cssi/SciSciGPT-SciSciNet huggingface dataset:
Association of CXCR6 with COVID-19 severity: delineating the host genetic factors in transcriptomic regulation.

The coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with ~ 80% asymptomatic or mild cases and ~ 5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms. We implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis, and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single-cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients. We discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on lung, and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis-expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung-resident memory CD8+ T (TRM) cells, we found a 2.24-fold decrease of cell proportion among CD8+ T cells and lower expression of CXCR6 in the severe patients than moderate patients. Pro-inflammatory transcriptional programs were highlighted in the TRM cellular trajectory from moderate to severe patients. CXCR6 from the 3p21.31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung TRM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis.
The conormal and Robin boundary value problems in nonsmooth domains satisfying a measure condition

Abstract We consider elliptic equations and systems in divergence form with the conormal or the Robin boundary conditions, with small BMO (bounded mean oscillation) or variably partially small BMO coefficients. We propose a new class of domains which are locally close to a half space (or convex domains) with respect to the Lebesgue measure in the system (or scalar, respectively) case, and obtain the W p 1 estimate for the conormal problem with the homogeneous boundary condition. Such condition is weaker than the Reifenberg flatness condition, for which the closeness is measured in terms of the Hausdorff distance, and the semi-convexity condition. For the conormal problem with inhomogeneous boundary conditions, we also assume that the domain is Lipschitz. By using these results, we obtain the W p 1 and weighted W p 1 estimates for the Robin problem in these domains.
Are Lengthy and Boilerplate Risk Factor Disclosures Inadequate? An Examination of Judicial and Regulatory Assessments of Risk Factor Language

Although formal guidance instructs firms to avoid issuing lengthy and boilerplate risk factor disclosures, regulators and users of financial statements note these disclosures continue to be excessively long and boilerplate. The persistence of these characteristics is particularly surprising given that prior research finds firms disclosing lengthy and boilerplate risk factors experience negative capital market consequences. We investigate two potential sources of firms’ incentives to issue such disclosures by examining how judicial and regulatory assessments of firms’ risk factor disclosures correlate with measures of disclosure length and disclosure boilerplate. Our results suggest that lengthier and more boilerplate risk factor disclosures are less likely to be considered inadequate under judicial and regulatory review. Specifically, risk factors that are lengthier and less specific are less likely to be found inadequate by judges in shareholder securities lawsuits. In addition, more standardized risk factor disclosures are less likely to be targeted by an SEC comment letter during the SEC’s filing review process. Further analysis finds that when risk factor language is assessed as adequate in judicial review, industry peers borrow that language more frequently. Finally, we find that judicial assessments of risk factor disclosures prompt industry peers to lengthen their own risk factor disclosures regardless of whether the risk disclosure was deemed adequate or not.
Strengthening SDN Security: Protocol Dialecting and Downgrade Attacks

Software-defined networking (SDN) has become a fundamental technology for data centers and 5G networks. Transport Layer Security (TLS) has been proposed as its single security layer for SDN networks; however, use of TLS is optional and TLS connections between the controller and switches are still vulnerable to downgrade attacks. In this paper, we propose a protocol dialecting approach to provide additional and customizable security assurance for network control messages. We consider and evaluate two dialecting approaches for OpenFlow protocol operation, adding per-message authentication to the SDN control channel that is independent of TLS and provides robustness against downgrade attacks targeting TLS. Furthermore, we measure the performance impact of using these dialecting primitives in a Mininet experiment. The results show a modest increase of communication latency of less than 22%.
Drivers of Cross-Border Banking in Sub-Saharan Africa

Using data collected from pan-African banks' (PABs), balance sheets and other sources (Orbis, Fitch), this study identifies some key patterns of cross-border investment in bank subsidiaries by key banking groups in sub-Saharan Africa (SSA) and discusses some of the determinants of this investment. Using a gravity model relating the annual value of a banking group's investment in the net equity of its subsidiaries to a set of explanatory variables, the analysis finds that cross-border banking is in part driven by a search for yield, diversification, and expansion for strategic reasons.
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning

Modeling the dynamics of real-world physical systems is critical for spatiotemporal prediction tasks, but challenging when data is limited. The scarcity of real-world data and the difficulty in reproducing the data distribution hinder directly applying meta-learning techniques. Although the knowledge of governing partial differential equations (PDE) of the data can be helpful for the fast adaptation to few observations, it is mostly infeasible to exactly find the equation for observations in real-world physical systems. In this work, we propose a framework, physics-aware meta-learning with auxiliary tasks whose spatial modules incorporate PDE-independent knowledge and temporal modules utilize the generalized features from the spatial modules to be adapted to the limited data, respectively. The framework is inspired by a local conservation law expressed mathematically as a continuity equation and does not require the exact form of governing equation to model the spatiotemporal observations. The proposed method mitigates the need for a large number of real-world tasks for meta-learning by leveraging spatial information in simulated data to meta-initialize the spatial modules. We apply the proposed framework to both synthetic and real-world spatiotemporal prediction tasks and demonstrate its superior performance with limited observations.
Focused ion beam deposited carbon-platinum nanowires for cryogenic resistive thermometry

Abstract The study of thermal effects, both classical and quantum, at cryogenic temperatures requires the use of on-chip, local, high-sensitivity thermometry. Carbon-platinum composites fabricated using focused ion beam (FIB) assisted deposition form a granular structure which is shown in this study to be uniquely suited for this application. Carbon-platinum thermometers deposited using a 24 pA ion beam current have high sensitivities below 1 K, comparable to the best cryogenic thermometers. In addition, these thermometers can be accurately placed to within 10s of nanometers on the chip using a mask-free process. They also have a weak magnetic field dependence, 3% change in resistance with applied magnetic fields from 0 to 8 T. Finally, these thermometers are integrable into a variety of nanoscale devices due to the existing wide spread use of FIB.
Disagreement about fundamentals: measurement and consequences

We propose a measure of disagreement, which reflects differences of opinion as opposed to information asymmetry, that can be extracted from sequences of analyst forecasts. Using a Bayesian theoretical framework, we prove that when analysts agree, a regression of an analyst’s forecast on the previous forecast issued by another analyst should have a slope coefficient of one. The magnitude of the estimated regression coefficient’s deviation from one is then employed as a disagreement measure. We validate the measure using tests tied to predicted relations between disagreement and trading volume and bid-ask spreads. Finally, we employ our measure to test for associations between disagreement and expected returns predicted by antecedent theoretical studies.
Structural basis of target DNA recognition by CRISPR-Cas12k for RNA-guided DNA transposition

Summary The type V-K CRISPR-Cas system, featured by Cas12k effector with a naturally inactivated RuvC domain and associated with Tn7-like transposon for RNA-guided DNA transposition, is a promising tool for precise DNA insertion. To reveal the mechanism underlying target DNA recognition, we determined a cryoelectron microscopy (cryo-EM) structure of Cas12k from cyanobacteria Scytonema hofmanni in complex with a single guide RNA (sgRNA) and a double-stranded target DNA. Coupled with mutagenesis and in vitro DNA transposition assay, our results revealed mechanisms for the recognition of the GGTT protospacer adjacent motif (PAM) sequence and the structural elements of Cas12k critical for RNA-guided DNA transposition. These structural and mechanistic insights should aid in the development of type V-K CRISPR-transposon systems as tools for genome editing.
On the Numerical Solution of the Near Field Refractor Problem

A numerical scheme is presented to solve the one source near field refractor problem to arbitrary precision and it is proved that for a given error, the scheme terminates in a finite number of iterations. The convergence of the algorithm depends upon proving appropriate Lipschitz estimates for the refractor measure. The algorithm is presented in general terms and has independent interest.
Reed-Muller Subcodes: Machine Learning-Aided Design of Efficient Soft Recursive Decoding

Reed-Muller (RM) codes are conjectured to achieve the capacity of any binary-input memoryless symmetric (BMS) channel, and are observed to have a comparable performance to that of random codes in terms of scaling laws. On the negative side, RM codes lack efficient decoders with performance close to that of a maximum likelihood decoder for general parameters. Also, they only admit certain discrete sets of rates. In this paper, we focus on subcodes of RM codes with flexible rates that can take any code dimension from 1 to $n$ . where $n$ is the blocklength. We first extend the recursive projection-aggregation (RPA) algorithm proposed recently by Ye and Abbe for decoding RM codes. To lower the complexity of our decoding algorithm, referred to as subRPA, we investigate different ways for pruning the projections. We then derive the soft-decision based version of our algorithm, called soft-subRPA, that is shown to improve upon the performance of subRPA. Furthermore, it enables training a machine learning (ML) model to search for good sets of projections that minimize the decoding error rate. Training our ML model enables achieving very close to the performance of full-projection decoding with a significantly reduced number of projections. For instance, our simulation results on a (64,14) RM subcode show almost identical performance for full-projection decoding and pruned-projection decoding with 15 projections picked via training our ML model. This is equivalent to lowering the complexity by a factor of more than 4 without sacrificing the decoding performance.
The dynamics of faculty hiring networks

Faculty hiring networks—who hires whose graduates as faculty—exhibit steep hierarchies, which can reinforce both social and epistemic inequalities in academia. Understanding the mechanisms driving these patterns would inform efforts to diversify the academy and shed new light on the role of hiring in shaping which scientific discoveries are made. Here, we investigate the degree to which structural mechanisms can explain hierarchy and other network characteristics observed in empirical faculty hiring networks. We study a family of adaptive rewiring network models, which reinforce institutional prestige within the hierarchy in five distinct ways. Each mechanism determines the probability that a new hire comes from a particular institution according to that institution’s prestige score, which is inferred from the hiring network’s existing structure. We find that structural inequalities and centrality patterns in real hiring networks are best reproduced by a mechanism of global placement power, in which a new hire is drawn from a particular institution in proportion to the number of previously drawn hires anywhere. On the other hand, network measures of biased visibility are better recapitulated by a mechanism of local placement power, in which a new hire is drawn from a particular institution in proportion to the number of its previous hires already present at the hiring institution. These contrasting results suggest that the underlying structural mechanism reinforcing hierarchies in faculty hiring networks is a mixture of global and local preference for institutional prestige. Under these dynamics, we show that each institution’s position in the hierarchy is remarkably stable, due to a dynamic competition that overwhelmingly favors more prestigious institutions. These results highlight the reinforcing effects of a prestige-based faculty hiring system, and the importance of understanding its ramifications on diversity and innovation in academia.
A matching procedure for sequential experiments that iteratively learns which covariates improve power

We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials exploiting some subjects' previous assessed responses. Subjects arrive sequentially and are either randomized or paired to a previously randomized subject and administered the alternate treatment. The pairing is made via a dynamic matching criterion that iteratively learns which specific covariates are important to the response. We develop estimators for the average treatment effect as well as an exact test. We illustrate our method's increase in efficiency and power over other allocation procedures in both simulated scenarios and a clinical trial dataset. An R package "SeqExpMatch" for use by practitioners is available on CRAN.
Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production.

Alcohol-forming fatty acyl reductases (FARs) catalyze the reduction of thioesters to alcohols and are key enzymes for microbial production of fatty alcohols. Many metabolic engineering strategies utilize FARs to produce fatty alcohols from intracellular acyl-CoA and acyl-ACP pools; however, enzyme activity, especially on acyl-ACPs, remains a significant bottleneck to high-flux production. Here, we engineer FARs with enhanced activity on acyl-ACP substrates by implementing a machine learning (ML)-driven approach to iteratively search the protein fitness landscape. Over the course of ten design-test-learn rounds, we engineer enzymes that produce over twofold more fatty alcohols than the starting natural sequences. We characterize the top sequence and show that it has an enhanced catalytic rate on palmitoyl-ACP. Finally, we analyze the sequence-function data to identify features, like the net charge near the substrate-binding site, that correlate with in vivo activity. This work demonstrates the power of ML to navigate the fitness landscape of traditionally difficult-to-engineer proteins.
EM-X-DL: Efficient Cross-device Deep Learning Side-channel Attack With Noisy EM Signatures

This work presents a Cross-device Deep-Learning based Electromagnetic (EM-X-DL) side-channel analysis (SCA) on AES-128, in the presence of a significantly lower signal-to-noise ratio (SNR) compared...
Identifying latent shared mobility preference segments in low-income communities: Ride-hailing, fixed-route bus, and mobility-on-demand transit

Abstract Concepts of Mobility-on-Demand (MOD) and Mobility as a Service (MaaS), which feature the integration of various shared-use mobility options, have gained widespread popularity in recent years. While these concepts promise great benefits to travelers, their heavy reliance on technology raises equity concerns as socially disadvantaged population groups can be left out in an era of on-demand mobility. This paper investigates the potential uptake of MOD transit services (integrated fixed-route and on-demand services) among travelers living in low-income communities. Specially, we analyze people's latent attitude towards three shared-use mobility services, including ride-hailing services, fixed-route transit, and MOD transit. We conduct a latent class cluster analysis of 825 survey respondents sampled from low-income neighborhoods in Detroit and Ypsilanti, Michigan. We identified three latent segments: shared-mode enthusiast, shared-mode opponent, and fixed-route transit loyalist. People from the shared-mode enthusiast segment often use ride-hailing services and live in areas with poor transit access, and they are likely to be the early adopters of MOD transit services. The shared-mode opponent segment mainly includes vehicle owners who lack interests in shared mobility options. The fixed-route transit loyalist segment includes a considerable share of low-income individuals who face technological barriers to use the MOD transit. We also find that males, college graduates, car owners, people with a mobile data plan, and people living in poor-transit-access areas have a higher level of preferences for MOD transit services. We conclude with policy recommendations for developing more accessible and equitable MOD transit services.
Resource allocation for different types of vaccines against COVID-19: Tradeoffs and synergies between efficacy and reach.

OBJECTIVE: Vaccine shortage and supply-chain challenges have caused limited access by many resource-limited countries during the COVID-19 pandemic. One of the primary decisions for a vaccine-ordering decision-maker is how to allocate the limited resources between different types of vaccines effectively. We studied the tradeoff between efficacy and reach of the two vaccine types that become available at different times. METHODS: We extended a Susceptible-Infected-Recovered-Deceased (SIR-D) model with vaccination, ran extensive simulations with different settings, and compared the level of infection attack rate (IAR) under different reach ratios between two vaccine types under different resource allocation decisions. RESULTS: We found that when there were limited resources, allocating resources to a vaccine with high efficacy that became available earlier than a vaccine with lower efficacy did not always lead to a lower IAR, particularly if the former could vaccinate less than 42.5% of the population (with the selected study parameters) who could have received the latter. Sensitivity analyses showed that this result stayed robust under different study parameters. CONCLUSIONS: Our results showed that a vaccine with lower resource requirements (wider reach) can significantly contribute to reducing IAR, even if it becomes available later in the pandemic, compared to a higher efficacy vaccine that becomes available earlier but requires more resources. Limited resource in vaccine distribution is significant challenge in many parts of the world that needs to be addressed to improve the global access to life-saving vaccines. Understanding the tradeoffs between efficacy and reach is critical for resource allocation decisions between different vaccine types for improving health outcomes.
Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs

Abstract Many-query problems – arising from, e.g., uncertainty quantification, Bayesian inversion, Bayesian optimal experimental design, and optimization under uncertainty – require numerous evaluations of a parameter-to-output map. These evaluations become prohibitive if this parametric map is high-dimensional and involves expensive solution of partial differential equations (PDEs). To tackle this challenge, we propose to construct surrogates for high-dimensional PDE-governed parametric maps in the form of derivative-informed projected neural networks (DIPNets) that parsimoniously capture the geometry and intrinsic low-dimensionality of these maps. Specifically, we compute Jacobians of these PDE-based maps, and project the high-dimensional parameters onto a low-dimensional derivative-informed active subspace; we also project the possibly high-dimensional outputs onto their principal subspace. This exploits the fact that many high-dimensional PDE-governed parametric maps can be well-approximated in low-dimensional parameter and output subspaces. We use the projection basis vectors in the active subspace as well as the principal output subspace to construct the weights for the first and last layers of the neural network, respectively. This frees us to train the weights in only the low-dimensional layers of the neural network. The architecture of the resulting neural network then captures, to first order, the low-dimensional structure and geometry of the parametric map. We demonstrate that the proposed projected neural network achieves greater generalization accuracy than a full neural network, especially in the limited training data regime afforded by expensive PDE-based parametric maps. Moreover, we show that the number of degrees of freedom of the inner layers of the projected network is independent of the parameter and output dimensions, and high accuracy can be achieved with weight dimension independent of the discretization dimension.
The MIT Humanoid Robot: Design, Motion Planning, and Control For Acrobatic Behaviors

Demonstrating acrobatic behavior of a humanoid robot such as flips and spinning jumps requires systematic approaches across hardware design, motion planning, and control. In this paper, we present a new humanoid robot design, an actuator-aware kino-dynamic motion planner, and a landing controller as part of a practical system design for highly dynamic motion control of the humanoid robot. To achieve the impulsive motions, we develop two new proprioceptive actuators. The actuator’s torque, velocity, and power limits are reflected in our kino-dynamic motion planner by approximating the configuration-dependent reaction force limits. For the landing control, we effectively integrate model-predictive control and whole-body impulse control by connecting them in a dynamically consistent way to accomplish both the long-time horizon optimal control and high-bandwidth full-body dynamics-based feedback. With the carefully designed hardware and control framework, we successfully demonstrate dynamic behaviors such as back flips, front flips, and spinning jumps in our realistic dynamics simulation.
Numerical Experiments in Error Control for Sound Propagation Using a Damping Layer Boundary Treatment

This paper presents results from numerical experiments for controlling the error caused by a damping layer boundary treatment when simulating the propagation of an acoustic signal from a continuous pressure source. The computations are with the 2D Linearized Euler Equations (LEE) for both a uniform mean flow and a steady parallel jet. The numerical experiments are with algorithms that are third, fifth, seventh and ninth order accurate in space and time. The numerical domain is enclosed in a damping layer boundary treatment. The damping is implemented in a time accurate manner, with simple polynomial damping profiles of second, fourth, sixth and eighth power. At the outer boundaries of the damping layer the propagating solution is uniformly set to zero. The complete boundary treatment is remarkably simple and intrinsically independant from the dimension of the spatial domain. The reported results show the relative effect on the error from the boundary treatment by varying the damping layer width, damping profile power, damping amplitude, propagtion time, grid resolution and algorithm order. The issue that is being addressed is not the accuracy of the numerical solution when compared to a mathematical solution, but the effect of the complete boundary treatment on the numerical solution, and to what degree the error in the numerical solution from the complete boundary treatment can be controlled. We report maximum relative absolute errors from just the boundary treatment that range from O[10-2] to O[10-7].
When Efficiency meets Equity in Congestion Pricing and Revenue Refunding Schemes

Congestion pricing has long been hailed as a means to mitigate traffic congestion; however, its practical adoption has been limited due to social inequity issues, e.g., low-income users are priced out off certain roads. This issue has spurred interest in the design of equitable mechanisms that refund the collected toll revenues to users. Although revenue refunding has been extensively studied, there has been no characterization of how such schemes can be designed to simultaneously achieve system efficiency and equity objectives. In this work, we bridge this gap through the study of congestion pricing and revenue refunding (CPRR) schemes in non-atomic congestion games. We first develop CPRR schemes, which in comparison to the untolled case, simultaneously (i) increase system efficiency and (ii) decrease wealth inequality, while being (iii) user-favorable: irrespective of their initial wealth or values-of-time (which may differ across users) users would experience a lower travel cost after the implementation of the proposed scheme. We then characterize the set of optimal user-favorable CPRR schemes that simultaneously maximize system efficiency and minimize wealth inequality. These results assume a well-studied behavior model of users minimizing a linear function of their travel times and tolls, without considering refunds. Overall, our work demonstrates that through appropriate refunding policies we can achieve system efficiency while reducing wealth inequality.
Decision Making in Star Networks with Incorrect Beliefs

Consider a Bayesian binary decision-making problem in star networks, where local agents make selfish decisions independently, and a fusion agent makes a final decision based on aggregated decisions and its own private signal. In particular, we assume all agents have private beliefs for the true prior probability, based on which they perform Bayesian decision making. We focus on the Bayes risk of the fusion agent and counterintuitively find that incorrect beliefs could achieve a smaller risk than that when agents know the true prior. It is of independent interest for sociotechnical system design that the optimal beliefs of local agents resemble human probability reweighting models from cumulative prospect theory. We also consider asymptotic characterization of the optimal beliefs and fusion agent's risk in the number of local agents. We find that the optimal risk of the fusion agent converges to zero exponentially fast as the number of local agents grows. Furthermore, having an identical constant belief is asymptotically optimal in the sense of the risk exponent. For additive Gaussian noise, the optimal belief turns out to be a simple function of only error costs and the risk exponent can be explicitly characterized.
Spin fluctuations yield zT enhancement in ferromagnets.

SUMMARY Thermal fluctuation of local magnetization intercoupled with charge carriers and phonons offers a path to enhance thermoelectric performance. The thermopower enhancement by spin fluctuations (SF) has been observed before. However, the crucial evidence for enhancing thermoelectric-figure-of-merit (zT) by SF has not been reported until now. Here we report that the SF leads to nearly 80% zT enhancement in ferromagnetic CrTe near and below TC∼335 K. The ferromagnetism is originated from the collective electronic and localized magnetic moments. The field-dependent transport properties demonstrate the profound impact of SF on the electrons and phonons. Under an external magnetic field, the enhancement in thermopower is suppressed, and the thermal conductivity is enhanced, evidencing the existence of a strong SF. The anomalous thermoelectric transport properties are analyzed based on theoretical models, and a good agreement with experimental data is found. This study contributes to the fundamental understanding of SF for designing high-performance spin-driven thermoelectrics.
Deep graph learning of inter-protein contacts.

MOTIVATION Inter-protein (interfacial) contact prediction is very useful for in silico structural characterization of protein-protein interactions. Although deep learning has been applied to this problem, its accuracy is not as good as intra-protein contact prediction. RESULTS We propose a new deep learning method GLINTER (Graph Learning of INTER-protein contacts) for interfacial contact prediction of dimers, leveraging a rotational invariant representation of protein tertiary structures and a pretrained language model of multiple sequence alignments (MSAs). Tested on the 13th and 14th CASP-CAPRI datasets, the average top L/10 precision achieved by GLINTER is 54% on the homodimers and 52% on all the dimers, much higher than 30% obtained by the latest deep learning method DeepHomo on the homodimers and 15% obtained by BIPSPI on all the dimers. Our experiments show that GLINTER-predicted contacts help improve selection of docking decoys. AVAILABILITY The software is available at https://github.com/zw2x/glinter. The data sets are available at https://github.com/zw2x/glinter/data.
Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexica.

People convey their intention and attitude through linguistic styles of the text that they write. In this study, we investigate lexicon usages across styles throughout two lenses: human perception and machine word importance, since words differ in the strength of the stylistic cues that they provide. To collect labels of human perception, we curate a new dataset, Hummingbird, on top of benchmarking style datasets. We have crowd workers highlight the representative words in the text that makes them think the text has the following styles: politeness, sentiment, offensiveness, and five emotion types. We then compare these human word labels with word importance derived from a popular fine-tuned style classifier like BERT. Our results show that the BERT often finds content words not relevant to the target style as important words used in style prediction, but humans do not perceive the same way even though for some styles (e.g., positive sentiment and joy) human- and machine-identified words share significant overlap for some styles.
Variance-Aware Off-Policy Evaluation with Linear Function Approximation

We study the off-policy evaluation (OPE) problem in reinforcement learning with linear function approximation, which aims to estimate the value function of a target policy based on the offline data collected by a behavior policy. We propose to incorporate the variance information of the value function to improve the sample efficiency of OPE. More specifically, for time-inhomogeneous episodic linear Markov decision processes (MDPs), we propose an algorithm, VA-OPE, which uses the estimated variance of the value function to reweight the Bellman residual in Fitted Q-Iteration. We show that our algorithm achieves a tighter error bound than the best-known result. We also provide a fine-grained characterization of the distribution shift between the behavior policy and the target policy. Extensive numerical experiments corroborate our theory.
Disappearing Cryptography in the Bounded Storage Model.

In this work, we study disappearing cryptography in thebounded storage model. Here, a component of the transmission, say a ciphertext, a digital signature, or even a program, is streamed bit by bit. The stream is too large for anyone to store in its entirety, meaning the transmission effectively disappears once the stream stops.
Two regimes of tidal-stream circularization by supermassive black holes

Stars that approach a supermassive black hole (SMBH) too closely can be disrupted by the tidal gravitational field of the SMBH. The resulting debris forms a tidal stream orbiting the SMBH which can collide with itself due to relativistic apsidal precession. These self-collisions dissipate energy, causing the stream to circularize. We perform kinematic simulations of these stream self-collisions to estimate the efficiency of this circularization as a function of SMBH mass $M_\bullet$ and penetration factor $\beta$, the ratio of the tidal radius to the pericenter distance. We uncover two distinct regimes depending on whether the time $t_c$ at which the most tightly bound debris circularizes is greater or less than the time $t_{\rm fb}$ at which the mass fallback rate peaks. The bolometric light curve of energy dissipated in the stream self-collisions has a single peak at $t > t_{\rm fb}$ in the slow circularization regime ($t_c > t_{\rm fb}$), but two peaks (one at $t < t_{\rm fb}$ and a second at $t_{\rm fb}$) in the fast circularization regime ($t_c < t_{\rm fb}$). Tidal streams will circularize in the slow (fast) regime for apsidal precession angles less (greater) than 0.2 radians which occur for $\beta \lesssim (\gtrsim) (M_\bullet/10^6M_\odot)^{-2/3}$. The observation of prominent double peaks in bolometric TDE light curves near the transition between these two regimes would strongly support our model of tidal-stream kinematics.
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs

Many modern complex data can be represented as a graph. In models dealing with graph-structured data, multivariate parameters are not just sparse but have structured sparsity and smoothness in the sense that both zero and non-zero parameters tend to cluster together. We propose a new prior for high dimensional parameters with graphical relations, referred to as a Tree-based Low-rank Horseshoe(T-LoHo) model, that generalizes the popular univariate Bayesian horseshoe shrinkage prior to the multivariate setting to detect structured sparsity and smoothness simultaneously. The prior can be embedded in many hierarchical high dimensional models. To illustrate its utility, we apply it to regularize a Bayesian high-dimensional regression problem where the regression coefficients are linked on a graph. The resulting clusters have flexible shapes and satisfy the cluster contiguity constraint with respect to the graph. We design an efficient Markov chain Monte Carlo algorithm that delivers full Bayesian inference with uncertainty measures for model parameters including the number of clusters. We offer theoretical investigations of the clustering effects and posterior concentration results. Finally, we illustrate the performance of the model with simulation studies and real data applications such as anomaly detection in road networks. The results indicate substantial improvements over other competing methods such as sparse fused lasso.
Efficient Bayesian Structural Equation Modeling in Stan

Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model estimation is complicated by the fact that we typically have multiple interdependent response variables and multiple latent variables (which may also be called random effects or hidden variables), often leading to slow and inefficient posterior sampling. In this paper, we describe and illustrate a general, efficient approach to Bayesian SEM estimation in Stan, contrasting it with previous implementations in R package blavaan (Merkle and Rosseel 2018). After describing the approaches in detail, we conduct a practical comparison under multiple scenarios. The comparisons show that the new approach is clearly better. We also discuss ways that the approach may be extended to other models that are of interest to psychometricians.
Quantum Extremal Surfaces and the Holographic Entropy Cone

Quantum states with geometric duals are known to satisfy a stricter set of entropy inequalities than those obeyed by general quantum systems. The set of allowed entropies derived using the Ryu-Takayanagi (RT) formula defines the Holographic Entropy Cone (HEC). These inequalities are no longer satisfied once general quantum corrections are included by employing the Quantum Extremal Surface (QES) prescription. Nevertheless, the structure of the QES formula allows for a controlled study of how quantum contributions from bulk entropies interplay with HEC inequalities. In this paper, we initiate an exploration of this problem by relating bulk entropy constraints to boundary entropy inequalities. In particular, we show that requiring the bulk entropies to satisfy the HEC implies that the boundary entropies also satisfy the HEC. Further, we also show that requiring the bulk entropies to obey monogamy of mutual information (MMI) implies the boundary entropies also obey MMI.
SoundStream: An End-to-End Neural Audio Codec

We present SoundStream, a novel neural audio codec that can efficiently compress speech, music and general audio at bitrates normally targeted by speech-tailored codecs. SoundStream relies on a model architecture composed by a fully convolutional encoder/decoder network and a residual vector quantizer, which are trained jointly end-to-end. Training leverages recent advances in text-to-speech and speech enhancement, which combine adversarial and reconstruction losses to allow the generation of high-quality audio content from quantized embeddings. By training with structured dropout applied to quantizer layers, a single model can operate across variable bitrates from 3kbps to 18kbps, with a negligible quality loss when compared with models trained at fixed bitrates. In addition, the model is amenable to a low latency implementation, which supports streamable inference and runs in real time on a smartphone CPU. In subjective evaluations using audio at 24kHz sampling rate, SoundStream at 3kbps outperforms Opus at 12kbps and approaches EVS at 9.6kbps. Moreover, we are able to perform joint compression and enhancement either at the encoder or at the decoder side with no additional latency, which we demonstrate through background noise suppression for speech.
Quantum gates robust to secular amplitude drifts

Quantum gates are typically vulnerable to imperfections in the classical control fields applied to physical qubits to drive the gates. One approach to reduce this source of error is to break the gate into parts, known as \textit{composite pulses} (CPs), that typically leverage the constancy of the error over time to mitigate its impact on gate fidelity. Here we extend this technique to suppress \textit{secular drifts} in Rabi frequency by regarding them as sums of \textit{power-law drifts} whose first-order effects on over- or under-rotation of the state vector add linearly. We show that composite pulses that suppress the power-law drifts $t^p$ for all $p \leq n$ are also high-pass filters of \textit{filter order} $n+1$ \cite{ball_walsh-synthesized_2015}. We present sequences that satisfy our proposed \textit{power law amplitude} $\text{PLA}(n)$ criteria, obtained with this technique, and compare their simulated performance under time-dependent amplitude errors to some traditional composite pulse sequences. We find that there is a range of noise frequencies for which the $\text{PLA}(n)$ sequences provide more error suppression than the traditional sequences, but in the low frequency limit, non-linear effects become more important for gate fidelity than frequency roll-off. As a result, the previously known $F_1$ sequence, which is one of the two solutions to the $\text{PLA}(1)$ criteria and furnishes suppression of both linear secular drift and the first order nonlinear effects, is a better noise filter than any of the other $\text{PLA}(n)$ sequences in the low frequency limit.
Omni: automated ensemble with unexpected models against adversarial evasion attack

Machine learning-based security detection models have become prevalent in modern malware and intrusion detection systems. However, previous studies show that such models are susceptible to adversarial evasion attacks. In this type of attack, inputs (i.e., adversarial examples) are specially crafted by intelligent malicious adversaries, with the aim of being misclassified by existing state-of-the-art models (e.g., deep neural networks). Once the attackers can fool a classifier to think that a malicious input is actually benign, they can render a machine learning-based malware or intrusion detection system ineffective. To help security practitioners and researchers build a more robust model against non-adaptive, white-box and non-targeted adversarial evasion attacks through the idea of ensemble model. We propose an approach called Omni, the main idea of which is to explore methods that create an ensemble of “unexpected models”; i.e., models whose control hyperparameters have a large distance to the hyperparameters of an adversary’s target model, with which we then make an optimized weighted ensemble prediction. In studies with five types of adversarial evasion attacks (FGSM, BIM, JSMA, DeepFool and Carlini-Wagner) on five security datasets (NSL-KDD, CIC-IDS-2017, CSE-CIC-IDS2018, CICAndMal2017 and the Contagio PDF dataset), we show Omni is a promising approach as a defense strategy against adversarial attacks when compared with other baseline treatments. When employing ensemble defense against adversarial evasion attacks, we suggest to create ensemble with unexpected models that are distant from the attacker’s expected model (i.e., target model) through methods such as hyperparameter optimization.
Historical Ethnic Conflicts and the Rise of Islamophobia in Modern China

In this paper, I show that narratives of historical conflicts between the Han Chinese and Muslims have been deployed to justify anti-Muslim sentiment and practices in modern and contemporary China. My study analyses Han Chinese narratives during and after the Northwest Muslim Rebellion – the largest ethnic conflict in 19th-century China. The historical narratives about the rebellion have passed down inter-generationally and have been reiterated and reconstructed to fuel modern-day bias against Muslims in the 20th century and beyond. My study contributes to the debate of Chinese Islamophobia by revealing how narratives of ethnic conflicts could help legitimize hostility against Muslims in modern China.
Predatory Lending and Community Development at Loggerheads

For decades, cities have invested in decaying neighborhoods, leading to increases in home values and home equity. As a result, these neighborhoods have become ready targets for predatory lenders, who market their abusive loans to financially unsophisticated homeowners with home equity and no relationships with traditional lenders. Some borrowers lose their homes; others forsake home repairs to avoid default and foreclosure. Neighborhoods that once were stable become littered with abandoned and neglected homes, resulting in increased crime, falling home values, rising demands for social services, and lower tax revenues. In the wake of the devastation done by predatory lenders, the question for policymakers is: what can be done? This paper seeks to answer this question. The paper opens by defining predatory lending. Next, the paper describes how the rise of securitization, deregulation of price terms, affordable lending incentives, bank closings, and historical credit discrimination together fueled the rise and institutionalization of predatory lending in the 1990s. Lastly, the paper evaluates different possible approaches to redressing predatory lending, including industry self-regulation, consumer education and counseling, Community Reinvestment Act oversight, criminal enforcement, existing private causes of action, and a suitability proposal.
Chapter 3 Higher dimensional issues in trade theory

Publisher Summary The extensive structure of modern trade theory has been built on a foundation of several extreme assumptions, including that of low and even dimensionality. This chapter is concerned with Heckscher–Ohlin theory of international trade in higher dimensions. This theory, in its standard two-commodity, two-factor version has dominated international trade theory for over 30 years. But this dominance has long been made uneasy by a widespread suspicion that world commerce does not accord well with the theoretical structure. There are two particular areas of concern. The first stems from the fact that the largest part of world trade involves the exchange of roughly similar products among similar economies, whereas the factor endowment theory, and comparative cost theory, generally teaches to look to international dissimilarities for the causes of trade. A large part of this actual trade is classified as intraindustry even with significant disaggregation. Thus probing its causes requires a high degree of disaggregation—that is, the explicit consideration of a large number of goods. The second area of concern stems from the Leontief Paradox. The sensitivity to higher dimensions of the basic propositions of the modern theory of international trade is the key issue for the practical relevance of the logical structure that has dominated trade theory in the past 30 years.
Integration of Lean Enterprise System, Total Quality Management and Six Sigma

The lean enterprise system, total quality management, six sigma, theory of constraints, agile manufacturing, and business process reengineering have been introduced as universally applicable best methods to improve the performance of enterprise operations through continuous process improvement and systemic planned enterprise change. Generally speaking, they represent practice-based, rather than theory-grounded, methods with common roots in manufacturing. Most of the literature on them is descriptive and prescriptive, aimed largely at a practitioner audience. Despite certain differences among them, they potentially complement each other in important ways. The lean enterprise system, total quality management and six sigma, in particular, are tightly interconnected as highly complementary approaches and can be brought together to define a first-approximation “core” integrated management system, with the lean enterprise system serving as the central organizing framework. Specific elements of the other approaches can be selectively incorporated into the “core” enterprise system to enrich its effectiveness. Concrete theoretical and computational developments in the future through an interdisciplinary research agenda centered on the design and development of networked enterprises as complex adaptive socio-technical systems, as well as the creation of a readily accessible observatory of evidence-based management practices, would represent important steps forward. Keywords: lean manufacturing; just-in-time-production (JIT); toyota production system (TPS); lean enterprise system; total quality management (TQM); six sigma; lean six sigma; theory of constraints (TOC); agile manufacturing; business process reengineering (BPR); enterprise change and transformation; evidence-based management practices
Plasmonics for beam engineering

This paper reviews our recent work on laser beam shaping using plasmonics. We demonstrated that by integrating properly designed plasmonic structures onto the facet of semiconductor lasers, their divergence angle can be dramatically reduced by more than one orders of magnitude, down to a few degrees.