cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimal Strategies for Communication and Remote Estimation With an Energy Harvesting Sensor

We consider a remote estimation problem with an energy harvesting sensor and a remote estimator. The sensor observes the state of a discrete-time source which may be a finite state Markov chain or a multidimensional linear Gaussian system. It harvests energy from its environment (say, for example, through a solar cell) and uses this energy for the purpose of communicating with the estimator. Due to randomness of the energy available for communication, the sensor may not be able to communicate all of the time. The sensor may also want to save its energy for future communications. The estimator relies on messages communicated by the sensor to produce real-time estimates of the source state. We consider the problem of finding a communication scheduling strategy for the sensor and an estimation strategy for the estimator that jointly minimizes the expected sum of communication and distortion costs over a finite time horizon. Our goal of joint optimization leads to a decentralized decision-making problem. By viewing the problem from the estimator's perspective, we obtain a dynamic programming characterization for the decentralized decision-making problem that involves optimization over functions. Under some symmetry assumptions on the source statistics and the distortion metric, we show that an optimal communication strategy is described by easily computable thresholds and that the optimal estimate is a simple function of the most recently received sensor observation.
Signal recovery on graphs: Random versus experimentally designed sampling

We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling. We propose a new class of smooth graph signals, called approximately bandlimited. We then propose two recovery strategies based on random sampling and experimentally designed sampling. The proposed recovery strategy based on experimentally designed sampling uses sampling scores, which is similar to the leverage scores used in the matrix approximation. We show that while both strategies are unbiased estimators for the low-frequency components, the convergence rate of experimentally designed sampling is much faster than that of random sampling when a graph is irregular1. We validate the proposed recovery strategies on three specific graphs: a ring graph, an Erdős-Renyi graph, and a star graph. The simulation results support the theoretical analysis.
Two sufficient conditions for rectifiable measures

We identify two sufficient conditions for locally finite Borel measures on R to give full mass to a countable family of Lipschitz images of R. The first condition, extending a prior result of Pajot, is a sufficient test in terms of L affine approximability for a locally finite Borel measure μ on R satisfying the global regularity hypothesis lim sup r↓0 μ(B(x, r))/r < ∞ at μ-a.e. x ∈ R to be m-rectifiable in the sense above. The second condition is an assumption on the growth rate of the 1-density that ensures a locally finite Borel measure μ on R with lim r↓0 μ(B(x, r))/r = ∞ at μ-a.e. x ∈ R
Detached red giant eclipsing binary twins: Rosetta stones to the galactic bulge

We identify 34 highly-probable detached, red giant eclipsing binary pairs among 315 candidates in Devor's catalog of $\sim$10,000 OGLE-II eclipsing binaries. We estimate that there should be at least 200 such systems in OGLE-III. We show that spectroscopic measurements of the metallicities and radial-velocity-derived masses of these systems would independently constrain both the age-metallicity and helium-metallicity relations of the Galactic Bulge, potentially breaking the age-helium degeneracy that currently limits our ability to characterize the Bulge stellar population. Mass and metallicity measurements alone would be sufficient to immediately validate or falsify recent claims about the age and helium abundance of the Bulge. A spectroscopic survey of these systems would constrain models of Milky Way assembly, as well as provide significant auxiliary science on research questions such as mass loss on the red giant branch. We discuss the theoretical uncertainties in stellar evolution models that would need to be accounted for to maximize the scientific yield.
Distribution-Specific Agnostic Boosting

We consider the problem of boosting the accuracy of weak learning algorithms in the agnostic learning framework of Haussler (1992) and Kearns et al. (1992). Known algorithms for this problem (Ben-David et al., 2001; Gavinsky, 2002; Kalai et al. , 2008) follow the same strategy as boosting algorithms in the PAC model: the weak learner is executed on the same target function but over different distributions on the domain. Application of such boosting algorithms usually requires a distribution-independent weak agnostic learners. Here we demonstrate boosting algorithms for the agnostic learning framework that only modify the distribution on the labels of the points (or, equivalently, modify the target function). This allows boosting a distribution-specific weak agnostic learner to a strong agnostic learner with respect to the same distribution. Our algorithm achieves the same guarantees on the final error as the boosting algorithms of Kalai et al. (2008) but is substantially simpler and more efficient. When applied to the weak agnostic parity learning algorithm of Goldreich and Levin (1989) our algorithm yields a simple PAC learning algorithm for DNF and an agnostic learning algorithm for decision trees over the uniform distribution using membership queries. These results substantially simplify Jackson’s famous DNF learning algorithm (1994) and the recent result of Gopalan et al. (2008). We also strengthen the connection to hard-core set constructions discovered by Klivans and Servedio (1999) by demonstrating that hard-core set constructions that achieve the optimal hard-core set size (given by Holenstein (2005) and Barak et al. (2009)) imply distribution-specific agnostic boosting algorithms. Conversely, our boosting algorithm gives a simple hard-core set construction with an (almost) optimal hard-core set size.
Hierarchical Evidence Accumulation in the Pseiki System and Experiments in Model-Driven Mobile Robot Navigation

Abstract In this paper, we will review the process of evidence accumulation in the PSEIKI system for expectation-driven interpretation of images of 3-D scenes. Expectations are presented to PSEIKI as a geometrical hierarchy of abstractions. PSEIKI's job is then to construct abstraction hierarchies in the perceived image taking cues from the abstraction hierarchies in the expectations. The Dempster-Shafer formalism is used for associating belief values with the different possible labels for the constructed abstractions in the perceived image. This system has been used successfully for autonomous navigation of a mobile robot in indoor environments.
Variational inference for the multi-armed contextual bandit

In many biomedical, science, and engineering problems, one must sequentially decide which action to take next so as to maximize rewards. One general class of algorithms for optimizing interactions with the world, while simultaneously learning how the world operates, is the multi-armed bandit setting and, in particular, the contextual bandit case. In this setting, for each executed action, one observes rewards that are dependent on a given 'context', available at each interaction with the world. The Thompson sampling algorithm has recently been shown to enjoy provable optimality properties for this set of problems, and to perform well in real-world settings. It facilitates generative and interpretable modeling of the problem at hand. Nevertheless, the design and complexity of the model limit its application, since one must both sample from the distributions modeled and calculate their expected rewards. We here show how these limitations can be overcome using variational inference to approximate complex models, applying to the reinforcement learning case advances developed for the inference case in the machine learning community over the past two decades. We consider contextual multi-armed bandit applications where the true reward distribution is unknown and complex, which we approximate with a mixture model whose parameters are inferred via variational inference. We show how the proposed variational Thompson sampling approach is accurate in approximating the true distribution, and attains reduced regrets even with complex reward distributions. The proposed algorithm is valuable for practical scenarios where restrictive modeling assumptions are undesirable.
Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring

This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to "when" to sample and "how many" samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards "where" to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria ("q-EI"), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).
Haglund–Haiman–Loehr type formulas for Hall–Littlewood polynomials of type B and C

In previous work we showed that two apparently unrelated formu- las for the Hall-Littlewood polynomials of type A are, in fact, closely related. The first is the tableau formula obtained by specializing q = 0 in the Haglund- Haiman-Loehr formula for Macdonald polynomials. The second is the type A instance of Schwer's formula (rephrased and rederived by Ram) for Hall- Littlewood polynomials of arbitrary finite type; Schwer's formula is in terms of so-called alcove walks, which originate in the work of Gaussent-Littelmann and of the author with Postnikov on discrete counterparts to the Littelmann path model. We showed that the tableau formula follows by "compressing" Ram's version of Schwer's formula. In this paper, we derive tableau formu- las for the Hall-Littlewood polynomials of type B and C by compressing the corresponding instances of Schwer's formula.
Polynomial bridgeland stable objects and reflexive sheaves

On a smooth projective threefold, we show that there are only two isomorphism types for the moduli of stable objects with respect to Bayer's standard polynomial Bridgeland stability - the moduli of Gieseker-stable sheaves and the moduli of PT-stable objects - under the following assumptions: no two of the stability vectors are collinear, and the degree and rank of the objects are relatively prime. We also describe a close relation between the intersection of the moduli spaces of PT-stable and dual-PT-stable objects, and the moduli of reflexive sheaves.
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion.

We study tensor completion in the agnostic setting. In the classical tensor completion problem, we receive $n$ entries of an unknown rank-$r$ tensor and wish to exactly complete the remaining entries. In agnostic tensor completion, we make no assumption on the rank of the unknown tensor, but attempt to predict unknown entries as well as the best rank-$r$ tensor. For agnostic learning of third-order tensors with the square loss, we give the first polynomial time algorithm that obtains a "fast" (i.e., $O(1/n)$-type) rate improving over the rate obtained by reduction to matrix completion. Our prediction error rate to compete with the best $d\times{}d\times{}d$ tensor of rank-$r$ is $\tilde{O}(r^{2}d^{3/2}/n)$. We also obtain an exact oracle inequality that trades off estimation and approximation error. Our algorithm is based on the degree-six sum-of-squares relaxation of the tensor nuclear norm. The key feature of our analysis is to show that a certain characterization for the subgradient of the tensor nuclear norm can be encoded in the sum-of-squares proof system. This unlocks the standard toolbox for localization of empirical processes under the square loss, and allows us to establish restricted eigenvalue-type guarantees for various tensor regression models, with tensor completion as a special case. The new analysis of the relaxation complements Barak and Moitra (2016), who gave slow rates for agnostic tensor completion, and Potechin and Steurer (2017), who gave exact recovery guarantees for the noiseless setting. Our techniques are user-friendly, and we anticipate that they will find use elsewhere.
Skill Acquisition via Automated Multi-Coordinate Cost Balancing

We propose a learning framework, named Multi-Coordinate Cost Balancing (MCCB), to address the problem of acquiring point-to-point movement skills from demonstrations. MCCB encodes demonstrations simultaneously in multiple differential coordinates that specify local geometric properties. MCCB generates reproductions by solving a convex optimization problem with a multi-coordinate cost function and linear constraints on the reproductions, such as initial, target, and via points. Further, since the relative importance of each coordinate system in the cost function might be unknown for a given skill, MCCB learns optimal weighting factors that balance the cost function. We demonstrate the effectiveness of MCCB via detailed experiments conducted on one handwriting dataset and three complex skill datasets.
Empirical likelihood test for diagonal symmetry

Abstract Energy distance is a statistical distance between the distributions of random variables, which characterizes the equality of the distributions. Utilizing the energy distance, we develop a nonparametric test for the diagonal symmetry, which is consistent against any fixed alternatives. The test statistic developed in this paper is based on the difference of two U -statistics. By applying the jackknife empirical likelihood approach, the standard limiting chi-square distribution with degree freedom of one is established and is used to determine critical value and p -value of the test. Simulation studies show that our method is competitive in terms of empirical sizes and empirical powers.
Learning Rhyming Constraints using Structured Adversaries

Existing recurrent neural language models often fail to capture higher-level structure present in text: for example, rhyming patterns present in poetry. Much prior work on poetry generation uses manually defined constraints which are satisfied during decoding using either specialized decoding procedures or rejection sampling. The rhyming constraints themselves are typically not learned by the generator. We propose an alternate approach that uses a structured discriminator to learn a poetry generator that directly captures rhyming constraints in a generative adversarial setup. By causing the discriminator to compare poems based only on a learned similarity matrix of pairs of line ending words, the proposed approach is able to successfully learn rhyming patterns in two different English poetry datasets (Sonnet and Limerick) without explicitly being provided with any phonetic information
Batched Multi-armed Bandits Problem

In this paper, we study the multi-armed bandit problem in the batched setting where the employed policy must split data into a small number of batches. While the minimax regret for the two-armed stochastic bandits has been completely characterized in \cite{perchet2016batched}, the effect of the number of arms on the regret for the multi-armed case is still open. Moreover, the question whether adaptively chosen batch sizes will help to reduce the regret also remains underexplored. In this paper, we propose the BaSE (batched successive elimination) policy to achieve the rate-optimal regrets (within logarithmic factors) for batched multi-armed bandits, with matching lower bounds even if the batch sizes are determined in an adaptive manner.
A language for distributed processing

The main question being addressed here is, what is a good way to program a multiple processor system (whether tightly or loosely coupled) to accomplish an integral distributed processing applicationƒ Writing concurrent programs for a uniprocessor is tough enough, but writing programs which interact and operate simultaneously in parallel can be a most difficult and frustrating experience. Opportunities abound for operational failures due to race conditions, for time-dependent bugs and for deadlock situations.
COMA: Communication and Obfuscation Management Architecture

In this paper, we introduce a novel Communication and Obfuscation Management Architecture (COMA) to handle the storage of the obfuscation key and to secure the communication to/from untrusted yet obfuscated circuits. COMA addresses three challenges related to the obfuscated circuits: First, it removes the need for the storage of the obfuscation unlock key at the untrusted chip. Second, it implements a mechanism by which the key sent for unlocking an obfuscated circuit changes after each activation (even for the same device), transforming the key into a dynamically changing license. Third, it protects the communication to/from the COMA protected device and additionally introduces two novel mechanisms for the exchange of data to/from COMA protected architectures: (1) a highly secure but slow double encryption, which is used for exchange of key and sensitive data (2) a high-performance and low-energy yet leaky encryption, secured by means of frequent key renewal. We demonstrate that compared to state-of-the-art key management architectures, COMA reduces the area overhead by 14%, while allowing additional features including unique chip authentication, enabling activation as a service (for IoT devices), reducing the side channel attack on key management architecture, and providing two new means of the secure communication to/from an COMA-secured untrusted chip.
Compression of Acoustic Event Detection Models With Quantized Distillation

Acoustic Event Detection (AED), aiming at detecting categories of events based on audio signals, has found application in many intelligent systems. Recently deep neural network significantly advances this field and reduces detection errors to a large scale. However how to efficiently execute deep models in AED has received much less attention. Meanwhile state-of-the-art AED models are based on large deep models, which are computational demanding and challenging to deploy on devices with constrained computational resources. In this paper, we present a simple yet effective compression approach which jointly leverages knowledge distillation and quantization to compress larger network (teacher model) into compact network (student model). Experimental results show proposed technique not only lowers error rate of original compact network by 15% through distillation but also further reduces its model size to a large extent (2% of teacher, 12% of full-precision student) through quantization.
Understanding Continuous and Pleasant Linear Sensations on the Forearm From a Sequential Discrete Lateral Skin-Slip Haptic Device

A continuous stroking sensation on the skin can convey messages or emotion cues. We seek to induce this sensation using a combination of illusory motion and lateral stroking via a haptic device. Our system provides discrete lateral skin-slip on the forearm with rotating tactors, which independently provide lateral skin-slip in a timed sequence. We vary the sensation by changing the angular velocity and delay between adjacent tactors, such that the apparent speed of the perceived stroke ranges from 2.5 to 48.2 cm/s. We investigated which actuation parameters create the most pleasant and continuous sensations through a user study with 16 participants. On average, the sensations were rated by participants as both continuous and pleasant. The most continuous and pleasant sensations were created by apparent speeds of 7.7 and 5.1 cm/s, respectively. We also investigated the effect of spacing between contact points on the pleasantness and continuity of the stroking sensation, and found that the users experience a pleasant and continuous linear sensation even when the space between contact points is relatively large (40 mm). Understanding how sequential discrete lateral skin-slip creates continuous linear sensations can influence the design and control of future wearable haptic devices.
A Game-Theoretic Analysis of Reallocation Mechanisms for Airport Landing Slots

As airport arrival capacities increasingly constrain the air transportation system, there is a need for mechanisms by which airlines can exchange landing slots amongst each other. We analyze two such mechanisms, scaled airline preferences and two-for-two trades, from a game-theoretic perspective. This paper investigates the extent to which strategic behavior on part of the airlines can impact the performance of each mechanism. In addition to increasing system efficiency, the reallocation mechanisms should exhibit desirable fairness and incentive properties, notions that we formally investigate in this paper. We show that neither mechanism has good incentive properties, and we develop simple, non-truthful strategies that airlines can use. Our empirical results show that for the scaled airline preferences mechanism, the best performing strategy depends greatly on the extent to which fairness is enforced. For the two-for-two trades mechanism, a simple threshold strategy can yield significant cost savings relative to the best-response strategy, and system efficiency increases when all airlines use the threshold strategy in equilibrium.
Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations.

Work using artificial languages as training input has shown that LSTMs are capable of inducing the stack-like data structures required to represent context-free and certain mildly context-sensitive languages — formal language classes which correspond in theory to the hierarchical structures of natural language. Here we present a suite of experiments probing whether neural language models trained on linguistic data induce these stack-like data structures and deploy them while incrementally predicting words. We study two natural language phenomena: center embedding sentences and syntactic island constraints on the filler–gap dependency. In order to properly predict words in these structures, a model must be able to temporarily suppress certain expectations and then recover those expectations later, essentially pushing and popping these expectations on a stack. Our results provide evidence that models can successfully suppress and recover expectations in many cases, but do not fully recover their previous grammatical state.
Basic physical properties of cubic boron arsenide

Cubic boron arsenide (BAs) is an emerging semiconductor material with a record-high thermal conductivity subject to intensive research interest for its applications in electronics thermal management. However, many fundamental properties of BAs remain unexplored experimentally since high-quality BAs single crystals have only been obtained very recently. Here, we report the systematic experimental measurements of important physical properties of BAs, including the bandgap, optical refractive index, elastic modulus, shear modulus, Poisson's ratio, thermal expansion coefficient, and heat capacity. In particular, light absorption and Fabry–Perot interference were used to measure an optical bandgap of 1.82 eV and a refractive index of 3.29 (657 nm) at room temperature. A picoultrasonic method, based on ultrafast optical pump probe spectroscopy, was used to measure a high elastic modulus of 326 GPa, which is twice that of silicon. Furthermore, temperature-dependent X-ray diffraction was used to measure a linear thermal expansion coefficient of 3.85 × 10−6 K−1; this value is very close to prototype semiconductors such as GaN, which underscores the promise of BAs for cooling high power and high frequency electronics. We also performed ab initio theory calculations and observed good agreement between the experimental and theoretical results. Importantly, this work aims to build a database (Table I) for the basic physical properties of BAs with the expectation that this semiconductor will inspire broad research and applications in electronics, photonics, and mechanics.
Cell-autonomous regulation of epithelial cell quiescence by calcium channel Trpv6

Epithelial homeostasis and regeneration require a pool of quiescent cells. How the quiescent cells are established and maintained is poorly understood. Here, we report that Trpv6, a cation channel responsible for epithelial Ca2+ absorption, functions as a key regulator of cellular quiescence. Genetic deletion and pharmacological blockade of Trpv6 promoted zebrafish epithelial cells to exit from quiescence and re-enter the cell cycle. Reintroducing Trpv6, but not its channel dead mutant, restored the quiescent state. Ca2+ imaging showed that Trpv6 is constitutively open in vivo. Mechanistically, Trpv6-mediated Ca2+ influx maintained the quiescent state by suppressing insulin-like growth factor (IGF)-mediated Akt-Tor and Erk signaling. In zebrafish epithelia and human colon carcinoma cells, Trpv6/TRPV6 elevated intracellular Ca2+ levels and activated PP2A, which down-regulated IGF signaling and promoted the quiescent state. Our findings suggest that Trpv6 mediates constitutive Ca2+ influx into epithelial cells to continuously suppress growth factor signaling and maintain the quiescent state.
Impact of 3D Antenna Radiation Patterns on TDOA-Based Wireless Localization of UAVs

Next big commercial applications of drones require to fly the drone beyond the visual line of sight (BVLOS). This inevitable ability to fly BVLOS will also necessitate the ability to keep track of the drone's location, in order to ensure successful completion of the intended service. In this context, we explore the fundamental limits of 3D localization of drones in conjunction with the effects of the 3D antenna radiation patterns. Although localization of drone/unmanned aerial vehicle (UAV) is a well-studied topic in the literature, its relationship to the antenna effects remains mostly unexplored. In this paper, we investigate the impact of antenna radiation pattern on the accuracy of time-difference-of-arrival (TDOA)-based localization of the UAV. Specifically, we consider a scenario where a fixed number of radio-frequency (RF) sensors, placed at some known locations on the ground, collect the TDOA measurements from the signals transmitted from the UAV, and estimate the location of the UAV from these observations. In order to study the impact of the antenna effects on the fundamental limits of the TDOA-based positioning scheme, we develop a simple analytical model to approximate the total antenna gains experienced by an air-to-ground (A2G) link, for various orientations of the antennas. We then derive the Cramer-Rao lower bound for the TDOA based localization scheme, for all three combinations of the transmit and the receive antenna orientations: vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH).
Dislocation Mechanisms in the GaN Lateral Overgrowth by Hydride Vapor Phase Epitaxy

We have carried out a series of lateral epitaxial overgrowths (LEO) of GaN through thin oxide windows by the hydride vapor phase epitaxy (HVPE) technique at different growth temperatures. High lateral growth rate at 1100°C allows coalescing of neighboring islands into a continuous and flat film, while the lower lateral growth rate at 1050°C produces triangular-shaped ridges over the growth windows. In either case, threading dislocations bend into laterally grown regions to relax the shear stress developed in the film during growth. In regions close to the mask edge, where the shear stress is highest, dislocations interact and multiply into arrays of edge dislocations lying parallel to the growth window. This multiplication and pileup of dislocations cause a large-angle tilting of the laterally grown regions. The tilt angle is high (∼8 degrees) when the growth is at 1050°C and becomes smaller (3-5 degrees) at 1100°C. At the coalescence of growth facets, a tilt-type grain boundary is formed. During the high-temperature lateral growth, the tensile stress in the GaN seed layer and the thermal stress from the mask layer both contribute to a high shear stress at the growth facets. Finite element stress simulations suggest that this shear stress may be sufficient to cause the observed excessive dislocation activities and tilting of LEO regions at high growth temperatures.
On demand uncertainty in the newsvendor model

Abstract I provide three comparative statics involving the level of demand uncertainty for the newsvendor model, two of which lead to robust predictions. I show that for distributions of demand that are greater in the dispersive order, both the expected (censored) sales and share of inventory sold fall. These monotone comparative statics occur despite the lack of one for the (optimal) inventory choice.
DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy

The next great leap toward improving treatment of cancer with radiation will require the combined use of online adaptive and magnetic resonance guided radiation therapy techniques with automatic X-ray beam orientation selection. Unfortunately, by uniting these advancements, we are met with a substantial expansion in the required dose information and consequential increase to the overall computational time imposed during radiation treatment planning, which cannot be handled by existing techniques for accelerating Monte Carlo dose calculation. We propose a deep convolutional neural network approach that unlocks new levels of acceleration and accuracy with regards to post-processed Monte Carlo dose results by relying on data-driven learned representations of low-level beamlet dose distributions instead of more limited filter-based denoising techniques that only utilize the information in a single dose input. Our method uses parallel U-Net branches acting on three input channels before mixing latent understanding to produce noise-free dose predictions. Our model achieves a normalized mean absolute error of only 0.106% compared with the ground truth dose contrasting the 25.7% error of the under sampled MC dose fed into the network at prediction time. Our model’s per-beamlet prediction time is ~220 ms, including Monte Carlo simulation and network prediction, with substantial additional acceleration expected from batched processing and combination with existing Monte Carlo acceleration techniques. Our method shows promise toward enabling clinical practice of advanced treatment technologies.
The Variational Gaussian Process

Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models. We develop the variational Gaussian process (VGP), a Bayesian nonparametric variational family, which adapts its shape to match complex posterior distributions. The VGP generates approximate posterior samples by generating latent inputs and warping them through random non-linear mappings; the distribution over random mappings is learned during inference, enabling the transformed outputs to adapt to varying complexity. We prove a universal approximation theorem for the VGP, demonstrating its representative power for learning any model. For inference we present a variational objective inspired by auto-encoders and perform black box inference over a wide class of models. The VGP achieves new state-of-the-art results for unsupervised learning, inferring models such as the deep latent Gaussian model and the recently proposed DRAW.
Motion of objects embedded in lipid bilayer membranes: Advection and effective viscosity.

An interfacial regularized Stokeslet scheme is presented to predict the motion of solid bodies (e.g., proteins or gel-phase domains) embedded within flowing lipid bilayer membranes. The approach provides a numerical route to calculate velocities and angular velocities in complex flow fields that are not amenable to simple Faxen-like approximations. Additionally, when applied to shearing motions, the calculations yield predictions for the effective surface viscosity of dilute rigid-body-laden membranes. In the case of cylindrical proteins, effective viscosity calculations are compared to two prior analytical predictions from the literature. Effective viscosity predictions for a dilute suspension of rod-shaped objects in the membrane are also presented.
Disclosure Processing Costs, Investors’ Information Choice, and Equity Market Outcomes: A Review

Abstract This paper reviews the literature examining how costs of monitoring for, acquiring, and analyzing firm disclosures – collectively, “disclosure processing costs” – affect investor information choices, trades, and market outcomes. The existence of disclosure processing costs means that disclosures are not “public” information as traditionally defined, but instead can be a form of costly private information. Conceptualizing disclosures as private information makes it clear that learning from disclosures is an active economic choice and that disclosure pricing cannot be perfectly efficient. We review the analytical and empirical literature on sources of processing costs and how these costs affect equity market outcomes, primarily within rational equilibria. We also discuss studies of the feedback effects of investors' processing costs on managers’ choices about disclosure and corporate actions. We conclude that disclosure processing costs have implications for a wide array of accounting research and phenomena, but we are only just beginning to understand their effects.
Theory of active chromatin remodeling

Nucleosome positioning controls the accessible regions of chromatin and plays essential roles in DNA-templated processes. ATP driven remodeling enzymes are known to be crucial for its establishment in vivo, but their nonequilibrium nature has hindered the development of a unified theoretical framework for nucleosome positioning. Using a perturbation theory, we show that the effect of these enzymes can be well approximated by effective equilibrium models with rescaled temperatures and interactions. Numerical simulations support the accuracy of the theory in predicting both kinetic and steady-state quantities, including the effective temperature and the radial distribution function, in biologically relevant regimes. The energy landscape view emerging from our study provides an intuitive understanding for the impact of remodeling enzymes in either reinforcing or overwriting intrinsic signals for nucleosome positioning, and may help improve the accuracy of computational models for its prediction in silico.
The Blessings of Multiple Causes

AbstractCausal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods assume that we observe all confounders, variables that affect both the causal...
Power trading and congestion management using real power rescheduling

Congestion is termed as the operating condition in which there is not enough transmission capacity to implement all the desired transactions. This paper deals with the power trading in electricity market to ensure regular supply at competitive rates. Bidding process of 75 Indian bus systems is analyzed. It is shown that how can congestion cost can be addressed through active power rescheduling with transmission line constraints.
Parameter identification in Markov chain choice models

Abstract This work studies the parameter identification problem for the Markov chain choice model of Blanchet, Gallego, and Goyal used in assortment planning. In this model, the product selected by a customer is determined by a Markov chain over the products, where the products in the offered assortment are absorbing states. The underlying parameters of the model were previously shown to be identifiable from the choice probabilities for the all-products assortment, together with choice probabilities for assortments of all-but-one products. Obtaining and estimating choice probabilities for such large assortments is not desirable in many settings. The main result of this work is that the parameters may be identified from assortments of sizes two and three, regardless of the total number of products. The result is obtained via a simple and efficient parameter recovery algorithm.
A Reputation-Based Contract for Repeated Crowdsensing With Costly Verification

A system operator asks a group of sensors toexert costly effort to collect accurate measurements of a value of interest over time. At each time, each sensor is asked to report its observation to the operator, and is suitably compensated for the costly effort it exerts. Since both the effort and the observation are private information for the sensor, a naive payment scheme which compensates the sensor based only on its self-reported values of the effort and the measurements will lead to both shirking and falsification of outcomes by the sensor. In this paper, we design an appropriate compensation scheme to incentivize the sensors to both exert costly effort and then reveal the resulting observation truthfully. To this end, we formulate the problem as a repeated game and propose a compensation scheme that employs stochastic verification by the operator coupled with an algorithm to assign a reputation to each sensor. By including the history of the behavior exerted by the sensor in determining present payments, we show that the operator can incentivize higher effort as well as more frequent truthtelling by the sensors.
Wide-field strain imaging with preferentially aligned nitrogen-vacancy centers in polycrystalline diamond

We report on wide-field optically detected magnetic resonance imaging of nitrogen-vacancy centers (NVs) in type IIa polycrystalline diamond. These studies reveal a heterogeneous crystalline environment that produces a varied density of NV centers, including preferential orientation within some individual crystal grains, but preserves long spin coherence times. Using the native NVs as nanoscale sensors, we introduce a three-dimensional strain imaging technique with high sensitivity ( Hz–1/2) and diffraction-limited resolution across a wide field of view.
Radioactive Artifacts: Historical Sources of Modern Radium Contamination

Radium has been distributed in a wide variety of devices during the early part of this century. Antique objects containing significant amounts of radium turn up at flea markets, antique shows, and antique dealers, in a variety of locations. These objects include radium in devices which were used by legitimate medical practitioners for legitimate medical purposes such as therapy, as well as a wide variety of “quack cures.” These devices may contain anywhere from a few nanocuries to as much as several hundred microcuries of radium. In addition to medical sources, a large variety of scientific instruments utilize radium in luminous dials. These instruments include compasses, azimuth indicators, and virtually any object which might require some form of calibration. In addition, the consumer market utilized a large amount of radium in the production of wrist watches, pocket watches, and clocks with luminous dials. Some of these watches contained as much as 4.5 μCi of radium, and between 1913 and 1920 about 70 gm was produced for the manufacture of luminous compounds. In addition to the large amount of radium produced for scientific and consumer utilization, there were a number of materials produced which were claimed to contain radium but in fact did not, further adding to the confusion in this area. The wide availability of radium is a result of the public's great fascination with radioactivity during the early part of this century and a belief in its curative properties. A number of objects were produced in order to trap the emanations of radium in water for persons to drink in order to benefit from their healing effects. Since the late 20s and early 30s the public's attitude towards radiation has shifted 180° and it is now considered an extremely dangerous and harmful material. However, even as late as the 1950s, there were still some items produced containing radioactivity which today would be unthinkable. The “Buck Rogers Mystery Ring” of the 1950s was activated with polonium. With the shift in public attitudes towards radioactivity, and increasing problems in disposal of radioactive materials, the disposal of radium presents a particularly perplexing problem. The radium which was produced in the early part of the century is still around in various forms and is extremely difficult to dispose of. All objects discovered claiming to contain radium should be taken seriously and should be properly surveyed. They then should either be stored in some area where the environment is protected from the radioactivity or if a very small amount of radium is present, they may be disposed of through one of several commercial sources. Any significant amount of radium is extraordinarily difficult and expensive to dispose of and there are only limited sites which will accept these materials. No clear cut, uniform mechanism for the handling of radioactive materials which turn up outside of the usual institutional sources, is currently in place.
Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices

Recent works propose neural network- (NN-) inspired analog-to-digital converters (NNADCs) and demonstrate their great potentials in many emerging applications. These NNADCs often rely on resistive random-access memory (RRAM) devices to realize the NN operations and require high-precision RRAM cells (6∼12-bit) to achieve a moderate quantization resolution (4∼8-bit). Such optimistic assumption of RRAM resolution, however, is not supported by fabrication data of RRAM arrays in large-scale production process. In this paper, we propose an NN-inspired super-resolution ADC based on low-precision RRAM devices by taking the advantage of a co-design methodology that combines a pipelined hardware architecture with a custom NN training framework. Results obtained from SPICE simulations demonstrate that our method leads to robust design of a 14-bit super-resolution ADC using 3-bit RRAM devices with improved power and speed performance and competitive figure-of-merits (FoMs). In addition to the linear uniform quantization, the proposed ADC can also support configurable high-resolution nonlinear quantization with high conversion speed and low conversion energy, enabling future intelligent analog-to-information interfaces for near-sensor analytics and processing.
Bayesian constraint relaxation

Prior information often takes the form of parameter constraints. Bayesian methods include such information through prior distributions having constrained support. By using posterior sampling algorithms, one can quantify uncertainty without relying on asymptotic approximations. However, sharply constrained priors are not necessary in some settings and tend to limit modelling scope to a narrow set of distributions that are tractable computationally. We propose to replace the sharp indicator function of the constraint with an exponential kernel, thereby creating a close-to-constrained neighbourhood within the Euclidean space in which the constrained subspace is embedded. This kernel decays with distance from the constrained space at a rate depending on a relaxation hyperparameter. By avoiding the sharp constraint, we enable use of off-the-shelf posterior sampling algorithms, such as Hamiltonian Monte Carlo, facilitating automatic computation in a broad range of models. We study the constrained and relaxed distributions under multiple settings and theoretically quantify their differences. Application of the method is illustrated through several novel modelling examples.
FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second

Current methods for body shape estimation either lack detail or require many images. They are usually architecturally complex and computationally expensive. We propose FACSIMILE (FAX), a method that estimates a detailed body from a single photo, lowering the bar for creating virtual representations of humans. Our approach is easy to implement and fast to execute, making it easily deployable. FAX uses an image-translation network which recovers geometry at the original resolution of the image. Counterintuitively, the main loss which drives FAX is on per-pixel surface normals instead of per-pixel depth, making it possible to estimate detailed body geometry without any depth supervision. We evaluate our approach both qualitatively and quantitatively, and compare with a state-of-the-art method.
Bordism-Finiteness and Semi-simple Group Actions

We give bordism-finiteness results for smooth S3-manifolds. Consider the class of oriented manifolds which admit an S1-action with isolated fixed points such that the action extends to an S3-action with fixed point. We exhibit various subclasses, characterized by an upper bound for the Euler characteristic and properties of the first Pontryagin class p1, for example p1 = 0, which contain only finitely many oriented bordism types in any given dimension. Also we show finiteness results for homotopy complex projective spaces and complete intersections with S3-action as above.
Semantic Processing Theory Could Help Explain the Effectiveness of Creativity Incentives: A Numerical Experiment

Two seminal accounting studies on creativity and incentives find that output creativity is insensitive to creative effort. They compare a one-dimensional quantity contract that measures only output...
Circuit walks in integral polyhedra

Abstract Circuits play a fundamental role in the theory of linear programming due to their intimate connection to algorithms of combinatorial optimization and the efficiency of the simplex method. We are interested in better understanding the properties of circuit walks in integral polyhedra. In this paper, we introduce a hierarchy for integral polyhedra based on different types of behavior exhibited by their circuit walks. Many problems in combinatorial optimization fall into the most interesting categories of this hierarchy — steps of circuit walks only stop at integer points, at vertices, or follow actual edges. We classify several classical families of polyhedra within the hierarchy, including 0 ∕ 1 -polytopes, polyhedra defined by totally unimodular matrices, and more specifically matroid polytopes, transportation polytopes, and partition polytopes. Finally, we prove three characterizations of the simple polytopes that appear in the bottom level of the hierarchy where all circuit walks are edge walks, showing that such polytopes constitute a generalization of simplices and parallelotopes.