cssi/SciSciGPT-SciSciNet huggingface dataset:
A single-cell view of the transcriptome during lateral root initiation in Arabidopsis thaliana

Root architecture is a major determinant of plant fitness and is under constant modification in response to favorable and unfavorable environmental stimuli. Beyond impacts on the primary root, the environment can alter the position, spacing, density, and length of secondary or lateral roots. Lateral root development is among the best-studied examples of plant organogenesis, yet there are still many unanswered questions about its earliest steps. Among the challenges faced in capturing these first molecular events is the fact that this process occurs in a small number of cells with unpredictable timing. Single-cell sequencing methods afford the opportunity to isolate the specific transcriptional changes occurring in cells undergoing this fate transition. Using this approach, we successfully captured the transcriptomes of initiating lateral root primordia in Arabidopsis thaliana and discovered many upregulated genes associated with this process. We developed a method to selectively repress target gene transcription in the xylem pole pericycle cells where lateral roots originate and demonstrated that the expression of several of these targets is required for normal root development. We also discovered subpopulations of cells in the pericycle and endodermal cell files that respond to lateral root initiation, highlighting the coordination across cell files required for this fate transition.
Using Graham v. Florida to Challenge Juvenile Transfer Laws

The article suggests that the recent Supreme Court opinion in Graham v. Florida abolishing life without parole sentences for juveniles (JLWOP) convicted of nonhomicide crimes, may be used to challenge juvenile transfer laws. Part I provides a description and analysis of the Graham opinion and reviews the Court's Eighth Amendment jurisprudence through to their recent ruling declaring JLWOP sentences for nonhomicide crimes unconstitutional. Part II argues that youth have a right to rehabilitation found under the state's police power. In addition, Graham discusses three types of difficulties that adult decisionmakers in the criminal justice system have with respect to youth that may be useful to challenge transfer laws. First, judges and experts have problems evaluating the culpability and maturity of youth. Second, adult perceptions of youth are biased by the severity and manner in which the crimes were conducted. Third, counsel have difficulty representing youth in the adult system. These factors apply to all youth prosecuted in the adult criminal system, regardless of offense charged or sentence imposed. Finally, Part III encourages lawyers to revisit these prior challenges in both individual cases and as part of impact litigation strategies to declare all transfer statutes, or portions of them, unconstitutional.
Cryo-EM structure of the yeast TREX complex and coordination with the SR-like protein Gbp2

The evolutionarily conserved TRanscript-EXport (TREX) complex plays central roles during mRNP (messenger ribonucleoprotein) maturation and export from the nucleus to the cytoplasm. In yeast, TREX is composed of the THO sub-complex (Tho2, Hpr1, Tex1, Mft1, and Thp2), the DEAD box ATPase Sub2, and Yra1. Here we present a 3.7 A cryo-EM structure of the yeast THO•Sub2 complex. The structure reveals the intimate assembly of THO revolving around its largest subunit Tho2. THO stabilizes a semi-open conformation of the Sub2 ATPase via interactions with Tho2. We show that THO interacts with the serine-arginine (SR)-like protein Gbp2 through both the RS domain and RRM domains of Gbp2. Cross-linking mass spectrometry analysis supports the extensive interactions between THO and Gbp2, further revealing that RRM domains of Gbp2 are in close proximity to the C-terminal domain of Tho2. We propose that THO serves as a landing pad to configure Gbp2 to facilitate its loading onto mRNP.
Structural causes and regime consequences: regimes as intervening variables

International regimes are defined as principles, norms, rules and decision making procedures around which actor expectations converge in a given issue area. As a starting point, regimes have been conceptualized as intervening variables, standing between basic causal factors and related outcomes and behaviour. There are three views about the importance of regimes: conventional structural orientations dismiss regimes as being at best ineffectual; Grotian orientations view regimes as an intimate component of the international system; and modified structural perspectives see regimes as significant only under certain constrained conditions. For Grotian and modified structuralist arguments, which endorse the view that regimes can influence outcomes and behavior, regime development is seen as a function of five basic causal variables: egoistic self interest, political power, diffuse norms and principles, custom and usage, and knowledge
An anisotropic regularity condition for the 3D incompressible Navier–Stokes equations for the entire exponent range

Abstract We show that a suitable weak solution to the incompressible Navier–Stokes equations on R 3 × ( − 1 , 1 ) is regular on R 3 × ( − 1 , 0 ] if ∂ 3 u belongs to M 2 p / ( 2 p − 3 ) , α ( ( − 1 , 0 ) ; L p ( R 3 ) ) for any α > 1 and p ∈ ( 3 / 2 , ∞ ) , which is a logarithmic-type variation of a Morrey space in time. For each α > 1 this space is, up to a logarithm, critical with respect to the scaling of the equations, and contains all spaces L q ( ( − 1 , 0 ) ; L p ( R 3 ) ) that are subcritical, that is for which 2 / q + 3 / p 2 .
Multilinear maximal operators associated to simplices

We establish $L^{p_1}\times\cdots\times L^{p_k}\to L^r$ and $\ell^{p_1}\times\cdots\times \ell^{p_k}\to \ell^r$ type bounds for multilinear maximal operators associated to averages over isometric copies of a given non-degenerate $k$-simplex in both the continuous and discrete settings. These provide natural extensions of $L^p\to L^p$ and $\ell^p\to \ell^p$ bounds for Stein's spherical maximal operator and the discrete spherical maximal operator, with each of these results serving as a key ingredient of the respective proofs.
Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs

In this paper we study the statistical properties of Laplacian smoothing, a graph-based approach to nonparametric regression. Under standard regularity conditions, we establish upper bounds on the error of the Laplacian smoothing estimator $\widehat{f}$, and a goodness-of-fit test also based on $\widehat{f}$. These upper bounds match the minimax optimal estimation and testing rates of convergence over the first-order Sobolev class $H^1(\mathcal{X})$, for $\mathcal{X}\subseteq \mathbb{R}^d$ and $1 \leq d < 4$; in the estimation problem, for $d = 4$, they are optimal modulo a $\log n$ factor. Additionally, we prove that Laplacian smoothing is manifold-adaptive: if $\mathcal{X} \subseteq \mathbb{R}^d$ is an $m$-dimensional manifold with $m < d$, then the error rate of Laplacian smoothing (in either estimation or testing) depends only on $m$, in the same way it would if $\mathcal{X}$ were a full-dimensional set in $\mathbb{R}^d$.
Multi-spatial scale dynamic interactions between functional sources reveal sex-specific changes in schizophrenia

Abstract We introduce an extension of independent component analysis (ICA), called multiscale ICA (msICA), and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. msICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and -common connectivity patterns in schizophrenia. Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (1) within the subcortical domain, (2) between the somatomotor and cerebellum domains, and (3) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.
Distributed Certifiably Correct Pose-Graph Optimization

This article presents the first certifiably correct algorithm for distributed pose-graph optimization (PGO), the backbone of modern collaborative simultaneous localization and mapping (CSLAM) and camera network localization (CNL) systems. Our method is based upon a sparse semidefinite relaxation that we prove provides globally optimal PGO solutions under moderate measurement noise (matching the guarantees enjoyed by the state-of-the-art centralized methods), but is amenable to distributed optimization using the low-rank Riemannian Staircase framework. To implement the Riemannian Staircase in the distributed setting, we develop Riemannian block coordinate descent (RBCD), a novel method for (locally) minimizing a function over a product of Riemannian manifolds. We also propose the first distributed solution verification and saddle escape methods to certify the global optimality of critical points recovered via RBCD, and to descend from suboptimal critical points (if necessary). All components of our approach are inherently decentralized: they require only local communication, provide privacy protection, and are easily parallelizable. Extensive evaluations on synthetic and real-world datasets demonstrate that the proposed method correctly recovers globally optimal solutions under moderate noise, and outperforms alternative distributed techniques in terms of solution precision and convergence speed.
CDPAM: Contrastive Learning for Perceptual Audio Similarity

Many speech processing methods based on deep learning require an automatic and differentiable audio metric for the loss function. The DPAM approach of Manocha et al. [1] learns a full-reference metric trained directly on human judgments, and thus correlates well with human perception. However, it requires a large number of human annotations and does not generalize well outside the range of perturbations on which it was trained. This paper introduces CDPAM –a metric that builds on and advances DPAM. The primary improvement is to combine contrastive learning and multi-dimensional representations to build robust models from limited data. In addition, we collect human judgments on triplet comparisons to improve generalization to a broader range of audio perturbations. CDPAM correlates well with human responses across nine varied datasets. We also show that adding this metric to existing speech synthesis and enhancement methods yields significant improvement, as measured by objective and subjective tests.
Torsion in the magnitude homology of graphs

Magnitude homology is a bigraded homology theory for finite graphs defined by Hepworth and Willerton, categorifying the power series invariant known as magnitude which was introduced by Leinster. We analyze the structure and implications of torsion in magnitude homology. We show that any finitely generated abelian group may appear as a subgroup of the magnitude homology of a graph, and, in particular, that torsion of a given prime order can appear in the magnitude homology of a graph and that there are infinitely many such graphs. Finally, we provide complete computations of magnitude homology of a class of outerplanar graphs and focus on the ranks of the groups along the main diagonal of magnitude homology.
Opioid use, motivation to quit, and treatment status related to COVID-19: a cross-sectional study.

Persons who use opioids may be at elevated risk of harm from the coronavirus disease 2019 (COVID-19) pandemic, yet few data currently exist that can be used to examine this risk. As part of a rapid response survey, this study measured opioid users’ perceptions of risk or harm from COVID-19, as well as potential changes in motivation to quit, frequency of use, and engagement with treatment. Data collected from Amazon’s Mechanical Turk (n = 562) were analyzed. Participants perceived modest risk elevation from COVID-19 due to their opioid use, and perceived moderate risk to themselves or their community from COVID-19. Since learning about COVID-19, 31.2% reported decreasing their opioid use, and 26.0% reported increased motivation to quit. Thirty-seven percent of participants reported both their use and motivation to quit stayed the same; 16.6% reported decreased use and increased motivation to quit. Participants who reported that their opioid use increased after learning about COVID-19, or whose motivation to quit opioids decreased, were more likely to also be engaged in treatment than those whose use or motivation stayed the same. These preliminary findings suggest that there likely is an association between COVID-19, opioid use, and treatment engagement that merits further in-depth investigation.
Fast quantum subroutines for the simplex method

We propose quantum subroutines for the simplex method that avoid classical computation of the basis inverse. For an \(m \times n\) constraint matrix with at most \(d_c\) nonzero elements per column, at most d nonzero elements per column or row of the basis, basis condition number \(\kappa \), and optimality tolerance \(\epsilon \), we show that pricing can be performed in \(\tilde{O}(\frac{1}{\epsilon }\kappa d \sqrt{n}(d_c n + d m))\) time, where the \(\tilde{O}\) notation hides polylogarithmic factors. If the ratio n/m is larger than a certain threshold, the running time of the quantum subroutine can be reduced to \(\tilde{O}(\frac{1}{\epsilon }\kappa d^{1.5} \sqrt{d_c} n \sqrt{m})\). The steepest edge pivoting rule also admits a quantum implementation, increasing the running time by a factor \(\kappa ^2\). Classically, pricing requires \(O(d_c^{0.7} m^{1.9} + m^{2 + o(1)} + d_c n)\) time in the worst case using the fastest known algorithm for sparse matrix multiplication, and \(O(d_c^{0.7} m^{1.9} + m^{2 + o(1)} + m^2n)\) with steepest edge. Furthermore, we show that the ratio test can be performed in \(\tilde{O}(\frac{t}{\delta } \kappa d^2 m^{1.5})\) time, where \(t, \delta \) determine a feasibility tolerance; classically, this requires \(O(m^2)\) time in the worst case. For well-conditioned sparse problems the quantum subroutines scale better in m and n, and may therefore have a worst-case asymptotic advantage. An important feature of our paper is that this asymptotic speedup does not depend on the data being available in some “quantum form”: the input of our quantum subroutines is the natural classical description of the problem, and the output is the index of the variables that should leave or enter the basis.
Network Topology Inference with Graphon Spectral Penalties

We consider the problem of inferring the unobserved edges of a graph from data supported on its nodes. In line with existing approaches, we propose a convex program for recovering a graph Laplacian that is approximately diagonalizable by a set of eigenvectors obtained from the second-order moment of the observed data. Unlike existing work, we incorporate prior knowledge about the distribution from where the underlying graph was drawn. In particular, we consider the case where the graph was drawn from a graphon model, and we supplement our convex optimization problem with a provably-valid regularizer on the spectrum of the graph to be recovered. We present the cases where the graphon model is assumed to be known and the more practical setting where the relevant features of the model are inferred from auxiliary network observations. Numerical experiments on synthetic and real-world data illustrate the advantage of leveraging the proposed graphon prior, even when the prior is imperfect.
A projection-based reformulation and decomposition algorithm for global optimization of a class of mixed integer bilevel linear programs

We propose an extended variant of the reformulation and decomposition algorithm for solving a special class of mixed-integer bilevel linear programs (MIBLPs) where continuous and integer variables are involved in both upper- and lower-level problems. In particular, we consider MIBLPs with upper-level constraints that involve lower-level variables. We assume that the inducible region is nonempty and all variables are bounded. By using the reformulation and decomposition scheme, an MIBLP is first converted into its equivalent single-level formulation, then computed by a column-and-constraint generation based decomposition algorithm. The solution procedure is enhanced by a projection strategy that does not require the relatively complete response property. To ensure its performance, we prove that our new method converges to the global optimal solution in a finite number of iterations. A large-scale computational study on random instances and instances of hierarchical supply chain planning are presented to demonstrate the effectiveness of the algorithm.
Real-Time Speech Frequency Bandwidth Extension

In this paper we propose a lightweight model for frequency bandwidth extension of speech signals, increasing the sampling frequency from 8kHz to 16kHz while restoring the high frequency content to a level almost indistinguishable from the 16kHz ground truth. The model architecture is based on SEANet (Sound EnhAncement Network), a wave-to-wave fully convolutional model, which uses a combination of feature losses and adversarial losses to reconstruct an enhanced version of the input speech. In addition, we propose a variant of SEANet that can be deployed on-device in streaming mode, achieving an architectural latency of 16ms. When profiled on a single core of a mobile CPU, processing one 16ms frame takes only 1.5ms. The low latency makes it viable for bi-directional voice communication systems.
Leveraging Pathogen Sequence and Contact Tracing Data to Enhance Vaccine Trials in Emerging Epidemics.

INTRODUCTION Advance planning of vaccine trials conducted during outbreaks increases our ability to rapidly define the efficacy and potential impact of a vaccine. Vaccine efficacy against infectiousness (VEI) is an important measure for understanding a vaccine's full impact, yet it is currently not identifiable in many trial designs because it requires knowledge of infectors' vaccination status. Recent advances in genomics have improved our ability to reconstruct transmission networks. We aim to assess if augmenting trials with pathogen sequence and contact tracing data can permit them to estimate VEI. METHODS We develop a transmission model with a vaccine trial in an outbreak setting, incorporate pathogen sequence data and contact tracing data, and assign probabilities to likely infectors. We then propose and evaluate the performance of an estimator of VEI. RESULTS We find that under perfect knowledge of infector-infectee pairs, we are able to accurately estimate VEI. Use of sequence data results in imperfect reconstruction of transmission networks, biasing estimates of VEI towards the null, with approaches using deep sequence data performing better than approaches using consensus sequence data. Inclusion of contact tracing data reduces the bias. CONCLUSION Pathogen genomics enhance identifiability of VEI, but imperfect transmission network reconstruction biases estimate toward the null and limits our ability to detect VEI. Given the consistent direction of the bias, estimates obtained from trials using these methods will provide lower bounds on the true VEI. A combination of sequence and epidemiologic data results in the most accurate estimates, underscoring the importance of contact tracing.
Privacy as a Planned Behavior: Effects of Situational Factors on Privacy Perceptions and Plans

To account for privacy perceptions and preferences in user models and develop personalized privacy systems, we need to understand how users make privacy decisions in various contexts. Existing studies of privacy perceptions and behavior focus on overall tendencies toward privacy, but few have examined the context-specific factors in privacy decision making. We conducted a survey on Mechanical Turk (N=401) based on the theory of planned behavior (TPB) to measure the way users’ perceptions of privacy factors and intent to disclose information are affected by three situational factors embodied hypothetical scenarios: information type, recipients’ role, and trust source. Results showed a positive relationship between subjective norms and perceived behavioral control, and between each of these and situational privacy attitude; all three constructs are significantly positively associated with intent to disclose. These findings also suggest that, situational factors predict participants’ privacy decisions through their influence on the TPB constructs.
How Social Norms and Social Identification Constrain Aggressive Reporting Behavior

This study examines how the source and nature of reporting standards jointly influence compliance with those standards. More specifically, I examine how decision makers’ identification with the source of the standards moderates compliance with different types of standards. Type refers to whether the accounting standard is descriptive or injunctive (i.e., prescriptive). Source refers to the entity promulgating the accounting standards. I conduct three experiments in which participants face a direct trade-off between reporting aggressively to maximize their personal wealth and reporting conservatively to adhere to a standard. I find that identification with the source causes less aggressive reporting for an injunctive standard, but when a standard is descriptive, identification has no effect or an opposite effect. When identification with the source is low, descriptive standards tend to work well compared to injunctive standards. With injunctive standards, persuasive factors, such as identification, likely influence financial managers’ aggressive reporting behavior.
Polysomnographic indicators of restorative sleep and body mass trajectories in the Wisconsin Sleep Cohort Study.

Study objectives Previous research suggests that reductions in restorative, slow-wave (N3), and rapid eye movement (REM) sleep are associated with weight gain and obesity in mid-to-late life. We extend prior work by examining how within-person (WP) changes and between-person (BP) differences in restorative sleep over several years are associated with body mass trajectories among participants in the Wisconsin Sleep Cohort Study (WSCS). Methods We used data from 4,862 polysomnographic (PSG) sleep studies and physical exams collected from 1,187 WSCS participants over an average duration of 14.9 years. Primary measures of interest included body mass index (BMI = kg/m2) and the percentages of time spent in N3 and REM sleep. We estimated a series of linear mixed regression models to examine how WP changes and BP differences in N3 and REM sleep affected BMI trajectories, controlling for other sleep measures, demographic characteristics, and health behaviors as potential confounders. Results Women in the WSCS experienced more rapid BMI gain than men. With some variation by sex, we found that (1) below-average N3 and REM sleep is associated with above-average BMI, and (2) within-person decreases in N3 and REM sleep over time are associated with gains in BMI. These findings persisted after adjustment for sleep duration and other potential confounders. Conclusions Our findings highlight the importance of PSG indices of restorative sleep in mid-to-late life, suggesting that future clinical treatments and public health policies will benefit from heightened attention to sleep quality.
How Variation in Internet Access, Digital Skills, and Media Use are Related to Rural Student Outcomes: GPA, SAT, and Educational Aspirations

Abstract Some have pointed to divides in the availability of fixed home broadband Internet access as a contributor to rural students’ lower levels of educational attainment. Based on standardized exams (SAT Suite) and a survey of rural Michigan students in grades 8–11, we find that rural students with broadband home Internet access are more interested in school and leave homework incomplete less often. However, the relationship to classroom grades (GPA) is relatively trivial. Yet, we find that students who are not dependent on a cell phone for Internet access and those with higher digital skills, especially social media skills, rank considerably higher on the SAT. Rural students with broadband Internet access are able to participate in a more diverse array of online media activities, which supports building digital skills. Any negative relationship between time spent on social media, video games, other digital media and educational outcomes is outweighed by the benefit to digital skills. However, aspects of rural culture; including the emphasis on activities such as sports, as a path to postsecondary schooling and upward, social mobility; may be stunting the positive relationship between access, digital skills, and educational aspirations. Whereas extra-curricular sports have no direct relationship to SAT performance, students who spend more time on sports receive higher grades and have higher educational aspirations than those with more digital skills. We discuss the implications for rural students’ access to human capital and how the unequal relationship between digital skills and performance in the classroom and on the SAT may perpetuate inequalities.
Combination of yttrium-90 radioembolization with stereotactic body radiation therapy in the treatment of portal vein tumor thrombosis.

PURPOSE Portal vein tumor thrombosis (PVTT) from cancer involving the liver carries a dismal prognosis, with median overall survival (OS) ranging from 2 to 5 months. While treatment with yttrium-90 (90Y) radioembolization alone may improve outcomes, overall prognosis remains poor. We hypothesize that the combination of 90Y radioembolization to the parenchymal component of the tumor and stereotactic body radiation therapy (SBRT) to the vascular component is a safe and effective means of improving outcomes. MATERIALS AND METHODS A single center retrospective review identified 12 patients with cancers involving the liver who received both 90Y radioembolization and SBRT to the PVTT between May 2015 to August 2020. Primary endpoint was the 90-day toxicity rate by the Common Terminology Criteria for Adverse Events version 5.0. Secondary endpoints were the best response rate based on the Response Evaluation Criteria in Solid Tumors v1.1, local control rate, portal vein (PV) patency rate, and median OS. RESULTS Patients received a median 90Y dose of 104.3 Gy (range, 83.3 to 131.7 Gy) and a median 5-fraction SBRT dose of 32.5 Gy (range, 27.5 to 50 Gy). There were no late toxicities reported, and only 7 acute grade 1 toxicities reported: elevation of liver function tests (17%), nausea (17%), fatigue (17%), and esophagitis (8%). Local control was 83%. 58% of patients had a patent PV after treatment. With a median follow-up time of 28 months, 1-year OS was 55% with a median OS of 14 months. CONCLUSION Combination 90Y radioembolization and SBRT appears to be safe and effective in the treatment of PVTT. Larger prospective studies are warranted to better evaluate this combination treatment approach.
Automatic gender detection in Twitter profiles for health-related cohort studies.

Objective Biomedical research involving social media data is gradually moving from population-level to targeted, cohort-level data analysis. Though crucial for biomedical studies, social media user's demographic information (eg, gender) is often not explicitly known from profiles. Here, we present an automatic gender classification system for social media and we illustrate how gender information can be incorporated into a social media-based health-related study. Materials and Methods We used a large Twitter dataset composed of public, gender-labeled users (Dataset-1) for training and evaluating the gender detection pipeline. We experimented with machine learning algorithms including support vector machines (SVMs) and deep-learning models, and public packages including M3. We considered users' information including profile and tweets for classification. We also developed a meta-classifier ensemble that strategically uses the predicted scores from the classifiers. We then applied the best-performing pipeline to Twitter users who have self-reported nonmedical use of prescription medications (Dataset-2) to assess the system's utility. Results and Discussion We collected 67 181 and 176 683 users for Dataset-1 and Dataset-2, respectively. A meta-classifier involving SVM and M3 performed the best (Dataset-1 accuracy: 94.4% [95% confidence interval: 94.0-94.8%]; Dataset-2: 94.4% [95% confidence interval: 92.0-96.6%]). Including automatically classified information in the analyses of Dataset-2 revealed gender-specific trends-proportions of females closely resemble data from the National Survey of Drug Use and Health 2018 (tranquilizers: 0.50 vs 0.50; stimulants: 0.50 vs 0.45), and the overdose Emergency Room Visit due to Opioids by Nationwide Emergency Department Sample (pain relievers: 0.38 vs 0.37). Conclusion Our publicly available, automated gender detection pipeline may aid cohort-specific social media data analyses (https://bitbucket.org/sarkerlab/gender-detection-for-public).
Oblique interactions between solitons and mean flows in the Kadomtsev-Petviashvili equation

The interaction of an oblique line soliton with a one-dimensional dynamic mean flow is analyzed using the Kadomtsev-Petviashvili II (KPII) equation. Building upon previous studies that examined the transmission or trapping of a soliton by a slowly varying rarefaction or oscillatory dispersive shock wave in one space and one time dimension, this paper allows for the incident soliton to approach the changing mean flow at a nonzero oblique angle. By deriving invariant quantities of the soliton-mean flow modulation equations$-$a system of three (1+1)-dimensional quasilinear, hyperbolic equations for the soliton and mean flow parameters$-$and positing the initial configuration as a Riemann problem in the modulation variables, it is possible to derive quantitative predictions regarding the evolution of the line soliton within the mean flow. It is found that the interaction between an oblique soliton and a changing mean flow leads to several novel features not observed in the (1+1)-dimensional reduced problem. Many of these interesting dynamics arise from the unique structure of the modulation equations that are nonstrictly hyperbolic, including a well-defined multivalued solution interpreted as a solution of the (2+1)-dimensional soliton-mean modulation equations, in which the soliton interacts with the mean flow and then wraps around to interact with it again. Finally, it is shown that the oblique interactions between solitons and dispersive shock wave solutions for the mean flow give rise to all three possible types of 2-soliton solutions of the KPII equation. The analytical findings are quantitatively supported by direct numerical simulations.
Why Dilated Convolutional Neural Networks: A Proof of Their Optimality.

One of the most effective image processing techniques is the use of convolutional neural networks that use convolutional layers. In each such layer, the value of the layer’s output signal at each point is a combination of the layer’s input signals corresponding to several neighboring points. To improve the accuracy, researchers have developed a version of this technique, in which only data from some of the neighboring points is processed. It turns out that the most efficient case—called dilated convolution—is when we select the neighboring points whose differences in both coordinates are divisible by some constant l. In this paper, we explain this empirical efficiency by proving that for all reasonable optimality criteria, dilated convolution is indeed better than possible alternatives.
Classical Proofs of Quantum Knowledge.

We define the notion of a proof of knowledge in the setting where the verifier is classical, but the prover is quantum, and where the witness that the prover holds is in general a quantum state. We establish simple properties of our definition, including that, if a nondestructive classical proof of quantum knowledge exists for some state, then that state can be cloned by an unbounded adversary, and that, under certain conditions on the parameters in our definition, a proof of knowledge protocol for a hard-to-clone state can be used as a (destructive) quantum money verification protocol. In addition, we provide two examples of protocols (both inspired by private-key classical verification protocols for quantum money schemes) which we can show to be proofs of quantum knowledge under our definition. In so doing, we introduce techniques for the analysis of such protocols which build on results from the literature on nonlocal games. Finally, we show that, under our definition, the verification protocol introduced by Mahadev (FOCS 2018) is a classical argument of quantum knowledge for QMA relations. In all cases, we construct an explicit quantum extractor that is able to produce a quantum witness given black-box quantum (rewinding) access to the prover, the latter of which includes the ability to coherently execute the prover’s black-box circuit controlled on a superposition of messages from the verifier.
COVID-19-Induced Colitis: A Novel Relationship During Troubling Times

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the resulting disease called coronavirus disease 2019 (COVID-19) has initiated a global health crisis declared by the World Health Organization (WHO). As the nature of this novel virus unfolds, there have been a variety of extra-pulmonary clinical presentations of COVID-19 affecting the gastrointestinal tract. We present a novel relationship between this disease and its clinical manifestation as COVID-19-induced colitis. Providing insight into this association will invariably facilitate earlier recognition and resultant treatment of COVID-19 along with reducing unintended exposure to healthcare workers.
“Alexa, Can I Program You?”: Student Perceptions of Conversational Artificial Intelligence Before and After Programming Alexa

Growing up in an artificial intelligence-filled world, with Siri and Amazon Alexa often within arm’s—or speech’s—reach, could have significant impact on children. Conversational agents could influence how students anthropomorphize computer systems or develop a theory of mind. Previous research has explored how conversational agents are used and perceived by children within and outside of learning contexts. This study investigates how middle and high school students’ perceptions of Alexa change through programming their own conversational agents in week-long AI education workshops. Specifically, we investigate the workshops’ influence on student perceptions of Alexa’s intelligence, friendliness, aliveness, safeness, trustworthiness, human-likeness, and feelings of closeness. We found that students felt Alexa was more intelligent and felt closer to Alexa after the workshops. We also found strong correlations between students’ perceptions of Alexa’s friendliness and trustworthiness, and safeness and trustworthiness. We recommend designers carefully consider personification, transparency, playfulness and utility when designing conversational agents for learning contexts.
Online Stochastic Max-Weight Bipartite Matching: Beyond Prophet Inequalities

The rich literature on online Bayesian selection problems has long focused on so-called prophet inequalities, which compare the gain of an online algorithm to that of a "prophet" who knows the future. An equally-natural, though significantly less well-studied benchmark is the optimum online algorithm, which may be omnipotent (i.e., computationally-unbounded), but not omniscient. What is the computational complexity of the optimum online? How well can a polynomial-time algorithm approximate it? Motivated by applications in ride hailing, we study the above questions for the online stochastic maximum-weight matching problem under vertex arrivals. This problem was recently introduced by Ezra, Feldman, Gravin and Tang (EC'20), who gave a 1/2-competitive algorithm for it. This is the best possible ratio, as this problem is a generalization of the original single-item prophet inequality. We present a polynomial-time algorithm which approximates optimal online within a factor of 0.51---beating the best-possible prophet inequality. At the core of our result are a new linear program formulation, an algorithm that tries to match the arriving vertices in two attempts, and an analysis that bounds the correlation resulting from the second attempts. In contrast, we show that it is PSPACE-hard to approximate this problem within some constant α
Ionization induced plasma grating and its applications in strong-field ionization measurements

An ionization induced plasma grating can be formed by spatially selective ionization of gases by the interference of two intersecting ultra-short laser pulses. The density modulation of a plasma grating can approach unity since the plasma is produced only where the two pulses constructively interfere and ionization does not occur in destructive interference regions. Such a large density modulation leads to efficient Thomson scattering of a second ultra-short probe pulse once the Bragg condition is satisfied. By measuring the scattering efficiency, it is possible to determine the absolute electron density in the plasma grating and thereby deduce the ionization degree for a given neutral gas density. In this paper, we demonstrate the usefulness of this concept by showing two applications: ionization degree measurement of strong-field ionization of atoms and molecules and characterization of extremely low-density gas jets. The former application is of particular interest for ionization physics studies in dense gases where the collision of the ionized electron with neighboring neutrals may become important-sometimes referred to as many-body ionization; and the latter is useful for plasma-based acceleration that requires extremely low-density plasmas.
Optogalvanic photodetachment spectroscopy

A new extension to optogalvanic spectroscopy, in which electrons detached from negative ions formed in the discharge are observed as a function of incident laser wavelength, has been developed. The determination of the electron affinities of I(-) and Cl(-) atomic ions is described. The potential of the technique for studying the spectroscopy of molecular negative ions is also discussed.
Terahertz Science, Technology, and Communication

The term "terahertz" has been ubiquitous in the arena of technology over the past couple of years. New applications are emerging every day which are exploiting the promises of terahertz - its small wavelength; capability of penetrating dust, clouds, and fog; and possibility of having large instantaneous bandwidth for high-speed communication channels. Until very recently, space-based instruments for astrophysics, planetary science, and Earth science missions have been the primary motivator for the development of terahertz sensors, sources, and systems. However, in recent years the emerging areas such as imaging from space platforms, surveillance of person-borne hidden weapons or contraband from a safe stand-off distance and reconnaissance, medical imaging and DNA sequencing, and in the world high speed communications have been the driving force for this area of research.
Good and bad boundaries in ultrasound compounding: preserving anatomic boundaries while suppressing artifacts

Ultrasound compounding is to combine sonographic information captured from different angles and produce a single image. It is important for multi-view reconstruction, but as of yet there is no consensus on best practices for compounding. Current popular methods inevitably suppress or altogether leave out bright or dark regions that are useful and potentially introduce new artifacts. In this work, we establish a new algorithm to compound the overlapping pixels from different viewpoints in ultrasound. Inspired by image fusion algorithms and ultrasound confidence, we uniquely leverage Laplacian and Gaussian pyramids to preserve the maximum boundary contrast without overemphasizing noise, speckles, and other artifacts in the compounded image, while taking the direction of the ultrasound probe into account. Besides, we designed an algorithm that detects the useful boundaries in ultrasound images to further improve the boundary contrast. We evaluate our algorithm by comparing it with previous algorithms both qualitatively and quantitatively, and we show that our approach not only preserves both light and dark details, but also somewhat suppresses noise and artifacts, rather than amplifying them. We also show that our algorithm can improve the performance of downstream tasks like segmentation. Our proposed method that is based on confidence, contrast, and both Gaussian and Laplacian pyramids appears to be better at preserving contrast at anatomic boundaries while suppressing artifacts than any of the other approaches we tested. This algorithm may have future utility with downstream tasks such as 3D ultrasound volume reconstruction and segmentation.
Resolution-based distillation for efficient histology image classification.

Abstract Developing deep learning models to analyze histology images has been computationally challenging, as the massive size of the images causes excessive strain on all parts of the computing pipeline. This paper proposes a novel deep learning-based methodology for improving the computational efficiency of histology image classification. The proposed approach is robust when used with images that have reduced input resolution, and it can be trained effectively with limited labeled data. Moreover, our approach operates at either the tissue- or slide-level, removing the need for laborious patch-level labeling. Our method uses knowledge distillation to transfer knowledge from a teacher model pre-trained at high resolution to a student model trained on the same images at a considerably lower resolution. Also, to address the lack of large-scale labeled histology image datasets, we perform the knowledge distillation in a self-supervised fashion. We evaluate our approach on three distinct histology image datasets associated with celiac disease, lung adenocarcinoma, and renal cell carcinoma. Our results on these datasets demonstrate that a combination of knowledge distillation and self-supervision allows the student model to approach and, in some cases, surpass the teacher model's classification accuracy while being much more computationally efficient. Additionally, we observe an increase in student classification performance as the size of the unlabeled dataset increases, indicating that there is potential for this method to scale further with additional unlabeled data. Our model outperforms the high-resolution teacher model for celiac disease in accuracy, F1-score, precision, and recall while requiring 4 times fewer computations. For lung adenocarcinoma, our results at 1.25× magnification are within 1.5% of the results for the teacher model at 10× magnification, with a reduction in computational cost by a factor of 64. Our model on renal cell carcinoma at 1.25× magnification performs within 1% of the teacher model at 5× magnification while requiring 16 times fewer computations. Furthermore, our celiac disease outcomes benefit from additional performance scaling with the use of more unlabeled data. In the case of 0.625× magnification, using unlabeled data improves accuracy by 4% over the tissue-level baseline. Therefore, our approach can improve the feasibility of deep learning solutions for digital pathology on standard computational hardware and infrastructures.
The structural dynamics of macropinosome formation and PI3-kinase-mediated sealing revealed by lattice light sheet microscopy.

Macropinosomes are formed by shaping actin-rich plasma membrane ruffles into large intracellular organelles in a phosphatidylinositol 3-kinase (PI3K)-coordinated manner. Here, we utilize lattice lightsheet microscopy and image visualization methods to map the three-dimensional structure and dynamics of macropinosome formation relative to PI3K activity. We show that multiple ruffling morphologies produce macropinosomes and that the majority form through collisions of adjacent PI3K-rich ruffles. By combining multiple volumetric representations of the plasma membrane structure and PI3K products, we show that PI3K activity begins early throughout the entire ruffle volume and continues to increase until peak activity concentrates at the base of the ruffle after the macropinosome closes. Additionally, areas of the plasma membrane rich in ruffling had increased PI3K activity and produced many macropinosomes of various sizes. Pharmacologic inhibition of PI3K activity had little effect on the rate and morphology of membrane ruffling, demonstrating that early production of 3′-phosphoinositides within ruffles plays a minor role in regulating their morphology. However, 3′-phosphoinositides are critical for the fusogenic activity that seals ruffles into macropinosomes. Taken together, these data indicate that local PI3K activity is amplified in ruffles and serves as a priming mechanism for closure and sealing of ruffles into macropinosomes. Macropinocytosis is a cellular process for the uptake of extracellular fluid. Here, the authors use lattice light sheet microscopy to examine the spatial dynamics of the plasma membrane, PI3K activity, and structural differences of various macrophage cell types during macropinocytosis.
Natural variation in the consequences of gene overexpression and its implications for evolutionary trajectories

Copy number variation through gene or chromosome amplification provides a route for rapid phenotypic variation and supports the long-term evolution of gene functions. Although the evolutionary importance of copy-number variation is known, little is understood about how genetic background influences its tolerance. Here, we measured fitness costs of over 4000 overexpressed genes in 15 Saccharomyces cerevisiae strains representing different lineages, to explore natural variation in tolerating gene overexpression (OE). Strain-specific effects dominated the fitness costs of gene OE. We report global differences in the consequences of gene OE, independent of the amplified gene, as well as gene-specific effects that were dependent on the genetic background. Natural variation in the response to gene OE could be explained by several models, including strain-specific physiological differences, resource limitations, and regulatory sensitivities. This work provides new insight on how genetic background influences tolerance to gene amplification and the evolutionary trajectories accessible to different backgrounds.
Local-to-global Urysohn width estimates

The notion of the Urysohn $d$-width measures to what extent a metric space can be approximated by a $d$-dimensional simplicial complex. We investigate how local Urysohn width bounds on a riemannian manifold affect its global width. We bound the $1$-width of a Riemannian manifold in terms of its first homology and the supremal width of its unit balls. Answering a question of Larry Guth, we give examples of $n$-manifolds of considerable $(n-1)$-width in which all unit balls have arbitrarily small $1$-width. We also give examples of topologically simple manifolds that are locally nearly low-dimensional.