cssi/SciSciGPT-SciSciNet huggingface dataset:
Photorefractive properties of Pb0.5Ba0.5Nb2O6 crystal

Tungsten bronze ferroelectrics with a morphotropic phase boundary (MPB) can have a number of enhanced properties compared with other conventional ferroelectric compounds because of the proximity in free energy of an alternative ferroelectric structure.1
Board Level Employee Representation and Tax Avoidance in Europe

In part due to recent disclosures of large-scale tax evasion (e.g. Panama Papers), corporate tax avoidance has become a prominent public policy issue around the world. An increasing amount of research on this topic has focused on identifying the determinants of tax avoidance at the company and country level. Many newer studies examine differences in corporate governance as one of these determinants. However, this literature almost entirely neglects the role of board level employee representation (BLER), despite the fact that this form of ‘stakeholder governance’ is widespread in Europe. This paper addresses this gap in the literature by examining the relationship between BLER and tax avoidance at the company level. Two mechanisms are identified through which BLER might influence corporate tax behavior: 1) reduction in agency costs through monitoring and 2) the voting power of workers as board members to enter into coalitions with management and/or shareholders. Based on a sample of 2343 European listed companies between 2012 and 2017, this paper shows that companies with BLER have a higher effective tax rate (ETR) than companies without workers on the board. The analysis suggests that the ability to form coalitions through voting power is a more significant channel for influencing tax behavior than the monitoring mechanism. The policy implications are that governments should consider ‘stakeholder governance’ such as BLER as one measure supporting their efforts to combat tax avoidance.
Maximum Likelihood Estimation of Power-law Degree Distributions via Friendship Paradox-based Sampling

This article considers the problem of estimating a power-law degree distribution of an undirected network using sampled data. Although power-law degree distributions are ubiquitous in nature, the w...
WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings

Intersectional bias is a bias caused by an overlap of multiple social factors like gender, sexuality, race, disability, religion, etc. A recent study has shown that word embedding models can be laden with biases against intersectional groups like African American females, etc. The first step towards tackling intersectional biases is to identify them. However, discovering biases against different intersectional groups remains a challenging task. In this work, we present WordBias, an interactive visual tool designed to explore biases against intersectional groups encoded in static word embeddings. Given a pretrained static word embedding, WordBias computes the association of each word along different groups like race, age, etc. and then visualizes them using a novel interactive interface. Using a case study, we demonstrate how WordBias can help uncover biases against intersectional groups like Black Muslim Males, Poor Females, etc. encoded in word embedding. In addition, we also evaluate our tool using qualitative feedback from expert interviews. The source code for this tool can be publicly accessed for reproducibility at github.com/bhavyaghai/WordBias.
Training Noisy Single-Channel Speech Separation with Noisy Oracle Sources: A Large Gap and a Small Step

As the performance of single-channel speech separation systems has improved, there has been a desire to move to more challenging conditions than the clean, near-field speech that initial systems were developed on. When training deep learning separation models, a need for ground truth leads to training on synthetic mixtures. As such, training in noisy conditions requires either using noise synthetically added to clean speech, preventing the use of in-domain data for a noisy-condition task, or training using mixtures of noisy speech, requiring the network to additionally separate the noise. We demonstrate the relative inseparability of noise and that this noisy speech paradigm leads to significant degradation of system performance. We also propose an SI-SDR–inspired training objective that tries to exploit the inseparability of noise to implicitly partition the signal and discount noise separation errors, enabling the training of better separation systems with noisy oracle sources.
Real-time gravitational replicas: formalism and a variational principle

This work is the first step in a two-part investigation of real-time replica wormholes. Here we study the associated real-time gravitational path integral and construct the variational principle that will define its saddle-points. We also describe the general structure of the resulting real-time replica wormhole saddles, setting the stage for construction of explicit examples. These saddles necessarily involve complex metrics, and thus are accessed by deforming the original real contour of integration. However, the construction of these saddles need not rely on analytic continuation, and our formulation can be used even in the presence of non-analytic boundary-sources. Furthermore, at least for replica- and CPT-symmetric saddles we show that the metrics may be taken to be real in regions spacelike separated from a so-called ‘splitting surface’. This feature is an important hallmark of unitarity in a field theory dual.
Deep Learning Identifies Neuroimaging Signatures Of Alzheimer’s Disease Using Structural And Synthesized Functional Mri Data

Current neuroimaging techniques provide paths to investigate the structure and function of the brain in vivo and have made great advances in understanding Alzheimer’s disease (AD). However, the group-level analyses prevalently used for investigation and understanding of the disease are not applicable for diagnosis of individuals. More recently, deep learning, which can efficiently analyze large-scale complex patterns in 3D brain images, has helped pave the way for computer-aided individual diagnosis by providing accurate and automated disease classification. Great progress has been made for classifying AD with deep learning models developed upon increasingly available structural MRI data. The lack of scale-matched functional neuroimaging data prevents such models from being further improved by observing functional changes in pathophysiology. Here we propose a potential solution by first learning a structural-to-functional transformation in brain MRI, and further synthesizing spatially matched functional images from large-scale structural scans. We evaluated our approach by building computational models to discriminate patients with AD from healthy normal subjects and demonstrated a performance boost after combining the structural and synthesized functional brain images into the same model. Furthermore, our regional analyses identified the temporal lobe to be the most predictive structural region and the parieto-occipital lobe to be the most predictive functional-region of our model, which are both in concordance with previous group-level neuroimaging findings. Together, we demonstrate the potential of deep learning with large-scale structural and synthesized functional MRI to impact AD classification and to identify AD’s neuroimaging signatures.
The "Legal Epidemiology" of Pandemic Control.

The “Legal Epidemiology” of Pandemic Control As the Covid pandemic reveals, law is a treatment to which many millions of people are routinely exposed. We should therefore devote more health researc...
Cortical response to naturalistic stimuli is largely predictable with deep neural networks

Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict neural responses to arbitrary stimuli can be very useful for studying brain function. However, existing models focus on limited aspects of naturalistic stimuli, ignoring the dynamic interactions of modalities in this inherently context-rich paradigm. Using movie-watching data from the Human Connectome Project, we build group-level models of neural activity that incorporate several inductive biases about neural information processing, including hierarchical processing, temporal assimilation, and auditory-visual interactions. We demonstrate how incorporating these biases leads to remarkable prediction performance across large areas of the cortex, beyond the sensory-specific cortices into multisensory sites and frontal cortex. Furthermore, we illustrate that encoding models learn high-level concepts that generalize to task-bound paradigms. Together, our findings underscore the potential of encoding models as powerful tools for studying brain function in ecologically valid conditions.
Investigating the transonic flutter boundary of the Benchmark Supercritical Wing

This paper builds on the computational aeroelastic results published previously and generated in support of the second Aeroelastic Prediction Workshop for the NASA Benchmark Supercritical Wing configuration. The computational results are obtained using FUN3D, an unstructured grid Reynolds-Averaged Navier-Stokes solver developed at the NASA Langley Research Center. The analysis results focus on understanding the dip in the transonic flutter boundary at a single Mach number (0.74), exploring an angle of attack range of 􀀀1 to 8 and dynamic pressures from wind off to beyond flutter onset. The rigid analysis results are examined for insights into the behavior of the aeroelastic system. Both static and dynamic aeroelastic simulation results are also examined.
Improving Generation and Evaluation of Visual Stories via Semantic Consistency

Story visualization is an underexplored task that falls at the intersection of many important research directions in both computer vision and natural language processing. In this task, given a series of natural language captions which compose a story, an agent must generate a sequence of images that correspond to the captions. Prior work has introduced recurrent generative models which outperform text-to-image synthesis models on this task. However, there is room for improvement of generated images in terms of visual quality, coherence and relevance. We present a number of improvements to prior modeling approaches, including (1) the addition of a dual learning framework that utilizes video captioning to reinforce the semantic alignment between the story and generated images, (2) a copy-transform mechanism for sequentially-consistent story visualization, and (3) MART-based transformers to model complex interactions between frames. We present ablation studies to demonstrate the effect of each of these techniques on the generative power of the model for both individual images as well as the entire narrative. Furthermore, due to the complexity and generative nature of the task, standard evaluation metrics do not accurately reflect performance. Therefore, we also provide an exploration of evaluation metrics for the model, focused on aspects of the generated frames such as the presence/quality of generated characters, the relevance to captions, and the diversity of the generated images. We also present correlation experiments of our proposed automated metrics with human evaluations.
XOR QA: Cross-lingual Open-Retrieval Question Answering

Multilingual question answering tasks typically assume that answers exist in the same language as the question. Yet in practice, many languages face both information scarcity—where languages have few reference articles—and information asymmetry—where questions reference concepts from other cultures. This work extends open-retrieval question answering to a cross-lingual setting enabling questions from one language to be answered via answer content from another language. We construct a large-scale dataset built on 40K information-seeking questions across 7 diverse non-English languages that TyDi QA could not find same-language answers for. Based on this dataset, we introduce a task framework, called Cross-lingual Open-Retrieval Question Answering (XOR QA), that consists of three new tasks involving cross-lingual document retrieval from multilingual and English resources. We establish baselines with state-of-the-art machine translation systems and cross-lingual pretrained models. Experimental results suggest that XOR QA is a challenging task that will facilitate the development of novel techniques for multilingual question answering. Our data and code are available at https://nlp.cs.washington.edu/xorqa/.
A comparison of six numerical methods for integrating a compartmental Hodgkin-Huxley type model

Abstract We compare six numerical integrators' performance when simulating a regular spiking cortical neuron model whose 74-compartments are equipped with eleven membrane ion channels and Calcium dynamics. Four methods are explicit and two are implicit; three are finite difference PDE methods, two are Runge-Kutta methods, and one an exponential time differencing method. Three methods are first-, two commonly considered second-, and one commonly considered fourth-order. Derivations show, and simulation data confirms, that Hodgkin-Huxley type cable equations render multiple order explicit RK methods as first-order methods. Illustrations compare accuracy, stability, variations of action potential phase and waveform statistics. Explicit methods were found unsuited for our model given their inability to control spiking waveform consistency up to 10 μ s less than the step size for onset of instability. While the backward-time central space method performed satisfactorily as a first order method for step sizes up to 80 μ s , performance of the Hines-Crank-Nicolson method, our only true second order method, was unmatched for step sizes of 1 − 100 μ s .
Almost optimal super-constant-pass streaming lower bounds for reachability

We give an almost quadratic n2−o(1) lower bound on the space consumption of any o(√logn)-pass streaming algorithm solving the (directed) s-t reachability problem. This means that any such algorithm must essentially store the entire graph. As corollaries, we obtain almost quadratic space lower bounds for additional fundamental problems, including maximum matching, shortest path, matrix rank, and linear programming. Our main technical contribution is the definition and construction of set hiding graphs, that may be of independent interest: we give a general way of encoding a set S ⊆ [k] as a directed graph with n = k 1 + o( 1 ) vertices, such that deciding whether i ∈ S boils down to deciding if ti is reachable from si, for a specific pair of vertices (si,ti) in the graph. Furthermore, we prove that our graph “hides” S, in the sense that no low-space streaming algorithm with a small number of passes can learn (almost) anything about S.
Revisiting the Stabilization Role of Public Banks: Public Debt Matters

This paper revisits the stabilization role of public banks and analyzes whether weak public finances may hinder this role. During the global financial crisis (GFC), public banks were widely used to counter the private credit crunch and prop up the economy. Using cross-country bank-level data for 125 advanced and developing economies for 1999-2018, the paper finds public bank lending to be less procyclical than private bank lending on average, particularly during busts. A key result, however, is that in developing economies with high public debt levels, public bank lending has been more procyclical, particularly outside of the GFC period. This finding suggests high public debt can limit the stabilization role of public banks during domestic busts, likely reflecting higher financing costs public banks face and lower subsidies they receive in economies with tighter budget constraints.
Stagnation Region Gas Film Cooling for Turbine Blade Leading-Edge Applications

An experimental investigation was conducted to model the film-cooling performance for a turbine-vane leading edge using the stagnation region of a cylinder in cross flow. Experiments were conducted with a single row of spanwise-angled coolant holes for a range of the coolant blowing ratio with a freestream-to-wall temperature ratio of about 2.1 and a Reynolds number of 170,000, characteristic of the gas-turbine environment. Data from local heat-flux measurements are presented for coolant-hole injection angles of 25, 35, and 45 deg with the row of holes located at three positions relative to the stagnation line on the cylinder. Results show the spanwise (hole-to-hole) variation of heat-flux reduction due to film cooling and indicate conditions for the optimum film-cooling performance.
The Refugees We Are: Solidarity, Asylum, and Critique in the European Constitutional Imagination

This Article aims to reimagine post-national legal solidarity. It does so by bringing debates over Habermasian constitutional theory to bear on the evolving use of mutual recognition and mutual trust in the EU’s Area of Freedom, Security, and Justice (AFSJ), particularly in the context of European asylum law and reforms to the Dublin Regulation. Insofar as critiques of Habermasian “constitutional patriotism” apply to the principle of mutual trust, the Article suggests why post-national solidarity requires fallibilism and dynamic responsiveness that exceed formalized rules of forbearance and respect. On this revised view, legal solidarity guarantees a particular form of adjudication through which individual litigants in a particular case challenge the transnational structural conditions that give rise to individual harm. Because it acknowledges that violations of individual rights are always potentially or in part the result of a collective systemic failure, this conception of solidarity restores meaning to the transformative “transfer” of sovereignty that post-national law had promised. In the field of asylum law, I detail how this application of solidarity would offer a much-needed corrective to structural imbalances in the existing Dublin regime. I conclude with reflections on the principle’s application in additional fields of EU law, as well.
IDENTIFICATION OF THE SOURCE FOR FULL PARABOLIC EQUATIONS

In this work, we consider the problem of identifying the time independent source for full parabolic equations in $\mathbb{R}^n$ from noisy data. This is an ill-posed problem in the sense of Hadamard. To compensate the factor that causes the instability, a family of parametric regularization operators is introduced, where the rule to select the value of the regularization parameter is included. This rule, known as regularization parameter choice rule, depends on the data noise level and the degree of smoothness that it is assumed for the source. The proof for the stability and convergence of the regularization criteria is presented and a H\"older type bound is obtained for the estimation error. Numerical examples are included to illustrate the effectiveness of this regularization approach.
MNL-Bandit with Knapsacks

In this paper, we study a dynamic assortment optimization problem under bandit feedback, where a seller with a fixed initial inventory of N substitutable products faces a sequence of i.i.d. customer arrivals (with an unknown distribution) over a time horizon of T periods, and needs to decide in each period on an assortment of products to offer to the customer to maximize the total expected revenue. Such a problem arises in many applications including online retail and recommendations. The seller has initially no (or only limited) information about the customer's preferences and needs to learn them through repeated interaction with the i.i.d. customers. Specifically, in each period, the seller offers an assortment to the customer; the customer makes a choice from the assortment according to the unknown preferences or choice model, and the seller only observes the eventual choice from the given assortment and needs to update the estimate and future actions under this bandit feedback. Therefore, this problem exemplifies the classical trade-off between exploitation and exploration: the seller needs to simultaneously gain information about the customer's preferences and offer revenue-maximizing assortments, while respecting the resource constraints.
The impact of temperature on labor quality: Umpire accuracy in Major League Baseball

Using data from Major League Baseball, I compute an objective measure of the home plate umpire's work quality- the accuracy of his ball and strike calls during a game- and measure how it varies with temperature. I find that an increase in game-time temperature from between 70F and 80F to above 95F decreases an umpire's accuracy by a little less than a percentage point, which is a 5.5% increase in the pitch-calling error rate when evaluated at the mean error rate of 13.3%. Restricting the sample to borderline pitches increases the magnitude of the hot-weather effect on accuracy to over a percentage point. My results indicate that very hot temperatures have a nontrivial, negative effect on the labor supply quality of a highly trained and highly skilled workforce in an important, high-revenue, and high-stakes industry, and suggest that protecting workers from daily variation in temperature can improve labor productivity.
Invariant Representation Learning for Treatment Effect Estimation

The defining challenge for causal inference from observational data is the presence of `confounders', covariates that affect both treatment assignment and the outcome. To address this challenge, practitioners collect and adjust for the covariates, hoping that they adequately correct for confounding. However, including every observed covariate in the adjustment runs the risk of including `bad controls', variables that \emph{induce} bias when they are conditioned on. The problem is that we do not always know which variables in the covariate set are safe to adjust for and which are not. To address this problem, we develop Nearly Invariant Causal Estimation (NICE). NICE uses invariant risk minimization (IRM) [Arj19] to learn a representation of the covariates that, under some assumptions, strips out bad controls but preserves sufficient information to adjust for confounding. Adjusting for the learned representation, rather than the covariates themselves, avoids the induced bias and provides valid causal inferences. We evaluate NICE on both synthetic and semi-synthetic data. When the covariates contain unknown collider variables and other bad controls, NICE performs better than adjusting for all the covariates.
Structural basis for template switching by a group II intron-encoded non-LTR-retroelement reverse transcriptase.

Reverse transcriptases (RTs) can switch template strands during complementary DNA synthesis, enabling them to join discontinuous nucleic acid sequences. Template switching (TS) plays crucial roles in retroviral replication and recombination, is used for adapter addition in RNA-Seq, and may contribute to retroelement fitness by increasing evolutionary diversity and enabling continuous complementary DNA synthesis on damaged templates. Here, we determined an X-ray crystal structure of a TS complex of a group II intron RT bound simultaneously to an acceptor RNA and donor RNA template–DNA primer heteroduplex with a 1-nt 3′-DNA overhang. The structure showed that the 3′ end of the acceptor RNA binds in a pocket formed by an N-terminal extension present in non–long terminal repeat–retroelement RTs and the RT fingertips loop, with the 3′ nucleotide of the acceptor base paired to the 1-nt 3′-DNA overhang and its penultimate nucleotide base paired to the incoming dNTP at the RT active site. Analysis of structure-guided mutations identified amino acids that contribute to acceptor RNA binding and a phenylalanine residue near the RT active site that mediates nontemplated nucleotide addition. Mutation of the latter residue decreased multiple sequential template switches in RNA-Seq. Our results provide new insights into the mechanisms of TS and nontemplated nucleotide addition by RTs, suggest how these reactions could be improved for RNA-Seq, and reveal common structural features for TS by non–long terminal repeat–retroelement RTs and viral RNA–dependent RNA polymerases.
Rough-Terrain Locomotion and Unilateral Contact Force Regulations With a Multi-Modal Legged Robot

Despite many accomplishments by legged robot designers, state-of-the-art bipedal robots are prone to falling over, cannot negotiate extremely rough terrains and cannot directly regulate unilateral contact forces. Our objective is to integrate merits of legged and aerial robots in a single platform. We will show that the thrusters in a bipedal legged robot called Harpy can be leveraged to stabilize the robot's frontal dynamics and permit jumping over large obstacles which is an unusual capability not reported before. In addition, we will capitalize on the thrusters action in Harpy and will show that one can avoid using costly optimization-based schemes by directly regulating contact forces using an Reference Governor (RGs). We will resolve gait parameters and re-plan them during gait cycles by only assuming well-tuned supervisory controllers. Then, we will focus on RG-based fine-tuning of the joints desired trajectories to satisfy unilateral contact force constraints.
Robust Segmentation of Cellular Ultrastructure on Sparsely Labeled 3D Electron Microscopy Images using Deep Learning

A deeper understanding of the cellular and subcellular organization of tumor cells and their interactions with the tumor microenvironment will shed light on how cancer evolves and guide effective therapy choices. Electron microscopy (EM) images can provide detailed view of the cellular ultrastructure and are being generated at an ever-increasing rate. However, the bottleneck in their analysis is the delineation of the cellular structures to enable interpretable rendering. We have mitigated this limitation by using deep learning, specifically, the ResUNet architecture, to segment cells and subcellular ultrastructure. Our initial prototype focuses on segmenting nuclei and nucleoli in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. Trained with sparse manual labels, our method results in accurate segmentation of nuclei and nucleoli with best Dice score of 0.99 and 0.98 respectively. This method can be extended to other cellular structures, enabling deeper analysis of inter- and intracellular state and interactions.
The Alternating Central Extension of the q-Onsager Algebra

The q-Onsager algebra $$O_q$$ is presented by two generators $$W_0$$ , $$W_1$$ and two relations, called the q-Dolan/Grady relations. Recently Baseilhac and Koizumi introduced a current algebra $${\mathcal {A}}_q$$ for $$O_q$$ . Soon afterwards, Baseilhac and Shigechi gave a presentation of $${\mathcal {A}}_q$$ by generators and relations. We show that these generators give a PBW basis for $${\mathcal {A}}_q$$ . Using this PBW basis, we show that the algebra $${\mathcal {A}}_q$$ is isomorphic to $$O_q \otimes {\mathbb {F}} [z_1, z_2, \ldots ]$$ , where $${\mathbb {F}}$$ is the ground field and $$\lbrace z_n \rbrace _{n=1}^\infty $$ are mutually commuting indeterminates. Recall the positive part $$U^+_q$$ of the quantized enveloping algebra $$U_q(\widehat{{\mathfrak {s}}{\mathfrak {l}}}_2)$$ . Our results show that $$O_q$$ is related to $${\mathcal {A}}_q$$ in the same way that $$U^+_q$$ is related to the alternating central extension of $$U^+_q$$ . For this reason, we propose to call $${\mathcal {A}}_q$$ the alternating central extension of $$O_q$$ .
Uniqueness and global optimality of the maximum likelihood estimator for the generalized extreme value distribution

Author(s): Zhang, Likun; Shaby, Benjamin A | Abstract: Abstract The three-parameter generalized extreme value distribution arises from classical univariate extreme value theory and is in common use for analysing the far tail of observed phenomena, yet important asymptotic properties of likelihood-based estimation under this standard model have not been established. In this paper, we prove that the maximum likelihood estimator is global and unique. An interesting secondary result entails the uniform consistency of a class of limit relations in a tight neighbourhood of the true shape parameter.
Effect of Damping Mode in Laboratory and Field-Scale Universal Distinct Element Code (UDEC) Models

In practical rock engineering, Universal Distinct Element Code (UDEC) is one of the most widely used two-dimensional software packages for simulating discontinuum behaviors of rocks and rockmasses. Over the years, this software has been used to study phenomena, such as grain-scale fracturing in laboratory specimens, spalling around tunnels and roadways, shearing and separation along discontinuities in jointed rockmasses, etc. One of the lesser discussed topics in context of explicit time-stepping discontinuum modeling in UDEC is the damping mode employed for such simulations. For static analysis, the ‘local’ (default) and ‘combined’ damping modes are generally used, but their effects on the emergent model response as well as their suitability to a particular problem are not well documented in the literature. To help bridge this gap, this study contrasts the responses of a laboratory-scale model, a hypothetical granite pillar model and a hypothetical coal mine entry model with ‘Local’ and ‘Combined’ damping modes. It was found that in small-scale simulations, the results using ‘Local’ and ‘Combined’ damping modes were similar, but the differences were significant in the field-scale models. In particular, ‘Local’ damping mode tended to suppress large deformations, predicted high pillar strengths and increased model run-time significantly. Although it is difficult to definitively establish which of the two damping mode leads to more realistic excavation-scale behavior in a given scenario, the authors suggest the use of ‘Combined’ damping for models of individual excavations where large deformations and block separations are expected.
Returns to Scale in Residential Construction: The Marginal Impact of Building Height

We use engineering data on building assemblies to estimate the average and marginal costs of developing multistory apartment buildings in the 50 largest cities in the United States. The majority of housing in the least affordable cities is in multistory buildings, but most of our knowledge of how housing is produced is limited to single-family homes. By focusing on the vertical margin, we discover non-linearities in the cost curve and directly estimate the returns to scale, which allow us to measure the impact of height regulations on break-even rents of new supply. We find that height regulation has large, negative effects on affordability, with increasing magnitudes for more expensive land markets.
Data-driven stochastic modeling of coarse-grained dynamics with finite-size effects using Langevin regression

Abstract Obtaining coarse-grained models that accurately incorporate finite-size effects is an important open challenge in the study of complex, multi-scale systems. We apply Langevin regression, a recently developed method for finding stochastic differential equation (SDE) descriptions of realistically-sampled time series data, to understand finite-size effects in the Kuramoto model of coupled oscillators. We find that across the entire bifurcation diagram, the dynamics of the Kuramoto order parameter are statistically consistent with an SDE whose drift term has the form predicted by the Ott–Antonsen ansatz in the N → ∞ limit. We find that the diffusion term is nearly independent of the bifurcation parameter, and has a magnitude decaying as N − 1 / 2 , consistent with the central limit theorem. This shows that the diverging fluctuations of the order parameter near the critical point are driven by a bifurcation in the underlying drift term, rather than increased stochastic forcing.
Optimization of Broadband Λ-type Quantum Memory Using Gaussian Pulses

We optimize the efficiency of broadband Λ-type quantum memories under the restriction of Gaussian-shape optical fields. We demonstrate an experimentally-simple path to enhancing memory efficiency over a wide range of broadband memory parameters.
Synthesized Trust Learning from Limited Human Feedback for Human-Load-Reduced Multi-Robot Deployments

Human multi-robot system (MRS) collaboration is demonstrating potentials in wide application scenarios due to the integration of human cognitive skills and a robot team’s powerful capability introduced by its multi-member structure. However, due to limited human cognitive capability, a human cannot simultaneously monitor multiple robots and identify the abnormal ones, largely limiting the efficiency of the human-MRS collaboration. There is an urgent need to proactively reduce unnecessary human engagements and further reduce human cognitive loads. Human trust in human MRS collaboration reveals human expectations on robot performance. Based on trust estimation, the work between a human and MRS will be reallocated that an MRS will self-monitor and only request human guidance in critical situations. Inspired by that, a novel Synthesized Trust Learning (STL) method was developed to model human trust in the collaboration. STL explores two aspects of human trust (trust level and trust preference), meanwhile accelerates the convergence speed by integrating active learning to reduce human workload. To validate the effectiveness of the method, tasks "searching victims in the context of city rescue" were designed in an open-world simulation environment, and a user study with 10 volunteers was conducted to generate real human trust feedback. The results showed that by maximally utilizing human feedback, the STL achieved higher accuracy in trust modeling with a few human feedback, effectively reducing human interventions needed for modeling an accurate trust, therefore reducing human cognitive load in the collaboration.
Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection

In this paper, we study the information theoretic bounds for exact recovery in sub-hypergraph models for community detection. We define a general model called the $m$ -uniform sub-hypergraph stochastic block model (m-ShSBM). Under the $m$ -ShSBM, we use Fano's inequality to identify the region of model parameters where any algorithm fails to exactly recover the planted communities with a large probability. We also identify the region where a Maximum Likelihood Estimation (MLE) algorithm succeeds to exactly recover the communities with high probability. Our bounds are tight up to a log( $k$ ) term and pertain to the community detection problems in various models such as the planted hypergraph stochastic block model, the planted densest sub-hypergraph model, and the planted multipartite hypergraph model.
Linear Programming Bounds for Almost-Balanced Binary Codes

We revisit the linear programming bounds for the size vs. distance trade-off for binary codes, focusing on the bounds for the almost-balanced case, when all pairwise distances are between $d$ and $n-d$ , where $d$ is the code distance and $n$ is the block length. We give an optimal solution to Delsarte's LP for the almost-balanced case with large distance $d\geq(n-\sqrt{n})/2+1$ , which shows that the optimal value of the LP coincides with the Grey-Rankin bound for self-complementary codes. We also show that a limitation of the asymptotic LP bound shown by Samorodnitsky, namely that it is at least the average of the first MRRW upper bound and Gilbert-Varshamov bound, continues to hold for the almost-balanced case.
On Graph Matching Using Generalized Seed Side-Information

In this paper, matching pairs of stocahstically generated graphs in the presence of generalized seed side-information is considered. The graph matching problem emerges naturally in various applications such as social network de-anonymization, image processing, DNA sequencing, and natural language processing. A pair of randomly generated labeled Erdos-Renyi graphs with pairwise correlated edges are considered. It is assumed that the matching strategy has access to the labeling of the vertices in the first graph, as well as a collection of shortlists — called ambiguity sets — of possible labels for the vertices of the second graph. The objective is to leverage the correlation among the edges of the graphs along with the side-information provided in the form of ambiguity sets to recover the labels of the vertices in the second graph. This scenario can be viewed as a generalization of the seeded graph matching problem, where the ambiguity sets take a specific form such that the exact labels for a subset of vertices in the second graph are known prior to matching. A matching strategy is proposed which operates by evaluating the joint typicality of the adjacency matrices of the graphs. Sufficient conditions on the edge statistics as well as ambiguity set statistics are derived under which the proposed matching strategy successfully recovers the labels of the vertices in the second graph. Additionally, Fano-type arguments are used to derive necessary conditions for successful seeded graph matching.
Deep learning-based surrogate modeling via physics-informed artificial image (PiAI) for strongly coupled multidisciplinary engineering systems

Abstract The design of strongly coupled multidisciplinary engineering systems is challenging since it is characterized by the complex interaction of different disciplines. Such complexity cannot be easily captured by explicit analytical solutions, which motivates the development of a surrogate model. Deep learning (DL) has gained considerable interest among existing surrogate modeling techniques because of the flexible non-linear formulation, comparability to data format diversity, and applicability to data-driven analysis. Notably, a convolution neural network (CNN) has been employed in multifold research to ameliorate the prediction accuracy of the surrogate model once images representing physical phenomena are utilized. Nevertheless, it is still questionable to guarantee the feasibility of the CNN-based surrogate model in the multi-physics domain due to (1) unreliable correlation representation between multi-domain design parameters and the coupled responses and (2) massive training costs. To address those issues, therefore, this research proposes a framework of CNN-based deep surrogate model (DSM), developing a novel input structure called physics-informed artificial image (PiAI). PiAI incubates (1) geometry-informed CAD representing physical uncertainties of engineering systems, (2) location-clarified filter improving CNN training accuracy, and (3) simulation conditions essentially required in the multi-physics analysis, which reinforces the prediction credibility. Moreover, in lieu of employing multi-modalities or multiple image channels, the proposed method applies unimodal-based single image inputs to escalate computational efficiency. The proposed framework’s efficacy and applicability are addressed in practical engineering design applications: a cantilever beam and a stretchable strain sensor.
Several Topological Indices of Random Caterpillars

In chemical graph theory, caterpillar trees have been an appealing model to represent the molecular structures of benzenoid hydrocarbon. Meanwhile, topological index has been thought of as a powerful tool for modeling quantitative structure-property relationship and quantitative structure-activity between molecules in chemical compounds. In this article, we consider a class of caterpillar trees that are incorporated with randomness, called random caterpillars, and investigate several popular topological indices of this random class, including Zagreb index, Randic index and Wiener index, etc. Especially, a central limit theorem is developed for the asymptotic distribution of the Zagreb index of random caterpillars.
Improved Method for Dealing with Discontinuities in Power System Transient Simulation Based on Frequency Response Optimized Integrators Considering Second Order Derivative

Potential disagreement in the result induced by discontinuities is revealed in this paper between a novel power system transient simulation scheme using numerical integrators considering second order derivative and conventional ones using numerical integrators considering first order derivative. The disagreement is due to the formula of the different numerical integrators. An improved method for dealing with discontinuities in the novel transient simulation scheme is proposed to resolve the disagreement. The effectiveness of the improved method is demonstrated and verified via numerical case studies. Although the disagreement is studied on and the improved method is proposed for a particular transient simulation scheme, similar conclusions also apply to other ones using numerical integrators considering high order derivative.
Rectifying Administrated ERC20 Tokens.

ERC20 token is the most popular type of Ethereum smart contract. The daily transaction volume of these tokens exceeds 100 billion dollars, which agitates the popular notions of “decentralized banking” and “tokenized economy”. Yet, it is a common misconception to assume that the decentralization of blockchain entails the decentralization of smart contracts deployed on this blockchain. In practice, the developers of smart contracts implement administrating patterns, such as censoring certain users, creating or destroying balances on demand, destroying smart contracts, or injecting arbitrary code. These routines, which are designed to tightly control the operation of these smart contracts, turn an ERC20 token into an administrated token—the type of Ethereum smart contract that we scrutinize in this research.
Members of the ELMOD protein family specify formation of distinct aperture domains on the Arabidopsis pollen surface.

Zooming in on cells reveals patterns on their outer surfaces. These patterns are actually a collection of distinct areas of the cell surface, each containing specific combinations of molecules. The outer layers of pollen grains consist of a cell wall, and a softer cell membrane that sits underneath. As a pollen grain develops, it recruits certain fats and proteins to specific areas of the cell membrane, known as ‘aperture domains’. The composition of these domains blocks the cell wall from forming over them, leading to gaps in the wall called ‘pollen apertures’. Pollen apertures can open and close, aiding reproduction and protecting pollen grains from dehydration. The number, location, and shape of pollen apertures vary between different plant species, but are consistent within the same species. In the plant species Arabidopsis thaliana, pollen normally develops three long and narrow, equally spaced apertures, but it remains unclear how pollen grains control the number and location of aperture domains. Zhou et al. found that mutations in two closely related A. thaliana proteins – ELMOD_A and MCR – alter the number and positions of pollen apertures. When A. thaliana plants were genetically modified so that they would produce different levels of ELMOD_A and MCR, Zhou et al. observed that when more of these proteins were present in a pollen grain, more apertures were generated on the pollen surface. This finding suggests that the levels of these proteins must be tightly regulated to control pollen aperture numbers. Further tests revealed that another related protein, called ELMOD_E, also has a role in domain formation. When artificially produced in developing pollen grains, it interfered with the activity of ELMOD_A and MCR, changing pollen aperture shape, number, and location. Zhou et al. identified a group of proteins that help control the formation of domains in the cell membranes of A. thaliana pollen grains. Further research will be required to determine what exactly these proteins do to promote formation of aperture domains and whether similar proteins control domain development in other organisms.
Itinerary planning for cooperative truck platooning

Abstract A cooperative truck platoon is a set of virtually linked trucks driving with a small intra-vehicle headway enabled by connected and automated vehicle technologies. One of the primary benefits of truck platooning is energy savings due to the reduction of aerodynamic drag on the platooned vehicles. The focus of this paper is on scheduling travel itineraries of a given set of trucks to facilitate the formation of platoons to maximize energy savings. By constructing a time-expanded network, we formulate the problem as a minimum concave-cost network flow problem, and devise a few solution methods to find the optimal or high-quality solutions. The solution methods include an outer approximation algorithm for solving a mixed-integer convex minimization reformulation of the problem, a dynamic-programming-based heuristic scalable to large-scale instances, and a fast approximation algorithm with guaranteed performance for a restrictive version of the problem. All the proposed algorithms are examined and benchmarked on medium to large networks under various test scenarios. The numerical results demonstrate the efficiency of the proposed methods and their applicability in real-world settings.
Recycling augmented Lagrangian preconditioner in an incompressible fluid solver

The paper discusses a reuse of matrix factorization as a building block in the Augmented Lagrangian (AL) and modified AL preconditioners for a non-symmetric saddle point linear algebraic systems. The strategy is applied to solve two-dimensional incompressible fluid problems with efficiency rates independent of the Reynolds number. The solver is then tested to simulate a motion of surface fluids, an example of 2D flows motivated by an interest in lateral fluidity of inextensible viscous membranes. Numerical examples include the Kelvin-Helmholtz instability problem posed on the sphere and on the torus.
Minimizing privilege for building HPC containers

HPC centers face increasing demand for software flexibility, and there is growing consensus that Linux containers are a promising solution. However, existing container build solutions require root privileges and cannot be used directly on HPC resources. This limitation is compounded as supercomputer diversity expands and HPC architectures become more dissimilar from commodity computing resources. Our analysis suggests this problem can best be solved with low-privilege containers. We detail relevant Linux kernel features, propose a new taxonomy of container privilege, and compare two open-source implementations: mostly-unprivileged rootless Podman and fully-unprivileged Charliecloud. We demonstrate that low-privilege container build on HPC resources works now and will continue to improve, giving normal users a better workflow to securely and correctly build containers. Minimizing privilege in this way can improve HPC user and developer productivity as well as reduce support workload for exascale applications.
On the nature of S II emission from Jupiter's hot plasma torus

An effective electron temperature T(e) of 80,000 K is indicated by the Voyager 1 encounter Jupiter hot torus emission rates in the 6731, 1256, 911 and reclassified 765 A transitions of S II. A set of 53 measurements of the S II red line doublet obtained at 5.9 Jupiter radii shows strong, irregular fluctuations in intensity, but no variation in the line ratio. At this distance from Jupiter, the torus is found to be longitudinally uniform in density; this is consonant with Voyager UVS findings, but contrary to magnetic anomaly model predictions. It is suggested that presently unidentified ion-ion and/or iron-atom reactions are responsible for the S II component irregular variations, in view of the fact that electron properties are regular and variable only over a small range in the hot torus at 5.9 Jupiter radii.