cssi/SciSciGPT-SciSciNet huggingface dataset:
Youth Trust in Social Media Companies and Expectations of Justice: Accountability and Repair After Online Harassment

Social media platforms aspire to deliver fair resolutions after online harassment. Platforms rely on sanctions like removing content or banning users but these punitive responses provide little opportunity for justice or reparation for targets of harassment. This may be especially important for youth, who experience pervasive harassment which can have uniquely harmful effects on their wellbeing. We conducted a text-message based survey with 832 U.S. adolescents and young adults, ages 14-24, to explore their attitudes towards social media companies' responses to online harassment. We find that youth are twice as likely (41% versus 20%) not to trust social media companies' ability to achieve a fair resolution as they are to trust them. Nearly two-thirds (62%) of youth expressed a preference for an apology from the offender after online harassment, and they were twice as likely to prefer a private apology to a public one (29% versus 14%). Preferences also vary by identity, revealing how a one-size-fits-all approach can harm some youth while benefitting others. We reflect on the opportunities and risks associated with institutional trust and restorative justice for supporting youth who experience online harassment.
Determination of two-photon exchange via e + p /e - p scattering with CLAS12

The proton elastic form factor ratio shows a discrepancy between measurements using the Rosenbluth technique in unpolarized beam and target experiments and measurements using polarization degrees of freedom. The proposed explanation of this discrepancy is uncorrected hard two-photon exchange (TPE), a type of radiative correction that is conventionally neglected. The effect size and agreement with theoretical predictions has been tested recently by three experiments. While the results support the existence of a small two-photon exchange effect, they cannot establish that theoretical treatments are valid. At larger momentum transfers, theory remains untested. This proposal aims to measure two-photon exchange over an extended and so far largely untested $$Q^2$$ and $$\varepsilon $$ range with high precision using the CLAS12 experiment. Such data are crucial to clearly confirm or rule out TPE as the driver for the discrepancy as well as test several theoretical approaches, believed valid in different parts of the tested $$Q^2$$ range.
The Hierarchy of Block Models

There exist various types of network block models such as the Stochastic Block Model (SBM), the Degree Corrected Block Model (DCBM), and the Popularity Adjusted Block Model (PABM). While this leads to a variety of choices, the block models do not have a nested structure. In addition, there is a substantial jump in the number of parameters from the DCBM to the PABM. The objective of this paper is formulation of a hierarchy of block model which does not rely on arbitrary identifiability conditions. We propose a Nested Block Model (NBM) that treats the SBM, the DCBM and the PABM as its particular cases with specific parameter values, and, in addition, allows a multitude of versions that are more complicated than DCBM but have fewer unknown parameters than the PABM. The latter allows one to carry out clustering and estimation without preliminary testing, to see which block model is really true.
Real entropy rigidity under quasi-conformal deformations

We set up a real entropy function $h_\Bbb{R}$ on the space $\mathcal{M}'_d$ of Mobius conjugacy classes of real rational maps of degree $d$ by assigning to each class the real entropy of a representative $f\in\Bbb{R}(z)$; namely, the topological entropy of its restriction $f\restriction_{\hat{\Bbb{R}}}$ to the real circle. We prove a rigidity result stating that $h_\Bbb{R}$ is locally constant on the subspace determined by real maps quasi-conformally conjugate to $f$. As examples of this result, we analyze real analytic stable families of hyperbolic and flexible Lattes maps with real coefficients along with numerous families of degree $d$ real maps of real entropy $\log(d)$. The latter discussion moreover entails a complete classification of maps of maximal real entropy.
On the control of discrete-event dynamical systems

We study a class of problems related to the supervisory control of a discrete-event system (DES), as formulated by Ramadge and Wonham, and we focus on the computational effort required for their solution. While the problem of supervisory control of a perfectly observed DES may be easily solved by dynamic programming, the problem becomes intractable (in the sense of complexity theory) when imperfectly observed systems are considered.
Identifying Coordinated Accounts on Social Media through Hidden Influence and Group Behaviours

Disinformation campaigns on social media, involving coordinated activities from malicious accounts towards manipulating public opinion, have become increasingly prevalent. Existing approaches to detect coordinated accounts either make very strict assumptions about coordinated behaviours, or require part of the malicious accounts in the coordinated group to be revealed in order to detect the rest. To address these drawbacks, we propose a generative model, AMDN-HAGE (Attentive Mixture Density Network with Hidden Account Group Estimation) which jointly models account activities and hidden group behaviours based on Temporal Point Processes (TPP) and Gaussian Mixture Model (GMM), to capture inherent characteristics of coordination which is, accounts that coordinate must strongly influence each other's activities, and collectively appear anomalous from normal accounts. To address the challenges of optimizing the proposed model, we provide a bilevel optimization algorithm with theoretical guarantee on convergence. We verified the effectiveness of the proposed method and training algorithm on real-world social network data collected from Twitter related to coordinated campaigns from Russia's Internet Research Agency targeting the 2016 U.S. Presidential Elections, and to identify coordinated campaigns related to the COVID-19 pandemic. Leveraging the learned model, we find that the average influence between coordinated account pairs is the highest. On COVID-19, we found coordinated group spreading anti-vaccination, anti-masks conspiracies that suggest the pandemic is a hoax and political scam.
Feedbacks: Financial Markets and Economic Activity

Our structural VAR with 10 monthly variables and identified by heteroscedasticity shows that credit and output growth are mostly positively associated. Negative reduced form responses to credit growth are attributed in our model to the monetary policy response to credit expansion shocks. Financial stress, measured by rises in interest rate spreads, is followed by declines in output and shrinkage of credit. We find two distinct sources of financial stress shocks. Neither credit aggregates nor rate spreads provide much advance warning of the 2008-9 crisis, but the spreads improve the model forecasts during the crisis.
Representational drift in primary olfactory cortex.

Perceptual constancy requires the brain to maintain a stable representation of sensory input. In the olfactory system, activity in primary olfactory cortex (piriform cortex) is thought to determine odour identity1–5. Here we present the results of electrophysiological recordings of single units maintained over weeks to examine the stability of odour-evoked responses in mouse piriform cortex. Although activity in piriform cortex could be used to discriminate between odorants at any moment in time, odour-evoked responses drifted over periods of days to weeks. The performance of a linear classifier trained on the first recording day approached chance levels after 32 days. Fear conditioning did not stabilize odour-evoked responses. Daily exposure to the same odorant slowed the rate of drift, but when exposure was halted the rate increased again. This demonstration of continuous drift poses the question of the role of piriform cortex in odour perception. This instability might reflect the unstructured connectivity of piriform cortex6–12, and may be a property of other unstructured cortices. All odours elicit a unique pattern of neuronal activity in primary olfactory cortex but these patterns drift over time, posing a problem for the perceptual constancy of odours.
Mutation of the nuclear pore complex component, aladin1, disrupts asymmetric cell division in Zea mays (maize)

The nuclear pore complex (NPC) regulates the movement of macromolecules between the nucleus and cytoplasm. Dysfunction of many components of the NPC results in human genetic diseases, including triple A syndrome (AAAS) as a result of mutations in ALADIN. Here we report a nonsense mutation in the maize ortholog, aladin1 (ali1-1), at the orthologous amino acid residue of an AAAS allele from humans, alters plant stature, tassel architecture, and asymmetric divisions of subsidiary mother cells (SMCs). Crosses with the stronger nonsense allele ali1-2 identified complex allele interactions for plant height and aberrant SMC division. RNA-seq analysis of the ali1-1 mutant identified compensatory transcript accumulation for other NPC components as well as gene expression consequences consistent with conservation of ALADIN1 functions between humans and maize. These findings demonstrate that ALADIN1 is necessary for normal plant development, shoot architecture, and asymmetric cell division in maize.
Bayesian Optimization over Hybrid Spaces

We consider the problem of optimizing hybrid structures (mixture of discrete and continuous input variables) via expensive black-box function evaluations. This problem arises in many real-world applications. For example, in materials design optimization via lab experiments, discrete and continuous variables correspond to the presence/absence of primitive elements and their relative concentrations respectively. The key challenge is to accurately model the complex interactions between discrete and continuous variables. In this paper, we propose a novel approach referred as Hybrid Bayesian Optimization (HyBO) by utilizing diffusion kernels, which are naturally defined over continuous and discrete variables. We develop a principled approach for constructing diffusion kernels over hybrid spaces by utilizing the additive kernel formulation, which allows additive interactions of all orders in a tractable manner. We theoretically analyze the modeling strength of additive hybrid kernels and prove that it has the universal approximation property. Our experiments on synthetic and six diverse real-world benchmarks show that HyBO significantly outperforms the state-of-the-art methods.
Shorter survival after liver pedicle clamping in patients undergoing liver resection for hepatocellular carcinoma revealed by a systematic review and meta-analysis

Liver pedicle clamping minimizes surgical bleeding during hepatectomy. However, by inducing ischemia-reperfusion injury to the remnant liver, pedicle clamping may be associated with tumor recurrence in the regenerating liver. Hepatocellular carcinoma (HCC) having a high rate of recurrence, evidences demonstrating an eventual association with pedicle clamping is strongly needed. We did a systematic review of the literature until April 2020, looking at studies reporting the impact of liver pedicle clamping on long-term outcomes in patients undergoing liver resection for HCC. Primary and secondary outcomes were overall survival (OS) and disease-free survival, respectively. Results were obtained by random-effect meta-analysis and expressed as standardized mean difference (SMD). Eleven studies were included, accounting for 8087 patients. Results of seven studies were pooled in a meta-analysis. Findings indicated that, as compared to control patients who did not receive liver pedicle clamping, those who did had a significantly shorter OS (SMD = -0.172, 95%CI: -0.298 to -0.047, p = 0.007, I2 = 76.8%) and higher tumor recurrence rates (odds ratio 1.36 1.01 to 1.83. p = 0.044, I2 = 50.7%). This meta-analysis suggests that liver pedicle clamping may have a deleterious impact on long-term outcomes. An individual patient-data meta-analysis of randomized trials evaluating liver pedicle clamping is urgently needed.
Perturbation expansions and error bounds for the truncated singular value decomposition

Abstract Truncated singular value decomposition is a reduced version of the singular value decomposition in which only a few largest singular values are retained. This paper presents a novel perturbation analysis for the truncated singular value decomposition for real matrices. First, we describe perturbation expansions for the singular value truncation of order r. We extend perturbation results for the singular subspace decomposition to derive the first-order perturbation expansion of the truncated operator about a matrix with rank greater than or equal to r. Observing that the first-order expansion can be greatly simplified when the matrix has exact rank r, we further show that the singular value truncation admits a simple second-order perturbation expansion about a rank-r matrix. Second, we introduce the first-known error bound on the linear approximation of the truncated singular value decomposition of a perturbed rank-r matrix. Our bound only depends on the least singular value of the unperturbed matrix and the norm of the perturbation matrix. Intriguingly, while the singular subspaces are known to be extremely sensitive to additive noises, the newly established error bound holds universally for perturbations with arbitrary magnitude. Finally, we demonstrate an application of our results to the analysis of the mean squared error associated with the TSVD-based matrix denoising solution.
Characterization and efficient Monte Carlo sampling of disordered microphases.

The disordered microphases that develop in the high-temperature phase of systems with competing short-range attractive and long-range repulsive (SALR) interactions result in a rich array of distinct morphologies, such as cluster, void cluster, and percolated (gel-like) fluids. These different structural regimes exhibit complex relaxation dynamics with marked heterogeneity and slowdown. The overall relationship between these structures and configurational sampling schemes, however, remains largely uncharted. Here, the disordered microphases of a schematic SALR model are thoroughly characterized, and structural relaxation functions adapted to each regime are devised. The sampling efficiency of various advanced Monte Carlo sampling schemes—Virtual-Move (VMMC), Aggregation-Volume-Bias (AVBMC), and Event-Chain (ECMC)—is then assessed. A combination of VMMC and AVBMC is found to be computationally most efficient for cluster fluids and ECMC to become relatively more efficient as density increases. These results offer a complete description of the equilibrium disordered phase of a simple microphase former as well as dynamical benchmarks for other sampling schemes.
Bayesian Nonstationary and Nonparametric Covariance Estimation for Large Spatial Data

In spatial statistics, it is often assumed that the spatial field of interest is stationary and its covariance has a simple parametric form, but these assumptions are not appropriate in many applications. Given replicate observations of a Gaussian spatial field, we propose nonstationary and nonparametric Bayesian inference on the spatial dependence. Instead of estimating the quadratic (in the number of spatial locations) entries of the covariance matrix, the idea is to infer a near-linear number of nonzero entries in a sparse Cholesky factor of the precision matrix. Our prior assumptions are motivated by recent results on the exponential decay of the entries of this Cholesky factor for Matern-type covariances under a specific ordering scheme. Our methods are highly scalable and parallelizable. We conduct numerical comparisons and apply our methodology to climate-model output, enabling statistical emulation of an expensive physical model.
Spintronics Meets Density Matrix Renormalization Group: Quantum Spin-Torque-Driven Nonclassical Magnetization Reversal and Dynamical Buildup of Long-Range Entanglement

A synergy between strongly electron-correlated physics, quantum transport theory, and quantum information science provides a long-sought quantum-mechanical description of spin torque, an effect at the heart of spintronic research.
Accurate and Robust Feature Importance Estimation under Distribution Shifts.

With increasing reliance on the outcomes of black-box models in critical applications, post-hoc explainability tools that do not require access to the model internals are often used to enable humans understand and trust these models. In particular, we focus on the class of methods that can reveal the influence of input features on the predicted outputs. Despite their wide-spread adoption, existing methods are known to suffer from one or more of the following challenges: computational complexities, large uncertainties and most importantly, inability to handle real-world domain shifts. In this paper, we propose PRoFILE (Producing Robust Feature Importances using Loss Estimates), a novel feature importance estimation method that addresses all these challenges. Through the use of a loss estimator jointly trained with the predictive model and a causal objective, PRoFILE can accurately estimate the feature importance scores even under complex distribution shifts, without any additional re-training. To this end, we also develop learning strategies for training the loss estimator, namely contrastive and dropout calibration, and find that it can effectively detect distribution shifts. Using empirical studies on several benchmark image and non-image data, we show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
Quantum Electrodynamics (QED) Renormalization is a Logical Paradox, Zeta Function Regularization is Logically Invalid, and Both are Mathematically Invalid

Quantum Electrodynamics (QED) renormalizaion is a paradox. It uses the Euler-Mascheroni constant, which is defined by a conditionally convergent series. But Riemann's series theorem proves that any conditionally convergent series can be rearranged to be divergent. This contradiction (a series that is both convergent and divergent) is a paradox in "classical" logic, intuitionistic logic, and Zermelo-Fraenkel set theory, and also contradicts the commutative and associative properties of addition. Therefore QED is mathematically invalid. Zeta function regularization equates two definitions of the Zeta function at domain values where they contradict (where the Dirichlet series definition is divergent and Riemann's definition is convergent). Doing so either creates a paradox (if Riemann's definition is true), or is logically invalid (if Riemann's definition is false). We show that Riemann's definition is false, because the derivation of Riemann's definition includes a contradiction: the use of both the Hankel contour and Cauchy's integral theorem. Also, a third definition of the Zeta function is proven to be false. The Zeta function has no zeros, so the Riemann hypothesis is a paradox, due to material implication and "vacuous subjects".
Geophysical inversion using petrophysical constraints with application to lithology differentiation

Petrophysical information is sometimes available from well logs, laboratory measurements, etc. It has been shown that incorporation of such information into geophysical inversions, especially joint inversion of multiple geophysical data sets collected based on fundamentally different physics, can greatly improve the recovered models. However, when the known petrophysical distribution or relationship is not applicable throughout the entire region under investigation, the application of petrophysical constraints to geophysical inversions could seriously distort the resulting models. In this study, we propose a new inversion algorithm that is capable of building the petrophysical information into the inversion, while overcoming the aforementioned problem. The lithology differentiation is accomplished by applying fuzzy c-means (FCM) clustering algorithm to the models inverted from this new method. We illustrate this method with a synthetic crosshole seismic example and a gravity survey that measures both surface and borehole gravity response.
Separating circles on the sphere by polygonal tilings

We say that a tiling separates discs of a packing in the Euclidean plane, if each tile contains exactly one member of the packing. It is a known elementary geometric problem to show that for each locally finite packing of circular discs, there exists a separating tiling with convex polygons. In this paper we show that this separating property remains true for circle packings on the sphere and in the hyperbolic plane. Moreover, we show that in the Euclidean plane circles are the only convex discs, whose packings with similar copies can be always separated by polygonal tilings. The analogous statement is not true on the sphere and it is not known in the hyperbolic plane.
Multi-IRS-assisted Multi-Cell Uplink MIMO Communications under Imperfect CSI: A Deep Reinforcement Learning Approach

Applications of intelligent reflecting surfaces (IRSs) in wireless networks have attracted significant attention recently. Most of the relevant literature is focused on the single cell setting where a single IRS is deployed and perfect channel state information (CSI) is assumed. In this work, we develop a novel methodology for multi-IRS-assisted multi-cell networks in the uplink. We consider the scenario in which (i) channels are dynamic and (ii) only partial CSI is available at each base station (BS); specifically, scalar effective channel powers from only a subset of user equipments (UE). We formulate the sum-rate maximization problem aiming to jointly optimize the IRS reflect beamformers, BS combiners, and UE transmit powers. In casting this as a sequential decision making problem, we propose a multi-agent deep reinforcement learning algorithm to solve it, where each BS acts as an independent agent in charge of tuning the local UE transmit powers, the local IRS reflect beamformer, and its combiners. We introduce an efficient information-sharing scheme that requires limited information exchange among neighboring BSs to cope with the non-stationarity caused by the coupling of actions taken by multiple BSs. Our numerical results show that our method obtains substantial improvement in average data rate compared to baseline approaches, e.g., fixed UE transmit power and maximum ratio combining.
Optimal Dynamic Allocation: Simplicity through Information Design

We study dynamic nonmonetary markets where objects are allocated to unit-demand agents with private types. An agent's value for an object is supermodular in her type and the quality of the object, and her payoff is quasilinear in her waiting cost. We analyze direct-revelation mechanisms that elicit agents' types and assign them to objects over time. We identify the welfare-maximizing mechanism and show that it can be implemented by a first-come first-served wait-list with deferrals when the marketmaker can design the information disclosed to agents about the objects. The optimal disclosure policy pools adjacent object types.
Joint quantile regression for spatial data

Linear quantile regression is a powerful tool to investigate how predictors may affect a response heterogeneously across different quantile levels. Unfortunately, existing approaches find it extremely difficult to adjust for any dependency between observation units, largely because such methods are not based upon a fully generative model of the data. For analyzing spatially indexed data, we address this difficulty by generalizing the joint quantile regression model of Yang and Tokdar (2017) and characterizing spatial dependence via a Gaussian or $t$ copula process on the underlying quantile levels of the observation units. A Bayesian semiparametric approach is introduced to perform inference of model parameters and carry out spatial quantile smoothing. An effective model comparison criteria is provided, particularly for selecting between different model specifications of tail heaviness and tail dependence. Extensive simulation studies and an application to particulate matter concentration in northeast US are presented to illustrate substantial gains in inference quality, accuracy and uncertainty quantification over existing alternatives.
Finding nonlinear system equations and complex network structures from data: A sparse optimization approach

In applications of nonlinear and complex dynamical systems, a common situation is that the system can be measured, but its structure and the detailed rules of dynamical evolution are unknown. The inverse problem is to determine the system equations and structure from time series. The principle of exploiting sparse optimization to find the equations of dynamical systems from data was first articulated in 2011 by the ASU group. The basic idea is to expand the system equations into a power series or a Fourier series of a finite number of terms and then to determine the vector of the expansion coefficients based solely on data through sparse optimization. This Tutorial presents a brief review of the recent progress in this area. Issues discussed include discovering the equations of stationary or nonstationary chaotic systems to enable the prediction of critical transition and system collapse, inferring the full topology of complex oscillator networks and social networks hosting evolutionary game dynamics, and identifying partial differential equations for spatiotemporal dynamical systems. Situations where sparse optimization works or fails are pointed out. The relation with the traditional delay-coordinate embedding method is discussed, and the recent development of a model-free, data-driven prediction framework based on machine learning is mentioned.
Sense-Bandits: AI-based Adaptation of Sensing Thresholds for Heterogeneous-technology Coexistence Over Unlicensed Bands

In this paper, we present Sense-Bandits, an AI-based framework for distributed adaptation of the sensing thresholds (STs) over shared spectrum. This framework specifically targets the coexistence of heterogenous technologies, e.g., Wi-Fi, 4G Licensed-Assisted Access (LAA), and 5G New Radio Unlicensed (NR-U), over unlicensed channels. To access the channel, a device compares the measured power with a predefined ST value and accordingly decides if the channel is idle or not. Improper setting of the ST values creates asymmetric sensing floors, resulting in collisions due to hidden terminals and/or reduction in the spatial reuse due to exposed terminals. Optimal ST setting is challenging because it requires global knowledge of mobility, traffic loads, and channel access behavior of all contending devices. Sense-Bandits tackles this problem by employing a clustering-based multi-armed bandit (MAB) algorithm, which adapts its learning behavior based on network dynamics. Clustering allows the algorithm to track network changes in real-time, ensuring fast learning of the best ST values by classifying the state and dynamics of coexisting networks. We develop a C++-based network simulator that implements Sense-Bandits and we apply it to evaluate the coexistence of Wi-Fi and 5G NR-U systems over the unlicensed 5 GHz U-NII bands. Our simulation results indicate that ST-adaptive devices employing Sense-Bandits do not harm neighboring devices that adopt a fixed ST value.
Source-independent time-lapse full-waveform inversion for VTI media

Time-lapse full-waveform inversion can provide high-resolution information about changes in the reservoir properties during hydrocarbon production and CO2 injection. However, the accuracy of the es...
Synthesizing Correlated Randomness using Algebraic Structured Codes

In this problem, Alice and Bob, are provided $X_{1}^{n}$ and $X_{2}^{n}$ that are IID $px_{1}x_{2}$ . Alice and Bob can communicate to Charles over (noiseless) links of rate $R_{1}$ and $R_{2}$ , respectively. Their goal is to enable Charles generate samples $Y^{n}$ such that the triple $(X_{1}^{n},\ X_{2}^{n},\ Y^{n})$ has a PMF that is close, in total variation, to $\prod p_{X_{1}X_{2}\mathrm{Y}}$ . In addition, the three parties may posses shared common randomness at rate $C$ . We address the problem of characterizing the set of rate triples $(R_{1},\ R_{2},\ C)$ for which the above goal can be accomplished. We build on our recent findings and propose a new coding scheme based on coset codes. We analyze its information-theoretic performance and derive a new inner bound. We identify examples for which the derived inner bound is analytically proven to contain rate triples that are not achievable via any known unstructured code based coding techniques. Our findings build on a variant of soft-covering which generalizes its applicability to the algebraic structured code ensembles. This adds to the advancement of the use structured codes in network information theory.
Smaller Hippocampal Volume Among Black and Latinx Youth Living in High-Stigma Contexts

Objective To determine whether structural and individual forms of stigma are associated with neurodevelopment in children. Method Stigma related to gender, race, and Latinx ethnicity was measured at the structural level using objective state-level indicators of social policies and prejudicial attitudes and at the individual level using self-reports of perceived discrimination. Respective associations of stigma with hippocampal volume and amygdala reactivity to threat were examined using data from the Adolescent Brain Cognitive Development (ABCD) Study (N = 11,534, mean age 9.9 years), the first multisite neuroimaging study that provided substantial variability in sociopolitical contexts and that included individual-level measures of stigma among youth. Results In a preregistered analysis, Black (B = −58.26, p = .023) and Latinx (B = −40.10, p = .044) youths in higher (vs lower) structural stigma contexts were found to have smaller hippocampal volume, controlling for total intracranial volume, demographics, and family socioeconomic status. This association was also observed at a trend-level among girls (p = .082). The magnitude of the difference in hippocampal volume between high and low structural stigma states was equivalent to the predicted impact of a $20,000 difference in annual family income in this sample. As hypothesized, structural stigma was not associated with hippocampal volume in nonstigmatized youths, providing evidence of specificity. Perceived discrimination was unrelated to hippocampal volume in stigmatized groups. No associations between perceived discrimination or structural stigma and amygdala reactivity to threat were observed. Conclusion This study provides novel evidence that an objective measure of structural stigma may be more strongly related to hippocampal volume than subjective perceptions of stigma, suggesting that contextual approaches to stigma could yield new insights into neurodevelopment among marginalized youth.
Fisher Information and Mutual Information Constraints

We consider the processing of statistical samples $X\sim P_{\theta}$ by a channel $p(y\vert x)$ , and characterize how the statistical information from the samples for estimating the parameter $\theta\in \mathbb{R}^{d}$ can scale with the mutual information or capacity of the channel. We show that if the statistical model has a sub-Gaussian score function, then the trace of the Fisher information matrix for estimating $\theta$ from $Y$ can scale at most linearly with the mutual information between $X$ and $Y$ . We apply this result to obtain minimax lower bounds in distributed statistical estimation problems, and obtain a tight preconstant for Gaussian mean estimation. We then show how our Fisher information bound can also imply mutual information or Jensen-Shannon divergence based distributed strong data processing inequalities.
Predicting CaO-(MgO)-Al2O3-SiO2 glass reactivity in alkaline environments from force field molecular dynamics simulations

Abstract In this investigation, force field-based molecular dynamics (MD) simulations have been employed to generate detailed structural representations for a range of amorphous quaternary CaO-MgO-Al2O3-SiO2 (CMAS) and ternary CaO-Al2O3-SiO2 (CAS) glasses. Comparison of the simulation results with select experimental X-ray and neutron total scattering and literature data reveals that the MD-generated structures have captured the key structural features of these CMAS and CAS glasses. Based on the MD-generated structural representations, we have developed two structural descriptors, specifically (i) average metal oxide dissociation energy (AMODE) and (ii) average self-diffusion coefficient (ASDC) of all the atoms at melting. Both structural descriptors are seen to more accurately predict the relative glass reactivity than the commonly used degree of depolymerization parameter, especially for the eight synthetic CAS glasses that span a wide compositional range. Hence these descriptors hold great promise for predicting CMAS and CAS glass reactivity in alkaline environments from compositional information.
Wave Asymptotics for Waveguides and Manifolds with Infinite Cylindrical Ends

We describe wave decay rates associated to embedded resonances and spectral thresholds for waveguides and manifolds with infinite cylindrical ends. We show that if the cut-off resolvent is polynomially bounded at high energies, as is the case in certain favorable geometries, then there is an associated asymptotic expansion, up to a $O(t^{-k_0})$ remainder, of solutions of the wave equation on compact sets as $t \to \infty$. In the most general such case we have $k_0=1$, and under an additional assumption on the infinite ends we have $k_0 = \infty$. If we localize the solutions to the wave equation in frequency as well as in space, then our results hold for quite general waveguides and manifolds with infinite cylindrical ends. To treat problems with and without boundary in a unified way, we introduce a black box framework analogous to the Euclidean one of Sjostrand and Zworski. We study the resolvent, generalized eigenfunctions, spectral measure, and spectral thresholds in this framework, providing a new approach to some mostly well-known results in the scattering theory of manifolds with cylindrical ends.
Dimension-free log-Sobolev inequalities for mixture distributions

Abstract We prove that if ( P x ) x ∈ X is a family of probability measures which satisfy the log-Sobolev inequality and whose pairwise chi-squared divergences are uniformly bounded, and μ is any mixing distribution on X , then the mixture ∫ P x d μ ( x ) satisfies a log-Sobolev inequality. In various settings of interest, the resulting log-Sobolev constant is dimension-free. In particular, our result implies a conjecture of Zimmermann and Bardet et al. that Gaussian convolutions of measures with bounded support enjoy dimension-free log-Sobolev inequalities.
Video Action Understanding

Many believe that the successes of deep learning on image understanding problems can be replicated in the realm of video understanding. However, due to the scale and temporal nature of video, the span of video understanding problems and the set of proposed deep learning solutions is arguably wider and more diverse than those of their 2D image siblings. Finding, identifying, and predicting actions are a few of the most salient tasks in this emerging and rapidly evolving field. With a pedagogical emphasis, this tutorial introduces and systematizes fundamental topics, basic concepts, and notable examples in supervised video action understanding. Specifically, we clarify a taxonomy of action problems, catalog and highlight video datasets, describe common video data preparation methods, present the building blocks of state-of-the-art deep learning model architectures, and formalize domain-specific metrics to baseline proposed solutions. This tutorial is intended to be accessible to a general computer science audience and assumes a conceptual understanding of supervised learning.
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks.

Existing research on making sense of deep neural networks often focuses on neuron-level interpretation, which may not adequately capture the bigger picture of how concepts are collectively encoded by multiple neurons. We present NEUROCARTOGRAPHY, an interactive system that scalably summarizes and visualizes concepts learned by neural networks. It automatically discovers and groups neurons that detect the same concepts, and describes how such neuron groups interact to form higher-level concepts and the subsequent predictions. NEUROCARTOGRAPHY introduces two scalable summarization techniques: (1) neuron clustering groups neurons based on the semantic similarity of the concepts detected by neurons (e.g., neurons detecting “dog faces” of different breeds are grouped); and (2) neuron embedding encodes the associations between related concepts based on how often they co-occur (e.g., neurons detecting “dog face” and “dog tail” are placed closer in the embedding space). Key to our scalable techniques is the ability to efficiently compute all neuron pairs' relationships, in time linear to the number of neurons instead of quadratic time. NEUROCARTOGRAPHY scales to large data, such as the ImageNet dataset with 1.2M images. The system's tightly coordinated views integrate the scalable techniques to visualize the concepts and their relationships, projecting the concept associations to a 2D space in Neuron Projection View, and summarizing neuron clusters and their relationships in Graph View. Through a large-scale human evaluation, we demonstrate that our technique discovers neuron groups that represent coherent, human-meaningful concepts. And through usage scenarios, we describe how our approaches enable interesting and surprising discoveries, such as concept cascades of related and isolated concepts. The NEUROCARTOGRAPHY visualization runs in modern browsers and is open-sourced.
Semi-Decentralized Federated Learning With Cooperative D2D Local Model Aggregations

Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we propose two timescale hybrid federated learning ( TT-HF ), a semi-decentralized learning architecture that combines the conventional device-to-server communication paradigm for federated learning with device-to-device (D2D) communications for model training. In TT-HF , during each global aggregation interval, devices (i) perform multiple stochastic gradient descent iterations on their individual datasets, and (ii) aperiodically engage in consensus procedure of their model parameters through cooperative, distributed D2D communications within local clusters. With a new general definition of gradient diversity, we formally study the convergence behavior of TT-HF , resulting in new convergence bounds for distributed ML. We leverage our convergence bounds to develop an adaptive control algorithm that tunes the step size, D2D communication rounds, and global aggregation period of TT-HF over time to target a sublinear convergence rate of $\mathcal {O}(1/t)$ while minimizing network resource utilization. Our subsequent experiments demonstrate that TT-HF significantly outperforms the current art in federated learning in terms of model accuracy and/or network energy consumption in different scenarios where local device datasets exhibit statistical heterogeneity. Finally, our numerical evaluations demonstrate robustness against outages caused by fading channels, as well favorable performance with non-convex loss functions.
Signatures of Topological Transitions in the Spin Susceptibility of Josephson Junctions

We theoretically investigate how the spin susceptibility of a planar Josephson junction is affected when the system transits into the topological superconducting state. We show that the magnetic flux and magnetic field dependence of the spin susceptibility closely maps the phase diagram of the system. In the absence of an external magnetic flux the system can self-tune into the topological superconducting state by minimizing its free energy. Self-tuned topological transitions are accompanied by sharp peaks in the spin susceptibility, which can therefore be used as measurable fingerprints for detecting the topological superconducting state. Away from the peaks, the amplitude of the spin susceptibility can provide qualitative information about the relative size of the topological gap. The signatures in the spin susceptibility are robust, even in junctions with narrow superconducting leads, where critical current minima may no longer serve as an indication of topological phase transitions. The predicted results could be particularly relevant for future experiments on realization and detection of the topological superconducting state in planar Josephson junctions.
RSS + : Pro-Active Risk Mitigation for AV Safety Layers based on RSS

Addressing safety for future autonomous vehicles (AVs) while maintaining a high practicability, i.e. not using excessive safety margins, getting stuck or not able to drive at all, is still an open research question. In this regard, the Responsibility Sensitive Safety (RSS) approach, which is a formal parametric safety model, is a promising concept and is currently gaining lots of attraction. With RSS it is possible to combine safety and practicability, if the model parameters are chosen in a reasonable manner, i.e. good enough to achieve a desirable level of safety, but not covering the physical worst case. However, as we will show in this work, this can lead to a not optimal behavior of the AV in extreme situations, when other traffic participants violate RSS assumptions. To overcome this, we propose an extension to RSS, called RSS +, which can mitigate many of these situations in a pro-active manner, without sacrificing practicability.
Horizontal Gene Transfer Drives the Evolution of Dependencies in Bacteria

Many naturally-occurring bacteria lead a lifestyle of metabolic dependency, i.e., they depend on others for crucial resources. We do not understand what factors drive bacteria towards this lifestyle, and how. Here, we systematically show that horizontal gene transfer (HGT) plays a crucial role in the evolution of dependencies in bacteria. Across 835 bacterial species, we map gene gain-loss dynamics on a deep evolutionary tree, and assess the impact of HGT and gene loss on bacterial metabolic networks. Our analyses suggest that genes acquired by HGT can affect which genes are later lost. Dependency evolution is contingent on earlier HGT because of two reasons. First, we find that HGT typically adds new catabolic routes to bacterial metabolic networks. This increases the chance of new metabolic interactions between bacteria, which is a prerequisite for dependency evolution. Second, we show that gaining new routes can promote the loss of specific ancestral routes (a mechanism we call "coupled gains and losses", CGLs). Phylogenetic patterns indicate that both types of dependencies — those mediated by CGLs and those purely by gene loss — are equally likely. Our results highlight HGT as an important driver of metabolic dependency evolution in bacteria.
The Fuel Cell: An Ideal Chemical Engineering Undergraduate Experiment

Fuel-cell based experiments embody principles in electrochemistry, thermodynamics, kinetics, and transport and are ideally suited for the chemical engineering curricula. Experiments using a hydrogen proton exchange membrane (PEM) fuel cell have been developed for the chemical engineering undergraduate laboratory. The experiments allow students to study the principles of fuel cell operation and familiarize themselves with fuel cell performance as a function of oxidant composition and operating temperature. Experimental data can be fit to a simple model from which ohmic losses, kinetic parameters, and limiting current density (mass transfer limitations) can be estimated and compared to measured or theoretical values. A list of equipment, supplies, costs, and vendors has been included.
Visualizing adverse events in clinical trials using correspondence analysis with R-package visae.

BACKGROUND Graphical displays and data visualization are essential components of statistical analysis that can lead to improved understanding of clinical trial adverse event (AE) data. Correspondence analysis (CA) has been introduced decades ago as a multivariate technique that can communicate AE contingency tables using two-dimensional plots, while quantifying the loss of information as other dimension reduction techniques such as principal components and factor analysis. METHODS We propose the application of stacked CA using contribution biplots as a tool to explore differences in AE data among treatments in clinical trials. We defined five levels of refinement for the analysis based on data derived from the Common Terminology Criteria for Adverse Events (CTCAE) grades, domains, terms and their combinations. In addition, we developed a Shiny app built in an R-package, visae, publicly available on Comprehensive R Archive Network (CRAN), to interactively investigate CA configurations based on the contribution to the explained variance and relative frequency of AEs. Data from two randomized controlled trials (RCT) were used to illustrate the proposed methods: NSABP R-04, a neoadjuvant rectal 2 × 2 factorial trial comparing radiation therapy with either capecitabine (Cape) or 5-fluorouracil (5-FU) alone with or without oxaliplatin (Oxa), and NSABP B-35, a double-blind RCT comparing tamoxifen to anastrozole in postmenopausal women with hormone-positive ductal carcinoma in situ. RESULTS In the R04 trial (n = 1308), CA biplots displayed the discrepancies between single agent treatments and their combinations with Oxa at all levels of AE classes, such that these discrepancies were responsible for the largest portion of the explained variability among treatments. In addition, an interaction effect when adding Oxa to Cape/5-FU was identified when the distance between Cape+Oxa and 5-FU + Oxa was observed to be larger than the distance between 5-FU and Cape, with Cape+Oxa and 5-FU + Oxa in different quadrants of the CA biplots. In the B35 trial (n = 3009), CA biplots showed different patterns for non-adherent Anastrozole and Tamoxifen compared with their adherent counterparts. CONCLUSION CA with contribution biplot is an effective tool that can be used to summarize AE data in a two-dimensional display while minimizing the loss of information and interpretation.
Coupling the Dirac and Einstein Equations Through Geometry

We show that the exterior algebra bundle over a curved spacetime can be used as framework in which both the Dirac and the Einstein equations can be obtained. These equations and their coupling follow from the variational principle applied to a Lagrangian constructed from natural geometric invariants. We also briefly indicate how other forces can potentially be incorporated within this geometric framework.
Separable Hamiltonian PDEs and Turning point principle for stability of gaseous stars

We consider stability of non-rotating gaseous stars modeled by the Euler-Poisson system. Under general assumptions on the equation of states, we proved a turning point principle (TPP) that the stability of the stars is entirely determined by the mass-radius curve parameterized by the center density. In particular, the stability can only change at extrema (i.e. local maximum or minimum points) of the total mass. For very general equation of states, TPP implies that for increasing center density the stars are stable up to the first mass maximum and unstable beyond this point until next mass extremum (a minimum). Moreover, we get a precise counting of unstable modes and exponential trichotomy estimates for the linearized Euler-Poisson system. To prove these results, we develop a general framework of separable Hamiltonian PDEs. The general approach is flexible and can be used for many other problems including stability of rotating and magnetic stars, relativistic stars and galaxies.