cssi/SciSciGPT-SciSciNet huggingface dataset:
Body size dependent dispersal influences stability in heterogeneous metacommunities

Body size affects key biological processes across the tree of life, with particular importance for food web dynamics and stability. Traits influencing movement capabilities depend strongly on body size, yet the effects of allometrically-structured dispersal on food web stability are less well understood than other demographic processes. Here we study the stability properties of spatially-arranged model food webs in which larger bodied species occupy higher trophic positions, while species’ body sizes also determine the rates at which they traverse spatial networks of heterogeneous habitat patches. Our analysis shows an apparent stabilizing effect of positive dispersal rate scaling with body size compared to negative scaling relationships or uniform dispersal. However, as the global coupling strength among patches increases, the benefits of positive body size-dispersal scaling disappear. A permutational analysis shows that breaking allometric dispersal hierarchies while preserving dispersal rate distributions rarely alters qualitative aspects of metacommunity stability. Taken together, these results suggest that the oft-predicted stabilizing effects of large mobile predators may, for some dimensions of ecological stability, be attributed to increased patch coupling per se, and not necessarily coupling by top trophic levels in particular.
Japan's Inbound Tourism Boom: Lessons for its Post-COVID-19 Revival

In this paper, we review developments in Japanese inbound tourism and investigate the main determinants of its rapid growth prior to the COVID-19 pandemic. Using a panel autoregressive distributed lag (ARDL) model with data on 34 tourism source markets from 1996Q1 to 2018Q4, we find that not only tourist income and tourism-related relative prices, also visa policies have had significant impacts on Japan's inbound tourism demand in the long run. In the short run, natural disasters have had large and prolonged effects on tourism. We then derive policy implications for the post-COVID-19 revival of Japanese inbound tourism.
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

Training and evaluation of fair classifiers is a challenging problem. This is partly due to the fact that most fairness metrics of interest depend on both the sensitive attribute information and label information of the data points. In many scenarios it is not possible to collect large datasets with such information. An alternate approach that is commonly used is to separately train an attribute classifier on data with sensitive attribute information, and then use it later in the ML pipeline to evaluate the bias of a given classifier. While such decoupling helps alleviate the problem of demographic scarcity, it raises several natural questions such as: how should the attribute classifier be trained?, and how should one use a given attribute classifier for accurate bias estimation? In this work we study this question from both theoretical and empirical perspectives. We first experimentally demonstrate that the test accuracy of the attribute classifier is not always correlated with its effectiveness in bias estimation for a downstream model. In order to further investigate this phenomenon, we analyze an idealized theoretical model and characterize the structure of the optimal classifier. Our analysis has surprising and counter-intuitive implications where in certain regimes one might want to distribute the error of the attribute classifier as unevenly as possible among the different subgroups. Based on our analysis we develop heuristics for both training and using attribute classifiers for bias estimation in the data scarce regime. We empirically demonstrate the effectiveness of our approach on real and simulated data.
Capturing non-Markovian dynamics on near-term quantum computers

The authors present a generalizable quantum algorithm to treat non-Markovian dynamics in open quantum systems using near-term quantum computers.
Irregular Metronomes as Assistive Devices to Promote Healthy Gait Patterns

Older adults and people suffering from neurodegenerative disease often experience difficulty controlling gait during locomotion, ultimately increasing their risk of falling. To combat these effects, researchers and clinicians have used metronomes as assistive devices to improve movement timing in hopes of reducing their risk of falling. Historically, researchers in this area have relied on metronomes with isochronous interbeat intervals, which may be problematic because normal healthy gait varies considerably from one step to the next. More recently, researchers have advocated the use of irregular metronomes embedded with statistical properties found in healthy populations. In this paper, we explore the effect of both regular and irregular metronomes on many statistical properties of interstride intervals. Furthermore, we investigate how these properties react to mechanical perturbation in the form of a halted treadmill belt while walking. Our results demonstrate that metronomes that are either isochronous or random break down the inherent structure of healthy gait. Metronomes with statistical properties similar to healthy gait seem to preserve those properties, despite a strong mechanical perturbation. We discuss the future development of this work in the context of networked augmented reality metronome devices.
Black hole perturbations of massive and partially massless spin-2 fields in (anti) de Sitter spacetime

We provide a systematic and comprehensive derivation of the linearized dynamics of massive and partially massless spin-2 particles in a Schwarzschild (anti) de Sitter black hole background, in four and higher spacetime dimensions. In particular, we show how to obtain the quadratic actions for the propagating modes and recast the resulting equations of motion in a Schrodinger-like form. In the case of partially massless fields in Schwarzschild de Sitter spacetime, we study the isospectrality between modes of different parity. In particular, we prove isospectrality analytically for modes with multipole number L = 1 in four spacetime dimensions, providing the explicit form of the underlying symmetry. We show that isospectrality between partially massless modes of different parity is broken in higher-dimensional Schwarzschild de Sitter spacetimes.
Frequency-dependent capacitors using paper

Measurements of capacitors made with paper sheets reveal a significant decrease in capacitance with increasing frequency from 10 to 100,000 Hz, offering a simple demonstration of complex dielectric phenomena using common equipment.
Shining a Light on Dark Patterns

Dark patterns are user interfaces whose designers knowingly confuse users, make it difficult for users to express their actual preferences, or manipulate users into taking certain actions. They typically exploit cognitive biases and prompt online consumers to purchase goods and services that they do not want, or to reveal personal information they would prefer not to disclose. Research by computer scientists suggests that dark patterns have proliferated in recent years, but there is no scholarship that examines dark patterns’ effectiveness in bending consumers to their designers’ will. This article provides the first public evidence of the power of dark patterns. It discusses the results of the authors’ large-scale experiment in which a representative sample of American consumers were randomly assigned to a control group, a group that was exposed to mild dark patterns, or a group that was exposed to aggressive dark patterns. All groups were told they had been automatically enrolled in an identity theft protection plan, and the experimental manipulation varied what acts were necessary for consumers to decline the plan. Users in the mild dark pattern condition were more than twice as likely to remain enrolled as those assigned to the control group, and users in the aggressive dark pattern condition were almost four times as likely to remain enrolled in the program. There were two other striking findings. First, whereas aggressive dark patterns generated a powerful backlash among consumers, mild dark patterns did not – suggesting that firms employing them generate substantial profits. Second, less educated subjects were significantly more susceptible to mild dark patterns than their well-educated counterparts. Both findings suggest that there is a particularly powerful case for legal interventions to curtail the use of mild dark patterns. The article concludes by examining legal frameworks for ameliorating the dark patterns problem. Many dark patterns appear to violate federal and state laws restricting the use of unfair and deceptive practices in trade. Moreover, in those instances where consumers enter into contracts after being exposed to dark patterns, their consent could be deemed voidable under contract law principles. The article proposes a quantitative bright-line rule for identifying impermissible dark patterns. Dark patterns are presumably proliferating because firms’ secret and proprietary A-B testing has revealed them to be profit maximizing. We show how similar A-B testing can be used to identify those dark patterns that are so manipulative that they ought to be deemed unlawful.
Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction.

We present a preliminary study of a knowledge graph created from season one of the television show Veronica Mars, which follows the eponymous young private investigator as she attempts to solve the murder of her best friend Lilly Kane. We discuss various techniques for mining the knowledge graph for clues and potential suspects. We also discuss best practice for collaboratively constructing knowledge graphs from television shows.
Hyperelliptic curves and newform coefficients

Abstract We study which integers are admissible as Fourier coefficients of even integer weight newforms. In the specific case of the tau-function, we show that for all odd primes l 100 and all integers m ≥ 1 , we have τ ( n ) ≠ ± l , ± 5 m . For general newforms f with even integer weight 2k and integer coefficients, we prove for most integers j dividing 2 k − 1 and all ordinary primes p that a f ( p 2 ) is never a j-th power. We prove a similar result for a f ( p 4 ) , conditional on the Frey-Mazur Conjecture. Our primary method involves relating questions about values of newforms to the existence of perfect powers in certain binary recurrence sequences, and makes use of bounds from the theory of linear forms in logarithms. The method extends without difficulty to a large family of Lebesgue-Nagell equations with fixed exponent. To prove results about general newforms, we also make use of the modular method and Ribet's level-lowering theorem.
Depot: Cloud Storage with Minimal Trust

This article describes the design, implementation, and evaluation of Depot, a cloud storage system that minimizes trust assumptions. Depot tolerates buggy or malicious behavior by any number of clients or servers, yet it provides safety and liveness guarantees to correct clients. Depot provides these guarantees using a two-layer architecture. First, Depot ensures that the updates observed by correct nodes are consistently ordered under Fork-Join-Causal consistency (FJC). FJC is a slight weakening of causal consistency that can be both safe and live despite faulty nodes. Second, Depot implements protocols that use this consistent ordering of updates to provide other desirable consistency, staleness, durability, and recovery properties. Our evaluation suggests that the costs of these guarantees are modest and that Depot can tolerate faults and maintain good availability, latency, overhead, and staleness even when significant faults occur.
CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded Systems

Due to the advent of modern embedded systems and mobile devices with constrained resources, there is a great demand for incredibly efficient deep neural networks for machine learning purposes. There is also a growing concern of privacy and confidentiality of user data within the general public when their data is processed and stored in an external server which has further fueled the need for developing such efficient neural networks for real-time inference on local embedded systems. The scope of our work presented in this paper is limited to image classification using a convolutional neural network. A Convolutional Neural Network (CNN) is a class of Deep Neural Network (DNN) widely used in the analysis of visual images captured by an image sensor, designed to extract information and convert it into meaningful representations for real-time inference of the input data. In this paper, we propose a neoteric variant of deep convolutional neural network architecture to ameliorate the performance of existing CNN architectures for real-time inference on embedded systems. We show that this architecture, dubbed CondenseNeXt, is remarkably efficient in comparison to the baseline neural network architecture, CondenseNet, by reducing trainable parameters and FLOPs required to train the network whilst maintaining a balance between the trained model size of less than 3.0 MB and accuracy trade-off resulting in an unprecedented computational efficiency.
Elasto-Plastic Analysis of Uniform Grillages Under Lateral Load

A method is presented for analyzing elastic-plastic grillages under arbitrary loads by finite difference calculus. The torsional stiffness of the beams is neglected. The introduction of two discontinuity functions representing the rotation at the plastic hinges permits a complete description of the structure from initial loading until collapse. The development of a collapse mechanism is signified by the vanishing of the determinant of the matrix of the equations determining the amount of rotation of the plastic hinges.
Client influence and auditor independence revisited: Evidence from auditor resignations

Abstract Financial scandals such as the Enron-Andersen debacle provoke concerns that auditors lack independence when faced with influential clients. Unlike previous studies that examine whether client influence affects audit quality on ongoing engagements (providing mixed results), we investigate whether client influence (which engenders “independence risk”) at the audit-office level affects auditor resignations from high engagement-risk clients. We construct summary measures of engagement risk, using client disclosures on Form 8-K filings, potential risk factors (e.g., litigation risk), and auditor action (e.g., issuance of a going concern opinion) on the previous year’s financial statements. Focusing on risky clients, we find that auditors are more likely on average to resign from influential clients, and this positive association holds for auditors that are less likely to have mechanisms in place to mitigate independence risk. Also, importantly, influential clients are prevalent across the spectrum of client size, and the positive association between client influence and auditor resignations holds for both large and small clients.
Data-informed crime prevention at convenience stores in Atlantic City

The Atlantic City Police Department intervened to reduce robberies with an evidence-based approach grounded in problem-oriented policing. Informed by risk terrain modeling and hot spot analysis, po...
The explicit Sato–Tate conjecture for primes in arithmetic progressions

Let τ(n) be Ramanujan’s tau function, defined by the discriminant modular form Δ(z) = q∏j=1∞(1 − qj)24 =∑ n=1∞τ(n)qn,q = e2πiz (this is the unique holomorphic normalized cuspidal newform of weight ...
Identification of Splice Regulators of Fibronectin-EIIIA and EIIIB by Direct Measurement of Exon Usage in a Flow-Cytometry Based CRISPR Screen

The extracellular matrix protein fibronectin (FN) is alternatively spliced in a variety of inflammatory conditions, resulting in increased inclusion of alternative exons EIIIA and EIIIB. Inclusion of these exons affects fibril formation, fibrosis, and inflammation. To define upstream regulators of alternative splicing in FN, we have developed an in vitro flow-cytometry based assay, using RNA-binding probes to determine alternative exon inclusion level in aortic endothelial cells. This approach allows us to detect exon inclusion in the primary transcripts themselves, rather than in surrogate splicing reporters. We validated this assay in cells with and without FN-EIIIA and -EIIIB expression. In a small-scale CRISPR KO screen of candidate regulatory splice factors, we successfully detected known regulators of EIIIA and EIIIB splicing, and detected several novel regulators. Finally, we show the potential in this approach to broadly interrogate upstream signaling pathways in aortic endothelial cells with a genome-wide CRISPR-KO screen, implicating the TNFalpha and RIG-I-like signaling pathways and genes involved in the regulation of fibrotic responses. Thus, we provide a novel means to screen the regulation of splicing of endogenous transcripts, and predict novel pathways in the regulation of FN-EIIIA inclusion.
Contrastive learning of strong-mixing continuous-time stochastic processes

Contrastive learning is a family of self-supervised methods where a model is trained to solve a classification task constructed from unlabeled data. It has recently emerged as one of the leading learning paradigms in the absence of labels across many different domains (e.g. brain imaging, text, images). However, theoretical understanding of many aspects of training, both statistical and algorithmic, remain fairly elusive. In this work, we study the setting of time series -- more precisely, when we get data from a strong-mixing continuous-time stochastic process. We show that a properly constructed contrastive learning task can be used to estimate the transition kernel for small-to-mid-range intervals in the diffusion case. Moreover, we give sample complexity bounds for solving this task and quantitatively characterize what the value of the contrastive loss implies for distributional closeness of the learned kernel. As a byproduct, we illuminate the appropriate settings for the contrastive distribution, as well as other hyperparameters in this setup.
Shock generated vorticity in spark discharges

Spark discharges induce a complex flow field consisting of a shock wave at early times (~ 1 µs), a pair of vortex rings, and a hot gas kernel. The vortex rings entrain ambient gas into the hot gas kernel and control its cooling and expansion. In this work, we investigate the shock wave's contribution in producing the vortex ring vorticity. We analyze high-speed (700 kHz) schlieren images of the shock wave for a range of electrical energies to measure the shock properties and estimate shock velocity and curvature. These measurements are combined with a model to calculate the vorticity generated. The measurements show that the highest vorticity is generated near the peak shock curvature location, and the shock curvature and strength increase with electrical energy deposited. A comparison of the vorticity estimated from the model to vorticity measurements from stereoscopic particle image velocimetry shows the results to be statistically equivalent. This suggests that the shock curvature and velocity contribute to the vortex rings induced by spark discharges.
Fiscal Cyclicality and Currency Risk Premia

I develop a model of real exchange rate determination that attributes a central role to the intertemporal government budget condition, which equates the market value of government debt to the present value of government surpluses. To enforce this equilibrium condition in the presence of nominal rigidities, the real exchange rate has to adjust in response to shocks to government surpluses. The model predicts that fiscal shocks account for real exchange rate movements, and the factor structure in fiscal shocks aligns with the factor structure in currency returns. Both predictions are confirmed in the sample of developed countries.
Small-scale turbulent dynamo in astrophysical environments: nonlinear dynamo and dynamo in a partially ionized plasma

Small-scale turbulent dynamo is responsible for the amplification of magnetic fields on scales smaller than the driving scale of turbulence in diverse astrophysical media. Most earlier dynamo theories concern the kinematic regime and small-scale magnetic field amplification. Here we review our recent progress in developing the theories for the nonlinear dynamo and the dynamo regime in a partially ionized plasma. The importance of reconnection diffusion of magnetic fields is identified for both the nonlinear dynamo and magnetic field amplification during gravitational contraction. For the dynamo in a partially ionized plasma, the coupling state between neutrals and ions and the ion-neutral collisional damping can significantly affect the dynamo behavior and the resulting magnetic field structure. We present both our analytical predictions and numerical tests with a two-fluid dynamo simulation on the dynamo features in this regime. In addition, to illustrate the astrophysical implications, we discuss several examples for the applications of the dynamo theory to studying magnetic field evolution in both preshock and postshock regions of supernova remnants, in weakly magnetized molecular clouds, during the (primordial) star formation, and during the first galaxy formation.
Geometric Loss For Deep Multiple Sclerosis Lesion Segmentation

Multiple sclerosis (MS) lesions occupy a small fraction of the brain volume, and are heterogeneous with regards to shape, size and locations, which poses a great challenge for training deep learning based segmentation models. We proposed a new geometric loss formula to address the data imbalance and exploit the geometric property of MS lesions. We showed that traditional region-based and boundary-aware loss functions can be associated with the formula. We further develop and instantiate two loss functions containing first- and second-order geometric information of lesion regions to enforce regularization on optimizing deep segmentation models. Experimental results on two MS lesion datasets with different scales, acquisition protocols and resolutions demonstrated the superiority of our proposed methods compared to other state-of-the-art methods.
Where Do Deep Fakes Look? Synthetic Face Detection via Gaze Tracking

Following the recent initiatives for the democratization of AI, deep fake generators have become increasingly popular and accessible, causing dystopian scenarios towards social erosion of trust. A particular domain, such as biological signals, attracted attention towards detection methods that are capable of exploiting authenticity signatures in real videos that are not yet faked by generative approaches. In this paper, we first propose several prominent eye and gaze features that deep fakes exhibit differently. Second, we compile those features into signatures and analyze and compare those of real and fake videos, formulating geometric, visual, metric, temporal, and spectral variations. Third, we generalize this formulation to the deep fake detection problem by a deep neural network, to classify any video in the wild as fake or real. We evaluate our approach on several deep fake datasets, achieving 92.48% accuracy on FaceForensics++, 80.0% on Deep Fakes (in the wild), 88.35% on CelebDF, and 99.27% on DeeperForensics datasets. Our approach outperforms most deep and biological fake detectors with complex network architectures without the proposed gaze signatures. We conduct ablation studies involving different features, architectures, sequence durations, and post-processing artifacts.
Magneto-optical properties of InSb for infrared spectral filtering

We present measurements of the Faraday effect in n-type InSb. The Verdet coefficient was determined for a range of carrier concentrations near 10 17 cm − 3 in the λ = 8– 12 μ m long-wave infrared regime. The absorption coefficient was measured and a figure of merit was calculated for each sample. From these measurements, we calculated the carrier effective mass and illustrated the variation of the figure of merit with the wavelength. A method for creating a tunable bandpass filter via the Faraday rotation is discussed along with preliminary results from a prototype device.
TreeToaster: Towards an IVM-Optimized Compiler

A compiler's optimizer operates over abstract syntax trees (ASTs), continuously applying rewrite rules to replace subtrees of the AST with more efficient ones. Especially on large source repositories, even simply finding opportunities for a rewrite can be expensive, as optimizer traverses the AST naively. In this paper, we leverage the need to repeatedly find rewrites, and explore options for making the search faster through indexing and incremental view maintenance (IVM). Concretely, we consider bolt-on approaches that make use of embedded IVM systems like DBToaster, as well as two new approaches: Label-indexing and TreeToaster, an AST-specialized form of IVM. We integrate these approaches into an existing just-in-time data structure compiler and show experimentally that TreeToaster can significantly improve performance with minimal memory overheads.
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping *

We describe an unsupervised method to detect and segment portions of images of live scenes that, at some point in time, are seen moving as a coherent whole, which we refer to as objects. Our method first partitions the motion field by minimizing the mutual information between segments. Then, it uses the segments to learn object models that can be used for detection in a static image. Static and dynamic models are represented by deep neural networks trained jointly in a bootstrapping strategy, which enables extrapolation to previously unseen objects. While the training process requires motion, the resulting object segmentation network can be used on either static images or videos at inference time. As the volume of seen videos grows, more and more objects are seen moving, priming their detection, which then serves as a regularizer for new objects, turning our method into unsupervised continual learning to segment objects. Our models are compared to the state of the art in both video object segmentation and salient object detection. In the six benchmark datasets tested, our models compare favorably even to those using pixel-level supervision, despite requiring no manual annotation.
Differential impact of mitigation policies and socioeconomic status on COVID-19 prevalence and social distancing in the United States.

The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents’ mobility across neighborhoods of different levels of socioeconomic disadvantage. This was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately. On both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation. Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.
Smooth solutions to the complex plateau problem

The paper builds on work of Du, Gao, and Yau. The main result provides a characterization of smooth solutions, up to normalization, of the complex Plateau problem for strongly pseudoconvex Calabi–Yau CR manifolds of dimension $2n - 1 \geq 5$ and in the hypersurface case when $n = 2$. The latter case was completely solved by Yau for $n \geq 3$ but only partially solved by Du and Yau for $n = 2$. As an application, we determine the existence of a link-theoretic invariant of normal isolated singularities that distinguishes smooth points from singular ones.
Countercyclical Fiscal Policy and Gender Employment: Evidence from the G-7 Countries

Would countercyclical fiscal policy during recessions improve or worsen the gender employment gap? We give an answer to this question by exploring the state-dependent impact of fiscal spending shocks on employment by gender in the G-7 countries. Using the local projection method, we find that, during recessions, a positive spending shock of 1 percent of GDP would, on average, lift female employment by 1 percent, while increasing male employment by 0.6 percent. Consequently such a shock would improve the female share of employment by 0.28 percentage point during recessions. Our findings are driven by disproportionate employment changes in female-friendly industries, occupations, and part-time jobs in response to fiscal spending shocks. The analysis suggests that fiscal stimulus, particularly during recessions, could achieve the twin objectives of supporting aggregate demand and improving gender gaps.
An Efficient Framework for Balancing Submodularity and Cost

In the classical selection problem, the input consists of a collection of elements and the goal is to pick a subset of elements from the collection such that some objective function ƒ is maximized. This problem has been studied extensively in the data-mining community and it has multiple applications including influence maximization in social networks, team formation and recommender systems. A particularly popular formulation that captures the needs of many such applications is one where the objective function ƒ is a monotone and non-negative submodular function. In these cases, the corresponding computational problem can be solved using a simple greedy (1-1/e)-approximation algorithm. In this paper, we consider a generalization of the above formulation where the goal is to optimize a function that maximizes the submodular function ƒ minus a linear cost function cost. This formulation appears as a more natural one, particularly when one needs to strike a balance between the value of the objective function and the cost being paid in order to pick the selected elements. We address variants of this problem both in an offline setting, where the collection is known apriori, as well as in online settings, where the elements of the collection arrive in an online fashion. We demonstrate that by using simple variants of the standard greedy algorithm (used for submodular optimization) we can design algorithms that have provable approximation guarantees, are extremely efficient and work very well in practice.
DeepFreight: A Model-free Deep-reinforcement-learning-based Algorithm for Multi-transfer Freight Delivery

With the freight delivery demands and shipping costs increasing rapidly, intelligent control of fleets to enable efficient and cost-conscious solutions becomes an important problem. In this paper, we propose DeepFreight, a model-free deep-reinforcement-learning-based algorithm for multi-transfer freight delivery, which includes two closely-collaborative components: truck-dispatch and package-matching. Specifically, a deep multi-agent reinforcement learning framework called QMIX is leveraged to learn a dispatch policy, with which we can obtain the multi-step joint vehicle dispatch decisions for the fleet with respect to the delivery requests. Then an efficient multi-transfer matching algorithm is executed to assign the delivery requests to the trucks. Also, DeepFreight is integrated with a Mixed-Integer Linear Programming optimizer for further optimization. The evaluation results shows that the proposed system is highly scalable and ensures a 100% delivery success while maintaining low delivery-time and fuel consumption.
Handle with Care: Regulatory Easing in Times of COVID-19

The policy response to the COVID-19 shock included regulatory easing across many jurisdictions to facilitate the flow of credit to the economy and mitigate a further ampli-fication of the shock through tighter financial conditions. Using an intraday event study,this paper examines how stock prices—a key driver in financial conditions—reacted to regulatory easing announcements in a sample of 18 advanced economies and 8 emerging markets. The paper finds that overall, regulatory easing announcements contributed to looser financial conditions, but effects varied across sectors and tools. Financial regulatory easing led to lower valuations for financial sector stocks, and higher valuations for non-financial sector stocks, particularly for industries that are more dependent on bank financing. Furthermore, valuations declined and financial conditions tightened following announcements related to easier bank capital regulation while equity valuation rose and financial conditions loosened after those about liquidity regulation. Effects from non-regulatory financial measures appear to be generally more muted.
Learning with invariances in random features and kernel models

A number of machine learning tasks entail a high degree of invariance: the data distribution does not change if we act on the data with a certain group of transformations. For instance, labels of images are invariant under translations of the images. Certain neural network architectures ---for instance, convolutional networks---are believed to owe their success to the fact that they exploit such invariance properties. With the objective of quantifying the gain achieved by invariant architectures, we introduce two classes of models: invariant random features and invariant kernel methods. The latter includes, as a special case, the neural tangent kernel for convolutional networks with global average pooling. We consider uniform covariates distributions on the sphere and hypercube and a general invariant target function. We characterize the test error of invariant methods in a high-dimensional regime in which the sample size and number of hidden units scale as polynomials in the dimension, for a class of groups that we call `degeneracy $\alpha$', with $\alpha \leq 1$. We show that exploiting invariance in the architecture saves a $d^\alpha$ factor ($d$ stands for the dimension) in sample size and number of hidden units to achieve the same test error as for unstructured architectures. Finally, we show that output symmetrization of an unstructured kernel estimator does not give a significant statistical improvement; on the other hand, data augmentation with an unstructured kernel estimator is equivalent to an invariant kernel estimator and enjoys the same improvement in statistical efficiency.
Membership Privacy for Machine Learning Models Through Knowledge Transfer.

Large capacity machine learning (ML) models are prone to membership inference attacks (MIAs), which aim to infer whether the target sample is a member of the target model's training dataset. The serious privacy concerns due to the membership inference have motivated multiple defenses against MIAs, e.g., differential privacy and adversarial regularization. Unfortunately, these defenses produce ML models with unacceptably low classification performances. Our work proposes a new defense, called distillation for membership privacy (DMP), against MIAs that preserves the utility of the resulting models significantly better than prior defenses. DMP leverages knowledge distillation to train ML models with membership privacy. We provide a novel criterion to tune the data used for knowledge transfer in order to amplify the membership privacy of DMP. Our extensive evaluation shows that DMP provides significantly better tradeoffs between membership privacy and classification accuracies compared to state-of-the-art MIA defenses. For instance, DMP achieves ~100% accuracy improvement over adversarial regularization for DenseNet trained on CIFAR100, for similar membership privacy (measured using MIA risk): when the MIA risk is 53.7%, adversarially regularized DenseNet is 33.6% accurate, while DMP-trained DenseNet is 65.3% accurate. We have released our code at github.com/vrt1shjwlkr/AAAI21-MIA-Defense.
Market Forces: Quantifying the Role of Top Credible Ad Servers in the Fake News Ecosystem

Larry Lessig argues that four modes regulate behavior in cyberspace: laws, markets, norms, and architecture. How can these four modes regulate the production and spread of fake news? In this paper, we focus on markets and empirically evaluate one particular market-based solution: top ad firms blacklisting fake news producers to eliminate their revenue sources. Our study reveals that fake and low-quality publishers demonstrate a higher tendency to serve more ads and to partner with risky ad servers than traditional news media with similar popularity and age. However, fake news publishers are still strongly reliant on credible ad servers. In fact, the top-10 credible ad servers alone account for 66.7% and 55.6% of fake and low-quality ad traffic respectively. Furthermore, our back-of-the-envelope calculation shows that, at the time of our data collection, the top-10 ad firms were receiving $985.7K to $1.15M monthly from web traffic on fake news sites, a negligible fraction of these firms' annual revenue. Overall, our findings suggest that having top ad firms blacklist known fake and low-quality publishers is a low-cost way to combat fake news.
Efficient Parallel Self-Adjusting Computation

Self-adjusting computation is an approach for automatically producing dynamic algorithms from static ones. It works by tracking control and data dependencies, and propagating changes through the dependencies when making an update. Extensively studied in the sequential setting, some results on parallel self-adjusting computation exist, but are only applicable to limited classes of computations, or are ad-hoc systems with no theoretical analysis of their performance. In this paper, we present the first system for parallel self-adjusting computation that applies to a wide class of nested parallel algorithms and provides theoretical bounds on the work and span of the resulting dynamic algorithms. Our bounds relate a "distance" measure between computations on different inputs to the cost of propagating an update. The main innovation in the paper is in using Series-Parallel trees (SP trees) to track sequential and parallel control dependencies to allow change propagation to be applied safely in parallel. We demonstrate several example applications, including algorithms for dynamic sequences and dynamic trees. Lastly, we show experimentally that our system allows algorithms to produce updated results over large datasets significantly faster than from-scratch execution, saving both work and parallel time.
Pauli Error Estimation via Population Recovery.

Motivated by estimation of quantum noise models, we study the problem of learning a Pauli channel, or more generally the Pauli error rates of an arbitrary channel. By employing a novel reduction to the "Population Recovery" problem, we give an extremely simple algorithm that learns the Pauli error rates of an n-qubit channel to precision e in 𝓁_∞ using just O(1/e²) log(n/e) applications of the channel. This is optimal up to the logarithmic factors. Our algorithm uses only unentangled state preparation and measurements, and the post-measurement classical runtime is just an O(1/e) factor larger than the measurement data size. It is also impervious to a limited model of measurement noise where heralded measurement failures occur independently with probability ≤ 1/4. We then consider the case where the noise channel is close to the identity, meaning that the no-error outcome occurs with probability 1-η. In the regime of small η we extend our algorithm to achieve multiplicative precision 1 ± e (i.e., additive precision eη) using just O(1/(e²η)) log(n/e) applications of the channel.
Uncertainty in the Hot Hand Fallacy: Detecting Streaky Alternatives to Random Bernoulli Sequences

We study a class of permutation tests of the randomness of a collection of Bernoulli sequences and their application to analyses of the human tendency to perceive streaks of consecutive successes as overly representative of positive dependence - the hot hand fallacy. In particular, we study permutation tests of the null hypothesis of randomness (i.e., that trials are i.i.d.) based on test statistics that compare the proportion of successes that directly follow k consecutive successes with either the overall proportion of successes or the proportion of successes that directly follow k consecutive failures. We characterize the asymptotic distributions of these test statistics and their permutation distributions under randomness, under a set of general stationary processes, and under a class of Markov chain alternatives, which allow us to derive their local asymptotic power. The results are applied to evaluate the empirical support for the hot hand fallacy provided by four controlled basketball shooting experiments. We establish that substantially larger data sets are required to derive an informative measurement of the deviation from randomness in basketball shooting. In one experiment, for which we were able to obtain data, multiple testing procedures reveal that one shooter exhibits a shooting pattern significantly inconsistent with randomness - supplying strong evidence that basketball shooting is not random for all shooters all of the time. However, we find that the evidence against randomness in this experiment is limited to this shooter. Our results provide a mathematical and statistical foundation for the design and validation of experiments that directly compare deviations from randomness with human beliefs about deviations from randomness, and thereby constitute a direct test of the hot hand fallacy.
Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare.

Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (EHR) has enabled machine learning advances in providing these predictions. However, many deep learning based methods are not satisfactory in solving several key challenges: 1) effectively utilizing disease domain knowledge; 2) collaboratively learning representations of patients and diseases; and 3) incorporating unstructured text. To address these issues, we propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. Our solution is able to capture structural features of both patients and diseases. The proposed model also utilizes unstructured text data by employing an attention regulation strategy and then integrates attentive text features into a sequential learning process. We conduct extensive experiments on two important healthcare problems to show the competitive prediction performance of the proposed method compared with various state-of-the-art models. We also confirm the effectiveness of learned representations and model interpretability by a set of ablation and case studies.
Convergent Extension Requires Adhesion-Dependent Biomechanical Integration of Cell Crawling and Junction Contraction

Convergent extension is an evolutionarily conserved collective cell movement that elongates the body axis of all animals and is required for the morphogenesis of several organ systems. Decades of study have revealed two distinct mechanisms of cell movement during CE, one based on cell crawling and the other on junction contraction. How these two behaviors collaborate during CE is not understood. Here, using quantitative live cell imaging we show that these two modes act both independently and in concert during CE, but that cell movement is more effective when the two modes are integrated via mechano-reciprocity. Based on these findings, we developed a novel computational model that for the first time treats crawling and contraction independently. This model not only confirmed the biomechanical efficacy of integrating the two modes, but also revealed for the first time how the two modes -and their integration- are influenced by cell adhesion. Finally, we use these new insights to further understand the complex CE phenotype resulting from loss of the C-cadherin interacting catenin Arvcf. These data are significant for providing new biomechanical and cell biological insights into a fundamental morphogenetic process that is implicated in human neural tube defects and skeletal dysplasias.
Lessons from the German Tank Problem

During World War II the German army used tanks to devastating advantage. The Allies needed accurate estimates of their tank production and deployment. They used two approaches to find these values: spies, and statistics. This note describes the statistical approach. Assuming the tanks are labeled consecutively starting at 1, if we observe $k$ serial numbers from an unknown number $N$ of tanks, with the maximum observed value $m$, then the best estimate for $N$ is $m(1 + 1/k) - 1$. This is now known as the German Tank Problem, and is a terrific example of the applicability of mathematics and statistics in the real world. The first part of the paper reproduces known results, specifically deriving this estimate and comparing its effectiveness to that of the spies. The second part presents a result we have not found in print elsewhere, the generalization to the case where the smallest value is not necessarily 1. We emphasize in detail why we are able to obtain such clean, closed-form expressions for the estimates, and conclude with an appendix highlighting how to use this problem to teach regression and how statistics can help us find functional relationships.