveals that less than 1% of returns are predictable in recent years. Given the high number of bi...
Revisiting Simple Neural Probabilistic Language Models.

Recent progress in language modeling has been driven not only by advances in neural architectures, but also through hardware and optimization improvements. In this paper, we revisit the neural probabilistic language model (NPLM) of Bengio et al. (2003), which simply concatenates word embeddings within a fixed window and passes the result through a feed-forward network to predict the next word. When scaled up to modern hardware, this model (despite its many limitations) performs much better than expected on word-level language model benchmarks. Our analysis reveals that the NPLM achieves lower perplexity than a baseline Transformer with short input contexts but struggles to handle long-term dependencies. Inspired by this result, we modify the Transformer by replacing its first self-attention layer with the NPLM’s local concatenation layer, which results in small but consistent perplexity decreases across three word-level language modeling datasets.
Thixotropy in viscoplastic drop impact on thin films

We report the first-ever experimental study of thixotropic aging in viscoplastic drop impact. A new dimensionless group is proposed and validated. The results will be useful in predicting splash behavior in a variety of applications from spray coating to fire suppression.
Selection-Free Non-Viral Method Revealed Highly Efficient Crispr-Cas9 Genome Editing of Human Pluripotent Stem Cells Guided by Cellular Autophagy

CRISPR-Cas9 mediated genome editing of human pluripotent stem cells (hPSCs) provides strong avenues for human disease modeling, drug discovery and cell replacement therapy. Genome editing of hPSCs is an extremely inefficient process and requires complex gene delivery and selection methods to improve edit efficiency which are not ideal for clinical applications. Here, we have shown a selection free simple lipofectamine based transfection method where a single plasmid encoding guide RNA (gRNA) and Cas9 selectively transfected hPSCs at the colony edges. Upon dissection and sequencing, the edge cells showed more than 30% edit frequency compared to the reported 3% rate under no selections. Increased cellular health of the edge cells as revealed by reduced autophagy gene-expressions is critical for such transfection pattern. Edge specific transfection was inhibited by blocking lysosomal activity which is essential for autophagy. Hence, our method provides robust scarless genome-editing of hPSCs which is ideal for translational research.
The Limitations of Limited Context for Constituency Parsing

Incorporating syntax into neural approaches in NLP has a multitude of practical and scientific benefits. For instance, a language model that is syntax-aware is likely to be able to produce better samples; even a discriminative model like BERT with a syntax module could be used for core NLP tasks like unsupervised syntactic parsing. Rapid progress in recent years was arguably spurred on by the empirical success of the Parsing-Reading-Predict architecture of (Shen et al., 2018a), later simplified by the Order Neuron LSTM of (Shen et al., 2019). Most notably, this is the first time neural approaches were able to successfully perform unsupervised syntactic parsing (evaluated by various metrics like F-1 score). However, even heuristic (much less fully mathematical) understanding of why and when these architectures work is lagging severely behind. In this work, we answer representational questions raised by the architectures in (Shen et al., 2018a, 2019), as well as some transition-based syntax-aware language models (Dyer et al., 2016): what kind of syntactic structure can current neural approaches to syntax represent? Concretely, we ground this question in the sandbox of probabilistic context-free-grammars (PCFGs), and identify a key aspect of the representational power of these approaches: the amount and directionality of context that the predictor has access to when forced to make parsing decision. We show that with limited context (either bounded, or unidirectional), there are PCFGs, for which these approaches cannot represent the max-likelihood parse; conversely, if the context is unlimited, they can represent the max-likelihood parse of any PCFG.
Ankyrin-R regulates fast-spiking interneuron excitability through perineuronal nets and Kv3.1b K + channels.

Neuronal ankyrins cluster and link membrane proteins to the actin and spectrin-based cytoskeleton. Among the three vertebrate ankyrins, little is known about neuronal Ankyrin-R (AnkR). We report AnkR is highly enriched in Pv+ fast-spiking interneurons in mouse and human. We identify AnkR-associated protein complexes including cytoskeletal proteins, cell adhesion molecules (CAMs), and perineuronal nets (PNNs). We show that loss of AnkR from forebrain interneurons reduces and disrupts PNNs, decreases anxiety-like behaviors, and changes the intrinsic excitability and firing properties of Pv+ fast-spiking interneurons. These changes are accompanied by a dramatic reduction in Kv3.1b K+ channels. We identify a novel AnkR-binding motif in Kv3.1b, and show that AnkR is both necessary and sufficient for Kv3.1b membrane localization in interneurons and at nodes of Ranvier. Thus, AnkR regulates Pv+ fast-spiking interneuron function by organizing ion channels, CAMs, and PNNs, and linking these to the underlying β1 spectrin-based cytoskeleton.
On the Interplay Between Fine-tuning and Composition in Transformers

Pre-trained transformer language models have shown remarkable performance on a variety of NLP tasks. However, recent research has suggested that phrase-level representations in these models reflect heavy influences of lexical content, but lack evidence of sophisticated, compositional phrase information. Here we investigate the impact of fine-tuning on the capacity of contextualized embeddings to capture phrase meaning information beyond lexical content. Specifically, we fine-tune models on an adversarial paraphrase classification task with high lexical overlap, and on a sentiment classification task. After fine-tuning, we analyze phrasal representations in controlled settings following prior work. We find that fine-tuning largely fails to benefit compositionality in these representations, though training on sentiment yields a small, localized benefit for certain models. In follow-up analyses, we identify confounding cues in the paraphrase dataset that may explain the lack of composition benefits from that task, and we discuss potential factors underlying the localized benefits from sentiment training.
Elastohydrodynamic Principles Applied to the Design of Helicopter Components

Elastohydrodynamic principles affecting the lubrication of transmission components are presented and discussed. Surface temperature of the transmission bearings and gears affect elastohydrodynamic film thickness. Traction forces and sliding as well as the inlet temperature determine surface temperatures. High contact ratio gears cause increased sliding and may run at higher surface temperatures. Component life is a function of the ratio of elastohydrodynamic film thickness to composite surface roughness. Lubricant starvation reduces elastrohydrodynamic film thickness and increases surface temperatures. Methods are presented which allow for the application of elastohydrodynamic principles to transmission design in order to increase system life and reliability.
New Insights into Classical Novae

We survey our understanding of classical novae—nonterminal, thermonuclear eruptions on the surfaces of white dwarfs in binary systems. The recent and unexpected discovery of GeV gamma rays from Gal...
CRY2 missense mutations suppress P53 and enhance cell growth

Disruption of circadian rhythms increases the risk of several types of cancer. Mammalian cryptochromes (CRY1 and CRY2) are circadian transcriptional repressors that are related to DNA-repair enzymes. While CRYs lack DNA-repair activity, they modulate the transcriptional response to DNA damage, and CRY2 can promote SKP1 cullin 1-F-box (SCF)FBXL3-mediated ubiquitination of c-MYC and other targets. Here, we characterize five mutations in CRY2 observed in human cancers in The Cancer Genome Atlas. We demonstrate that two orthologous mutations of mouse CRY2 (D325H and S510L) accelerate the growth of primary mouse fibroblasts expressing high levels of c-MYC. Neither mutant affects steady-state levels of overexpressed c-MYC, and they have divergent impacts on circadian rhythms and on the ability of CRY2 to interact with SCFFBXL3 Unexpectedly, stable expression of either CRY2 D325H or of CRY2 S510L robustly suppresses P53 target-gene expression, suggesting that this may be a primary mechanism by which they influence cell growth.
Thermoelectric conversion for space nuclear power

A lightweight, high performance nuclear reactor power system can offer significant advantages for many space missions. Conceptual design has been completed for the SP-100, a system which utilizes many thermoelectric converters and is capable of delivering 100 kilowatts of electrical power. A reference design, using thermoelectric materials with an average figure of merit of 0.001/K and a reactor heat pipe temperature of 1500 K, is presented which has a mass of 2280 kg not including contingency. The sensitivity of system mass to changes in the configuration and thermoelectric material properties are presented
Structural intermediates observed only in intact Escherichia coli indicate a mechanism for TonB-dependent transport

Bacteria must obtain nutrients from their surrounding environment in order to survive. In Gram-negative bacteria, proteins in the outer membrane surrounding the cell actively transport carbohydrates and trace nutrients like iron into the cell’s interior. Although the structures of many of these transport proteins have been determined, the mechanism they use to move molecules across the membrane is poorly understood. To better understand this process, Nilaweera, Nyenhuis and Cafiso examined the structure of BtuB, a transport protein found in the outer membrane of Escherichia coli that is responsible for absorbing vitamin B12. Previous experiments analyzing the structure of BtuB, and other similar transporters, have been carried out on purified proteins that were extracted from the outer membrane. However, these isolated proteins fail to replicate the transport activity observed in bacterial cells. Nilaweera, Nyenhuis and Cafiso therefore wanted to see how the structure of BtuB changes when it is still enclosed in the membrane of E. coli. This revealed that BtuB undergoes large structural changes when it binds to vitamin B12, suggesting that this is an important part of the transport process. However, when purified BtuB was placed into an artificial membrane, these structural changes did not occur. This indicates that the cellular environment in the bacteria is needed for BtuB to carry out its transport role, and explains why previous experiments using purified proteins struggled to see this structural shift. This work highlights the importance of studying bacterial membrane proteins in their native cell environment. BtuB and similar transporters represent a large family of proteins unique to Gram-negative bacteria that have an impact on human health. Since these proteins are structurally alike, the results of this study may help resolve the transport mechanisms of other proteins, ultimately leading to new ways to control bacterial growth.
Autonomous navigation for deep space missions

Navigation (determining where the spacecraft is at any given time, controlling its path to achieve desired targets), performed using ground-in- the-loop techniques: (1) Data includes 2-way radiometric (Doppler, range), interferometric (Delta- Differential One-way Range), and optical (images of natural bodies taken by onboard camera) (2) Data received on the ground, processed to determine orbit, commands sent to execute maneuvers to control orbit. A self-contained, onboard, autonomous navigation system can: (1) Eliminate delays due to round-trip light time (2) Eliminate the human factors in ground-based processing (3) Reduce turnaround time from navigation update to minutes, down to seconds (4) React to late-breaking data. At JPL, we have developed the framework and computational elements of an autonomous navigation system, called AutoNav. It was originally developed as one of the technologies for the Deep Space 1 mission, launched in 1998; subsequently used on three other spacecraft, for four different missions. The primary use has been on comet missions to track comets during flybys, and impact one comet.
Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year's dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015-19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45-0.81, 0.29-0.55, and 0.27-0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.
Dynamic Enumeration of Similarity Joins

This paper considers enumerating answers to similarity-join queries under dynamic updates: Given two sets of n points A,B in ℝ^d, a metric ϕ(⋅), and a distance threshold r > 0, report all pairs of points (a, b) ∈ A × B with ϕ(a,b) ≤ r. Our goal is to store A,B into a dynamic data structure that, whenever asked, can enumerate all result pairs with worst-case delay guarantee, i.e., the time between enumerating two consecutive pairs is bounded. Furthermore, the data structure can be efficiently updated when a point is inserted into or deleted from A or B. We propose several efficient data structures for answering similarity-join queries in low dimension. For exact enumeration of similarity join, we present near-linear-size data structures for 𝓁₁, 𝓁_∞ metrics with log^{O(1)} n update time and delay. We show that such a data structure is not feasible for the 𝓁₂ metric for d ≥ 4. For approximate enumeration of similarity join, where the distance threshold is a soft constraint, we obtain a unified linear-size data structure for 𝓁_p metric, with log^{O(1)} n delay and update time. In high dimensions, we present an efficient data structure with worst-case delay-guarantee using locality sensitive hashing (LSH).
THE RADIO SYNCHROTRON BACKGROUND A COSMIC CONUNDRUM

It has recently become apparent that the background level of diffuse radio emission on the sky is significantly higher than the level that can result from known extragalactic radio source classes or our Galaxy given our current understanding of its large-scale structure.~ In contrast to the more well-known and well-constrained cosmological and astrophysical backgrounds at microwave, infrared, optical/UV, X-ray, and gamma-ray wavelengths, this ``radio synchrotron background'' at radio wavelengths provides clear motivation for considering the possibilities of new astrophysical sources and new particle-based emission mechanisms.
Gated recurrent units viewed through the lens of continuous time dynamical systems

In recent years, the efficacy of using artificial recurrent neural networks to model cortical dynamics has been a topic of interest. Gated recurrent units (GRUs) are specialized memory elements for building these recurrent neural networks. Despite their incredible success in natural language, speech, video processing, and extracting dynamics underlying neural data, little is understood about the specific dynamics representable in a GRU network, and how these dynamics play a part in performance and generalization. As a result, it is both difficult to know a priori how successful a GRU network will perform on a given task, and also their capacity to mimic the underlying behavior of their biological counterparts. Using a continuous time analysis, we gain intuition on the inner workings of GRU networks. We restrict our presentation to low dimensions, allowing for a comprehensive visualization. We found a surprisingly rich repertoire of dynamical features that includes stable limit cycles (nonlinear oscillations), multi-stable dynamics with various topologies, and homoclinic bifurcations. At the same time GRU networks are limited in their inability to produce continuous attractors, which are hypothesized to exist in biological neural networks. We contextualize the usefulness of different kinds of observed dynamics and support our claims experimentally.
A Vision of Responsible Research in Operations Management

Are we contributing positively to the society at large by research that we conduct in the field of Operations Management? Currently, the answer is probably closer to “no” than to “yes.” We often do...
Trellis BMA: Coded Trace Reconstruction on IDS Channels for DNA Storage

Sequencing a DNA strand, as part of the read process in DNA storage, produces multiple noisy copies which can be combined to produce better estimates of the original strand; this is called trace reconstruction. One can reduce the error rate further by introducing redundancy in write sequence and this is called coded trace reconstruction. In this paper, we model the DNA storage channel as an insertion-deletion-substitution (IDS) channel and design both encoding schemes and low-complexity decoding algorithms for coded trace reconstruction. We introduce Trellis BMA, a new reconstruction algorithm whose complexity is linear in the number of traces, and compare its performance to previous algorithms. Our results show that it reduces the error rate on both simulated and experimental data. The performance comparisons in this paper are based on the Clustered Nanopore Reads Dataset publicly released with this paper. Our hope is that this dataset will enable research progress by allowing objective comparisons between candidate algorithms.
Statistical reliability and the seasonal cycle - comments on 'Bottom pressure measurements across the Antarctic Circumpolar Current and their relation to the wind'

The influence of the seasonal cycle on the sample correlation between two time series is discussed. It is shown that any narrow band energetic signal common to both time series can dramatically reduce the statistical reliability of time-lagged sample cross correlation values by reducing the effective number of degrees of freedom in the records. The results are used to discuss the statistical analysis of wind-forced transport of the Antarctic Circumpolar Current through Drake Passage given in a recent paper by Wearn and Baker (Deep-Sea Research, 27, 875–888, 1980). It is suggested that their high correlation between Drake Passage transport and circumpolar averaged zonal wind stress may largely be due to the presence of a strong semi-annual signal in both time series. This reduces the statistical significance of their conclusion that this reflects a dynamical coupling between the atmosphere and the ocean. Their conclusion that the southern hemisphere analyzed with fields are of high quality (based on the high correlation with Drake Passage transport) is also questioned.
Bounds on expected propagation time of probabilistic zero forcing

Abstract Probabilistic zero forcing is a coloring game played on a graph where the goal is to color every vertex blue starting with an initial blue vertex set. As long as the graph G is connected, if at least 1 vertex is blue then eventually all of the vertices will be colored blue. The most studied parameter in probabilistic zero forcing is the expected propagation time ept ( G ) . We significantly improve on upper bounds for ept ( G ) by Geneson and Hogben and by Chan et al. in terms of a graph’s order and radius. We prove the bound ept ( G ) = O r log n r . We also show using Doob’s Optional Stopping Theorem that ept ( G ) ≤ n 2 + O ( log n ) . Finally, we derive an explicit lower bound ept ( G ) ≥ log 2 log 2 ( 2 n ) .
Advanced Heart Failure Epidemiology and Outcomes: A Population-Based Study.

Abstract Objectives The goal of this study was to evaluate the prevalence, characteristics, and outcomes of patients with advanced heart failure (HF) in a geographically defined population. Background Some patients with HF progress to advanced HF, characterized by debilitating HF symptoms refractory to therapy. Limited data are available on the epidemiology and outcomes of patients with advanced HF. Methods This was a population-based cohort study of all Olmsted County, Minnesota, adults with and without HF from 2007 to 2017. The 2018 European Society of Cardiology advanced HF diagnostic criteria were operationalized and applied to all patients with HF. Hospitalization and mortality in advanced HF, overall and according to ejection fraction (EF) type (reduced EF Results Of 6,836 adults with HF, 936 (13.7%) met criteria for advanced HF. The prevalence of advanced HF increased with age and was higher in men. At advanced HF diagnosis, 396 (42.3%) patients had HFrEF, 134 (14.3%) had HFmrEF, and 406 (43.4%) had HFpEF. The median (interquartile range) time from advanced HF diagnosis to death was 12.2 months (3.7-29.9 months). The mean rate of hospitalization was 2.91 (95% CI: 2.78-3.06) per person-year in the first year after advanced HF diagnosis. There were no differences in risks of all-cause mortality or hospitalization by EF. Patients with advanced HFpEF were at lower risk for cardiovascular mortality compared with advanced HFrEF (HR: 0.79; 95% CI: 0.65-0.97). Conclusions In this population-based study, more than one-half of patients with advanced HF had mid-range or preserved EF, and survival was poor regardless of EF.
Complete families of indecomposable non-simple abelian varieties

Given a fixed product of non-isogenous abelian varieties at least one of which is general, we show how to construct complete families of indecomposable abelian varieties whose very general fiber is isogenous to the given product and whose connected monodromy group is a product of symplectic groups or is a unitary group. As a consequence, we show how to realize any product of symplectic groups of total rank g as the connected monodromy group of a complete family of $$g'$$ -dimensional abelian varieties for any $$g'\ge g$$ . These methods also yield a construction of a new Kodaira fibration with fiber genus 4.
Differential roles of RIG-I like receptors in SARS-CoV-2 infection.

Retinoic acid-inducible gene I (RIG-I) and melanoma differentiation-associated protein 5 (MDA5) sense viral RNA and activate antiviral immune responses. Herein we investigate their functions in human epithelial cells, the primary and initial target of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A deficiency in MDA5, RIG-I or mitochondrial antiviral signaling protein (MAVS) enhanced viral replication. The expression of the type I/III interferon (IFN) during infection was impaired in MDA5-/- and MAVS-/-, but not in RIG-I-/-, when compared to wild type (WT) cells. The mRNA level of full-length angiotensin-converting enzyme 2 (ACE2), the cellular entry receptor for SARS-CoV-2, was ~ 2.5-fold higher in RIG-I-/- than WT cells. These data demonstrate MDA5 as the predominant SARS-CoV-2 sensor, IFN-independent induction of ACE2 and anti-SARS-CoV-2 role of RIG-I in epithelial cells.
Direct detection of the myosin super-relaxed state and interacting-heads motif in solution.

The interacting-heads motif (IHM) is a structure of myosin that has been proposed to modulate cardiac output by occluding myosin molecules from undergoing the force-generating cycle. It is hypothesized to be the structural basis for the super-relaxed state (SRX), a low-ATPase kinetic state thought to be cardioprotective. The goal of the present study was to test this hypothesis by determining directly and quantitatively the fractions of myosin in the IHM and SRX under the same conditions in solution. To detect the structural IHM, we used time-resolved fluorescence resonance energy transfer to quantitate two distinct populations. One population was observed at a center distance of 2.0 nm, whereas the other was not detectable by fluorescence resonance energy transfer, implying a distance greater than 4 nm. We confirmed the IHM assignment to the 2.0-nm population by applying the same cross-linking protocol used previously to image the IHM by electron microscopy. Under the same conditions, we also measured the fraction of myosin in the SRX using stopped-flow kinetics. Our results show that the populations of SRX and IHM myosin were similar, unless treated with mavacamten, a drug that recently completed phase III clinical trials to treat hypertrophic cardiomyopathy and is proposed to act by stabilizing both the SRX and IHM. However, we found that mavacamten had a much greater effect on the SRX (55% increase) than on the IHM (4% increase). We conclude that the IHM structure is sufficient but not necessary to produce the SRX kinetic state.
On the Connectivity and Giant Component Size of Random K-out Graphs Under Randomly Deleted Nodes

Random K-out graphs, denoted $\mathbb{H}(n;K)$ , are generated by each of the $n$ nodes drawing $K$ out-edges towards $K$ distinct nodes selected uniformly at random, and then ignoring the orientation of the arcs. Recently, random K-out graphs have been used in applications as diverse as random (pairwise) key predistribution in ad-hoc networks, anonymous message routing in crypto-currency networks, and differentially-private federated averaging. In many applications, connectivity of the random K-out graph when some of its nodes are dishonest, have failed, or have been captured is of practical interest. We provide a comprehensive set of results on the connectivity and giant component size of $\mathbb{H}(n;K_{n},\gamma_{n})$ , i.e., random K-out graph when $\gamma_{n}$ of its nodes, selected uniformly at random, are deleted. First, we derive conditions for $K_{n}$ and $n$ that ensure, with high probability (whp), the connectivity of the remaining graph when the number of deleted nodes is $\gamma_{n}=\Omega(n)$ and $\gamma_{n}=o(n)$ , respectively. Next, we derive conditions for $\mathbb{H}(n;K_{n}, \gamma_{n})$ to have a giant component, i.e., a connected subgraph with $\Omega(n)$ nodes, whp. This is also done for different scalings of $\gamma_{n}$ and upper bounds are provided for the number of nodes outside the giant component. Simulation results are presented to validate the usefulness of the results in the finite node regime.
An exact cutting plane method for k-submodular function maximization

Abstract A natural and important generalization of submodularity – k -submodularity – applies to set functions with k arguments and appears in a broad range of applications, such as infrastructure design, machine learning, and healthcare. In this paper, we study maximization problems with k -submodular objective functions. We propose valid linear inequalities, namely the k -submodular inequalities, for the hypograph of any k -submodular function. This class of inequalities serves as a novel generalization of the well-known submodular inequalities. We show that maximizing a k -submodular function is equivalent to solving a mixed-integer linear program with exponentially many k -submodular inequalities. Using this representation in a delayed constraint generation framework, we design the first exact algorithm, that is not a complete enumeration method, to solve general k -submodular maximization problems. Our computational experiments on the multi-type sensor placement problems demonstrate the efficiency of our algorithm in constrained nonlinear k -submodular maximization problems for which no alternative compact mixed-integer linear formulations are available. The computational experiments show that our algorithm significantly outperforms the only available exact solution method—exhaustive search. Problems that would require over 13 years to solve by exhaustive search can be solved within ten minutes using our method.
The Argument for Bonds in Strategic Asset Allocation

A number of well-known practitioners such as Warren Buffett and Jeremy Siegel have long advocated a strategic asset allocation in which investors hold a majority of their assets in equities. However, in this simple straightforward study of investable indices we find that in order to maximize the well-known Sharpe ratio, most investors over the period 1995–2019 would have been better off holding a majority of their assets in bond indices rather than stock indices. While interest rates have decreased during this twenty-five-year period and thus enhanced bond relative returns, our results are quite convincing. Even though our results are obviously ex post, investors very often make decisions on future investment based on past performance and thus our results could provide guidance to investors for future investing decisions. TOPICS:Wealth management, passive strategies, portfolio construction, statistical methods Key Findings ▪ In order to maximize the Sharpe ratio, most investors over the period 1995-2019 would have been better off holding a majority of their assets in bond indices rather than stock indices. ▪ For a portfolio at the 4% annualized standard deviation level, the percentage that should have been held in a U.S. aggregate bond index over the time period studied is 79% on average. As the risk level increases, the percentage held in the U.S. bond index decreases substantially in favor of investing in an emerging market bond index. For a portfolio at the 10% annualized standard deviation level, the percentage that should have been held in an emerging market bond index is approximately 63% on average. ▪ Bond indices, whether they be domestic or emerging markets indices, should have been a much larger proportion of one’s portfolio than is often stated by finance professionals and commentators.
A semiparametric Bayesian approach to epidemics, with application to the spread of the coronavirus MERS in South Korea in 2015

We consider incomplete observations of stochastic processes governing the spread of infectious diseases through finite populations by way of contact. We propose a flexible semiparametric modeling framework with at least three advantages. First, it enables researchers to study the structure of a population contact network and its impact on the spread of infectious diseases. Second, it can accommodate short- and long-tailed degree distributions and detect potential superspreaders, who represent an important public health concern. Third, it addresses the important issue of incomplete data. Starting from first principles, we show when the incomplete-data generating process is ignorable for the purpose of Bayesian inference for the parameters of the population model. We demonstrate the semiparametric modeling framework by simulations and an application to the partially observed MERS epidemic in South Korea in 2015. We conclude with an extended discussion of open questions and directions for future research.
Zeros of a binomial combination of Chebyshev polynomials

For 0 < α < 1, we study the zeros of the sequence of polynomials {Pm(z)}m=0∞ generated by the reciprocal of (1 − t)α(1 − 2zt + t2), expanded as a power series in t. Equivalently, this sequence is o...
Investigating Robustness of Dialog Models to Popular Figurative Language Constructs

Humans often employ figurative language use in communication, including during interactions with dialog systems. Thus, it is important for real-world dialog systems to be able to handle popular figurative language constructs like metaphor and simile. In this work, we analyze the performance of existing dialog models in situations where the input dialog context exhibits use of figurative language. We observe large gaps in handling of figurative language when evaluating the models on two open domain dialog datasets. When faced with dialog contexts consisting of figurative language, some models show very large drops in performance compared to contexts without figurative language. We encourage future research in dialog modeling to separately analyze and report results on figurative language in order to better test model capabilities relevant to real-world use. Finally, we propose lightweight solutions to help existing models become more robust to figurative language by simply using an external resource to translate figurative language to literal (non-figurative) forms while preserving the meaning to the best extent possible.
Hyperuniform scalar random fields for lensless, multispectral imaging systems.

We propose a novel framework for the systematic design of lensless imaging systems based on the hyperuniform random field solutions of nonlinear reaction-diffusion equations from pattern formation theory. Specifically, we introduce a new class of imaging point-spread functions (PSFs) with enhanced isotropic behavior and controllable sparsity. We investigate PSFs and modulated transfer functions for a number of nonlinear models and demonstrate that two-phase isotropic random fields with hyperuniform disorder are ideally suited to construct imaging PSFs with improved performances compared to PSFs based on Perlin noise. Additionally, we introduce a phase retrieval algorithm based on non-paraxial Rayleigh–Sommerfeld diffraction theory and introduce diffractive phase plates with PSFs designed from hyperuniform random fields, called hyperuniform phase plates (HPPs). Finally, using high-fidelity object reconstruction, we demonstrate improved image quality using engineered HPPs across the visible range. The proposed framework is suitable for high-performance lensless imaging systems for on-chip microscopy and spectroscopy applications.
Neuronal population models reveal specific linear conductance controllers sufficient to rescue preclinical disease phenotypes.

Summary Preclinical drug candidates are screened for their ability to ameliorate in vitro neuronal electrophysiology, and go/no-go decisions progress drugs to clinical trials based on population means across cells and animals. However, these measures do not mitigate clinical endpoint risk. Population-based modeling captures variability across multiple electrophysiological measures from healthy, disease, and drug phenotypes. We pursued optimizing therapeutic targets by identifying coherent sets of ion channel target modulations for recovering heterogeneous wild-type (WT) population excitability profiles from a heterogeneous Huntington’s disease (HD) population. Our approach combines mechanistic simulations with population modeling of striatal neurons using evolutionary optimization algorithms to design ‘virtual drugs’. We introduce efficacy metrics to score populations and rank virtual drug candidates. We found virtual drugs using heuristic approaches that performed better than single target modulators and standard classification methods. We compare a real drug to virtual candidates and demonstrate a novel in silico triaging method.
A CLT for dependent random variables with an application to an infinite system of interacting diffusion processes

We present a central limit theorem for stationary random fields that are short-range dependent and asymptotically independent. As an application, we present a central limit theorem for an infinite family of interacting Ito-type diffusion processes.
GPR-based Model Reconstruction System for Underground Utilities Using GPRNet

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) instruments to detect and locate underground objects (i.e. rebars, utility pipes). Many of the previous researches focus on GPR image-based feature detection only, and none can process sparse GPR measurements to successfully reconstruct a very fine and detailed 3D model of underground objects for better visualization. To address this problem, this paper presents a novel robotic system to collect GPR data, localize the underground utilities, and reconstruct the underground objects’ dense point cloud model. This system is composed of three modules: 1) visual-inertial-based GPR data collection module which tags the GPR measurements with positioning information provided by an omnidirectional robot; 2) a deep neural network (DNN) migration module to interpret the raw GPR B-scan image into a cross-section of object model; 3) a DNN-based 3D reconstruction module, i.e., GPRNet, to generate underground utility model with the fine 3D point cloud. In this paper, both the quantitative and qualitative experiment results verify our method that can generate a dense and complete point cloud model of pipe-shaped utilities based on a sparse input, i.e., GPR raw data, with incompleteness and various noise. The experiment results on synthetic data as well as field test data further support the effectiveness of our approach.
Hard X-ray polarimetry—an overview of the method, science drivers, and recent findings

The last decade has seen a leapfrog in the interest of X-ray polarimetry with a number of new polarization measurements in hard X-rays from AstroSat, POLAR, GAP, and PoGO+. The measurements provide some interesting insights into various astrophysical phenomena such as coronal geometry and disk–jet connection in black hole X-ray binaries, hard X-ray emission mechanism in pulsars, and gamma-ray bursts. They also highlight an increase in polarization with energy, which makes hard X-ray polarimetry extremely appealing. There are a number of confirmed hard X-ray polarimetry experiments which along with the existing instruments (AstroSat and INTEGRAL) make this field further exciting. Polarization experiments may also see significant progress in sensitivity with new developments in scintillator readouts, active pixel sensors, and cadmium zinc telluride detectors. In particular, the advent of hard X-ray focusing optics will enable the designing of compact focal plane polarimeters with a multifold enhancement in sensitivity. In this review, we will focus on the recent polarimetry findings and science potential of hard X-ray polarimetry along with possible improvements in the measurement techniques.
Classification and Visualization of Genotype x Phenotype Interactions in Biomass Sorghum.

We introduce a simple approach to understanding the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. The pipeline involves training deep convolutional neural networks (CNNs) to differentiate between images of plants with reference and alternate versions of various SNPs, and then using visualization approaches to highlight what the classification networks key on. We demonstrate the capacity of deep CNNs at performing this classification task, and show the utility of these visualizations on RGB imagery of biomass sorghum captured by the TERRA-REF gantry. We focus on several different genetic markers with known phenotypic expression, and discuss the possibilities of using this approach to uncover genotype x phenotype relationships.
Towards System for Knowledge Representation of Campaign Experimentation

The campaign is an experimentation construct for codesign activity wherein multiple researchers carry out computational experiments that individually contribute to a shared goal. The larger objective of our research is a system that exists in the experimental environment that constructs a knowledge representation of campaigns and products both produced and consumed such that the campaign can as efficient as possible and the products richly contextualized for reuse. Using campaign experiments running on the Summit machine at Oak Ridge National Labs, we demonstrate early results of support for discovery queries and for detecting when two sweeps are similar.
A Matching Mechanism with Anticipatory Tolls for Congestion Pricing

This paper presents a matching mechanism for assigning drivers to routes where the drivers pay a toll for the marginal delay they impose on other drivers. The simple matching mechanism is derived from the deterministic algorithm for online bipartite matching proposed in [1]. The toll, which is anticipatory in design, is an adaption of one proposed in [2]. Our research proves that the matching mechanism proposed here is Pareto user-optimal, that is, it is fair to all drivers and achieves a competitive ratio of 1 + log(m), where $m$ is the number of available routes, when applied with a goal of minimizing total network travel cost.
Theoretical guidelines for editing ecological communities.

Abstract Having control over species abundances and community resilience is of great interest for experimental, agricultural, industrial and conservation purposes. Here, we theoretically explore the possibility of manipulating ecological communities by modifying pairwise interactions. Specifically, we establish which interaction values should be modified, and by how much, in order to alter the composition or resilience of a community towards a favorable direction. While doing so, we also take into account the experimental difficulties in making such modifications by including in our optimization process, a cost parameter, which penalizes large modifications. In addition to prescribing what changes should be made to interspecies interactions given some modification cost, our approach also serves to establish the limits of community control, i.e. how well can one approach an ecological goal at best, even when not constrained by cost.
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification

Extreme multi-label text classification (XMC) seeks to find relevant labels from an extreme large label collection for a given text input. Many real-world applications can be formulated as XMC problems, such as recommendation systems, document tagging and semantic search. Recently, transformer based XMC methods, such as X-Transformer and LightXML, have shown significant improvement over other XMC methods. Despite leveraging pre-trained transformer models for text representation, the fine-tuning procedure of transformer models on large label space still has lengthy computational time even with powerful GPUs. In this paper, we propose a novel recursive approach, XR-Transformer to accelerate the procedure through recursively fine-tuning transformer models on a series of multi-resolution objectives related to the original XMC objective function. Empirical results show that XR-Transformer takes significantly less training time compared to other transformer-based XMC models while yielding better state-of-the-art results. In particular, on the public Amazon-3M dataset with 3 million labels, XR-Transformer is not only 20x faster than X-Transformer but also improves the Precision@1 from 51% to 54%.
Disclosure Sentiment: Machine Learning vs. Dictionary Methods

We compare the ability of dictionary-based and machine-learning methods to capture disclosure sentiment at 10-K filing and conference-call dates. Like Loughran and McDonald [Loughran T, McDonald B ...
CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator

Accurately backpropagating the gradient through categorical variables is a challenging task that arises in various domains, such as training discrete latent variable models. To this end, we propose CARMS, an unbiased estimator for categorical random variables based on multiple mutually negatively correlated (jointly antithetic) samples. CARMS combines REINFORCE with copula based sampling to avoid duplicate samples and reduce its variance, while keeping the estimator unbiased using importance sampling. It generalizes both the ARMS antithetic estimator for binary variables, which is CARMS for two categories, as well as LOORF/VarGrad, the leave-one-out REINFORCE estimator, which is CARMS with independent samples. We evaluate CARMS on several benchmark datasets on a generative modeling task, as well as a structured output prediction task, and find it to outperform competing methods including a strong self-control baseline. The code is publicly available.
Multiplier U-processes: Sharp bounds and applications

The theory for multiplier empirical processes has been one of the central topics in the development of the classical theory of empirical processes, due to its wide applicability to various statistical problems. In this paper, we develop theory and tools for studying multiplier U-processes, a natural higher-order generalization of the multiplier empirical processes. To this end, we develop a multiplier inequality that quantifies the moduli of continuity of the multiplier U-process in terms of that of the (decoupled) symmetrized U-process. The new inequality finds a variety of applications including (i) multiplier and bootstrap central limit theorems for U-processes, (ii) general theory for bootstrap M-estimators based on U-statistics, and (iii) theory for M-estimation under general complex sampling designs, again based on U-statistics.