cssi/SciSciGPT-SciSciNet huggingface dataset:
Selection on VPS13A linked to migration in a songbird

Animal migration demands an interconnected suite of adaptations for individuals to navigate over long distances. This trait complex is crucial for small birds whose migratory behaviors-such as directionality-are more likely innate, rather than being learned as in many longer-lived birds. Identifying causal genes has been a central goal of migration ecology, and this endeavor has been furthered by genome-scale comparisons. However, even the most successful studies of migration genetics have achieved low-resolution associations, identifying large chromosomal regions that encompass hundreds of genes, one or more of which might be causal. Here we leverage the genomic similarity among golden-winged (Vermivora chrysoptera) and blue-winged (V. cyanoptera) warblers to identify a single gene-vacuolar protein sorting 13A (VPS13A)-that is associated with distinct differences in migration to Central American (CA) or South American (SA) wintering areas. We find reduced sequence variation in this gene region for SA wintering birds, and show this is the likely result of natural selection on this locus. In humans, variants of VPS13A are linked to the neurodegenerative disorder chorea-acanthocytosis. This association provides one of the strongest gene-level associations with avian migration differences.
Optimal Best Markovian Arm Identification with Fixed Confidence

We give a complete characterization of the sampling complexity of best Markovian arm identification in one-parameter Markovian bandit models. We derive instance specific nonasymptotic and asymptotic lower bounds which generalize those of the IID setting. We analyze the Track-and-Stop strategy, initially proposed for the IID setting, and we prove that asymptotically it is at most a factor of four apart from the lower bound. Our one-parameter Markovian bandit model is based on the notion of an exponential family of stochastic matrices for which we establish many useful properties. For the analysis of the Track-and-Stop strategy we derive a novel and optimal concentration inequality for Markov chains that may be of interest in its own right.
The Secretomes of Painful Versus Nonpainful Human Schwannomatosis Tumor Cells Differentially Influence Sensory Neuron Gene Expression and Sensitivity

Schwannomatosis is a multiple tumor syndrome in which patients develop benign tumors along peripheral nerves throughout the body. The first symptom with which schwannomatosis patients often present, prior to discovery of tumors, is pain. This pain can be debilitating and is often inadequately alleviated by pharmacological approaches. Schwannomatosis-associated pain can be localized to the area of a tumor, or widespread. Moreover, not all tumors are painful, and the occurrence of pain is often unrelated to tumor size or location. We speculate that some individual tumors, but not others, secrete factors that act on nearby nerves to augment nociception by producing neuronal sensitization or spontaneous neuronal firing. We created cell lines from human SWN tumors with varying degrees of pain. We have found that conditioned medium (CM) collected from painful SWN tumors, but not that from nonpainful SWN tumors, sensitized DRG neurons, causing increased sensitivity to depolarization by KCl, increased response to noxious TRPV1 and TRPA1 agonists and also upregulated the expression of pain-associated genes in DRG cultures. Multiple cytokines were also detected at higher levels in CM from painful tumors. Taken together our data demonstrate a differential ability of painful versus non-painful human schwannomatosis tumor cells to secrete factors that augment sensory neuron responsiveness, and thus identify a potential determinant of pain heterogeneity in schwannomatosis.
An effective Chebotarev density theorem for families of number fields, with an application to $\ell$-torsion in class groups

We prove a new effective Chebotarev density theorem for Galois extensions $$L/\mathbb {Q}$$ that allows one to count small primes (even as small as an arbitrarily small power of the discriminant of L); this theorem holds for the Galois closures of “almost all” number fields that lie in an appropriate family of field extensions. Previously, applying Chebotarev in such small ranges required assuming the Generalized Riemann Hypothesis. The error term in this new Chebotarev density theorem also avoids the effect of an exceptional zero of the Dedekind zeta function of L, without assuming GRH. We give many different “appropriate families,” including families of arbitrarily large degree. To do this, we first prove a new effective Chebotarev density theorem that requires a zero-free region of the Dedekind zeta function. Then we prove that almost all number fields in our families yield such a zero-free region. The innovation that allows us to achieve this is a delicate new method for controlling zeroes of certain families of non-cuspidalL-functions. This builds on, and greatly generalizes the applicability of, work of Kowalski and Michel on the average density of zeroes of a family of cuspidalL-functions. A surprising feature of this new method, which we expect will have independent interest, is that we control the number of zeroes in the family of L-functions by bounding the number of certain associated fields with fixed discriminant. As an application of the new Chebotarev density theorem, we prove the first nontrivial upper bounds for $$\ell $$-torsion in class groups, for all integers $$\ell \ge 1$$, applicable to infinite families of fields of arbitrarily large degree.
Geometric estimates for complex Monge–Ampère equations

We prove uniform gradient and diameter estimates for a family of geometric complex Monge-Ampere equations. Such estimates can be applied to study geometric regularity of singular solutions of complex Monge-Ampere equations. We also prove a uniform diameter estimate for collapsing families of twisted Kahler-Einstein metrics on Kahler manifolds of nonnegative Kodaira dimensions.
Investigation of Supported Metal Oxide Species with Shell-Isolated Nanoparticle-Enhanced Raman Spectroscopy

Raman spectroscopy is a widely employed tool for characterization of heterogeneous catalysts, although it suffers somewhat from poor sensitivity at low concentrations or under in situ conditions. I...
Twists of hyperelliptic curves by integers in progressions modulo $p$

Let $f(x)$ be a nonconstant polynomial with integer coefficients and nonzero discriminant. We study the distribution modulo primes of the set of squarefree integers $d$ such that the curve $dy^2=f(x)$ has a nontrivial rational or integral point.
IL-10-producing B cells are enriched in murine pericardial adipose tissues and ameliorate the outcome of acute myocardial infarction.

Acute myocardial infarction (MI) provokes an inflammatory response in the heart that removes damaged tissues to facilitate tissue repair/regeneration. However, overactive and prolonged inflammation compromises healing, which may be counteracted by antiinflammatory mechanisms. A key regulatory factor in an inflammatory response is the antiinflammatory cytokine IL-10, which can be produced by a number of immune cells, including subsets of B lymphocytes. Here, we investigated IL-10–producing B cells in pericardial adipose tissues (PATs) and their role in the healing process following acute MI in mice. We found that IL-10–producing B cells were enriched in PATs compared to other adipose depots throughout the body, with the majority of them bearing a surface phenotype consistent with CD5+ B-1a cells (CD5+ B cells). These cells were detected early in life, maintained a steady presence during adulthood, and resided in fat-associated lymphoid clusters. The cytokine IL-33 and the chemokine CXCL13 were preferentially expressed in PATs and contributed to the enrichment of IL-10–producing CD5+ B cells. Following acute MI, the pool of CD5+ B cells was expanded in PATs. These cells accumulated in the infarcted heart during the resolution of MI-induced inflammation. B cell-specific deletion of IL-10 worsened cardiac function, exacerbated myocardial injury, and delayed resolution of inflammation following acute MI. These results revealed enrichment of IL-10–producing B cells in PATs and a significant contribution of these cells to the antiinflammatory processes that terminate MI-induced inflammation. Together, these findings have identified IL-10–producing B cells as therapeutic targets to improve the outcome of MI.
Chiral piperidines from acyclic amines via enantioselective, radical-mediated δ C-H cyanation.

Piperidines are the most prevalent heterocycle found in medicines. Yet, while they are often chiral, there remain no robust methods for their asymmetric syntheses. To solve this challenge, we have interrupted the century-old Hofmann-Loffler-Freytag (HLF) reaction to afford this privileged heterocycle. The catalytic, regio- and enantio- selective δ C-H cyanation of acyclic amines described herein, incorporates a carbonyl equivalent selectively at the δ position. This δ C-H cyanation is enabled by a chiral Cu catalyst, which both initiates and terminates intramolecular hydrogen atom transfer (HAT) by an N-centered radical relay mechanism. The broad scope and utility of this highly enantioselective method for δ C-C formation is presented, as well as conversion of the resulting enantioenriched δ amino nitriles to a family of chiral piperidines. Experiments probing the chemo-, regio-, and enantio- selectivity of this HAT mechanism are also included to enable extension to other stereoselective δ C-H functionalizations.
Topological generation results for free unitary and orthogonal groups

We show that for every N ≥ 3 the free unitary group UN+ is topologically generated by its classical counterpart UN and the lower-rank UN−1+. This allows for a uniform inductive proof that a number ...
Changing the speed of optical coherence in free space.

It is typically assumed that the fluctuations associated with a stationary broadband incoherent field propagate in free space at the speed of light in vacuum c. Here we introduce the concept of “coherence group velocity” which, in analogy to the group velocity of coherent pulses, is the speed of the peak of the coherence function. We confirm experimentally that incorporating a judicious spatio-temporal spectral structure into a field allows tuning its coherence group velocity in free space. Utilizing light from a superluminescent diode, we interferometrically measure the group delay encountered by the cross-correlation of a structured field synthesized from this source with the unstructured diode field. By tracking the propagation of this cross-correlation function, we measure coherence group velocities in the range of 12c to −6c.
How kinesin waits for ATP affects the nucleotide and load dependence of the stepping kinetics

Conventional kinesin, responsible for directional transport of cellular vesicles, takes multiple nearly uniform 8.2-nm steps by consuming one ATP molecule per step as it walks toward the plus end of the microtubule (MT). Despite decades of intensive experimental and theoretical studies, there are gaps in the elucidation of key steps in the catalytic cycle of kinesin. How the motor waits for ATP to bind to the leading head is controversial. Two experiments using a similar protocol have arrived at different conclusions. One asserts that kinesin waits for ATP in a state with both the heads bound to the MT, whereas the other shows that ATP binds to the leading head after the trailing head detaches. To discriminate between the 2 scenarios, we developed a minimal model, which analytically predicts the outcomes of a number of experimental observable quantities (the distribution of run length, the distribution of velocity [[Formula: see text]], and the randomness parameter) as a function of an external resistive force (F) and ATP concentration ([T]). The differences in the predicted bimodality in [Formula: see text] as a function of F between the 2 models may be amenable to experimental testing. Most importantly, we predict that the F and [T] dependence of the randomness parameters differ qualitatively depending on the waiting states. The randomness parameters as a function of F and [T] can be quantitatively measured from stepping trajectories with very little prejudice in data analysis. Therefore, an accurate measurement of the randomness parameter and the velocity distribution as a function of load and nucleotide concentration could resolve the apparent controversy.
Who’s prone to drone? A global time-series analysis of armed uninhabited aerial vehicle proliferation:

What determines whether countries pursue and obtain armed drones? Using an original time-series dataset, we conduct the first comprehensive analysis of armed drone proliferation from 1994 to 2019. ...
Morphology and development of the Portuguese man of war, Physalia physalis

The Portuguese man of war, Physalia physalis, is one of the most conspicuous, but poorly understood members of the pleuston, a community of organisms that occupy a habitat at the sea-air interface. Physalia physalis is a siphonophore that uses a gas-filled float as a sail to catch the wind. The development, morphology, and colony organization of P. physalis is very different from all other siphonophores. Here, we look at live and fixed larval and juvenile specimens, and use optical projection tomography to build on existing knowledge about the morphology and development of this species. We also propose a framework for homologizing the axes with other siphonophores, and also suggest that the tentacle bearing zooids should be called tentacular palpons. Previous descriptions of P. physalis larvae, especially descriptions of budding order, were often framed with the mature colony in mind. However, we use the simpler organization of larvae and the juvenile specimens to inform our understanding of the morphology, budding order, and colony organization in the mature specimen. Finally, we review what is known about the ecology and lifecycle of P. physalis.
Bilateral Wilms’ Tumor

6 Recent studies indicate a favorable outcome with bilateral Wilms’ tumor. From 1971 to 1985, ten children between the ages of 8 months and 5 years were treated for this disease. During the early part of the series, five patients had nephrectomy on one side and partial nephrectomy on the other side, and one patient had bilateral partial nephrectomies at the same operation. More recently four patients initially had a biopsy of both tumors and lymph node sampling followed by chemotherapy. At the secondlook procedure two patients had multiple biopsies because there was no obvious tumor. Histologically there was no tumor on the third-look procedure in these two patients. Nine patients had a favorable histology of the Wilms’ tumor. One patient had a favorable histology on one side and an unfavorable type of histology on the other side. Eight patients are surviving between 8 months and 13 years. Two died of extensive diesase within 16 months of diagnosis. One patient had an unfavorable histology. The good results following partial nephrectomies have led us to attempt to conserve additional tissue, as has been done in the last four patients. Our early results suggest biopsy of the tumor followed by chemotherapy, then a second look, and if necessary, third-look procedures may result in preservation of functioning renal tissue. o 1988 by Grune & Stratton, inc.
An optically isolated hybrid two-stage current transformer for measurements at high voltage

The development of a high-voltage (HV) current measurement system for on-site/in-situ calibrations in power systems is described in the paper. The system is based on an optically isolated hybrid two-stage current transformer with electronic circuitry that performs A/D conversion. It uses a fiber optic link for data transmission to a ground station and a laser-driven fiber optic link for supplying power to the electronic circuitry of the remote module at HV. The uncertainty of the current-ratio measurement is estimated to be less than 100middot10-6 both in-phase and quadrature
Paradox Elimination in Dempster-Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion.

Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster–Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature.
Theoretical nous in the posterior analytics

Abstract According to Aristotle's definition of episteme (understanding) in the Posterior Analytics, you have episteme of the proposition that P when you know why P, and you know that it is necessary that P. Episteme is therefore only available for propositions which have an explanation, i.e. the theorems of the science. It is a demanding cognitive state, since knowing the explanation of a proposition in a science requires being able to demonstrate or prove it. Aristotle occasionally refers to the counterpart notion to episteme which applies to propositions which lack an explanation, i.e. the first principles of a science. This counterpart notion is nous, or non-demonstrative understanding. Aristotle never defines it, but it should turn out to be an equally demanding cognitive state to achieve. This paper proposes that you have nous of the proposition that P when you know that nothing explains why P, you know the various ways in which the proposition that P features in explanations in the science, and you know that it is necessary that P.
Resilient living materials built by printing bacterial spores

Materials can be made multifunctional by embedding them with living cells that perform sensing, synthesis, energy production, and physical movement. A challenge is that the conditions needed for living cells are not conducive to materials processing and require continuous water and nutrients. Here, we present a three dimensional (3D) printer that can mix material and cell streams to build 3D objects. Bacillus subtilis spores were printed within the material and germinated on its exterior surface, including spontaneously in new cracks. The material was resilient to extreme stresses, including desiccation, solvents, osmolarity, pH, ultraviolet light, and γ-radiation. Genetic engineering enabled the bacteria to respond to stimuli or produce chemicals on demand. As a demonstration, we printed custom-shaped hydrogels containing bacteria that can sense or kill Staphylococcus aureus, a causative agent of infections. This work demonstrates materials endued with living functions that can be used in applications that require storage or exposure to environmental stresses.
On finite volume discretization of infiltration dynamics in tumor growth models

We address numerical challenges in solving hyperbolic free boundary problems described by spherically symmetric conservation laws that arise in the modeling of tumor growth due to immune cell infiltrations. In this work, we normalize the radial coordinate to transform the free boundary problem to a fixed boundary one, and utilize finite volume methods to discretize the resulting equations. We show that the conventional finite volume methods fail to preserve constant solutions and the incompressibility condition, and they typically lead to inaccurate, if not wrong, solutions even for very simple tests. These issues are addressed in a new finite volume framework with segregated flux computations that satisfy sufficient conditions for ensuring the so-called totality conservation law and the geometric conservation law. We focus on describing the new framework by enhancing the conventional first-order upwind scheme, and the methodology extends to second-order ones. The numerical performance is assessed by various benchmark tests to show that the enhanced method is able to preserve the incompressibility constraint and produce much more accurate solutions than the conventional one.
Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations

Assemblies of modular subsystems are being pressed into service to perform sensing, reasoning, and decision making in high-stakes, time-critical tasks in areas such as transportation, healthcare, and industrial automation. We address the opportunity to maximize the utility of an overall computing system by employing reinforcement learning to guide the configuration of the set of interacting modules that comprise the system. The challenge of doing system-wide optimization is a combinatorial problem. Local attempts to boost the performance of a specific module by modifying its configuration often leads to losses in overall utility of the system's performance as the distribution of inputs to downstream modules changes drastically. We present metareasoning techniques which consider a rich representation of the input, monitor the state of the entire pipeline, and adjust the configuration of modules on-the-fly so as to maximize the utility of a system's operation. We show significant improvement in both real-world and synthetic pipelines across a variety of reinforcement learning techniques.
An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer

Combining anti-cancer therapies with orthogonal modes of action, such as direct cytotoxicity and immunostimulatory, hold promise for expanding clinical benefit to patients with metastatic disease. For instance, a chemotherapy agent Oxaliplatin (OXP) in combination with Interleukin-12 (IL-12) can eliminate pre-existing liver metastatic colorectal cancer and protect from relapse in a murine model. However, the underlying dynamics associated with the targeted biology and the combinatorial space consisting of possible dosage and timing of each therapy present challenges for optimizing treatment regimens. To address some of these challenges, we developed a predictive simulation platform for optimizing dose and timing of the combination therapy involving Mifepristone-induced IL-12 and chemotherapy agent OXP. A multi-scale mathematical model comprised of impulsive ordinary differential equations was developed to describe the interaction between the immune system and tumor cells in response to the combined IL-12 and OXP therapy. An ensemble of model parameters were calibrated to published experimental data using a genetic algorithm and used to represent three different phenotypes: responders, partial-responders, and non-responders. The multi-scale model captures tumor growth patterns of the three phenotypic responses observed in mice in response to the combination therapy against a tumor re-challenge and was used to explore the impacts of changing the dose and timing of the mixed immune-chemotherapy on tumor growth subjected to a tumor re-challenge in mice. An increased ratio of CD8 + T effectors to regulatory T cells during and after treatment was key to improve tumor control in the responder cohort. Sensitivity analysis indicates that combined OXP and IL-12 therapy worked more efficiently in responders by increased priming of T cells, enhanced CD8 + T cell-mediated killing, and functional inhibition of regulatory T cells. In a virtual cohort that mimics non-responders and partial-responders, simulations show that an increased dose of OXP alone would improve the response. In addition, enhanced IL-12 expression alone or an increased number of treatment cycles of the mixed immune-chemotherapy can barely improve tumor control for non-responders and partial responders. Overall, this study illustrates how mechanistic models can be used for in silico screening of the optimal therapeutic dose and timing in combined cancer treatment strategies.
Zero-sum analogues of van der Waerden’s theorem on arithmetic progressions

Let $r$ and $k$ be positive integers with $r \mid k$. Denote by $w_{\mathrm{\mathfrak{z}}}(k;r)$ the minimum integer such that every coloring $\chi:[1,w_{\mathrm{\mathfrak{z}}}(k;r)] \rightarrow \{0,1,\dots,r-1\}$ admits a $k$-term arithmetic progression $a,a+d,\dots,a+(k-1)d$ with $\sum_{j=0}^{k-1} \chi(a+jd) \equiv 0 \,(\mathrm{mod }\,r)$. We investigate these numbers as well as a "mixed" monochromatic/zero-sum analogue. We also present an interesting reciprocity between the van der Waerden numbers and $w_{\mathrm{\mathfrak{z}}}(k;r)$.
A Humanistic Narrative for Responsible Management Learning: An Ontological Perspective

Why has responsible management been so difficult and why is the chorus of stakeholders demanding such responsibility getting louder? We argue that management learning has been framed within the narrative of economism. As such, we argue that managers need to be aware of the paradigmatic frame of the dominant economistic narrative and learn to transcend it. We also argue that for true managerial responsibility, an alternative humanistic narrative is more fit for purpose. This humanistic narrative is based on epistemological metaphors and ontological insights that integrate the latest insights from evolutionists suggesting that humans only survived by being responsible. This understanding has consequences for responsible management learning in that it focuses on dignity literacy, balance orientation, as well as creativity and innovation for the common good. We argue that managerial learning within a humanistic paradigm is more likely to lead to ethical and sustainable business conduct.
X-Ramanujan graphs

Let X be an infinite graph of bounded degree; e.g., the Cayley graph of a free product of finite groups. If G is a finite graph covered by X, it is said to be X-Ramanujan if its second-largest eigenvalue λ2(G) is at most the spectral radius ρ(X) of X, and more generally k-quasi-X-Ramanujan if λk(G) is at most ρ(X). In case X is the infinite Δ-regular tree, this reduces to the well known notion of a finite Δ-regular graph being Ramanujan. Inspired by the Interlacing Polynomials method of Marcus, Spielman, and Srivastava, we show the existence of infinitely many k-quasi-X-Ramanujan graphs for a variety of infinite X. In particular, X need not be a tree; our analysis is applicable whenever X is what we call an additive product graph. This additive product is a new construction of an infinite graph A1 ⌖ ··· ⌖ Ac from finite "atom" graphs A1,...,Ac over a common vertex set. It generalizes the notion of the free product graph A1 * ··· * Ac when the atoms Aj are vertex-transitive, and it generalizes the notion of the universal covering tree when the atoms Aj are single-edge graphs. Key to our analysis is a new graph polynomial α(A1,...,Ac; x) that we call the additive characteristic polynomial. It generalizes the well known matching polynomial μ(G; x) in case the atoms Aj are the single edges of G, and it generalizes the r-characteristic polynomial introduced in [Rav16, LR18]. We show that α(A1,..., Ac; x) is real-rooted, and all of its roots have magnitude at most ρ(A1 ⌖ ··· ⌖ Ac). This last fact is proven by generalizing Godsil's notion of treelike walks on a graph G to a notion of freelike walks on a collection of atoms A1,..., Ac.
Comparison of Eulerian Bin and Lagrangian Particle-Based Schemes in Simulations of Pi Chamber Dynamics and Microphysics

AbstractThis paper presents a comparison of simulations applying either a traditional Eulerian bin microphysics or a novel particle-based Lagrangian approach to represent CCN activation and cloud d...
Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

PURPOSEMany antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the m...
Microresonator-Enhanced, Waveguide-Coupled Emission from Silicon Defect Centers for Superconducting Optoelectronic Networks

Superconducting optoelectronic networks could achieve scales unmatched in hardware-based neuromorphic computing. After summarizing recent progress in this area, we report new results in cryogenic silicon photonic light sources, components central to these architectures.
Aluminum nitride two-dimensional-resonant-rods

In the last few decades, bulk-acoustic-wave filters have been essential components of 3G-to-4G radios. These devices rely on the high electromechanical coupling coefficient (kt2 ∼ 7%), attained by aluminum nitride (AlN) film-bulk-acoustic-resonators (FBARs), to achieve a wideband and low-loss frequency response. As the resonance frequency of FBARs is set by their thickness, the integration of multiple FBARs, to form filters, can only be attained through the adoption of frequency tuning fabrication steps, such as mass loading or trimming. However, as the ability to reliably control these steps significantly decays for thinner (or higher frequency) FBARs, manufacturing FBAR-based filters, addressing the needs of emerging IoT and 5G applications, is becoming more and more challenging. Consequently, there is a quest for new acoustic resonant components, simultaneously exhibiting high-kt2 and a lithographic frequency tunability. In this work, a novel class of AlN resonators is presented. These radio frequency devices, labeled as two-dimensional-resonant-rods (2DRRs), exploit, for the first time, the unconventional acoustic behavior exhibited by a forest of locally resonant rods, built in the body of a profiled AlN layer that is sandwiched between a bottom un-patterned metal plate and a top metallic grating. 2DRRs exhibit unexplored modal features that make them able to achieve high-kt2, a significant lithographic frequency tunability, and a relaxed lithographic resolution, while relying on an optimal AlN crystalline orientation. The operation of 2DRRs is discussed, in this work, by means of analytical and finite-element-methods. The measured performance of the first fabricated 2DRR, operating around 2.4 GHz and showing a kt2 in excess of 7.4%, is also reported.
ICLab: A Global, Longitudinal Internet Censorship Measurement Platform

Researchers have studied Internet censorship for nearly as long as attempts to censor contents have taken place. Most studies have however been limited to a short period of time and / or a few countries; the few exceptions have traded off detail for breadth of coverage. Collecting enough data for a comprehensive, global, longitudinal perspective remains challenging.In this work, we present ICLab, an Internet measurement platform specialized for censorship research. It achieves a new balance between breadth of coverage and detail of measurements, by using commercial VPNs as vantage points distributed around the world. ICLab has been operated continuously since late 2016. It can currently detect DNS manipulation and TCP packet injection, and overt "block pages" however they are delivered. ICLab records and archives raw observations in detail, making retrospective analysis with new techniques possible. At every stage of processing, ICLab seeks to minimize false positives and manual validation.Within 53,906,532 measurements of individual web pages, collected by ICLab in 2017 and 2018, we observe blocking of 3,602 unique URLs in 60 countries. Using this data, we compare how different blocking techniques are deployed in different regions and/or against different types of content. Our longitudinal monitoring pinpoints changes in censorship in India and Turkey concurrent with political shifts, and our clustering techniques discover 48 previously unknown block pages. ICLab’s broad and detailed measurements also expose other forms of network interference, such as surveillance and malware injection.
Supervised Adversarial Alignment of Single-Cell RNA-seq Data

Dimensionality reduction is an important first step in the analysis of single cell RNA-seq (scRNA-seq) data. In addition to enabling the visualization of the profiled cells, such representations are used by many downstream analyses methods ranging from pseudo-time reconstruction to clustering to alignment of scRNA-seq data from different experiments, platforms, and labs. Both supervised and unsupervised methods have been proposed to reduce the dimension of scRNA-seq. However, all methods to date are sensitive to batch effects. When batches correlate with cell types, as is often the case, their impact can lead to representations that are batch rather than cell type specific. To overcome this we developed a domain adversarial neural network model for learning a reduced dimension representation of scRNA-seq data. The adversarial model tries to simultaneously optimize two objectives. The first is the accuracy of cell type assignment and the second is the inability to distinguish the batch (domain). We tested the method by using the resulting representation to align several different datasets. As we show, by overcoming batch effects our method was able to correctly separate cell types, improving on several prior methods suggested for this task. Analysis of the top features used by the network indicates that by taking the batch impact into account, the reduced representation is much better able to focus on key genes for each cell type.
Games for Fairness and Interpretability

As Machine Learning (ML) systems becomes more ubiquitous, ensuring the fair and equitable application of their underlying algorithms is of paramount importance. We argue that one way to achieve this is to proactively cultivate public pressure for ML developers to design and develop fairer algorithms — and that one way to cultivate public pressure while simultaneously serving the interests and objectives of algorithm developers is through gameplay. We propose a new class of games — “games for fairness and interpretability” — as one example of an incentive-aligned approach for producing fairer and more equitable algorithms. Games for fairness and interpretability are carefully-designed games with mass appeal. They are inherently engaging, provide insights into how machine learning models work, and ultimately produce data that helps researchers and developers improve their algorithms. We highlight several possible examples of games, their implications for fairness and interpretability, how their proliferation could creative positive public pressure by narrowing the gap between algorithm developers and the general public, and why the machine learning community could benefit from them.
The spillover effects of state spending

Abstract This paper estimates the extent to which state spending is influenced by the spending of neighboring states, examining several different metrics of neighborliness in order to better identify the channels for interstate spillovers. It improves on previous studies by taking advantage of exogenous shocks to state medical spending to abstract from correlated local conditions and by allowing for spatial-autocorrelation. Focusing on mandated increases in medical spending, the analysis shows that each dollar of state spending causes spending in neighboring states to increase by almost 90 cents. The most predictive measure of neighborliness is the degree of population mobility between states, suggesting that rational concerns about adverse migration may drive the interdependence of state spending policy.
Naturally Rehearsing Passwords.

We introduce quantitative usability and security models to guide the design of password management schemes — systematic strategies to help users create and remember multiple passwords. In the same way that security proofs in cryptography are based on complexity-theoretic assumptions (e.g., hardness of factoring and discrete logarithm), we quantify usability by introducing usability assumptions. In particular, password management relies on assumptions about human memory, e.g., that a user who follows a particular rehearsal schedule will successfully maintain the corresponding memory. These assumptions are informed by research in cognitive science and can be tested empirically. Given rehearsal requirements and a user’s visitation schedule for each account, we use the total number of extra rehearsals that the user would have to do to remember all of his passwords as a measure of the usability of the password scheme. Our usability model leads us to a key observation: password reuse benefits users not only by reducing the number of passwords that the user has to memorize, but more importantly by increasing the natural rehearsal rate for each password. We also present a security model which accounts for the complexity of password management with multiple accounts and associated threats, including online, offline, and plaintext password leak attacks. Observing that current password management schemes are either insecure or unusable, we present Shared Cues — a new scheme in which the underlying secret is strategically shared across accounts to ensure that most rehearsal requirements are satisfied naturally while simultaneously providing strong security. The construction uses the Chinese Remainder Theorem to achieve these competing goals.
Measuring Forecasting Skill from Text

People vary in their ability to make accurate predictions about the future. Prior studies have shown that some individuals can predict the outcome of future events with consistently better accuracy. This leads to a natural question: what makes some forecasters better than others? In this paper we explore connections between the language people use to describe their predictions and their forecasting skill. Datasets from two different forecasting domains are explored: (1) geopolitical forecasts from Good Judgment Open, an online prediction forum and (2) a corpus of company earnings forecasts made by financial analysts. We present a number of linguistic metrics which are computed over text associated with people’s predictions about the future including: uncertainty, readability, and emotion. By studying linguistic factors associated with predictions, we are able to shed some light on the approach taken by skilled forecasters. Furthermore, we demonstrate that it is possible to accurately predict forecasting skill using a model that is based solely on language. This could potentially be useful for identifying accurate predictions or potentially skilled forecasters earlier.
Incorporating External Knowledge through Pre-training for Natural Language to Code Generation

Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents. Motivated by the intuition that developers usually retrieve resources on the web when writing code, we explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation. Our evaluations show that combining the two sources with data augmentation and retrieval-based data re-sampling improves the current state-of-the-art by up to 2.2% absolute BLEU score on the code generation testbed CoNaLa. The code and resources are available at https://github.com/neulab/external-knowledge-codegen.
Bandits for BMO Functions

We study the bandit problem where the underlying expected reward is a Bounded Mean Oscillation (BMO) function. BMO functions are allowed to be discontinuous and unbounded, and are useful in modeling signals with infinities in the do-main. We develop a toolset for BMO bandits, and provide an algorithm that can achieve poly-log $\delta$-regret -- a regret measured against an arm that is optimal after removing a $\delta$-sized portion of the arm space.
Automatic Shortcut Removal for Self-Supervised Representation Learning

In self-supervised visual representation learning, a feature extractor is trained on a "pretext task" for which labels can be generated cheaply, without human annotation. A central challenge in this approach is that the feature extractor quickly learns to exploit low-level visual features such as color aberrations or watermarks and then fails to learn useful semantic representations. Much work has gone into identifying such "shortcut" features and hand-designing schemes to reduce their effect. Here, we propose a general framework for mitigating the effect shortcut features. Our key assumption is that those features which are the first to be exploited for solving the pretext task may also be the most vulnerable to an adversary trained to make the task harder. We show that this assumption holds across common pretext tasks and datasets by training a "lens" network to make small image changes that maximally reduce performance in the pretext task. Representations learned with the modified images outperform those learned without in all tested cases. Additionally, the modifications made by the lens reveal how the choice of pretext task and dataset affects the features learned by self-supervision.
Blow-Up Solutions to 3D Euler are Hydrodynamically Unstable

We study the interaction between the stability, and the propagation of regularity, for solutions to the incompressible 3D Euler equation. It is still unknown whether a solution with smooth initial data can develop a singularity in finite time. This article explains why the prediction of such a blow-up, via direct numerical experiments, is so difficult. It is described how, in such a scenario, the solution becomes unstable as time approaches the blow-up time.
Competing Bandits in Matching Markets.

Stable matching, a classical model for two-sided markets, has long been studied with little consideration for how each side's preferences are learned. With the advent of massive online markets powered by data-driven matching platforms, it has become necessary to better understand the interplay between learning and market objectives. We propose a statistical learning model in which one side of the market does not have a priori knowledge about its preferences for the other side and is required to learn these from stochastic rewards. Our model extends the standard multi-armed bandits framework to multiple players, with the added feature that arms have preferences over players. We study both centralized and decentralized approaches to this problem and show surprising exploration-exploitation trade-offs compared to the single player multi-armed bandits setting.
A comparative analysis of host–parasitoid models with density dependence preceding parasitism

We present a systematic comparison and analysis of four discrete-time, host–parasitoid models. For each model, we specify that density-dependent effects occur prior to parasitism in the life cycle ...
A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales

Many studies have identified the role of localized and distributed cognitive functionality by mapping either local task-related activity or distributed functional connectivity (FC). However, few studies have directly explored the relationship between a brain region's localized task activity and its distributed task FC. Here we systematically evaluated the differential contributions of task-related activity and FC changes to identify a relationship between localized and distributed processes across the cortical hierarchy. We found that across multiple tasks, the magnitude of regional task-evoked activity was high in unimodal areas, but low in transmodal areas. In contrast, we found that task-state FC was significantly reduced in unimodal areas relative to transmodal areas. This revealed a strong negative relationship between localized task activity and distributed FC across cortical regions that was associated with the previously reported principal gradient of macroscale organization. Moreover, this dissociation corresponded to hierarchical cortical differences in the intrinsic timescale estimated from resting-state fMRI and region myelin content estimated from structural MRI. Together, our results contribute to a growing literature illustrating the differential contributions of a hierarchical cortical gradient representing localized and distributed cognitive processes.
FastVA: Deep Learning Video Analytics Through Edge Processing and NPU in Mobile

Many mobile applications have been developed to apply deep learning for video analytics. Although these advanced deep learning models can provide us with better results, they also suffer from the high computational overhead which means longer delay and more energy consumption when running on mobile devices. To address this issue, we propose a framework called FastVA, which supports deep learning video analytics through edge processing and Neural Processing Unit (NPU) in mobile. The major challenge is to determine when to offload the computation and when to use NPU. Based on the processing time and accuracy requirement of the mobile application, we study two problems: Max-Accuracy where the goal is to maximize the accuracy under some time constraints, and Max-Utility where the goal is to maximize the utility which is a weighted function of processing time and accuracy. We formulate them as integer programming problems and propose heuristics based solutions. We have implemented FastVA on smartphones and demonstrated its effectiveness through extensive evaluations.
nPINNs: Nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications

Abstract Physics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integro-differential equations with sparse, noisy, unstructured, and multi-fidelity data. PINNs incorporate all available information, including governing equations (reflecting physical laws), initial-boundary conditions, and observations of quantities of interest, into a loss function to be minimized, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal Laplace operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius δ goes to zero, and to the fractional Laplacian as δ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We provide theoretical convergence rates with respect to δ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, δ and α, characterizing the kernel of the unified operator. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon, that is, different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order α ( y ) , which is more suitable for modeling wall-bounded turbulence, e.g. turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as δ. More importantly, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.