cssi/SciSciGPT-SciSciNet huggingface dataset:
Electronic surveillance as employee control: a procedural justice interpretation

Abstract Despite a significant growth and mounting popular interest in electronic monitoring and surveillance of workers, there has not been a great deal of systematic research into the relationship among various monitoring techniques, employee reactions to such monitoring, and outcomes such as employee satisfaction, organizational commitment, job performance and job stress. By applying the concept of procedural justice, this paper develops propositions to guide research on electronic control systems.
A two-stage estimator for probit models with structural group effects

This paper outlines a two-stage technique for estimation and inference in probit models with structural group effects. The structural group specification belongs to a broader class of random components models. In particular, individuals in a given group share a common component in the specification of the conditional mean of a latent variable. For a number of computational reasons, existing random effects models are impractical for estimation and inference in this type of problem. Our two-stage estimator provides an easily estimable alternative to the random effects specification. In addition, we conduct a Monte Carlo simulation comparing the performance of alternative es- timators, and find that the two-stage estimator is superior ~ both in terms of estimation and inference ~- to traditional estimators.
Alfred Chandler: His Vision and Achievement

How do we measure Alfred Chandler's achievement? What forces shaped his vision? What is his place in the pantheon of historians and social scientists? Might he rank with sociologists such as Talcott Parsons or even Max Weber? Economists such as Kenneth Arrow or even Joseph Schumpeter? With political scientists such as Louis Hartz, Robert Dahl, and Seymour Martin Lipset? It's too early to make these kinds of judgments, but some answers are certainly possible about his place among historians.
A model of asset pricing under country risk

Abstract I develop a formal model that could provide quantitative guidance to practitioners who use sovereign yield spreads in emerging market asset valuation. The model provides analytical formulas relating emerging market stock P/E ratios (and expected returns) to the corresponding average yield spread in sovereign bonds. In the model, sovereign yield spreads carry information about the likelihood of a negative regime change in an emerging market (“country risk”), under the common assumption that the regime change is associated with a hostile renegotiation of the country's foreign debt. In the model, country risk is priced because the regime change may be endogenously associated with bad states of the global economy. Data from emerging markets are consistent with some of the model's quantitative and qualitative predictions.
Determining damages from the operation of bidding rings: An analysis of the post-auction 'knockout' sale*

This paper studies ‘knockout’ auctions, typically organized by bidding rings, in which the winning bidder makes side-payments to all losing bidders. These side-payments provide an incentive for the ring members to bid higher than they would have in an identical public auction. As a consequence, neither the realized price nor the total payments of the winner are unbiased estimates of the item's price in the absence of collusion. This paper evaluates the extent of this overestimate in the independent private values case, for first and second price post-auction knockouts. Bids are not independent of the sharing rule but transfers from the winning bidder are. Further, bidder payoffs are independent of both the auction format and the sharing rule. The “overbidding” in the knockout is increasing with the dispersion of bidder valuations and of significant empirical relevance. This paper's results can be used to obtain an unbiased assessment of the damages inflicted on the seller.
Insights from a Tax-systems Perspective

The tax-systems perspective considers a variety of costs and behavioral margins often ignored in standard tax analysis: administrative and compliance costs, evasion and avoidance behavior, and multiple nonrate tax-system instruments (for example, withholding and public disclosure). We show how the standard optimal tax framework can be augmented to include these new sources of cost and behavioral response by considering some enduring tax policy questions: What is the optimal commodity tax base breadth? How does enforcement effort targeted to avoidance behavior affect optimal progressivity? What fraction of returns should be audited? Should small firms be excluded from a tax system? (JEL codes: H20, H21, H24-H26).
Clearly erroneous executions

Abstract We examine the cancellation of erroneous executions on equity exchanges in the United States. Self-regulatory organizations of the National Market System are able to cancel large numbers of trades that are deemed to be clearly erroneous. We explore the market response to cancellations by comparing erroneous trades against matched trades that are eligible to be deemed erroneous, but are never reported as erroneous. We analyze the relation between the cancellation of erroneous executions and the market environment, paying particular attention to the information dissemination process from exchange officials to market participants. We find that clearly erroneous executions have detrimental effects on market quality.
Automated Versus Floor Trading: An Analysis of Execution Costs on the Paris and New York Exchanges

A global trend towards automated trading systems raises the important question of whether execution costs are, in fact, lower than on trading f loors. This paper compares the trade execution costs of similar stocks in an automated trading structure ~Paris Bourse! and a f loor-based trading structure ~NYSE!. Results indicate that execution costs are higher in Paris than in New York after controlling for differences in adverse selection, relative tick size, and economic attributes across samples. These results suggest that the present form of the automated trading system may not be able to fully replicate the benefits of human intermediation on a trading f loor. A TRADING MECHANISM IS DEF INED by the distinctive set of rules that govern the
The Theory of Informative Fictions: A Character-Based Approach to False News and other Misinformation

This paper derives a theory of informative fictions. Common forms of misinformation--fake news, rumors, and conspiracy theories—while dysfunctional for communicating property information—information about the state and operation of things—can actually be valuable for communicating character information—information about the motivations of social agents. It is argued that narratives containing “false facts” can effectively portray a speaker’s theory of another individual’s character. Thus, such narratives are useful for gathering information about leaders and other important individuals who are evaluated in the community. After deriving the theory, TIF is used to derive propositions predicting the empirical conditions under which misinformation will be accepted, tolerated or promoted. The implications of the theory for addressing the normative problem of misinformation are also discussed.
How much stock return predictability can we expect from an asset pricing model

Abstract We provide a new upper bound on the R-squared of a predictive regression of stock returns on predictable variables, tightening substantially Ross's (2005) bound. An empirical application illustrates that while Ross's bound is not binding, our bound does.
The joint use of regulation and strict liability with multidimensional care and uncertain conviction

The purpose of this paper is to explore the joint use of regulation and strict liability when firms can take care in both observable and unobservable dimensions and when the firm’s conviction for damages is uncertain. Much of the literature concerning joint use regards management of the judgment-proof problem; the take-home result of our paper is that if the harming party can take both observable and unobservable care, then joint use can improve welfare even in the absence of judgment-proofness. This is true even when penalty multipliers are allowed, provided social welfare is negatively related to the firm’s expected liability costs. In fact, use of penalty multipliers further strengthens the case for joint use.
Household Strategies of Coping with Shocks in Post‐crisis Russia

The paper looks at the subjective evaluations of a number of coping strategies that respondents undertook to mitigate the effect on their welfare of the Russian financial crisis. Given the dramatic drop in formal cash incomes, how are people able to adapt? What are the relative importance and effectiveness of coping strategies to resist hardship in reducing poverty for different groups of households? The results of the analysis indicate that the choices of survival strategies are determined by the level of human capital in the household. The higher the household human capital, the more likely the household is to choose active strategies. Households with low human capital, households headed by pensioners, and low-educated households are more likely to be socially excluded. A specific set of policy interventions should be targeted on such households to avoid the entrenchment of poverty, and the trend toward marginalization and impoverishment of these groups should be expressly monitored.
The reduced-order hybrid Monte Carlo sampling smoother

Summary Hybrid Monte Carlo sampling smoother is a fully non-Gaussian four-dimensional data assimilation algorithm that works by directly sampling the posterior distribution formulated in the Bayesian framework. The smoother in its original formulation is computationally expensive owing to the intrinsic requirement of running the forward and adjoint models repeatedly. Here we present computationally efficient versions of the hybrid Monte Carlo sampling smoother based on reduced-order approximations of the underlying model dynamics. The schemes developed herein are tested numerically using the shallow-water equations model on Cartesian coordinates. The results reveal that the reduced-order versions of the smoother are capable of accurately capturing the posterior probability density, while being significantly faster than the original full-order formulation. Copyright © 2016 John Wiley & Sons, Ltd.
Reflections on Group Action and the Law of the Workplace Symposium: The Changing Workplace

Sixty years after the National Labor Relations Act (NLRA) was passed, collective action appears moribund. Current analysis burying and praising the NLRA has focused primarily on the changed economic realities of the product and labor markets. Yet there is another story to be told involving a comparable transformation of the legal culture. Relying in part on empirical analysis of court decisions, I argue that changes in federal workplace law over the past thirty years have undermined the concept of group action-in particular collective bargaining-as a preferred means of regulating the employment relationship. These changes are the product of leading institutional actors that share general legal responsibility for regulating the workplace: the federal courts of appeals, the Supreme Court, and the Congress. To be sure, the diminished legal role for group action in labor relations is in part a function of the diminished power of unions in an economy increasingly subject to global competition and rapid technological change. I want to suggest, however, that changes in the legal status of group action may be cause as well as effect. When the actions of the legislative and judicial branches of government indicate that collective bargaining has become an anachronistic means of promoting employee interests, one inevitable consequence is a loss of legitimacy for unions as the enablers of group action. This loss of legitimacy encourages the business community and the general public to erode and belittle the role of unions, thus making it more difficult for unions to adjust to new economic realities. Accordingly, a proper appreciation for how the legal system has devalued group action may assist in understanding the steady decline of collective bargaining in the American workplace. Part I examines how group action has been diminished by Congress and the federal courts over the past thirty years. It briefly summarizes the goals of the NLRA, goals that Congress meant to be achieved through collective action in the workplace. It then describes how Congress since 1963 has enacted a series of workplace regulatory statutes that have effectively subordinated the role of group action by making individual rights preeminent. Finally, Part I demonstrates that in a legal world where group action is the exception, both the Supreme Court and the courts of appeals have thwarted the original legislative commitment to collective bargaining when interpreting the NLRA. Part II raises questions about the subversion of a legislative scheme that has not been revised by Congress for nearly half a century. It discusses different factors that may have led the courts to turn away from vigorous enforcement of the NLRA's core idea. In this context, Part II also probes the extent to which judicial disrespect for collective bargaining may be a natural consequence of the statutory aging process.
An Ensemble-based Approach by Fine-Tuning the Deep Transfer Learning Models to Classify Pneumonia from Chest X-Ray Images.

Pneumonia is caused by viruses, bacteria, or fungi that infect the lungs, which, if not diagnosed, can be fatal and lead to respiratory failure. More than 250,000 individuals in the United States, mainly adults, are diagnosed with pneumonia each year, and 50,000 die from the disease. Chest Radiography (X-ray) is widely used by radiologists to detect pneumonia. It is not uncommon to overlook pneumonia detection for a well-trained radiologist, which triggers the need for improvement in the diagnosis's accuracy. In this work, we propose using transfer learning, which can reduce the neural network's training time and minimize the generalization error. We trained, fine-tuned the state-of-the-art deep learning models such as InceptionResNet, MobileNetV2, Xception, DenseNet201, and ResNet152V2 to classify pneumonia accurately. Later, we created a weighted average ensemble of these models and achieved a test accuracy of 98.46%, precision of 98.38%, recall of 99.53%, and f1 score of 98.96%. These performance metrics of accuracy, precision, and f1 score are at their highest levels ever reported in the literature, which can be considered a benchmark for the accurate pneumonia classification.
Name Space Analysis: Verification of Named Data Network Data Planes

Named Data Networking (NDN) has many forwarding behaviors, strategies, and protocols to enable the benefits of Information-Centric Networking. This additional functionality introduces complexity, motivating the need for a tool to help reason about and verify that basic properties of an NDN data plane are guaranteed. This paper proposes Name Space Analysis (NSA), a network verification framework to model and analyze NDN data planes. NSA can take as input one or more snapshots, each representing a state of the data plane. It then provides the verification result against specified properties. NSA builds on the theory of Header Space Analysis, and extends it in a number of ways, e.g. , supporting variable-sized headers with flexible formats, introduction of name space functions, allowing for name-based properties such as content reachability and name leakage-freedom, and multi-snapshot verification such as equivalence checks. These important additions reflect the behavior and requirements of NDN, requiring modeling and verification foundations fundamentally different from those of traditional host-centric networks. As a case study, we show how NSA can detect name space conflicts in NDN, which can be often hard to catch. Leveraging the learning from this study, we outline a conflict detection and resolution protocol and a name space registry to avoid such conflicts. We have implemented NSA and identified a number of optimizations to enhance the efficiency of verification. Results from our evaluations, using snapshots from various synthetic test cases and the real-world NDN testbed, show how NSA is effective, in finding errors, has good performance, and is scalable.
Evaluation of scatter rejection and correction performance of 2D antiscatter grids in cone beam computed tomography.

PURPOSE We have been investigating two-dimensional (2D) antiscatter grids (2D ASGs) to reduce scatter fluence and improve image quality in cone beam computed tomography (CBCT). In this work, two different aspects of 2D ASGs, their scatter rejection and correction capability, were investigated in CBCT experiments. To correct residual scatter transmitted through the 2D ASG, it was used as a scatter measurement device with a novel method: grid-based scatter sampling. METHODS Three focused 2D ASG prototypes with grid ratios of 8, 12, and 16 were developed for linac-mounted offset detector CBCT geometry. In the first phase, 2D ASGs were used as a scatter rejection device, and the effect of grid ratio on CT number accuracy and contrast-to-noise ratio (CNR) evaluated in CBCT images. In the second phase, a grid-based scatter sampling method which exploits the signal modulation characteristics of the 2D ASG's septal shadows to measure and correct residual scatter transmitted through the grid was implemented. To evaluate CT number accuracy, the percent change in CT numbers was measured by changing the phantom from head to pelvis size and configuration. RESULTS When 2D ASG was used as a scatter rejection device, CT number accuracy increased and the CT number variation due to change in phantom dimensions was reduced from 23% to 2-6%. A grid ratio of 16 yielded the lowest CT number variation. All three 2D ASGs yielded improvement in CNR, up to a factor of two in pelvis-sized phantoms. When 2D ASG prototypes were used for both scatter rejection and correction, CT number variations were reduced further, to 1.3-2.6%. In comparisons with a clinical CBCT system and a high-performance radiographic ASG, 2D ASG provided higher CT number accuracy under the same imaging conditions. CONCLUSIONS When 2D ASG is used solely as a scatter rejection device, substantial improvement in CT number accuracy can be achieved by increasing the grid ratio. Two-dimensional ASGs also provided significant CNR improvement even at lower grid ratios. Two-dimensional ASGs used in conjunction with the grid-based scatter sampling method provided further improvement in CT number accuracy, irrespective of the grid ratio, while preserving 2D ASGs' capacity to improve CNR. The combined effect of scatter rejection and residual scatter correction by 2D ASG may accelerate implementation of new techniques in CBCT that require high quantitative accuracy, such as radiotherapy dose calculation and dual energy CBCT.
Multi-Scale Thermal to Visible Face Verification via Attribute Guided Synthesis

Thermal-to-visible face verification is a challenging problem due to the large domain discrepancy between the modalities. Existing approaches either attempt to synthesize visible faces from thermal faces or learn domain-invariant robust features from these modalities for cross-modal matching. In this paper, we use attributes extracted from visible images to synthesize attribute-preserved visible images from thermal imagery for cross-modal matching. A pre-trained attribute predictor network is used to extract the attributes from the visible image. Then, a novel multi-scale generator is proposed to synthesize the visible image from the thermal image guided by the extracted attributes. Finally, a pre-trained VGG-Face network is leveraged to extract features from the synthesized image and the input visible image for verification. Extensive experiments evaluated on three datasets (ARL Face Database, Visible and Thermal Paired Face Database, and Tufts Face Database) demonstrate that the proposed method achieves state-of-the-art performance. In particular, it achieve around 2.41%, 2.85% and 1.77% improvements in Equal Error Rate (EER) over the state-of-the-art methods on the ARL Face Database, Visible and Thermal Paired Face Database, and Tufts Face Database, respectively. An extended dataset (ARL Face Dataset volume III) consisting of polarimetric thermal faces of 121 subjects is also introduced in this paper. Furthermore, an ablation study is conducted to demonstrate the effectiveness of different modules in the proposed method.
Digital Signal Processing for the Event Horizon Telescope

The Event Horizon Telescope (EHT) [1] is an earth-size very long baseline interferometry (VLBI) array, operating at the shortest radio wavelengths of about 1 millimeter, corresponding to radio frequencies 230 GHz and higher. It has an extremely fine angular resolution of the order of 20 microarcseconds. For super massive black holes (SMBH) which are relatively nearby and sufficiently massive, this is the angular scale subtended by the event horizon. Relativistically lensed emission from the black hole's accretion disk and jet can be directly observed. Retrofitting wideband digital instruments and atomic clocks to existing $\lambda$ 1 mm radio telescopes last year led to the first image of the shadow of a black hole. I cover the scientific significance of the image and the technology that took the data.
Tracking the Coupling of Single Emitters to Plasmonic Nanoantennas with Single-Molecule Super-Resolution Imaging

Metal nanoantennas enable the manipulation of light emission and detection at the single photon level by confining light into very small volumes. Emitters coupled to these plasmonic structures are ...
Emerging carbon nanotube electronic circuits, modeling and performance

In this paper, a CNT interconnect model is presented and combined with a CNT-FET model to study the performance of CNT-FET circuits at very high frequencies. An all complementary CNT-FET inverter pair using CNT interconnection is modeled and characterized. Cadence/Spectre simulations show that CNT-FET circuits can operate at GHz frequencies and CNT interconnects are able to provide enough bandwidth for GHz operation of CNT-FETs circuits.
Loss of Prdm12 during development, but not in mature nociceptors, causes defects in pain sensation

Prdm12 is a key transcription factor in nociceptor neurogenesis. Mutations of Prdm12 cause congenital insensitivity to pain (CIP) from failure of nociceptor development. However, precisely how deletion of Prdm12 during development or adulthood affects nociception is unknown. Here, we employ tissue- and temporal-specific knockout mouse models to test the function of Prdm12 during development and in adulthood. We find that constitutive loss of Prdm12 causes deficiencies in proliferation during sensory neurogenesis. We also demonstrate that conditional knockout from dorsal root ganglia (DRGs) during embryogenesis causes defects in nociception. In contrast, we find that, in adult DRGs, Prdm12 is dispensable for most pain-sensation and injury-induced hypersensitivity. Using transcriptomic analysis, we find mostly unique changes in adult Prdm12 knockout DRGs compared with embryonic knockout and that PRDM12 is likely a transcriptional activator in the adult. Overall, we find that the function of PRDM12 changes over developmental time.
Rif1 regulates telomere length through conserved HEAT repeats

In budding yeast, Rif1 negatively regulates telomere length, but the mechanism of this regulation has remained elusive. Previous work identified several functional domains of Rif1, but none of these has been shown to mediate telomere length. To define Rif1 domains responsible for telomere regulation, we localized truncations of Rif1 to a single specific telomere and measured telomere length of that telomere compared to bulk telomeres. We found that a domain in the N-terminus containing HEAT repeats, Rif1177-996, was sufficient for length regulation when tethered to the telomere. Charged residues in this region were previously proposed to mediate DNA binding. We found that mutation of these residues disrupted telomere length regulation even when Rif1 was tethered to the telomere. Mutation of other conserved residues in this region, which were not predicted to interact with DNA, also disrupted telomere length maintenance, while mutation of conserved residues distal to this region did not. Our data suggest that conserved amino acids in the region from 436 to 577 play a functional role in telomere length regulation, which is separate from their proposed DNA binding function. We propose that the Rif1 HEAT repeats region represents a protein-protein binding interface that mediates telomere length regulation.
Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification

Due to the sheer scale of the Internet of Things (IoT) and 5G, the wireless spectrum is becoming severely congested. For this reason, wireless devices will need to continuously adapt to current spectrum conditions by changing their communication parameters in real-time. Therefore, wireless signal classification (WSC) will become a compelling necessity to decode fast-changing signals from dynamic transmitters. Thanks to its capability of classifying complex phenomena without explicit mathematical modeling, deep learning (DL) has been demonstrated to be a key enabler of WSC. Although DL can achieve a very high accuracy under certain conditions, recent research has unveiled that the wireless channel can disrupt the features learned by the DL model during training, thus drastically reducing the classification performance in real-world live settings. Since retraining classifiers is cumbersome after deployment, existing work has leveraged the usage of carefully-tailored Finite Impulse Response (FIR) filters that, when applied at the transmitter’s side, can restore the features that are lost because of the the channel actions, i.e., waveform synthesis. However, these approaches compute FIRs using offline optimization strategies, which limits their efficacy in highly-dynamic channel settings. In this paper, we improve the state of the art by proposing Chares, a Deep Reinforcement Learning (DRL)-based framework for channel-resilient adaptive waveform synthesis. Chares adapts to new and unseen channel conditions by optimally computing through DRL the FIRs in real time. Chares is a DRL agent whose architecture is based upon the Twin Delayed Deep Deterministic Policy Gradients (TD3), which requires minimal feedback from the receiver and explores a continuous action space for best performance. Chares has been extensively evaluated on two well-known datasets with an extensive number of channels. We have also evaluated the real-time latency of Chares with an implementation on field-programmable gate array (FPGA). Results show that Chares increases the accuracy up to 4.1x when no waveform synthesis is performed, by 1.9x with respect to existing work, and can compute new actions within 41µs.
When Fair Ranking Meets Uncertain Inference

Existing fair ranking systems, especially those designed to be demographically fair, assume that accurate demographic information about individuals is available to the ranking algorithm. In practice, however, this assumption may not hold --- in real-world contexts like ranking job applicants or credit seekers, social and legal barriers may prevent algorithm operators from collecting peoples' demographic information. In these cases, algorithm operators may attempt to infer peoples' demographics and then supply these inferences as inputs to the ranking algorithm. In this study, we investigate how uncertainty and errors in demographic inference impact the fairness offered by fair ranking algorithms. Using simulations and three case studies with real datasets, we show how demographic inferences drawn from real systems can lead to unfair rankings. Our results suggest that developers should not use inferred demographic data as input to fair ranking algorithms, unless the inferences are extremely accurate.
Efficient Learning-based Scheduling for Information Freshness in Wireless Networks

Motivated by the recent trend of integrating artificial intelligence into the Internet-of-Things (IoT), we consider the problem of scheduling packets from multiple sensing sources to a central controller over a wireless network. Here, packets from different sensing sources have different values or degrees of importance to the central controller for intelligent decision making. In such a setup, it is critical to provide timely and valuable information for the central controller. In this paper, we develop a parameterized maximum-weight type scheduling policy that combines both the AoI metrics and Upper Confidence Bound (UCB) estimates in its weight measure with parameter η. Here, UCB estimates balance the tradeoff between exploration and exploitation in learning and are critical for yielding a small cumulative regret. We show that our proposed algorithm yields the running average total age at most by O(N2η). We also prove that our proposed algorithm achieves the cumulative regret over time horizon T at most by $O(NT/\eta + \sqrt {NT\log T} )$. This reveals a tradeoff between the cumulative regret and the running average total age: when increasing η, the cumulative regret becomes smaller, but is at the cost of increasing running average total age. Simulation results are provided to evaluate the efficiency of our proposed algorithm.
Elliptic curves with Galois-stable cyclic subgroups of order 4

Infinitely many elliptic curves over $$\mathbf{Q }$$ have a Galois-stable cyclic subgroup of order 4. Such subgroups come in pairs, which intersect in their subgroups of order 2. Let $$N_j(X)$$ denote the number of elliptic curves over $$\mathbf{Q }$$ with at least j pairs of Galois-stable cyclic subgroups of order 4, and height at most X. In this article we show that $$N_1(X) = c_{1,1}X^{1/3}+c_{1,2}X^{1/6}+O(X^{0.105})$$ . We also show, as $$X\rightarrow \infty $$ , that $$N_2(X)=c_{2,1}X^{1/6}+o(X^{1/12})$$ , the precise nature of the error term being related to the prime number theorem and the zeros of the Riemann zeta-function in the critical strip. Here, $$c_{1,1}= 0.95740\ldots $$ , $$c_{1,2}=- 0.87125\ldots $$ , and $$c_{2,1}= 0.035515\ldots $$ are calculable constants. Lastly, we show no elliptic curve over Q has more than 2 pairs of Galois-stable cyclic subgroups of order 4.
New short proofs to some stability theorems

Abstract We present new short proofs to both the exact and the stability result of two extremal problems. The first result is about the extension of Turan’s theorem to hypergraphs, and the second result is about cancellative hypergraphs. Our proofs are concise and straightforward, but give a sharper version of stability theorems to both problems.
On the High Accuracy Limitation of Adaptive Property Estimation

Recent years have witnessed the success of adaptive (or unified) approaches in estimating symmetric properties of discrete distributions, where one first obtains a distribution estimator independent of the target property, and then plugs the estimator into the target property as the final estimator. Several such approaches have been proposed and proved to be adaptively optimal, i.e. they achieve the optimal sample complexity for a large class of properties within a low accuracy, especially for a large estimation error $\varepsilon\gg n^{-1/3}$ where $n$ is the sample size. In this paper, we characterize the high accuracy limitation, or the penalty for adaptation, for all such approaches. Specifically, we show that under a mild assumption that the distribution estimator is close to the true sorted distribution in expectation, any adaptive approach cannot achieve the optimal sample complexity for every $1$-Lipschitz property within accuracy $\varepsilon \ll n^{-1/3}$. In particular, this result disproves a conjecture in [Acharya et al. 2017] that the profile maximum likelihood (PML) plug-in approach is optimal in property estimation for all ranges of $\varepsilon$, and confirms a conjecture in [Han and Shiragur, 2021] that their competitive analysis of the PML is tight.
Low-Resource Expressive Text-To-Speech Using Data Augmentation

While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired speaking style. In this work, we present a novel 3-step methodology to circumvent the costly operation of recording large amounts of target data in order to build expressive style voices with as little as 15 minutes of such recordings. First, we augment data via voice conversion by leveraging recordings in the desired speaking style from other speakers. Next, we use that synthetic data on top of the available recordings to train a TTS model. Finally, we fine-tune that model to further increase quality. Our evaluations show that the proposed changes bring significant improvements over non-augmented models across many perceived aspects of synthesised speech. We demonstrate the proposed approach on 2 styles (news-caster and conversational), on various speakers, and on both single and multi-speaker models, illustrating the robustness of our approach.1
Local cohomology of module of differentials of integral extensions

Abstract The main focus of this paper is on determining the highest non-vanishing local cohomology modules of Ω B / R , Ω B / V ( Ω B / k ) where R is either a complete regular local ring or a complete local normal domain with coefficient ring V (field k) and B is its integral closure in an algebraic extension of Q ( R ) . Similar problem is also studied over a normal domain R containing a field k of characteristic 0. In this connection new observations on the direct summand property for integral extensions are also presented.
Semantic Modeling for Food Recommendation Explanations

With the increased use of AI methods to provide recommendations in the health, specifically dietary recommendation space, there is also an increased need for explainability of those recommendations. Such explanations would benefit users of recommendation systems by empowering them with justifications for following the system’s suggestions. We present the Food Explanation Ontology (FEO) that provides a formalism for modeling explanations to users for food-related recommendations. FEO models food recommendations, using concepts from the explanation domain to create responses to user questions about food recommendations they receive from AI systems such as personalized knowledge base question answering systems. FEO uses a modular, extensible structure that lends itself to a variety of explanations while still preserving important semantic details to accurately represent explanations of food recommendations. In order to evaluate this system, we used a set of competency questions derived from explanation types present in literature that are relevant to food recommendations. Our motivation with the use of FEO is to empower users to make decisions about their health, fully equipped with an understanding of the AI recommender systems as they relate to user questions, by providing reasoning behind their recommendations in the form of explanations. Resource Website: https://tetherless-world.github.io/food-explanation-ontology Ontology Link: https://purl.org/heals/food-explanation-ontology/
Dynamical correlation energy of metals in large basis sets from downfolding and composite approaches.

Coupled-cluster theory with single and double excitations (CCSD) is a promising ab initio method for the electronic structure of three-dimensional metals, for which second-order perturbation theory (MP2) diverges in the thermodynamic limit. However, due to the high cost and poor convergence of CCSD with respect to basis size, applying CCSD to periodic systems often leads to large basis set errors. In a common “composite” method, MP2 is used to recover the missing dynamical correlation energy through a focal-point correction, but the inadequacy of finite-order perturbation theory for metals raises questions about this approach. Here, we describe how high-energy excitations treated by MP2 can be “downfolded” into a low-energy active space to be treated by CCSD. Comparing how the composite and downfolding approaches perform for the uniform electron gas, we find that the latter converges more quickly with respect to the basis set size. Nonetheless, the composite approach is surprisingly accurate because it removes the problematic MP2 treatment of double excitations near the Fermi surface. Using this method to estimate the CCSD correlation energy in the combined complete basis set and thermodynamic limits, we find that CCSD recovers 85%–90% of the exact correlation energy at rs = 4. We also test the composite approach with the direct random-phase approximation used in place of MP2, yielding a method that is typically (but not always) more cost effective due to the smaller number of orbitals that need to be included in the more expensive CCSD calculation.
Elastocaloric effect in amorphous polymer networks undergoing mechanotropic phase transitions

Deformations of amorphous polymer networks prepared with significant concentrations of liquid crystalline mesogens have been recently reported to undergo mechanotropic phase transitions. Here, we report that mechanotropic phase transitions are accompanied by an elastocaloric response ($\mathrm{\ensuremath{\Delta}}T=2.9$ K). Applied uniaxial strain to the elastomeric polymer network transitions the organization of the material from a disordered, amorphous state (order parameter $Q=0$) to the nematic phase ($Q=0.47$). Both the magnitude of the elastocaloric temperature change and the mechanically induced order parameter are dependent on the concentration of liquid crystal mesogens in the material.
Uncovering Latent Biases in Text: Method and Application to Peer Review.

Quantifying systematic disparities in numerical quantities such as employment rates and wages between population subgroups provides compelling evidence for the existence of societal biases. However, biases in the text written for members of different subgroups (such as in recommendation letters for male and non-male candidates), though widely reported anecdotally, remain challenging to quantify. In this work, we introduce a novel framework to quantify bias in text caused by the visibility of subgroup membership indicators. We develop a nonparametric estimation and inference procedure to estimate this bias. We then formalize an identification strategy to causally link the estimated bias to the visibility of subgroup membership indicators, provided observations from time periods both before and after an identity-hiding policy change. We identify an application wherein "ground truth" bias can be inferred to evaluate our framework, instead of relying on synthetic or secondary data. Specifically, we apply our framework to quantify biases in the text of peer reviews from a reputed machine learning conference before and after the conference adopted a double-blind reviewing policy. We show evidence of biases in the review ratings that serves as "ground truth", and show that our proposed framework accurately detects these biases from the review text without having access to the review ratings.
Faster Game Solving via Predictive Blackwell Approachability: Connecting Regret Matching and Mirror Descent

Blackwell approachability is a framework for reasoning about repeated games with vector-valued payoffs. We introduce predictive Blackwell approachability, where an estimate of the next payoff vector is given, and the decision maker tries to achieve better performance based on the accuracy of that estimator. In order to derive algorithms that achieve predictive Blackwell approachability, we start by showing a powerful connection between four well-known algorithms. Follow-the-regularized-leader (FTRL) and online mirror descent (OMD) are the most prevalent regret minimizers in online convex optimization. In spite of this prevalence, the regret matching (RM) and regret matching+ (RM+) algorithms have been preferred in the practice of solving large-scale games (as the local regret minimizers within the counterfactual regret minimization framework). We show that RM and RM+ are the algorithms that result from running FTRL and OMD, respectively, to select the halfspace to force at all times in the underlying Blackwell approachability game. By applying the predictive variants of FTRL or OMD to this connection, we obtain predictive Blackwell approachability algorithms, as well as predictive variants of RM and RM+. In experiments across 18 common zero-sum extensive-form benchmark games, we show that predictive RM+ coupled with counterfactual regret minimization converges vastly faster than the fastest prior algorithms (CFR+, DCFR, LCFR) across all games but two of the poker games, sometimes by two or more orders of magnitude.
Fundamental Limits of Many-User MAC With Finite Payloads and Fading

Consider a (multiple-access) wireless communication system where users are connected to a unique base station over a shared-spectrum radio links. Each user has a fixed number $k$ of bits to send to the base station, and his signal gets attenuated by a random channel gain (quasi-static fading). In this paper we consider the many-user asymptotics of Chen-Chen-Guo’2017, where the number of users grows linearly with the blocklength. Differently, though, we adopt a per-user probability of error (PUPE) criterion (as opposed to classical joint-error probability criterion). Under PUPE the finite energy-per-bit communication is possible, and we are able to derive bounds on the tradeoff between energy and spectral efficiencies. We reconfirm the curious behaviour (previously observed for non-fading MAC) of the possibility of almost perfect multi-user interference (MUI) cancellation for user densities below a critical threshold. Further, we demonstrate the suboptimality of standard solutions such as orthogonalization (i.e. TDMA/FDMA) and treating interference as noise (i.e. pseudo-random CDMA without multi-user detection). Notably, the problem treated here can be seen as a variant of support recovery in compressed sensing for the unusual definition of sparsity with one non-zero entry per each contiguous section of $2^{k}$ coordinates. This identifies our problem with that of the sparse regression codes (SPARCs) and hence our results can be equivalently understood in the context of SPARCs with sections of length 2100. Finally, we discuss the relation of the almost perfect MUI cancellation property and the replica-method predictions.
Vitamin A aldehyde-taurine adduct and the visual cycle

Visual pigment consists of opsin covalently linked to the vitamin A-derived chromophore, 11-cis-retinaldehyde. Photon absorption causes the chromophore to isomerize from the 11-cis- to all-trans-retinal configuration. Continued light sensitivity necessitates the regeneration of 11-cis-retinal via a series of enzyme-catalyzed steps within the visual cycle. During this process, vitamin A aldehyde is shepherded within photoreceptors and retinal pigment epithelial cells to facilitate retinoid trafficking, to prevent nonspecific reactivity, and to conserve the 11-cis configuration. Here we show that redundancy in this system is provided by a protonated Schiff base adduct of retinaldehyde and taurine (A1-taurine, A1T) that forms reversibly by nonenzymatic reaction. A1T was present as 9-cis, 11-cis, 13-cis, and all-trans isomers, and the total levels were higher in neural retina than in retinal pigment epithelium (RPE). A1T was also more abundant under conditions in which 11-cis-retinaldehyde was higher; this included black versus albino mice, dark-adapted versus light-adapted mice, and mice carrying the Rpe65-Leu450 versus Rpe65-450Met variant. Taurine levels paralleled these differences in A1T. Moreover, A1T was substantially reduced in mice deficient in the Rpe65 isomerase and in mice deficient in cellular retinaldehyde-binding protein; in these models the production of 11-cis-retinal is compromised. A1T is an amphiphilic small molecule that may represent a mechanism for escorting retinaldehyde. The transient Schiff base conjugate that the primary amine of taurine forms with retinaldehyde would readily hydrolyze to release the retinoid and thus may embody a pool of 11-cis-retinal that can be marshalled in photoreceptor cells.
Forebody effects on drogue drag in supersonic flow.

Forebody shape effects on drogue drag tested for various forebody-drogue configurations in supersonic flow
A unified energy-optimality criterion predicts human navigation paths and speeds

Navigating our physical environment requires changing directions and turning. Despite its ecological importance, we do not have a unified theoretical account of non-straight-line human movement. Here, we present a unified optimality criterion that predicts disparate non-straight-line walking phenomena, with straight-line walking as a special case. We first characterized the metabolic cost of turning, deriving the cost landscape as a function of turning radius and rate. We then generalized this cost landscape to arbitrarily complex trajectories, allowing the velocity direction to deviate from body orientation (holonomic walking). We used this generalized optimality criterion to mathematically predict movement patterns in multiple contexts of varying complexity: walking on prescribed paths, turning in place, navigating an angled corridor, navigating freely with end-point constraints, walking through doors, and navigating around obstacles. In these tasks, humans moved at speeds and paths predicted by our optimality criterion, slowing down to turn and never using sharp turns. We show that the shortest path between two points is, counterintuitively, often not energy-optimal, and, indeed, humans do not use the shortest path in such cases. Thus, we have obtained a unified theoretical account that predicts human walking paths and speeds in diverse contexts. Our model focuses on walking in healthy adults; future work could generalize this model to other human populations, other animals, and other locomotor tasks.
Free vibrations of delaminated beams

Free vibration of laminated composite beams is studied. The effect of interply delaminations on natural frequencies and mode shapes is evaluated both analytically and experimentally. The equation of motion and associated boundary conditions are derived for the free vibration of a composite beam with a delamination of arbitrary size and location. A generalized variational principle is used to formulate the equation of motion, taking into account the interlaminar stress concentration at the crack-tips. This is accomplished by introducing a 'crack function' into the beam's compatibility relations. This function has its maximum value at the crack tip and decays exponentially in the longitudinal direction. The rate of exponential decay is determined by a least-square fit with the experimental results. The effect of coupling between longitudinal vibration and bending vibration is considered in the present study. This coupling effect is found to significantly affect the natural frequencies and mode shapes of the delaminated beam.
Optimal Dynamic Futures Portfolios Under a Multiscale Central Tendency Ornstein-Uhlenbeck Model

We study the problem of dynamically trading multiple futures whose underlying asset price follows a multiscale central tendency Ornstein-Uhlenbeck (MCTOU) model. Under this model, we derive the closed-form no-arbitrage prices for the futures contracts. Applying a utility maximization approach, we solve for the optimal trading strategies under different portfolio configurations by examining the associated system of Hamilton-Jacobi-Bellman (HJB) equations. The optimal strategies depend on not only the parameters of the underlying asset price process, but also the risk premia embedded in the futures prices. Numerical examples are provided to illustrate the investor's optimal positions and optimal wealth over time.